Add GitHub ML run 20260928T052909000000Z-ab099699
Browse files
coverage/20260928T052909000000Z-ab099699.json
ADDED
|
@@ -0,0 +1,1318 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"band_complete": false,
|
| 3 |
+
"complete_sweep": false,
|
| 4 |
+
"date_field": "created",
|
| 5 |
+
"end": "2026-09-28",
|
| 6 |
+
"limitations": [
|
| 7 |
+
"GitHub Search coverage gaps are recorded per query and date partition.",
|
| 8 |
+
"Search results do not establish that the discovered repositories are ML projects."
|
| 9 |
+
],
|
| 10 |
+
"mode": "star-crawl",
|
| 11 |
+
"queries": [
|
| 12 |
+
{
|
| 13 |
+
"coverage_gap": false,
|
| 14 |
+
"coverage_gap_reason": null,
|
| 15 |
+
"incomplete_results": false,
|
| 16 |
+
"pages_scanned": 1,
|
| 17 |
+
"query": "\"protein-protein interaction prediction\" in:readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 18 |
+
"query_id": "bio.protein-interactions",
|
| 19 |
+
"range_end": "2026-09-28",
|
| 20 |
+
"range_start": "2008-01-01",
|
| 21 |
+
"search_limit_reached": false,
|
| 22 |
+
"status": "complete",
|
| 23 |
+
"total_count": 0
|
| 24 |
+
},
|
| 25 |
+
{
|
| 26 |
+
"coverage_gap": false,
|
| 27 |
+
"coverage_gap_reason": null,
|
| 28 |
+
"incomplete_results": false,
|
| 29 |
+
"pages_scanned": 1,
|
| 30 |
+
"query": "\"protein structure prediction\" in:description stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 31 |
+
"query_id": "bio.protein-structure",
|
| 32 |
+
"range_end": "2026-09-28",
|
| 33 |
+
"range_start": "2008-01-01",
|
| 34 |
+
"search_limit_reached": false,
|
| 35 |
+
"status": "complete",
|
| 36 |
+
"total_count": 0
|
| 37 |
+
},
|
| 38 |
+
{
|
| 39 |
+
"coverage_gap": false,
|
| 40 |
+
"coverage_gap_reason": null,
|
| 41 |
+
"incomplete_results": false,
|
| 42 |
+
"pages_scanned": 1,
|
| 43 |
+
"query": "enhancer prediction genomics in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 44 |
+
"query_id": "bio.regulatory-sequence-modeling",
|
| 45 |
+
"range_end": "2026-09-28",
|
| 46 |
+
"range_start": "2008-01-01",
|
| 47 |
+
"search_limit_reached": false,
|
| 48 |
+
"status": "complete",
|
| 49 |
+
"total_count": 6
|
| 50 |
+
},
|
| 51 |
+
{
|
| 52 |
+
"coverage_gap": false,
|
| 53 |
+
"coverage_gap_reason": null,
|
| 54 |
+
"incomplete_results": false,
|
| 55 |
+
"pages_scanned": 1,
|
| 56 |
+
"query": "\"RNA language model\" in:description stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 57 |
+
"query_id": "bio.rna-language-models",
|
| 58 |
+
"range_end": "2026-09-28",
|
| 59 |
+
"range_start": "2008-01-01",
|
| 60 |
+
"search_limit_reached": false,
|
| 61 |
+
"status": "complete",
|
| 62 |
+
"total_count": 0
|
| 63 |
+
},
|
| 64 |
+
{
|
| 65 |
+
"coverage_gap": false,
|
| 66 |
+
"coverage_gap_reason": null,
|
| 67 |
+
"incomplete_results": false,
|
| 68 |
+
"pages_scanned": 1,
|
| 69 |
+
"query": "\"RNA structure prediction\" in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 70 |
+
"query_id": "bio.rna-structure",
|
| 71 |
+
"range_end": "2026-09-28",
|
| 72 |
+
"range_start": "2008-01-01",
|
| 73 |
+
"search_limit_reached": false,
|
| 74 |
+
"status": "complete",
|
| 75 |
+
"total_count": 0
|
| 76 |
+
},
|
| 77 |
+
{
|
| 78 |
+
"coverage_gap": false,
|
| 79 |
+
"coverage_gap_reason": null,
|
| 80 |
+
"incomplete_results": false,
|
| 81 |
+
"pages_scanned": 1,
|
| 82 |
+
"query": "\"scRNA-seq\" in:readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 83 |
+
"query_id": "bio.scrna-seq",
|
| 84 |
+
"range_end": "2026-09-28",
|
| 85 |
+
"range_start": "2008-01-01",
|
| 86 |
+
"search_limit_reached": false,
|
| 87 |
+
"status": "complete",
|
| 88 |
+
"total_count": 0
|
| 89 |
+
},
|
| 90 |
+
{
|
| 91 |
+
"coverage_gap": false,
|
| 92 |
+
"coverage_gap_reason": null,
|
| 93 |
+
"incomplete_results": false,
|
| 94 |
+
"pages_scanned": 1,
|
| 95 |
+
"query": "\"single-cell\" in:description stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 96 |
+
"query_id": "bio.single-cell",
|
| 97 |
+
"range_end": "2026-09-28",
|
| 98 |
+
"range_start": "2008-01-01",
|
| 99 |
+
"search_limit_reached": false,
|
| 100 |
+
"status": "complete",
|
| 101 |
+
"total_count": 0
|
| 102 |
+
},
|
| 103 |
+
{
|
| 104 |
+
"coverage_gap": false,
|
| 105 |
+
"coverage_gap_reason": null,
|
| 106 |
+
"incomplete_results": false,
|
| 107 |
+
"pages_scanned": 1,
|
| 108 |
+
"query": "\"spatial transcriptomics\" in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 109 |
+
"query_id": "bio.spatial-transcriptomics",
|
| 110 |
+
"range_end": "2026-09-28",
|
| 111 |
+
"range_start": "2008-01-01",
|
| 112 |
+
"search_limit_reached": false,
|
| 113 |
+
"status": "complete",
|
| 114 |
+
"total_count": 1
|
| 115 |
+
},
|
| 116 |
+
{
|
| 117 |
+
"coverage_gap": false,
|
| 118 |
+
"coverage_gap_reason": null,
|
| 119 |
+
"incomplete_results": false,
|
| 120 |
+
"pages_scanned": 1,
|
| 121 |
+
"query": "\"transcriptomics\" in:description stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 122 |
+
"query_id": "bio.transcriptomics",
|
| 123 |
+
"range_end": "2026-09-28",
|
| 124 |
+
"range_start": "2008-01-01",
|
| 125 |
+
"search_limit_reached": false,
|
| 126 |
+
"status": "complete",
|
| 127 |
+
"total_count": 0
|
| 128 |
+
},
|
| 129 |
+
{
|
| 130 |
+
"coverage_gap": false,
|
| 131 |
+
"coverage_gap_reason": null,
|
| 132 |
+
"incomplete_results": false,
|
| 133 |
+
"pages_scanned": 1,
|
| 134 |
+
"query": "\"variant effect prediction\" in:readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 135 |
+
"query_id": "bio.variant-effects",
|
| 136 |
+
"range_end": "2026-09-28",
|
| 137 |
+
"range_start": "2008-01-01",
|
| 138 |
+
"search_limit_reached": false,
|
| 139 |
+
"status": "complete",
|
| 140 |
+
"total_count": 0
|
| 141 |
+
},
|
| 142 |
+
{
|
| 143 |
+
"coverage_gap": false,
|
| 144 |
+
"coverage_gap_reason": null,
|
| 145 |
+
"incomplete_results": false,
|
| 146 |
+
"pages_scanned": 1,
|
| 147 |
+
"query": "\"ADMET prediction\" in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 148 |
+
"query_id": "chem-admet",
|
| 149 |
+
"range_end": "2026-09-28",
|
| 150 |
+
"range_start": "2008-01-01",
|
| 151 |
+
"search_limit_reached": false,
|
| 152 |
+
"status": "complete",
|
| 153 |
+
"total_count": 0
|
| 154 |
+
},
|
| 155 |
+
{
|
| 156 |
+
"coverage_gap": false,
|
| 157 |
+
"coverage_gap_reason": null,
|
| 158 |
+
"incomplete_results": false,
|
| 159 |
+
"pages_scanned": 1,
|
| 160 |
+
"query": "\"binding affinity prediction\" in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 161 |
+
"query_id": "chem-binding-affinity",
|
| 162 |
+
"range_end": "2026-09-28",
|
| 163 |
+
"range_start": "2008-01-01",
|
| 164 |
+
"search_limit_reached": false,
|
| 165 |
+
"status": "complete",
|
| 166 |
+
"total_count": 0
|
| 167 |
+
},
|
| 168 |
+
{
|
| 169 |
+
"coverage_gap": false,
|
| 170 |
+
"coverage_gap_reason": null,
|
| 171 |
+
"incomplete_results": false,
|
| 172 |
+
"pages_scanned": 1,
|
| 173 |
+
"query": "\"catalyst discovery\" in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 174 |
+
"query_id": "chem-catalyst-discovery",
|
| 175 |
+
"range_end": "2026-09-28",
|
| 176 |
+
"range_start": "2008-01-01",
|
| 177 |
+
"search_limit_reached": false,
|
| 178 |
+
"status": "complete",
|
| 179 |
+
"total_count": 0
|
| 180 |
+
},
|
| 181 |
+
{
|
| 182 |
+
"coverage_gap": false,
|
| 183 |
+
"coverage_gap_reason": null,
|
| 184 |
+
"incomplete_results": false,
|
| 185 |
+
"pages_scanned": 1,
|
| 186 |
+
"query": "topic:cheminformatics stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 187 |
+
"query_id": "chem-cheminformatics",
|
| 188 |
+
"range_end": "2026-09-28",
|
| 189 |
+
"range_start": "2008-01-01",
|
| 190 |
+
"search_limit_reached": false,
|
| 191 |
+
"status": "complete",
|
| 192 |
+
"total_count": 0
|
| 193 |
+
},
|
| 194 |
+
{
|
| 195 |
+
"coverage_gap": false,
|
| 196 |
+
"coverage_gap_reason": null,
|
| 197 |
+
"incomplete_results": false,
|
| 198 |
+
"pages_scanned": 1,
|
| 199 |
+
"query": "\"drug discovery\" in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 200 |
+
"query_id": "chem-drug-discovery",
|
| 201 |
+
"range_end": "2026-09-28",
|
| 202 |
+
"range_start": "2008-01-01",
|
| 203 |
+
"search_limit_reached": false,
|
| 204 |
+
"status": "complete",
|
| 205 |
+
"total_count": 3
|
| 206 |
+
},
|
| 207 |
+
{
|
| 208 |
+
"coverage_gap": false,
|
| 209 |
+
"coverage_gap_reason": null,
|
| 210 |
+
"incomplete_results": false,
|
| 211 |
+
"pages_scanned": 1,
|
| 212 |
+
"query": "\"machine learning interatomic potential\" in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 213 |
+
"query_id": "chem-interatomic-potentials",
|
| 214 |
+
"range_end": "2026-09-28",
|
| 215 |
+
"range_start": "2008-01-01",
|
| 216 |
+
"search_limit_reached": false,
|
| 217 |
+
"status": "complete",
|
| 218 |
+
"total_count": 0
|
| 219 |
+
},
|
| 220 |
+
{
|
| 221 |
+
"coverage_gap": false,
|
| 222 |
+
"coverage_gap_reason": null,
|
| 223 |
+
"incomplete_results": false,
|
| 224 |
+
"pages_scanned": 1,
|
| 225 |
+
"query": "\"molecular docking\" in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 226 |
+
"query_id": "chem-molecular-docking",
|
| 227 |
+
"range_end": "2026-09-28",
|
| 228 |
+
"range_start": "2008-01-01",
|
| 229 |
+
"search_limit_reached": false,
|
| 230 |
+
"status": "complete",
|
| 231 |
+
"total_count": 0
|
| 232 |
+
},
|
| 233 |
+
{
|
| 234 |
+
"coverage_gap": false,
|
| 235 |
+
"coverage_gap_reason": null,
|
| 236 |
+
"incomplete_results": false,
|
| 237 |
+
"pages_scanned": 1,
|
| 238 |
+
"query": "\"molecular dynamics\" \"machine learning\" in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 239 |
+
"query_id": "chem-molecular-dynamics",
|
| 240 |
+
"range_end": "2026-09-28",
|
| 241 |
+
"range_start": "2008-01-01",
|
| 242 |
+
"search_limit_reached": false,
|
| 243 |
+
"status": "complete",
|
| 244 |
+
"total_count": 0
|
| 245 |
+
},
|
| 246 |
+
{
|
| 247 |
+
"coverage_gap": false,
|
| 248 |
+
"coverage_gap_reason": null,
|
| 249 |
+
"incomplete_results": false,
|
| 250 |
+
"pages_scanned": 1,
|
| 251 |
+
"query": "\"molecular generation\" in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 252 |
+
"query_id": "chem-molecular-generation",
|
| 253 |
+
"range_end": "2026-09-28",
|
| 254 |
+
"range_start": "2008-01-01",
|
| 255 |
+
"search_limit_reached": false,
|
| 256 |
+
"status": "complete",
|
| 257 |
+
"total_count": 0
|
| 258 |
+
},
|
| 259 |
+
{
|
| 260 |
+
"coverage_gap": false,
|
| 261 |
+
"coverage_gap_reason": null,
|
| 262 |
+
"incomplete_results": false,
|
| 263 |
+
"pages_scanned": 1,
|
| 264 |
+
"query": "MoleculeNet in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 265 |
+
"query_id": "chem-molecular-property-benchmark",
|
| 266 |
+
"range_end": "2026-09-28",
|
| 267 |
+
"range_start": "2008-01-01",
|
| 268 |
+
"search_limit_reached": false,
|
| 269 |
+
"status": "complete",
|
| 270 |
+
"total_count": 0
|
| 271 |
+
},
|
| 272 |
+
{
|
| 273 |
+
"coverage_gap": false,
|
| 274 |
+
"coverage_gap_reason": null,
|
| 275 |
+
"incomplete_results": false,
|
| 276 |
+
"pages_scanned": 1,
|
| 277 |
+
"query": "molecular spectroscopy \"machine learning\" in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 278 |
+
"query_id": "chem-molecular-spectroscopy",
|
| 279 |
+
"range_end": "2026-09-28",
|
| 280 |
+
"range_start": "2008-01-01",
|
| 281 |
+
"search_limit_reached": false,
|
| 282 |
+
"status": "complete",
|
| 283 |
+
"total_count": 0
|
| 284 |
+
},
|
| 285 |
+
{
|
| 286 |
+
"coverage_gap": false,
|
| 287 |
+
"coverage_gap_reason": null,
|
| 288 |
+
"incomplete_results": false,
|
| 289 |
+
"pages_scanned": 1,
|
| 290 |
+
"query": "\"molecular property prediction\" in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 291 |
+
"query_id": "chem-property-prediction",
|
| 292 |
+
"range_end": "2026-09-28",
|
| 293 |
+
"range_start": "2008-01-01",
|
| 294 |
+
"search_limit_reached": false,
|
| 295 |
+
"status": "complete",
|
| 296 |
+
"total_count": 0
|
| 297 |
+
},
|
| 298 |
+
{
|
| 299 |
+
"coverage_gap": false,
|
| 300 |
+
"coverage_gap_reason": null,
|
| 301 |
+
"incomplete_results": false,
|
| 302 |
+
"pages_scanned": 1,
|
| 303 |
+
"query": "\"protein-ligand docking\" in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 304 |
+
"query_id": "chem-protein-ligand-docking",
|
| 305 |
+
"range_end": "2026-09-28",
|
| 306 |
+
"range_start": "2008-01-01",
|
| 307 |
+
"search_limit_reached": false,
|
| 308 |
+
"status": "complete",
|
| 309 |
+
"total_count": 0
|
| 310 |
+
},
|
| 311 |
+
{
|
| 312 |
+
"coverage_gap": false,
|
| 313 |
+
"coverage_gap_reason": null,
|
| 314 |
+
"incomplete_results": false,
|
| 315 |
+
"pages_scanned": 1,
|
| 316 |
+
"query": "\"quantum chemistry\" \"machine learning\" in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 317 |
+
"query_id": "chem-quantum-chemistry",
|
| 318 |
+
"range_end": "2026-09-28",
|
| 319 |
+
"range_start": "2008-01-01",
|
| 320 |
+
"search_limit_reached": false,
|
| 321 |
+
"status": "complete",
|
| 322 |
+
"total_count": 5
|
| 323 |
+
},
|
| 324 |
+
{
|
| 325 |
+
"coverage_gap": false,
|
| 326 |
+
"coverage_gap_reason": null,
|
| 327 |
+
"incomplete_results": false,
|
| 328 |
+
"pages_scanned": 1,
|
| 329 |
+
"query": "\"reaction condition prediction\" in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 330 |
+
"query_id": "chem-reaction-conditions",
|
| 331 |
+
"range_end": "2026-09-28",
|
| 332 |
+
"range_start": "2008-01-01",
|
| 333 |
+
"search_limit_reached": false,
|
| 334 |
+
"status": "complete",
|
| 335 |
+
"total_count": 0
|
| 336 |
+
},
|
| 337 |
+
{
|
| 338 |
+
"coverage_gap": false,
|
| 339 |
+
"coverage_gap_reason": null,
|
| 340 |
+
"incomplete_results": false,
|
| 341 |
+
"pages_scanned": 1,
|
| 342 |
+
"query": "\"reaction prediction\" in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 343 |
+
"query_id": "chem-reaction-prediction",
|
| 344 |
+
"range_end": "2026-09-28",
|
| 345 |
+
"range_start": "2008-01-01",
|
| 346 |
+
"search_limit_reached": false,
|
| 347 |
+
"status": "complete",
|
| 348 |
+
"total_count": 0
|
| 349 |
+
},
|
| 350 |
+
{
|
| 351 |
+
"coverage_gap": false,
|
| 352 |
+
"coverage_gap_reason": null,
|
| 353 |
+
"incomplete_results": false,
|
| 354 |
+
"pages_scanned": 1,
|
| 355 |
+
"query": "\"reaction yield prediction\" in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 356 |
+
"query_id": "chem-reaction-yield",
|
| 357 |
+
"range_end": "2026-09-28",
|
| 358 |
+
"range_start": "2008-01-01",
|
| 359 |
+
"search_limit_reached": false,
|
| 360 |
+
"status": "complete",
|
| 361 |
+
"total_count": 0
|
| 362 |
+
},
|
| 363 |
+
{
|
| 364 |
+
"coverage_gap": false,
|
| 365 |
+
"coverage_gap_reason": null,
|
| 366 |
+
"incomplete_results": false,
|
| 367 |
+
"pages_scanned": 1,
|
| 368 |
+
"query": "\"computer aided synthesis planning\" in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 369 |
+
"query_id": "chem-retrosynthesis",
|
| 370 |
+
"range_end": "2026-09-28",
|
| 371 |
+
"range_start": "2008-01-01",
|
| 372 |
+
"search_limit_reached": false,
|
| 373 |
+
"status": "complete",
|
| 374 |
+
"total_count": 0
|
| 375 |
+
},
|
| 376 |
+
{
|
| 377 |
+
"coverage_gap": false,
|
| 378 |
+
"coverage_gap_reason": null,
|
| 379 |
+
"incomplete_results": false,
|
| 380 |
+
"pages_scanned": 1,
|
| 381 |
+
"query": "retrosynthesis in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 382 |
+
"query_id": "chem-retrosynthesis-planning",
|
| 383 |
+
"range_end": "2026-09-28",
|
| 384 |
+
"range_start": "2008-01-01",
|
| 385 |
+
"search_limit_reached": false,
|
| 386 |
+
"status": "complete",
|
| 387 |
+
"total_count": 0
|
| 388 |
+
},
|
| 389 |
+
{
|
| 390 |
+
"coverage_gap": false,
|
| 391 |
+
"coverage_gap_reason": null,
|
| 392 |
+
"incomplete_results": false,
|
| 393 |
+
"pages_scanned": 1,
|
| 394 |
+
"query": "\"virtual screening\" in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 395 |
+
"query_id": "chem-virtual-screening",
|
| 396 |
+
"range_end": "2026-09-28",
|
| 397 |
+
"range_start": "2008-01-01",
|
| 398 |
+
"search_limit_reached": false,
|
| 399 |
+
"status": "complete",
|
| 400 |
+
"total_count": 0
|
| 401 |
+
},
|
| 402 |
+
{
|
| 403 |
+
"coverage_gap": false,
|
| 404 |
+
"coverage_gap_reason": null,
|
| 405 |
+
"incomplete_results": false,
|
| 406 |
+
"pages_scanned": 1,
|
| 407 |
+
"query": "\"audio diffusion\" generative model in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 408 |
+
"query_id": "cross26.audio-diffusion",
|
| 409 |
+
"range_end": "2026-09-28",
|
| 410 |
+
"range_start": "2008-01-01",
|
| 411 |
+
"search_limit_reached": false,
|
| 412 |
+
"status": "complete",
|
| 413 |
+
"total_count": 1
|
| 414 |
+
},
|
| 415 |
+
{
|
| 416 |
+
"coverage_gap": false,
|
| 417 |
+
"coverage_gap_reason": null,
|
| 418 |
+
"incomplete_results": false,
|
| 419 |
+
"pages_scanned": 1,
|
| 420 |
+
"query": "\"neural vocoder\" in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 421 |
+
"query_id": "cross26.audio-neural-vocoder",
|
| 422 |
+
"range_end": "2026-09-28",
|
| 423 |
+
"range_start": "2008-01-01",
|
| 424 |
+
"search_limit_reached": false,
|
| 425 |
+
"status": "complete",
|
| 426 |
+
"total_count": 2
|
| 427 |
+
},
|
| 428 |
+
{
|
| 429 |
+
"coverage_gap": false,
|
| 430 |
+
"coverage_gap_reason": null,
|
| 431 |
+
"incomplete_results": false,
|
| 432 |
+
"pages_scanned": 1,
|
| 433 |
+
"query": "\"self-supervised speech representation\" in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 434 |
+
"query_id": "cross26.audio-self-supervised-speech",
|
| 435 |
+
"range_end": "2026-09-28",
|
| 436 |
+
"range_start": "2008-01-01",
|
| 437 |
+
"search_limit_reached": false,
|
| 438 |
+
"status": "complete",
|
| 439 |
+
"total_count": 2
|
| 440 |
+
},
|
| 441 |
+
{
|
| 442 |
+
"coverage_gap": false,
|
| 443 |
+
"coverage_gap_reason": null,
|
| 444 |
+
"incomplete_results": false,
|
| 445 |
+
"pages_scanned": 1,
|
| 446 |
+
"query": "\"singing voice synthesis\" in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 447 |
+
"query_id": "cross26.audio-singing-synthesis",
|
| 448 |
+
"range_end": "2026-09-28",
|
| 449 |
+
"range_start": "2008-01-01",
|
| 450 |
+
"search_limit_reached": false,
|
| 451 |
+
"status": "complete",
|
| 452 |
+
"total_count": 1
|
| 453 |
+
},
|
| 454 |
+
{
|
| 455 |
+
"coverage_gap": false,
|
| 456 |
+
"coverage_gap_reason": null,
|
| 457 |
+
"incomplete_results": false,
|
| 458 |
+
"pages_scanned": 1,
|
| 459 |
+
"query": "\"speech enhancement transformer\" in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 460 |
+
"query_id": "cross26.audio-speech-enhancement-transformer",
|
| 461 |
+
"range_end": "2026-09-28",
|
| 462 |
+
"range_start": "2008-01-01",
|
| 463 |
+
"search_limit_reached": false,
|
| 464 |
+
"status": "complete",
|
| 465 |
+
"total_count": 0
|
| 466 |
+
},
|
| 467 |
+
{
|
| 468 |
+
"coverage_gap": false,
|
| 469 |
+
"coverage_gap_reason": null,
|
| 470 |
+
"incomplete_results": false,
|
| 471 |
+
"pages_scanned": 1,
|
| 472 |
+
"query": "\"antibody design\" machine learning in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 473 |
+
"query_id": "cross26.biomed-antibody-design",
|
| 474 |
+
"range_end": "2026-09-28",
|
| 475 |
+
"range_start": "2008-01-01",
|
| 476 |
+
"search_limit_reached": false,
|
| 477 |
+
"status": "complete",
|
| 478 |
+
"total_count": 0
|
| 479 |
+
},
|
| 480 |
+
{
|
| 481 |
+
"coverage_gap": false,
|
| 482 |
+
"coverage_gap_reason": null,
|
| 483 |
+
"incomplete_results": false,
|
| 484 |
+
"pages_scanned": 1,
|
| 485 |
+
"query": "\"drug response prediction\" machine learning in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 486 |
+
"query_id": "cross26.biomed-drug-response",
|
| 487 |
+
"range_end": "2026-09-28",
|
| 488 |
+
"range_start": "2008-01-01",
|
| 489 |
+
"search_limit_reached": false,
|
| 490 |
+
"status": "complete",
|
| 491 |
+
"total_count": 0
|
| 492 |
+
},
|
| 493 |
+
{
|
| 494 |
+
"coverage_gap": false,
|
| 495 |
+
"coverage_gap_reason": null,
|
| 496 |
+
"incomplete_results": false,
|
| 497 |
+
"pages_scanned": 1,
|
| 498 |
+
"query": "\"peptide design\" generative model in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 499 |
+
"query_id": "cross26.biomed-peptide-design",
|
| 500 |
+
"range_end": "2026-09-28",
|
| 501 |
+
"range_start": "2008-01-01",
|
| 502 |
+
"search_limit_reached": false,
|
| 503 |
+
"status": "complete",
|
| 504 |
+
"total_count": 0
|
| 505 |
+
},
|
| 506 |
+
{
|
| 507 |
+
"coverage_gap": false,
|
| 508 |
+
"coverage_gap_reason": null,
|
| 509 |
+
"incomplete_results": false,
|
| 510 |
+
"pages_scanned": 1,
|
| 511 |
+
"query": "\"retinal vessel segmentation\" deep learning in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 512 |
+
"query_id": "cross26.biomed-retinal-vessel-segmentation",
|
| 513 |
+
"range_end": "2026-09-28",
|
| 514 |
+
"range_start": "2008-01-01",
|
| 515 |
+
"search_limit_reached": false,
|
| 516 |
+
"status": "complete",
|
| 517 |
+
"total_count": 0
|
| 518 |
+
},
|
| 519 |
+
{
|
| 520 |
+
"coverage_gap": false,
|
| 521 |
+
"coverage_gap_reason": null,
|
| 522 |
+
"incomplete_results": false,
|
| 523 |
+
"pages_scanned": 1,
|
| 524 |
+
"query": "\"whole slide image\" deep learning in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 525 |
+
"query_id": "cross26.biomed-whole-slide-analysis",
|
| 526 |
+
"range_end": "2026-09-28",
|
| 527 |
+
"range_start": "2008-01-01",
|
| 528 |
+
"search_limit_reached": false,
|
| 529 |
+
"status": "complete",
|
| 530 |
+
"total_count": 2
|
| 531 |
+
},
|
| 532 |
+
{
|
| 533 |
+
"coverage_gap": false,
|
| 534 |
+
"coverage_gap_reason": null,
|
| 535 |
+
"incomplete_results": false,
|
| 536 |
+
"pages_scanned": 1,
|
| 537 |
+
"query": "\"causal bandit\" machine learning in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 538 |
+
"query_id": "cross26.causal-bandits",
|
| 539 |
+
"range_end": "2026-09-28",
|
| 540 |
+
"range_start": "2008-01-01",
|
| 541 |
+
"search_limit_reached": false,
|
| 542 |
+
"status": "complete",
|
| 543 |
+
"total_count": 0
|
| 544 |
+
},
|
| 545 |
+
{
|
| 546 |
+
"coverage_gap": false,
|
| 547 |
+
"coverage_gap_reason": null,
|
| 548 |
+
"incomplete_results": false,
|
| 549 |
+
"pages_scanned": 1,
|
| 550 |
+
"query": "\"Bayesian structural time series\" causal in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 551 |
+
"query_id": "cross26.causal-bayesian-structural-time-series",
|
| 552 |
+
"range_end": "2026-09-28",
|
| 553 |
+
"range_start": "2008-01-01",
|
| 554 |
+
"search_limit_reached": false,
|
| 555 |
+
"status": "complete",
|
| 556 |
+
"total_count": 1
|
| 557 |
+
},
|
| 558 |
+
{
|
| 559 |
+
"coverage_gap": false,
|
| 560 |
+
"coverage_gap_reason": null,
|
| 561 |
+
"incomplete_results": false,
|
| 562 |
+
"pages_scanned": 1,
|
| 563 |
+
"query": "\"doubly robust\" treatment effect machine learning in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 564 |
+
"query_id": "cross26.causal-doubly-robust",
|
| 565 |
+
"range_end": "2026-09-28",
|
| 566 |
+
"range_start": "2008-01-01",
|
| 567 |
+
"search_limit_reached": false,
|
| 568 |
+
"status": "complete",
|
| 569 |
+
"total_count": 1
|
| 570 |
+
},
|
| 571 |
+
{
|
| 572 |
+
"coverage_gap": false,
|
| 573 |
+
"coverage_gap_reason": null,
|
| 574 |
+
"incomplete_results": false,
|
| 575 |
+
"pages_scanned": 1,
|
| 576 |
+
"query": "\"instrumental variable\" machine learning in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 577 |
+
"query_id": "cross26.causal-instrumental-variable",
|
| 578 |
+
"range_end": "2026-09-28",
|
| 579 |
+
"range_start": "2008-01-01",
|
| 580 |
+
"search_limit_reached": false,
|
| 581 |
+
"status": "complete",
|
| 582 |
+
"total_count": 3
|
| 583 |
+
},
|
| 584 |
+
{
|
| 585 |
+
"coverage_gap": false,
|
| 586 |
+
"coverage_gap_reason": null,
|
| 587 |
+
"incomplete_results": false,
|
| 588 |
+
"pages_scanned": 1,
|
| 589 |
+
"query": "\"synthetic control\" machine learning in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 590 |
+
"query_id": "cross26.causal-synthetic-control",
|
| 591 |
+
"range_end": "2026-09-28",
|
| 592 |
+
"range_start": "2008-01-01",
|
| 593 |
+
"search_limit_reached": false,
|
| 594 |
+
"status": "complete",
|
| 595 |
+
"total_count": 0
|
| 596 |
+
},
|
| 597 |
+
{
|
| 598 |
+
"coverage_gap": false,
|
| 599 |
+
"coverage_gap_reason": null,
|
| 600 |
+
"incomplete_results": false,
|
| 601 |
+
"pages_scanned": 1,
|
| 602 |
+
"query": "\"battery degradation prediction\" machine learning in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 603 |
+
"query_id": "cross26.chem-battery-degradation",
|
| 604 |
+
"range_end": "2026-09-28",
|
| 605 |
+
"range_start": "2008-01-01",
|
| 606 |
+
"search_limit_reached": false,
|
| 607 |
+
"status": "complete",
|
| 608 |
+
"total_count": 0
|
| 609 |
+
},
|
| 610 |
+
{
|
| 611 |
+
"coverage_gap": false,
|
| 612 |
+
"coverage_gap_reason": null,
|
| 613 |
+
"incomplete_results": false,
|
| 614 |
+
"pages_scanned": 1,
|
| 615 |
+
"query": "\"catalyst activity prediction\" machine learning in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 616 |
+
"query_id": "cross26.chem-catalyst-activity",
|
| 617 |
+
"range_end": "2026-09-28",
|
| 618 |
+
"range_start": "2008-01-01",
|
| 619 |
+
"search_limit_reached": false,
|
| 620 |
+
"status": "complete",
|
| 621 |
+
"total_count": 0
|
| 622 |
+
},
|
| 623 |
+
{
|
| 624 |
+
"coverage_gap": false,
|
| 625 |
+
"coverage_gap_reason": null,
|
| 626 |
+
"incomplete_results": false,
|
| 627 |
+
"pages_scanned": 1,
|
| 628 |
+
"query": "\"crystal property prediction\" machine learning in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 629 |
+
"query_id": "cross26.chem-crystal-property",
|
| 630 |
+
"range_end": "2026-09-28",
|
| 631 |
+
"range_start": "2008-01-01",
|
| 632 |
+
"search_limit_reached": false,
|
| 633 |
+
"status": "complete",
|
| 634 |
+
"total_count": 0
|
| 635 |
+
},
|
| 636 |
+
{
|
| 637 |
+
"coverage_gap": false,
|
| 638 |
+
"coverage_gap_reason": null,
|
| 639 |
+
"incomplete_results": false,
|
| 640 |
+
"pages_scanned": 1,
|
| 641 |
+
"query": "\"polymer generation\" machine learning in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 642 |
+
"query_id": "cross26.chem-polymer-generation",
|
| 643 |
+
"range_end": "2026-09-28",
|
| 644 |
+
"range_start": "2008-01-01",
|
| 645 |
+
"search_limit_reached": false,
|
| 646 |
+
"status": "complete",
|
| 647 |
+
"total_count": 0
|
| 648 |
+
},
|
| 649 |
+
{
|
| 650 |
+
"coverage_gap": false,
|
| 651 |
+
"coverage_gap_reason": null,
|
| 652 |
+
"incomplete_results": false,
|
| 653 |
+
"pages_scanned": 1,
|
| 654 |
+
"query": "\"reaction center prediction\" machine learning in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 655 |
+
"query_id": "cross26.chem-reaction-center",
|
| 656 |
+
"range_end": "2026-09-28",
|
| 657 |
+
"range_start": "2008-01-01",
|
| 658 |
+
"search_limit_reached": false,
|
| 659 |
+
"status": "complete",
|
| 660 |
+
"total_count": 0
|
| 661 |
+
},
|
| 662 |
+
{
|
| 663 |
+
"coverage_gap": false,
|
| 664 |
+
"coverage_gap_reason": null,
|
| 665 |
+
"incomplete_results": false,
|
| 666 |
+
"pages_scanned": 1,
|
| 667 |
+
"query": "\"air quality forecasting\" machine learning in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 668 |
+
"query_id": "cross26.geo-air-quality-forecasting",
|
| 669 |
+
"range_end": "2026-09-28",
|
| 670 |
+
"range_start": "2008-01-01",
|
| 671 |
+
"search_limit_reached": false,
|
| 672 |
+
"status": "complete",
|
| 673 |
+
"total_count": 0
|
| 674 |
+
},
|
| 675 |
+
{
|
| 676 |
+
"coverage_gap": false,
|
| 677 |
+
"coverage_gap_reason": null,
|
| 678 |
+
"incomplete_results": false,
|
| 679 |
+
"pages_scanned": 1,
|
| 680 |
+
"query": "\"sea level prediction\" machine learning in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 681 |
+
"query_id": "cross26.geo-sea-level-prediction",
|
| 682 |
+
"range_end": "2026-09-28",
|
| 683 |
+
"range_start": "2008-01-01",
|
| 684 |
+
"search_limit_reached": false,
|
| 685 |
+
"status": "complete",
|
| 686 |
+
"total_count": 0
|
| 687 |
+
},
|
| 688 |
+
{
|
| 689 |
+
"coverage_gap": false,
|
| 690 |
+
"coverage_gap_reason": null,
|
| 691 |
+
"incomplete_results": false,
|
| 692 |
+
"pages_scanned": 1,
|
| 693 |
+
"query": "\"soil moisture downscaling\" machine learning in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 694 |
+
"query_id": "cross26.geo-soil-moisture-downscaling",
|
| 695 |
+
"range_end": "2026-09-28",
|
| 696 |
+
"range_start": "2008-01-01",
|
| 697 |
+
"search_limit_reached": false,
|
| 698 |
+
"status": "complete",
|
| 699 |
+
"total_count": 0
|
| 700 |
+
},
|
| 701 |
+
{
|
| 702 |
+
"coverage_gap": false,
|
| 703 |
+
"coverage_gap_reason": null,
|
| 704 |
+
"incomplete_results": false,
|
| 705 |
+
"pages_scanned": 1,
|
| 706 |
+
"query": "\"urban heat island prediction\" machine learning in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 707 |
+
"query_id": "cross26.geo-urban-heat-prediction",
|
| 708 |
+
"range_end": "2026-09-28",
|
| 709 |
+
"range_start": "2008-01-01",
|
| 710 |
+
"search_limit_reached": false,
|
| 711 |
+
"status": "complete",
|
| 712 |
+
"total_count": 0
|
| 713 |
+
},
|
| 714 |
+
{
|
| 715 |
+
"coverage_gap": false,
|
| 716 |
+
"coverage_gap_reason": null,
|
| 717 |
+
"incomplete_results": false,
|
| 718 |
+
"pages_scanned": 1,
|
| 719 |
+
"query": "\"wildfire spread prediction\" machine learning in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 720 |
+
"query_id": "cross26.geo-wildfire-spread",
|
| 721 |
+
"range_end": "2026-09-28",
|
| 722 |
+
"range_start": "2008-01-01",
|
| 723 |
+
"search_limit_reached": false,
|
| 724 |
+
"status": "complete",
|
| 725 |
+
"total_count": 0
|
| 726 |
+
},
|
| 727 |
+
{
|
| 728 |
+
"coverage_gap": false,
|
| 729 |
+
"coverage_gap_reason": null,
|
| 730 |
+
"incomplete_results": false,
|
| 731 |
+
"pages_scanned": 1,
|
| 732 |
+
"query": "\"differentiable rendering\" in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 733 |
+
"query_id": "cross26.graphics-differentiable-rendering",
|
| 734 |
+
"range_end": "2026-09-28",
|
| 735 |
+
"range_start": "2008-01-01",
|
| 736 |
+
"search_limit_reached": false,
|
| 737 |
+
"status": "complete",
|
| 738 |
+
"total_count": 2
|
| 739 |
+
},
|
| 740 |
+
{
|
| 741 |
+
"coverage_gap": false,
|
| 742 |
+
"coverage_gap_reason": null,
|
| 743 |
+
"incomplete_results": false,
|
| 744 |
+
"pages_scanned": 1,
|
| 745 |
+
"query": "\"implicit neural representation\" in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 746 |
+
"query_id": "cross26.graphics-implicit-neural-representation",
|
| 747 |
+
"range_end": "2026-09-28",
|
| 748 |
+
"range_start": "2008-01-01",
|
| 749 |
+
"search_limit_reached": false,
|
| 750 |
+
"status": "complete",
|
| 751 |
+
"total_count": 0
|
| 752 |
+
},
|
| 753 |
+
{
|
| 754 |
+
"coverage_gap": false,
|
| 755 |
+
"coverage_gap_reason": null,
|
| 756 |
+
"incomplete_results": false,
|
| 757 |
+
"pages_scanned": 1,
|
| 758 |
+
"query": "\"neural radiance field\" in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 759 |
+
"query_id": "cross26.graphics-neural-radiance-field",
|
| 760 |
+
"range_end": "2026-09-28",
|
| 761 |
+
"range_start": "2008-01-01",
|
| 762 |
+
"search_limit_reached": false,
|
| 763 |
+
"status": "complete",
|
| 764 |
+
"total_count": 5
|
| 765 |
+
},
|
| 766 |
+
{
|
| 767 |
+
"coverage_gap": false,
|
| 768 |
+
"coverage_gap_reason": null,
|
| 769 |
+
"incomplete_results": false,
|
| 770 |
+
"pages_scanned": 1,
|
| 771 |
+
"query": "\"occupancy network\" 3D in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 772 |
+
"query_id": "cross26.graphics-occupancy-network",
|
| 773 |
+
"range_end": "2026-09-28",
|
| 774 |
+
"range_start": "2008-01-01",
|
| 775 |
+
"search_limit_reached": false,
|
| 776 |
+
"status": "complete",
|
| 777 |
+
"total_count": 0
|
| 778 |
+
},
|
| 779 |
+
{
|
| 780 |
+
"coverage_gap": false,
|
| 781 |
+
"coverage_gap_reason": null,
|
| 782 |
+
"incomplete_results": false,
|
| 783 |
+
"pages_scanned": 1,
|
| 784 |
+
"query": "\"6D pose estimation\" in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 785 |
+
"query_id": "cross26.graphics-sixd-pose",
|
| 786 |
+
"range_end": "2026-09-28",
|
| 787 |
+
"range_start": "2008-01-01",
|
| 788 |
+
"search_limit_reached": false,
|
| 789 |
+
"status": "complete",
|
| 790 |
+
"total_count": 1
|
| 791 |
+
},
|
| 792 |
+
{
|
| 793 |
+
"coverage_gap": false,
|
| 794 |
+
"coverage_gap_reason": null,
|
| 795 |
+
"incomplete_results": false,
|
| 796 |
+
"pages_scanned": 1,
|
| 797 |
+
"query": "\"contact-rich manipulation\" learning in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 798 |
+
"query_id": "cross26.robotics-contact-rich-manipulation",
|
| 799 |
+
"range_end": "2026-09-28",
|
| 800 |
+
"range_start": "2008-01-01",
|
| 801 |
+
"search_limit_reached": false,
|
| 802 |
+
"status": "complete",
|
| 803 |
+
"total_count": 0
|
| 804 |
+
},
|
| 805 |
+
{
|
| 806 |
+
"coverage_gap": false,
|
| 807 |
+
"coverage_gap_reason": null,
|
| 808 |
+
"incomplete_results": false,
|
| 809 |
+
"pages_scanned": 1,
|
| 810 |
+
"query": "\"model predictive control\" robot learning in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 811 |
+
"query_id": "cross26.robotics-model-predictive-control",
|
| 812 |
+
"range_end": "2026-09-28",
|
| 813 |
+
"range_start": "2008-01-01",
|
| 814 |
+
"search_limit_reached": false,
|
| 815 |
+
"status": "complete",
|
| 816 |
+
"total_count": 0
|
| 817 |
+
},
|
| 818 |
+
{
|
| 819 |
+
"coverage_gap": false,
|
| 820 |
+
"coverage_gap_reason": null,
|
| 821 |
+
"incomplete_results": false,
|
| 822 |
+
"pages_scanned": 1,
|
| 823 |
+
"query": "\"tactile policy learning\" in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 824 |
+
"query_id": "cross26.robotics-tactile-policy",
|
| 825 |
+
"range_end": "2026-09-28",
|
| 826 |
+
"range_start": "2008-01-01",
|
| 827 |
+
"search_limit_reached": false,
|
| 828 |
+
"status": "complete",
|
| 829 |
+
"total_count": 0
|
| 830 |
+
},
|
| 831 |
+
{
|
| 832 |
+
"coverage_gap": false,
|
| 833 |
+
"coverage_gap_reason": null,
|
| 834 |
+
"incomplete_results": false,
|
| 835 |
+
"pages_scanned": 1,
|
| 836 |
+
"query": "\"trajectory optimization\" robot learning in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 837 |
+
"query_id": "cross26.robotics-trajectory-optimization",
|
| 838 |
+
"range_end": "2026-09-28",
|
| 839 |
+
"range_start": "2008-01-01",
|
| 840 |
+
"search_limit_reached": false,
|
| 841 |
+
"status": "complete",
|
| 842 |
+
"total_count": 0
|
| 843 |
+
},
|
| 844 |
+
{
|
| 845 |
+
"coverage_gap": false,
|
| 846 |
+
"coverage_gap_reason": null,
|
| 847 |
+
"incomplete_results": false,
|
| 848 |
+
"pages_scanned": 1,
|
| 849 |
+
"query": "\"visual servoing\" deep learning in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 850 |
+
"query_id": "cross26.robotics-visual-servoing",
|
| 851 |
+
"range_end": "2026-09-28",
|
| 852 |
+
"range_start": "2008-01-01",
|
| 853 |
+
"search_limit_reached": false,
|
| 854 |
+
"status": "complete",
|
| 855 |
+
"total_count": 1
|
| 856 |
+
},
|
| 857 |
+
{
|
| 858 |
+
"coverage_gap": false,
|
| 859 |
+
"coverage_gap_reason": null,
|
| 860 |
+
"incomplete_results": false,
|
| 861 |
+
"pages_scanned": 1,
|
| 862 |
+
"query": "\"learned turbulence closure\" in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 863 |
+
"query_id": "cross26.science-learned-turbulence-closure",
|
| 864 |
+
"range_end": "2026-09-28",
|
| 865 |
+
"range_start": "2008-01-01",
|
| 866 |
+
"search_limit_reached": false,
|
| 867 |
+
"status": "complete",
|
| 868 |
+
"total_count": 0
|
| 869 |
+
},
|
| 870 |
+
{
|
| 871 |
+
"coverage_gap": false,
|
| 872 |
+
"coverage_gap_reason": null,
|
| 873 |
+
"incomplete_results": false,
|
| 874 |
+
"pages_scanned": 1,
|
| 875 |
+
"query": "\"neural weather model\" in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 876 |
+
"query_id": "cross26.science-neural-weather-model",
|
| 877 |
+
"range_end": "2026-09-28",
|
| 878 |
+
"range_start": "2008-01-01",
|
| 879 |
+
"search_limit_reached": false,
|
| 880 |
+
"status": "complete",
|
| 881 |
+
"total_count": 0
|
| 882 |
+
},
|
| 883 |
+
{
|
| 884 |
+
"coverage_gap": false,
|
| 885 |
+
"coverage_gap_reason": null,
|
| 886 |
+
"incomplete_results": false,
|
| 887 |
+
"pages_scanned": 1,
|
| 888 |
+
"query": "\"ocean forecasting\" deep learning in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 889 |
+
"query_id": "cross26.science-ocean-forecasting",
|
| 890 |
+
"range_end": "2026-09-28",
|
| 891 |
+
"range_start": "2008-01-01",
|
| 892 |
+
"search_limit_reached": false,
|
| 893 |
+
"status": "complete",
|
| 894 |
+
"total_count": 0
|
| 895 |
+
},
|
| 896 |
+
{
|
| 897 |
+
"coverage_gap": false,
|
| 898 |
+
"coverage_gap_reason": null,
|
| 899 |
+
"incomplete_results": false,
|
| 900 |
+
"pages_scanned": 1,
|
| 901 |
+
"query": "\"physics-informed operator learning\" in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 902 |
+
"query_id": "cross26.science-physics-informed-operator",
|
| 903 |
+
"range_end": "2026-09-28",
|
| 904 |
+
"range_start": "2008-01-01",
|
| 905 |
+
"search_limit_reached": false,
|
| 906 |
+
"status": "complete",
|
| 907 |
+
"total_count": 0
|
| 908 |
+
},
|
| 909 |
+
{
|
| 910 |
+
"coverage_gap": false,
|
| 911 |
+
"coverage_gap_reason": null,
|
| 912 |
+
"incomplete_results": false,
|
| 913 |
+
"pages_scanned": 1,
|
| 914 |
+
"query": "\"quantum error correction\" machine learning in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 915 |
+
"query_id": "cross26.science-quantum-error-correction",
|
| 916 |
+
"range_end": "2026-09-28",
|
| 917 |
+
"range_start": "2008-01-01",
|
| 918 |
+
"search_limit_reached": false,
|
| 919 |
+
"status": "complete",
|
| 920 |
+
"total_count": 2
|
| 921 |
+
},
|
| 922 |
+
{
|
| 923 |
+
"coverage_gap": false,
|
| 924 |
+
"coverage_gap_reason": null,
|
| 925 |
+
"incomplete_results": false,
|
| 926 |
+
"pages_scanned": 1,
|
| 927 |
+
"query": "\"hand-object interaction\" deep learning in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 928 |
+
"query_id": "cross27.vision.hand-object-interaction",
|
| 929 |
+
"range_end": "2026-09-28",
|
| 930 |
+
"range_start": "2008-01-01",
|
| 931 |
+
"search_limit_reached": false,
|
| 932 |
+
"status": "complete",
|
| 933 |
+
"total_count": 1
|
| 934 |
+
},
|
| 935 |
+
{
|
| 936 |
+
"coverage_gap": false,
|
| 937 |
+
"coverage_gap_reason": null,
|
| 938 |
+
"incomplete_results": false,
|
| 939 |
+
"pages_scanned": 1,
|
| 940 |
+
"query": "\"neural surface reconstruction\" in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 941 |
+
"query_id": "cross27.vision.neural-surface-reconstruction",
|
| 942 |
+
"range_end": "2026-09-28",
|
| 943 |
+
"range_start": "2008-01-01",
|
| 944 |
+
"search_limit_reached": false,
|
| 945 |
+
"status": "complete",
|
| 946 |
+
"total_count": 1
|
| 947 |
+
},
|
| 948 |
+
{
|
| 949 |
+
"coverage_gap": false,
|
| 950 |
+
"coverage_gap_reason": null,
|
| 951 |
+
"incomplete_results": false,
|
| 952 |
+
"pages_scanned": 1,
|
| 953 |
+
"query": "\"referring image segmentation\" in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 954 |
+
"query_id": "cross27.vision.referring-segmentation",
|
| 955 |
+
"range_end": "2026-09-28",
|
| 956 |
+
"range_start": "2008-01-01",
|
| 957 |
+
"search_limit_reached": false,
|
| 958 |
+
"status": "complete",
|
| 959 |
+
"total_count": 0
|
| 960 |
+
},
|
| 961 |
+
{
|
| 962 |
+
"coverage_gap": false,
|
| 963 |
+
"coverage_gap_reason": null,
|
| 964 |
+
"incomplete_results": false,
|
| 965 |
+
"pages_scanned": 1,
|
| 966 |
+
"query": "\"semantic correspondence\" image matching in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 967 |
+
"query_id": "cross27.vision.semantic-correspondence",
|
| 968 |
+
"range_end": "2026-09-28",
|
| 969 |
+
"range_start": "2008-01-01",
|
| 970 |
+
"search_limit_reached": false,
|
| 971 |
+
"status": "complete",
|
| 972 |
+
"total_count": 0
|
| 973 |
+
},
|
| 974 |
+
{
|
| 975 |
+
"coverage_gap": false,
|
| 976 |
+
"coverage_gap_reason": null,
|
| 977 |
+
"incomplete_results": false,
|
| 978 |
+
"pages_scanned": 1,
|
| 979 |
+
"query": "\"video object segmentation\" deep learning in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 980 |
+
"query_id": "cross27.vision.video-object-segmentation",
|
| 981 |
+
"range_end": "2026-09-28",
|
| 982 |
+
"range_start": "2008-01-01",
|
| 983 |
+
"search_limit_reached": false,
|
| 984 |
+
"status": "complete",
|
| 985 |
+
"total_count": 2
|
| 986 |
+
},
|
| 987 |
+
{
|
| 988 |
+
"coverage_gap": false,
|
| 989 |
+
"coverage_gap_reason": null,
|
| 990 |
+
"incomplete_results": false,
|
| 991 |
+
"pages_scanned": 1,
|
| 992 |
+
"query": "\"visual prompt\" segmentation model in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 993 |
+
"query_id": "cross27.vision.visual-prompting",
|
| 994 |
+
"range_end": "2026-09-28",
|
| 995 |
+
"range_start": "2008-01-01",
|
| 996 |
+
"search_limit_reached": false,
|
| 997 |
+
"status": "complete",
|
| 998 |
+
"total_count": 3
|
| 999 |
+
},
|
| 1000 |
+
{
|
| 1001 |
+
"coverage_gap": false,
|
| 1002 |
+
"coverage_gap_reason": null,
|
| 1003 |
+
"incomplete_results": false,
|
| 1004 |
+
"pages_scanned": 1,
|
| 1005 |
+
"query": "\"activation checkpointing\" in:description stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 1006 |
+
"query_id": "efficiency.activation-checkpointing",
|
| 1007 |
+
"range_end": "2026-09-28",
|
| 1008 |
+
"range_start": "2008-01-01",
|
| 1009 |
+
"search_limit_reached": false,
|
| 1010 |
+
"status": "complete",
|
| 1011 |
+
"total_count": 0
|
| 1012 |
+
},
|
| 1013 |
+
{
|
| 1014 |
+
"coverage_gap": false,
|
| 1015 |
+
"coverage_gap_reason": null,
|
| 1016 |
+
"incomplete_results": false,
|
| 1017 |
+
"pages_scanned": 1,
|
| 1018 |
+
"query": "AutoML in:description stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 1019 |
+
"query_id": "efficiency.automl",
|
| 1020 |
+
"range_end": "2026-09-28",
|
| 1021 |
+
"range_start": "2008-01-01",
|
| 1022 |
+
"search_limit_reached": false,
|
| 1023 |
+
"status": "complete",
|
| 1024 |
+
"total_count": 4
|
| 1025 |
+
},
|
| 1026 |
+
{
|
| 1027 |
+
"coverage_gap": false,
|
| 1028 |
+
"coverage_gap_reason": null,
|
| 1029 |
+
"incomplete_results": false,
|
| 1030 |
+
"pages_scanned": 1,
|
| 1031 |
+
"query": "\"communication efficient training\" in:description stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 1032 |
+
"query_id": "efficiency.communication-efficient-training",
|
| 1033 |
+
"range_end": "2026-09-28",
|
| 1034 |
+
"range_start": "2008-01-01",
|
| 1035 |
+
"search_limit_reached": false,
|
| 1036 |
+
"status": "complete",
|
| 1037 |
+
"total_count": 0
|
| 1038 |
+
},
|
| 1039 |
+
{
|
| 1040 |
+
"coverage_gap": false,
|
| 1041 |
+
"coverage_gap_reason": null,
|
| 1042 |
+
"incomplete_results": false,
|
| 1043 |
+
"pages_scanned": 1,
|
| 1044 |
+
"query": "\"machine learning compiler\" in:description stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 1045 |
+
"query_id": "efficiency.compiler-optimization",
|
| 1046 |
+
"range_end": "2026-09-28",
|
| 1047 |
+
"range_start": "2008-01-01",
|
| 1048 |
+
"search_limit_reached": false,
|
| 1049 |
+
"status": "complete",
|
| 1050 |
+
"total_count": 0
|
| 1051 |
+
},
|
| 1052 |
+
{
|
| 1053 |
+
"coverage_gap": false,
|
| 1054 |
+
"coverage_gap_reason": null,
|
| 1055 |
+
"incomplete_results": false,
|
| 1056 |
+
"pages_scanned": 1,
|
| 1057 |
+
"query": "\"distributed training\" in:description stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 1058 |
+
"query_id": "efficiency.distributed-training",
|
| 1059 |
+
"range_end": "2026-09-28",
|
| 1060 |
+
"range_start": "2008-01-01",
|
| 1061 |
+
"search_limit_reached": false,
|
| 1062 |
+
"status": "complete",
|
| 1063 |
+
"total_count": 1
|
| 1064 |
+
},
|
| 1065 |
+
{
|
| 1066 |
+
"coverage_gap": false,
|
| 1067 |
+
"coverage_gap_reason": null,
|
| 1068 |
+
"incomplete_results": false,
|
| 1069 |
+
"pages_scanned": 1,
|
| 1070 |
+
"query": "\"edge AI\" in:description stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 1071 |
+
"query_id": "efficiency.edge-ai",
|
| 1072 |
+
"range_end": "2026-09-28",
|
| 1073 |
+
"range_start": "2008-01-01",
|
| 1074 |
+
"search_limit_reached": false,
|
| 1075 |
+
"status": "complete",
|
| 1076 |
+
"total_count": 0
|
| 1077 |
+
},
|
| 1078 |
+
{
|
| 1079 |
+
"coverage_gap": false,
|
| 1080 |
+
"coverage_gap_reason": null,
|
| 1081 |
+
"incomplete_results": false,
|
| 1082 |
+
"pages_scanned": 1,
|
| 1083 |
+
"query": "\"energy efficient machine learning\" in:description stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 1084 |
+
"query_id": "efficiency.energy-efficient-ml",
|
| 1085 |
+
"range_end": "2026-09-28",
|
| 1086 |
+
"range_start": "2008-01-01",
|
| 1087 |
+
"search_limit_reached": false,
|
| 1088 |
+
"status": "complete",
|
| 1089 |
+
"total_count": 0
|
| 1090 |
+
},
|
| 1091 |
+
{
|
| 1092 |
+
"coverage_gap": false,
|
| 1093 |
+
"coverage_gap_reason": null,
|
| 1094 |
+
"incomplete_results": false,
|
| 1095 |
+
"pages_scanned": 1,
|
| 1096 |
+
"query": "\"FlashAttention\" in:description stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 1097 |
+
"query_id": "efficiency.flash-attention",
|
| 1098 |
+
"range_end": "2026-09-28",
|
| 1099 |
+
"range_start": "2008-01-01",
|
| 1100 |
+
"search_limit_reached": false,
|
| 1101 |
+
"status": "complete",
|
| 1102 |
+
"total_count": 0
|
| 1103 |
+
},
|
| 1104 |
+
{
|
| 1105 |
+
"coverage_gap": false,
|
| 1106 |
+
"coverage_gap_reason": null,
|
| 1107 |
+
"incomplete_results": false,
|
| 1108 |
+
"pages_scanned": 1,
|
| 1109 |
+
"query": "\"GPU kernel\" in:description stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 1110 |
+
"query_id": "efficiency.gpu-kernels",
|
| 1111 |
+
"range_end": "2026-09-28",
|
| 1112 |
+
"range_start": "2008-01-01",
|
| 1113 |
+
"search_limit_reached": false,
|
| 1114 |
+
"status": "complete",
|
| 1115 |
+
"total_count": 0
|
| 1116 |
+
},
|
| 1117 |
+
{
|
| 1118 |
+
"coverage_gap": false,
|
| 1119 |
+
"coverage_gap_reason": null,
|
| 1120 |
+
"incomplete_results": false,
|
| 1121 |
+
"pages_scanned": 1,
|
| 1122 |
+
"query": "\"gradient-based optimization\" in:description stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 1123 |
+
"query_id": "efficiency.gradient-optimization",
|
| 1124 |
+
"range_end": "2026-09-28",
|
| 1125 |
+
"range_start": "2008-01-01",
|
| 1126 |
+
"search_limit_reached": false,
|
| 1127 |
+
"status": "complete",
|
| 1128 |
+
"total_count": 0
|
| 1129 |
+
},
|
| 1130 |
+
{
|
| 1131 |
+
"coverage_gap": false,
|
| 1132 |
+
"coverage_gap_reason": null,
|
| 1133 |
+
"incomplete_results": false,
|
| 1134 |
+
"pages_scanned": 1,
|
| 1135 |
+
"query": "\"hyperparameter optimization\" in:description stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 1136 |
+
"query_id": "efficiency.hyperparameter-optimization",
|
| 1137 |
+
"range_end": "2026-09-28",
|
| 1138 |
+
"range_start": "2008-01-01",
|
| 1139 |
+
"search_limit_reached": false,
|
| 1140 |
+
"status": "complete",
|
| 1141 |
+
"total_count": 1
|
| 1142 |
+
},
|
| 1143 |
+
{
|
| 1144 |
+
"coverage_gap": false,
|
| 1145 |
+
"coverage_gap_reason": null,
|
| 1146 |
+
"incomplete_results": false,
|
| 1147 |
+
"pages_scanned": 1,
|
| 1148 |
+
"query": "\"inference engine\" \"machine learning\" in:description,readme stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 1149 |
+
"query_id": "efficiency.inference-systems",
|
| 1150 |
+
"range_end": "2026-09-28",
|
| 1151 |
+
"range_start": "2008-01-01",
|
| 1152 |
+
"search_limit_reached": false,
|
| 1153 |
+
"status": "complete",
|
| 1154 |
+
"total_count": 12
|
| 1155 |
+
},
|
| 1156 |
+
{
|
| 1157 |
+
"coverage_gap": false,
|
| 1158 |
+
"coverage_gap_reason": null,
|
| 1159 |
+
"incomplete_results": false,
|
| 1160 |
+
"pages_scanned": 1,
|
| 1161 |
+
"query": "\"knowledge distillation\" in:description stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 1162 |
+
"query_id": "efficiency.knowledge-distillation",
|
| 1163 |
+
"range_end": "2026-09-28",
|
| 1164 |
+
"range_start": "2008-01-01",
|
| 1165 |
+
"search_limit_reached": false,
|
| 1166 |
+
"status": "complete",
|
| 1167 |
+
"total_count": 0
|
| 1168 |
+
},
|
| 1169 |
+
{
|
| 1170 |
+
"coverage_gap": false,
|
| 1171 |
+
"coverage_gap_reason": null,
|
| 1172 |
+
"incomplete_results": false,
|
| 1173 |
+
"pages_scanned": 1,
|
| 1174 |
+
"query": "\"KV cache\" in:description stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 1175 |
+
"query_id": "efficiency.kv-cache",
|
| 1176 |
+
"range_end": "2026-09-28",
|
| 1177 |
+
"range_start": "2008-01-01",
|
| 1178 |
+
"search_limit_reached": false,
|
| 1179 |
+
"status": "complete",
|
| 1180 |
+
"total_count": 1
|
| 1181 |
+
},
|
| 1182 |
+
{
|
| 1183 |
+
"coverage_gap": false,
|
| 1184 |
+
"coverage_gap_reason": null,
|
| 1185 |
+
"incomplete_results": false,
|
| 1186 |
+
"pages_scanned": 1,
|
| 1187 |
+
"query": "LoRA in:description stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 1188 |
+
"query_id": "efficiency.lora",
|
| 1189 |
+
"range_end": "2026-09-28",
|
| 1190 |
+
"range_start": "2008-01-01",
|
| 1191 |
+
"search_limit_reached": false,
|
| 1192 |
+
"status": "complete",
|
| 1193 |
+
"total_count": 4
|
| 1194 |
+
},
|
| 1195 |
+
{
|
| 1196 |
+
"coverage_gap": false,
|
| 1197 |
+
"coverage_gap_reason": null,
|
| 1198 |
+
"incomplete_results": false,
|
| 1199 |
+
"pages_scanned": 1,
|
| 1200 |
+
"query": "\"low-rank factorization\" in:description stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 1201 |
+
"query_id": "efficiency.low-rank-factorization",
|
| 1202 |
+
"range_end": "2026-09-28",
|
| 1203 |
+
"range_start": "2008-01-01",
|
| 1204 |
+
"search_limit_reached": false,
|
| 1205 |
+
"status": "complete",
|
| 1206 |
+
"total_count": 0
|
| 1207 |
+
},
|
| 1208 |
+
{
|
| 1209 |
+
"coverage_gap": false,
|
| 1210 |
+
"coverage_gap_reason": null,
|
| 1211 |
+
"incomplete_results": false,
|
| 1212 |
+
"pages_scanned": 1,
|
| 1213 |
+
"query": "\"mixed precision training\" in:description stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 1214 |
+
"query_id": "efficiency.mixed-precision-training",
|
| 1215 |
+
"range_end": "2026-09-28",
|
| 1216 |
+
"range_start": "2008-01-01",
|
| 1217 |
+
"search_limit_reached": false,
|
| 1218 |
+
"status": "complete",
|
| 1219 |
+
"total_count": 0
|
| 1220 |
+
},
|
| 1221 |
+
{
|
| 1222 |
+
"coverage_gap": false,
|
| 1223 |
+
"coverage_gap_reason": null,
|
| 1224 |
+
"incomplete_results": false,
|
| 1225 |
+
"pages_scanned": 1,
|
| 1226 |
+
"query": "\"mixture of experts\" in:description stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 1227 |
+
"query_id": "efficiency.mixture-of-experts",
|
| 1228 |
+
"range_end": "2026-09-28",
|
| 1229 |
+
"range_start": "2008-01-01",
|
| 1230 |
+
"search_limit_reached": false,
|
| 1231 |
+
"status": "complete",
|
| 1232 |
+
"total_count": 2
|
| 1233 |
+
},
|
| 1234 |
+
{
|
| 1235 |
+
"coverage_gap": false,
|
| 1236 |
+
"coverage_gap_reason": null,
|
| 1237 |
+
"incomplete_results": false,
|
| 1238 |
+
"pages_scanned": 1,
|
| 1239 |
+
"query": "\"model compression\" in:description stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 1240 |
+
"query_id": "efficiency.model-compression",
|
| 1241 |
+
"range_end": "2026-09-28",
|
| 1242 |
+
"range_start": "2008-01-01",
|
| 1243 |
+
"search_limit_reached": false,
|
| 1244 |
+
"status": "complete",
|
| 1245 |
+
"total_count": 0
|
| 1246 |
+
},
|
| 1247 |
+
{
|
| 1248 |
+
"coverage_gap": false,
|
| 1249 |
+
"coverage_gap_reason": null,
|
| 1250 |
+
"incomplete_results": false,
|
| 1251 |
+
"pages_scanned": 1,
|
| 1252 |
+
"query": "\"neural architecture search\" in:description stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 1253 |
+
"query_id": "efficiency.neural-architecture-search",
|
| 1254 |
+
"range_end": "2026-09-28",
|
| 1255 |
+
"range_start": "2008-01-01",
|
| 1256 |
+
"search_limit_reached": false,
|
| 1257 |
+
"status": "complete",
|
| 1258 |
+
"total_count": 0
|
| 1259 |
+
},
|
| 1260 |
+
{
|
| 1261 |
+
"coverage_gap": false,
|
| 1262 |
+
"coverage_gap_reason": null,
|
| 1263 |
+
"incomplete_results": false,
|
| 1264 |
+
"pages_scanned": 1,
|
| 1265 |
+
"query": "\"optimizer state sharding\" in:description stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 1266 |
+
"query_id": "efficiency.optimizer-state-sharding",
|
| 1267 |
+
"range_end": "2026-09-28",
|
| 1268 |
+
"range_start": "2008-01-01",
|
| 1269 |
+
"search_limit_reached": false,
|
| 1270 |
+
"status": "complete",
|
| 1271 |
+
"total_count": 0
|
| 1272 |
+
},
|
| 1273 |
+
{
|
| 1274 |
+
"coverage_gap": false,
|
| 1275 |
+
"coverage_gap_reason": null,
|
| 1276 |
+
"incomplete_results": false,
|
| 1277 |
+
"pages_scanned": 1,
|
| 1278 |
+
"query": "\"parameter-efficient fine-tuning\" in:description stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 1279 |
+
"query_id": "efficiency.parameter-efficient-finetuning",
|
| 1280 |
+
"range_end": "2026-09-28",
|
| 1281 |
+
"range_start": "2008-01-01",
|
| 1282 |
+
"search_limit_reached": false,
|
| 1283 |
+
"status": "complete",
|
| 1284 |
+
"total_count": 0
|
| 1285 |
+
},
|
| 1286 |
+
{
|
| 1287 |
+
"coverage_gap": false,
|
| 1288 |
+
"coverage_gap_reason": null,
|
| 1289 |
+
"incomplete_results": false,
|
| 1290 |
+
"pages_scanned": 1,
|
| 1291 |
+
"query": "\"post-training quantization\" in:description stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 1292 |
+
"query_id": "efficiency.post-training-quantization",
|
| 1293 |
+
"range_end": "2026-09-28",
|
| 1294 |
+
"range_start": "2008-01-01",
|
| 1295 |
+
"search_limit_reached": false,
|
| 1296 |
+
"status": "complete",
|
| 1297 |
+
"total_count": 0
|
| 1298 |
+
},
|
| 1299 |
+
{
|
| 1300 |
+
"coverage_gap": false,
|
| 1301 |
+
"coverage_gap_reason": null,
|
| 1302 |
+
"incomplete_results": false,
|
| 1303 |
+
"pages_scanned": 1,
|
| 1304 |
+
"query": "pruning in:description stars:5000..9999 created:2008-01-01..2026-09-28",
|
| 1305 |
+
"query_id": "efficiency.pruning",
|
| 1306 |
+
"range_end": "2026-09-28",
|
| 1307 |
+
"range_start": "2008-01-01",
|
| 1308 |
+
"search_limit_reached": false,
|
| 1309 |
+
"status": "complete",
|
| 1310 |
+
"total_count": 0
|
| 1311 |
+
}
|
| 1312 |
+
],
|
| 1313 |
+
"requests_used": 100,
|
| 1314 |
+
"run_id": "20260928T052909000000Z-ab099699",
|
| 1315 |
+
"star_band": "5000..9999",
|
| 1316 |
+
"start": "2008-01-01",
|
| 1317 |
+
"started_at": "2026-09-28T05:29:09Z"
|
| 1318 |
+
}
|
data/observations/2026/09/28/20260928T052909000000Z-ab099699.jsonl
ADDED
|
@@ -0,0 +1,53 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2011-09-06T22:24:59Z","description":"Distributed Asynchronous Hyperparameter Optimization in Python","domains":["automl","optimization"],"fork":false,"forks":1075,"github_id":2337852,"homepage":"http://hyperopt.github.io/hyperopt","language":"Python","license":"NOASSERTION","methods":["hyperparameter-optimization"],"name":"hyperopt/hyperopt","novelty_signals":["description","github-topics","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2026-09-21T21:30:55Z","query_ids":["efficiency.hyperparameter-optimization"],"stars":7597,"topics":["hacktoberfest"],"updated_at":"2026-09-21T12:13:57Z","url":"https://github.com/hyperopt/hyperopt"}
|
| 2 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2014-03-03T16:08:07Z","description":"H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.","domains":["automl","optimization","tabular-and-structured-data"],"fork":false,"forks":2020,"github_id":17371412,"homepage":"http://h2o.ai","language":"Jupyter Notebook","license":"Apache-2.0","methods":["automl","ensemble-learning","gradient-boosting","k-means","random-forest","support-vector-machine"],"name":"h2oai/h2o-3","novelty_signals":["description","github-topics","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2026-09-25T08:28:44Z","query_ids":["efficiency.automl"],"stars":7510,"topics":["automl","big-data","data-science","deep-learning","distributed","ensemble-learning","gbm","gpu","h2o","h2o-automl","hadoop","java","machine-learning","naive-bayes","opensource","pca","python","r","random-forest","spark"],"updated_at":"2026-09-26T19:11:23Z","url":"https://github.com/h2oai/h2o-3"}
|
| 3 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2014-07-19T06:02:47Z","description":"A curated list of awesome R packages, frameworks and software.","domains":["biology","causal-ml","computational-biology","econometrics","genomics","time-series"],"fork":false,"forks":1512,"github_id":22003158,"homepage":null,"language":"R","license":null,"methods":["causal-inference","prediction","regulatory-genomics","sequence-model","structural-time-series"],"name":"qinwf/awesome-R","novelty_signals":["description","github-topics","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2025-09-18T16:46:19Z","query_ids":["bio.regulatory-sequence-modeling","cross26.causal-bayesian-structural-time-series"],"stars":6517,"topics":["awesome","awesome-list","data-analysis","data-science","list","r","rstats"],"updated_at":"2026-09-27T17:46:47Z","url":"https://github.com/qinwf/awesome-R"}
|
| 4 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2015-09-24T23:20:28Z","description":"Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology","domains":["chemistry","computational-chemistry","drug-discovery","health-and-biomedicine","materials-science","science-and-engineering"],"fork":false,"forks":2326,"github_id":43098215,"homepage":"https://deepchem.io/","language":"Python","license":"MIT","methods":["property-prediction","quantum-chemistry","ranking","representation-learning","surrogate-modeling","virtual-screening"],"name":"deepchem/deepchem","novelty_signals":["description","github-topics","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2026-08-20T19:01:34Z","query_ids":["chem-drug-discovery","chem-quantum-chemistry"],"stars":7019,"topics":["biology","deep-learning","drug-discovery","hacktoberfest","materials-science","quantum-chemistry"],"updated_at":"2026-09-26T16:42:59Z","url":"https://github.com/deepchem/deepchem"}
|
| 5 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2015-11-26T16:53:05Z","description":"A list of awesome Robotics resources","domains":["computer-vision","robotics","robotics-and-control"],"fork":false,"forks":1092,"github_id":46938122,"homepage":null,"language":null,"license":"NOASSERTION","methods":["deep-learning","visual-servoing"],"name":"kiloreux/awesome-robotics","novelty_signals":["description","github-topics","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2024-09-22T20:24:10Z","query_ids":["cross26.robotics-visual-servoing"],"stars":7205,"topics":["robotics"],"updated_at":"2026-09-27T15:32:23Z","url":"https://github.com/kiloreux/awesome-robotics"}
|
| 6 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2016-02-27T14:56:35Z","description":"😎Awesome GIS is a collection of geospatial related sources, including cartographic tools, geoanalysis tools, developer tools, data, conference & communities, news, massive open online course, some amazing map sites, and more.","domains":["causal-ml","econometrics"],"fork":false,"forks":869,"github_id":52671720,"homepage":null,"language":null,"license":"MIT","methods":["causal-inference","instrumental-variables"],"name":"sshuair/awesome-gis","novelty_signals":["description","github-topics","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2026-07-21T09:10:54Z","query_ids":["cross26.causal-instrumental-variable"],"stars":5543,"topics":["awesome","geo","geospatial","geospatial-library","geotools","gis","map","spatial-analysis"],"updated_at":"2026-09-28T03:36:29Z","url":"https://github.com/sshuair/awesome-gis"}
|
| 7 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2016-04-27T04:13:03Z","description":"A list of popular github projects related to deep learning","domains":["3d-vision","chemistry","computational-chemistry","drug-discovery","graphics","inference-systems","ml-systems"],"fork":false,"forks":1255,"github_id":57182702,"homepage":null,"language":"Python","license":"MIT","methods":["implicit-representation","inference-optimization","inference-serving","kernel-optimization","neural-rendering","property-prediction","quantum-chemistry","ranking","representation-learning","surrogate-modeling","virtual-screening"],"name":"aymericdamien/TopDeepLearning","novelty_signals":["description","github-topics","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2026-07-15T02:19:53Z","query_ids":["chem-drug-discovery","chem-quantum-chemistry","cross26.graphics-neural-radiance-field","efficiency.inference-systems"],"stars":6357,"topics":["deep-learning","machine-learning","pytorch","tensorflow"],"updated_at":"2026-09-27T09:44:07Z","url":"https://github.com/aymericdamien/TopDeepLearning"}
|
| 8 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2016-05-06T18:05:42Z","description":"Long list of geospatial tools and resources","domains":["causal-ml","econometrics","science-and-engineering"],"fork":false,"forks":807,"github_id":58225794,"homepage":null,"language":null,"license":"CC0-1.0","methods":["causal-inference","instrumental-variables"],"name":"sacridini/Awesome-Geospatial","novelty_signals":["description","github-topics","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2026-09-23T16:51:11Z","query_ids":["cross26.causal-instrumental-variable"],"stars":5303,"topics":["awesome","awesome-list","data-analysis","deep-learning","earth-observation","geoprocessing","geospatial","geospatial-analysis","geotools","gis","machine-learning","map","remote-sensing","spatial-analysis","spatial-data"],"updated_at":"2026-09-28T02:31:42Z","url":"https://github.com/sacridini/Awesome-Geospatial"}
|
| 9 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2016-05-30T18:44:46Z","description":null,"domains":["biology","computational-biology","genomics","health-and-biomedicine","medical-imaging"],"fork":false,"forks":1129,"github_id":60031662,"homepage":null,"language":null,"license":null,"methods":["deep-learning","prediction","regulatory-genomics","sequence-model","whole-slide-analysis"],"name":"beamandrew/medical-data","novelty_signals":["query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2023-11-19T22:52:15Z","query_ids":["bio.regulatory-sequence-modeling","cross26.biomed-whole-slide-analysis"],"stars":6034,"topics":[],"updated_at":"2026-09-26T22:21:34Z","url":"https://github.com/beamandrew/medical-data"}
|
| 10 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2017-06-05T19:58:10Z","description":"Awesome Books","domains":["chemistry","computational-chemistry"],"fork":false,"forks":822,"github_id":93441336,"homepage":null,"language":null,"license":null,"methods":["property-prediction","quantum-chemistry","surrogate-modeling"],"name":"linsa-io/books","novelty_signals":["description","github-topics","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2026-03-04T15:38:20Z","query_ids":["chem-quantum-chemistry"],"stars":7675,"topics":["awesome","awesome-list","books","education","free","learn-anything","learning","list","pdf","resources"],"updated_at":"2026-09-28T02:26:45Z","url":"https://github.com/linsa-io/books"}
|
| 11 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2017-09-24T02:23:36Z","description":"A curated list of awesome embedded programming.","domains":["inference-systems","ml-systems"],"fork":false,"forks":1211,"github_id":104612770,"homepage":"https://nhivp.github.io/Awesome-Embedded/","language":null,"license":"Unlicense","methods":["inference-optimization","inference-serving","kernel-optimization"],"name":"nhivp/Awesome-Embedded","novelty_signals":["description","github-topics","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2026-09-06T05:27:33Z","query_ids":["efficiency.inference-systems"],"stars":9153,"topics":["ai","autosar","awesome","beaglebone","bootloader","embedded","embedded-ai","embedded-gui","freertos","linux-programming","machine-learning","microcontroller","msp430","operating-systems","raspberry-pi","rtos","stm32","tiva-c-series","tm4c123"],"updated_at":"2026-09-28T02:23:40Z","url":"https://github.com/nhivp/Awesome-Embedded"}
|
| 12 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2017-09-24T06:25:27Z","description":"机器人视觉 移动机器人 VS-SLAM ORB-SLAM2 深度学习目标检测 yolov3 行为检测 opencv PCL 机器学习 无人驾驶","domains":["computer-vision","segmentation","video"],"fork":false,"forks":2782,"github_id":104623696,"homepage":null,"language":"C++","license":null,"methods":["video-object-segmentation"],"name":"Ewenwan/MVision","novelty_signals":["description","github-topics","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2024-07-09T10:25:36Z","query_ids":["cross27.vision.video-object-segmentation"],"stars":8733,"topics":["cnn","deep-learning","machine-vision","opencv","pcl","robot","slam"],"updated_at":"2026-09-28T01:17:34Z","url":"https://github.com/Ewenwan/MVision"}
|
| 13 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2017-10-12T03:19:02Z","description":"Curated list of awesome GAN applications and demo","domains":["audio","generative-modeling","speech"],"fork":false,"forks":814,"github_id":106639043,"homepage":null,"language":null,"license":"CC0-1.0","methods":["neural-vocoder","speech-synthesis"],"name":"nashory/gans-awesome-applications","novelty_signals":["description","github-topics","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2026-06-08T10:40:14Z","query_ids":["cross26.audio-neural-vocoder"],"stars":5106,"topics":["applications","curated-list","demonstration","generative-adversarial-network","github","papers"],"updated_at":"2026-09-21T11:15:29Z","url":"https://github.com/nashory/gans-awesome-applications"}
|
| 14 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2017-11-19T23:18:20Z","description":"AutoML library for deep learning","domains":["automl","optimization"],"fork":false,"forks":1388,"github_id":111340129,"homepage":"http://autokeras.com/","language":"Python","license":"Apache-2.0","methods":["automl"],"name":"keras-team/autokeras","novelty_signals":["description","github-topics","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2025-11-25T02:26:35Z","query_ids":["efficiency.automl"],"stars":9332,"topics":["autodl","automated-machine-learning","automl","deep-learning","keras","machine-learning","neural-architecture-search","python","tensorflow"],"updated_at":"2026-09-21T17:46:58Z","url":"https://github.com/keras-team/autokeras"}
|
| 15 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2017-11-26T17:37:44Z","description":"A log of things I'm learning","domains":["inference-systems","ml-systems","natural-language-processing"],"fork":false,"forks":877,"github_id":112103066,"homepage":"https://twitter.com/amitness","language":null,"license":"MIT","methods":["inference-optimization","inference-serving","kernel-optimization"],"name":"amitness/learning","novelty_signals":["description","github-topics","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2026-09-01T08:43:11Z","query_ids":["efficiency.inference-systems"],"stars":6956,"topics":["deep-learning","generative-ai","learning-resources","llms","machine-learning","nlp","python"],"updated_at":"2026-09-27T13:08:23Z","url":"https://github.com/amitness/learning"}
|
| 16 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2017-12-01T14:48:35Z","description":"links to conference publications in graph-based deep learning","domains":["chemistry","computational-chemistry","graph-learning"],"fork":false,"forks":781,"github_id":112751115,"homepage":null,"language":"Jupyter Notebook","license":"MIT","methods":["graph-neural-network","neural-network","property-prediction","quantum-chemistry","surrogate-modeling"],"name":"naganandy/graph-based-deep-learning-literature","novelty_signals":["description","github-topics","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2026-09-28T03:38:21Z","query_ids":["chem-quantum-chemistry"],"stars":5098,"topics":["conference-publications","deep-learning","graph","graph-convolutional-networks","graph-neural-networks","graph-representation-learning","neural-networks"],"updated_at":"2026-09-28T03:38:25Z","url":"https://github.com/naganandy/graph-based-deep-learning-literature"}
|
| 17 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2017-12-13T00:45:11Z","description":"End-to-End Speech Processing Toolkit","domains":["audio","music","natural-language-processing","speech","speech-and-audio"],"fork":false,"forks":2436,"github_id":114054873,"homepage":"https://espnet.github.io/espnet/","language":"Python","license":"Apache-2.0","methods":["neural-vocoder","singing-synthesis","speech-synthesis"],"name":"espnet/espnet","novelty_signals":["description","github-topics","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2026-09-27T21:44:35Z","query_ids":["cross26.audio-neural-vocoder","cross26.audio-singing-synthesis"],"stars":9972,"topics":["chainer","deep-learning","end-to-end","kaldi","machine-translation","pytorch","singing-voice-synthesis","speaker-diarization","speech-enhancement","speech-recognition","speech-separation","speech-synthesis","speech-translation","spoken-language-understanding","text-to-speech","voice-conversion"],"updated_at":"2026-09-27T21:03:22Z","url":"https://github.com/espnet/espnet"}
|
| 18 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2017-12-14T23:41:49Z","description":"Python framework for creating, editing, and running Noisy Intermediate-Scale Quantum (NISQ) circuits.","domains":["physics","quantum-computing"],"fork":false,"forks":1276,"github_id":114306758,"homepage":"https://quantumai.google/cirq","language":"Python","license":"Apache-2.0","methods":["machine-learning","quantum-error-correction"],"name":"quantumlib/Cirq","novelty_signals":["description","github-topics","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2026-09-28T03:28:01Z","query_ids":["cross26.science-quantum-error-correction"],"stars":5075,"topics":["algorithms","api","cirq","google","google-quantum","nisq","python","quantum","quantum-algorithms","quantum-circuit","quantum-circuit-simulator","quantum-computer-simulator","quantum-computing","quantum-development-kit","quantum-information","quantum-programming","quantum-programming-language","quantum-simulation","sdk","simulation"],"updated_at":"2026-09-28T03:28:04Z","url":"https://github.com/quantumlib/Cirq"}
|
| 19 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2018-02-10T01:57:50Z","description":"A curated list of awesome self-supervised methods","domains":["3d-vision","audio","computer-vision","graphics","natural-language-processing","reinforcement-learning","robotics","robotics-and-control","speech"],"fork":false,"forks":836,"github_id":120975327,"homepage":null,"language":null,"license":null,"methods":["6d-perception","pose-estimation","reinforcement-learning","self-supervised-learning","speech-representation"],"name":"jason718/awesome-self-supervised-learning","novelty_signals":["description","github-topics","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2026-02-24T08:30:02Z","query_ids":["cross26.audio-self-supervised-speech","cross26.graphics-sixd-pose"],"stars":6429,"topics":["computer-vision","deep-learning","machine-learning","natural-language-processing","reinforcement-learning","robotics","self-supervised"],"updated_at":"2026-09-24T01:38:14Z","url":"https://github.com/jason718/awesome-self-supervised-learning"}
|
| 20 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2018-03-11T02:34:38Z","description":"2027 tech & software engineering internships for students — new grad US roles, summer & full-year","domains":["physics","quantum-computing"],"fork":false,"forks":799,"github_id":124716861,"homepage":"https://zapplyjobs.github.io/Internships-2027/","language":"HTML","license":"NOASSERTION","methods":["machine-learning","quantum-error-correction"],"name":"zapplyjobs/Internships-2027","novelty_signals":["description","github-topics","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2026-09-28T05:20:50Z","query_ids":["cross26.science-quantum-error-correction"],"stars":5199,"topics":["2027","applications","college","early-career","hiring","internship","internships","interviews","job-search","jobboard","jobs","jobs-2027","junior","new-grad-jobs","newgrad","remote-job","summer-internship","swe","tech-internship","university"],"updated_at":"2026-09-28T05:20:54Z","url":"https://github.com/zapplyjobs/Internships-2027"}
|
| 21 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2018-04-04T13:28:06Z","description":"The cryptography-based networking stack for building unstoppable networks with LoRa, Packet Radio, WiFi and everything in between.","domains":["efficient-ml","language"],"fork":false,"forks":543,"github_id":128066037,"homepage":"https://reticulum.network","language":"Python","license":"NOASSERTION","methods":["lora","low-rank-adaptation","parameter-efficient-fine-tuning"],"name":"markqvist/Reticulum","novelty_signals":["description","github-topics","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2026-09-27T12:55:43Z","query_ids":["efficiency.lora"],"stars":7394,"topics":["lora","mesh","mesh-networks","network","networking-stack","packet-radio","reticulum","rns"],"updated_at":"2026-09-28T02:50:30Z","url":"https://github.com/markqvist/Reticulum"}
|
| 22 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2018-04-23T16:28:52Z","description":"A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch","domains":["distributed-ml","machine-learning-systems","ml-systems"],"fork":false,"forks":1529,"github_id":130725814,"homepage":null,"language":"Python","license":"BSD-3-Clause","methods":["data-parallelism","distributed-training","model-parallelism"],"name":"NVIDIA/apex","novelty_signals":["description","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2026-09-28T03:29:44Z","query_ids":["efficiency.distributed-training"],"stars":9001,"topics":[],"updated_at":"2026-09-28T03:29:55Z","url":"https://github.com/NVIDIA/apex"}
|
| 23 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2018-05-31T13:07:04Z","description":"DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.","domains":["causal-ml","econometrics"],"fork":false,"forks":1057,"github_id":135584946,"homepage":"https://www.pywhy.org/dowhy","language":"Python","license":"MIT","methods":["causal-inference","instrumental-variables"],"name":"py-why/dowhy","novelty_signals":["description","github-topics","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2026-09-27T17:25:10Z","query_ids":["cross26.causal-instrumental-variable"],"stars":8332,"topics":["bayesian-networks","causal-inference","causal-machine-learning","causal-models","causality","data-science","do-calculus","graphical-models","machine-learning","python3","treatment-effects"],"updated_at":"2026-09-28T02:55:08Z","url":"https://github.com/py-why/dowhy"}
|
| 24 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2019-05-03T05:08:15Z","description":"A curated list of applied machine learning and data science notebooks and libraries across different industries (by @firmai)","domains":["biology","chemistry","computational-biology","computational-chemistry","drug-discovery","genomics"],"fork":false,"forks":1248,"github_id":184701688,"homepage":"https://www.sov.ai/","language":"Jupyter Notebook","license":null,"methods":["prediction","property-prediction","quantum-chemistry","ranking","regulatory-genomics","representation-learning","sequence-model","surrogate-modeling","virtual-screening"],"name":"firmai/industry-machine-learning","novelty_signals":["description","github-topics","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2024-10-04T12:55:18Z","query_ids":["bio.regulatory-sequence-modeling","chem-drug-discovery","chem-quantum-chemistry"],"stars":7485,"topics":["data-science","datascience","example","firmai","jupyter-notebook","machine-learning","practical-machine-learning","python"],"updated_at":"2026-09-25T13:32:51Z","url":"https://github.com/firmai/industry-machine-learning"}
|
| 25 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2019-05-27T23:03:48Z","description":"Reading list for research topics in multimodal machine learning","domains":["biology","computational-biology","computer-vision","genomics","health-and-biomedicine","medical-imaging","multimodal-learning","natural-language-processing","reinforcement-learning","robotics-and-control"],"fork":false,"forks":898,"github_id":188919037,"homepage":null,"language":null,"license":"MIT","methods":["deep-learning","prediction","regulatory-genomics","reinforcement-learning","sequence-model","whole-slide-analysis"],"name":"pliang279/awesome-multimodal-ml","novelty_signals":["description","github-topics","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2024-08-20T19:46:33Z","query_ids":["bio.regulatory-sequence-modeling","cross26.biomed-whole-slide-analysis"],"stars":6935,"topics":["computer-vision","deep-learning","healthcare","machine-learning","multimodal-learning","natural-language-processing","reading-list","reinforcement-learning","representation-learning","robotics","speech-processing"],"updated_at":"2026-09-28T02:27:36Z","url":"https://github.com/pliang279/awesome-multimodal-ml"}
|
| 26 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2019-07-09T02:08:58Z","description":"Uplift modeling and causal inference with machine learning algorithms","domains":["causal-ml","econometrics"],"fork":false,"forks":879,"github_id":195914972,"homepage":null,"language":"Python","license":"NOASSERTION","methods":["causal-inference","doubly-robust-estimation","treatment-effect-estimation"],"name":"uber/causalml","novelty_signals":["description","github-topics","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2026-08-20T15:25:36Z","query_ids":["cross26.causal-doubly-robust"],"stars":6009,"topics":["causal-inference","incubation","machine-learning","uplift-modeling"],"updated_at":"2026-09-26T08:26:51Z","url":"https://github.com/uber/causalml"}
|
| 27 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2019-10-25T02:23:45Z","description":"PyTorch3D is FAIR's library of reusable components for deep learning with 3D data","domains":["3d-vision","graphics"],"fork":false,"forks":1467,"github_id":217433767,"homepage":"https://pytorch3d.org/","language":"Python","license":"NOASSERTION","methods":["differentiable-rendering","implicit-representation","inverse-rendering","neural-rendering"],"name":"facebookresearch/pytorch3d","novelty_signals":["description","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2026-09-15T11:00:35Z","query_ids":["cross26.graphics-differentiable-rendering","cross26.graphics-neural-radiance-field"],"stars":9971,"topics":[],"updated_at":"2026-09-26T08:49:57Z","url":"https://github.com/facebookresearch/pytorch3d"}
|
| 28 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2019-11-14T21:09:40Z","description":"A PyTorch Library for Accelerating 3D Deep Learning Research","domains":["3d-vision","graphics"],"fork":false,"forks":628,"github_id":221787282,"homepage":null,"language":"Python","license":"Apache-2.0","methods":["differentiable-rendering","inverse-rendering","neural-network"],"name":"NVIDIAGameWorks/kaolin","novelty_signals":["description","github-topics","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2026-09-18T19:12:46Z","query_ids":["cross26.graphics-differentiable-rendering"],"stars":5178,"topics":["3d-deep-learning","artificial-intelligence","camera-api","cuda","differentiable-lighting","differentiable-rendering","gaussian-splatting","interactive-visualizations","neural-networks","nvidia-warp","physics-simulation","pytorch","rasterization"],"updated_at":"2026-09-28T02:34:17Z","url":"https://github.com/NVIDIAGameWorks/kaolin"}
|
| 29 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2019-11-23T18:40:48Z","description":"Open-source, low-code AutoML platform for Python. PyCaret 4.0: sklearn-native engine + React control plane.","domains":["automl","optimization"],"fork":false,"forks":1842,"github_id":223636350,"homepage":"https://pycaret.org","language":"Python","license":"NOASSERTION","methods":["automl"],"name":"pycaret/pycaret","novelty_signals":["description","github-topics","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2026-07-23T00:34:25Z","query_ids":["efficiency.automl"],"stars":9847,"topics":["anomaly-detection","automl","classification","clustering","data-science","fastapi","machine-learning","ml","mlops","open-source","pycaret","python","react","regression","scikit-learn"],"updated_at":"2026-09-26T08:18:28Z","url":"https://github.com/pycaret/pycaret"}
|
| 30 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2019-12-10T05:45:37Z","description":"Reverse Engineering Resources About All Platforms(Windows/Linux/macOS/Android/iOS/IoT) And Every Aspect! (More than 3500 open source tools and 2300 posts&videos)","domains":["inference-systems","ml-systems"],"fork":false,"forks":904,"github_id":227038548,"homepage":null,"language":null,"license":null,"methods":["inference-optimization","inference-serving","kernel-optimization"],"name":"alphaSeclab/awesome-reverse-engineering","novelty_signals":["description","github-topics","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2021-09-01T16:55:49Z","query_ids":["efficiency.inference-systems"],"stars":5068,"topics":["android-security","angr","apk-analysis","binaryninja","binnavi","cuckoo","dynamorio","frida","gdb","ghidra","ida-plugin","idapro","idapython","intelpt","radare2","reverse-engineering","windbg","x64dbg"],"updated_at":"2026-09-26T17:34:55Z","url":"https://github.com/alphaSeclab/awesome-reverse-engineering"}
|
| 31 |
+
{"archived":true,"candidate_status":"candidate","created_at":"2020-03-12T03:52:47Z","description":"Google Brain AutoML","domains":["automl","computer-vision","optimization"],"fork":false,"forks":1463,"github_id":246738676,"homepage":null,"language":"Jupyter Notebook","license":"Apache-2.0","methods":["automl"],"name":"google/automl","novelty_signals":["description","github-topics","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2025-03-02T07:46:39Z","query_ids":["efficiency.automl"],"stars":6474,"topics":["automl","efficientdet","efficientnet","efficientnetv2","object-detection"],"updated_at":"2026-09-21T02:01:09Z","url":"https://github.com/google/automl"}
|
| 32 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2020-04-05T08:29:57Z","description":"A PyTorch implementation of NeRF (Neural Radiance Fields) that reproduces the results.","domains":["3d-vision","graphics"],"fork":false,"forks":1124,"github_id":253188774,"homepage":null,"language":"Python","license":"MIT","methods":["implicit-representation","neural-rendering"],"name":"yenchenlin/nerf-pytorch","novelty_signals":["description","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2024-07-25T10:27:44Z","query_ids":["cross26.graphics-neural-radiance-field"],"stars":6053,"topics":[],"updated_at":"2026-09-27T11:13:00Z","url":"https://github.com/yenchenlin/nerf-pytorch"}
|
| 33 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2020-11-26T16:10:56Z","description":"A curated list of awesome neural radiance fields papers","domains":["3d-vision","graphics","implicit-representation"],"fork":false,"forks":597,"github_id":316280031,"homepage":null,"language":"TeX","license":"MIT","methods":["implicit-representation","neural-rendering","neural-surface-reconstruction"],"name":"awesome-NeRF/awesome-NeRF","novelty_signals":["description","github-topics","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2025-01-06T11:03:37Z","query_ids":["cross26.graphics-neural-radiance-field","cross27.vision.neural-surface-reconstruction"],"stars":6775,"topics":["nerf"],"updated_at":"2026-09-27T07:32:12Z","url":"https://github.com/awesome-NeRF/awesome-NeRF"}
|
| 34 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2021-09-15T07:16:24Z","description":"An ultimately comprehensive paper list of Vision Transformer/Attention, including papers, codes, and related websites","domains":["computer-vision","human-object-interaction","prompt-learning","segmentation","video","vision","vision-language"],"fork":false,"forks":498,"github_id":406653146,"homepage":null,"language":null,"license":null,"methods":["hand-object-interaction","self-attention","transformer","video-object-segmentation","visual-prompting"],"name":"cmhungsteve/Awesome-Transformer-Attention","novelty_signals":["description","github-topics","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2024-07-30T06:57:18Z","query_ids":["cross27.vision.hand-object-interaction","cross27.vision.video-object-segmentation","cross27.vision.visual-prompting"],"stars":5046,"topics":["attention-mechanism","attention-mechanisms","awesome-list","computer-vision","deep-learning","detr","papers","self-attention","transformer","transformer-architecture","transformer-awesome","transformer-cv","transformer-models","transformer-with-cv","transformers","vision-transformer","visual-transformer","vit"],"updated_at":"2026-09-26T20:39:13Z","url":"https://github.com/cmhungsteve/Awesome-Transformer-Attention"}
|
| 35 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2022-04-15T01:56:44Z","description":"An awesome & curated list of best LLMOps tools for developers","domains":["inference-systems","ml-systems"],"fork":false,"forks":1070,"github_id":481805419,"homepage":null,"language":"Shell","license":"CC0-1.0","methods":["inference-optimization","inference-serving","kernel-optimization"],"name":"tensorchord/Awesome-LLMOps","novelty_signals":["description","github-topics","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2026-05-21T09:12:50Z","query_ids":["efficiency.inference-systems"],"stars":5950,"topics":["ai-development-tools","awesome-list","llmops","mlops"],"updated_at":"2026-09-26T19:09:59Z","url":"https://github.com/tensorchord/Awesome-LLMOps"}
|
| 36 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2022-05-11T06:31:22Z","description":"A collaborative list of public APIs for developers","domains":["biology","computational-biology","genomics"],"fork":false,"forks":1158,"github_id":490985210,"homepage":"https://publicapis.dev","language":"JavaScript","license":"MIT","methods":["prediction","regulatory-genomics","sequence-model"],"name":"marcelscruz/public-apis","novelty_signals":["description","github-topics","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2026-09-28T05:03:04Z","query_ids":["bio.regulatory-sequence-modeling"],"stars":9508,"topics":["apis","awesome","awesome-list","hacktoberfest","lists","open-source","public-api","public-apis","resources"],"updated_at":"2026-09-28T05:03:08Z","url":"https://github.com/marcelscruz/public-apis"}
|
| 37 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2022-05-23T22:06:13Z","description":"Implementation of Imagen, Google's Text-to-Image Neural Network, in Pytorch","domains":["audio","generative-ai","multimodal-learning"],"fork":false,"forks":792,"github_id":495587598,"homepage":null,"language":"Python","license":"MIT","methods":["audio-generation","diffusion","neural-network"],"name":"lucidrains/imagen-pytorch","novelty_signals":["description","github-topics","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2024-10-07T13:00:53Z","query_ids":["cross26.audio-diffusion"],"stars":8429,"topics":["artificial-intelligence","deep-learning","imagination-machine","text-to-image","text-to-video"],"updated_at":"2026-09-23T14:07:25Z","url":"https://github.com/lucidrains/imagen-pytorch"}
|
| 38 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2022-07-01T11:54:47Z","description":"⚡ TabPFN: Foundation Model for Tabular Data ⚡","domains":["inference-systems","ml-systems","tabular-and-structured-data"],"fork":false,"forks":794,"github_id":509436902,"homepage":"http://priorlabs.ai","language":"Python","license":"Apache-2.0","methods":["inference-optimization","inference-serving","kernel-optimization"],"name":"PriorLabs/TabPFN","novelty_signals":["description","github-topics","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2026-09-26T15:19:38Z","query_ids":["efficiency.inference-systems"],"stars":8067,"topics":["data-science","foundation-models","machine-learning","tabpfn","tabular-data"],"updated_at":"2026-09-27T20:29:39Z","url":"https://github.com/PriorLabs/TabPFN"}
|
| 39 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2023-01-22T13:50:45Z","description":"SD-Trainer. LoRA & Dreambooth training scripts & GUI use kohya-ss's trainer, for diffusion model.","domains":["efficient-ml","generative-modeling","language"],"fork":false,"forks":703,"github_id":591961298,"homepage":null,"language":"Python","license":"AGPL-3.0","methods":["diffusion","lora","low-rank-adaptation","parameter-efficient-fine-tuning"],"name":"Akegarasu/lora-scripts","novelty_signals":["description","github-topics","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2026-08-21T16:02:55Z","query_ids":["efficiency.lora"],"stars":6149,"topics":["dreambooth","finetune","lora","stable-diffusion"],"updated_at":"2026-09-28T03:25:34Z","url":"https://github.com/Akegarasu/lora-scripts"}
|
| 40 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2023-02-09T18:22:52Z","description":"This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc","domains":["multimodal-learning","prompt-learning","speech-and-audio","vision-language"],"fork":false,"forks":768,"github_id":599716050,"homepage":"https://discord.gg/m88xfYMbK6","language":"TypeScript","license":"Apache-2.0","methods":["transformer","visual-prompting"],"name":"promptslab/Awesome-Prompt-Engineering","novelty_signals":["description","github-topics","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2026-09-27T11:50:31Z","query_ids":["cross27.vision.visual-prompting"],"stars":6345,"topics":["chatgpt","chatgpt-api","deep-learning","few-shot-learning","gpt","gpt-3","machine-learning","openai","prompt","prompt-based-learning","prompt-engineering","prompt-generator","prompt-learning","prompt-toolkit","prompt-tuning","promptengineering","text-to-image","text-to-speech","text-to-video"],"updated_at":"2026-09-27T17:26:07Z","url":"https://github.com/promptslab/Awesome-Prompt-Engineering"}
|
| 41 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2023-03-22T14:28:32Z","description":"Implementation of the LLaMA language model based on nanoGPT. Supports flash attention, Int8 and GPTQ 4bit quantization, LoRA and LLaMA-Adapter fine-tuning, pre-training. Apache 2.0-licensed.","domains":["efficient-ml","language","machine-learning-systems","natural-language-processing"],"fork":false,"forks":517,"github_id":617504730,"homepage":null,"language":"Python","license":"Apache-2.0","methods":["lora","low-rank-adaptation","parameter-efficient-fine-tuning","quantization"],"name":"Lightning-AI/lit-llama","novelty_signals":["description","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2025-07-01T16:31:39Z","query_ids":["efficiency.lora"],"stars":6080,"topics":[],"updated_at":"2026-09-26T21:00:57Z","url":"https://github.com/Lightning-AI/lit-llama"}
|
| 42 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2023-04-17T10:34:17Z","description":"A curated list of recent diffusion models for video generation, editing, and various other applications.","domains":["3d-vision","generative-modeling","graphics"],"fork":false,"forks":374,"github_id":628959446,"homepage":null,"language":null,"license":null,"methods":["diffusion","implicit-representation","neural-rendering"],"name":"showlab/Awesome-Video-Diffusion","novelty_signals":["description","github-topics","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2026-09-21T21:27:17Z","query_ids":["cross26.graphics-neural-radiance-field"],"stars":5789,"topics":["awesome","diffusion-models","motion-customization","video-editing","video-generation","video-generation-evaluation"],"updated_at":"2026-09-28T04:00:10Z","url":"https://github.com/showlab/Awesome-Video-Diffusion"}
|
| 43 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2023-07-07T06:26:00Z","description":"[EMNLP'23, ACL'24] To speed up LLMs' inference and enhance LLM's perceive of key information, compress the prompt and KV-Cache, which achieves up to 20x compression with minimal performance loss.","domains":["efficient-ml","inference-systems","language","natural-language-processing"],"fork":false,"forks":431,"github_id":663368311,"homepage":"https://llmlingua.com/","language":"Python","license":"MIT","methods":["kv-cache-optimization","memory-optimization"],"name":"microsoft/LLMLingua","novelty_signals":["description","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2026-09-10T23:37:23Z","query_ids":["efficiency.kv-cache"],"stars":6710,"topics":[],"updated_at":"2026-09-28T04:33:34Z","url":"https://github.com/microsoft/LLMLingua"}
|
| 44 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2023-09-04T23:34:24Z","description":"Daily updated list of Tech Company OAs and Interview Problems. Save your time from finding them all over the internet.","domains":["inference-systems","ml-systems"],"fork":false,"forks":368,"github_id":687227685,"homepage":"https://www.fastprep.io/problems","language":"Python","license":null,"methods":["inference-optimization","inference-serving","kernel-optimization"],"name":"perixtar/Tech-OA-Interview-Questions","novelty_signals":["description","github-topics","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2026-09-28T05:04:42Z","query_ids":["efficiency.inference-systems"],"stars":5272,"topics":["amazon","amazon-interview","amazon-interview-practice","amazon-interview-preparation","fulltime","internship","interview-preparation","interview-questions","online-assessments","university"],"updated_at":"2026-09-28T05:04:47Z","url":"https://github.com/perixtar/Tech-OA-Interview-Questions"}
|
| 45 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2023-09-12T14:19:42Z","description":"The paper list of the 86-page SCIS cover paper \"The Rise and Potential of Large Language Model Based Agents: A Survey\" by Zhiheng Xi et al.","domains":["audio","biology","computational-biology","genomics","natural-language-processing","speech"],"fork":false,"forks":499,"github_id":690609896,"homepage":"https://arxiv.org/abs/2309.07864","language":null,"license":null,"methods":["prediction","regulatory-genomics","self-supervised-learning","sequence-model","speech-representation"],"name":"WooooDyy/LLM-Agent-Paper-List","novelty_signals":["description","github-topics","paper-reference","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2025-09-12T03:08:58Z","query_ids":["bio.regulatory-sequence-modeling","cross26.audio-self-supervised-speech"],"stars":8221,"topics":["agent","large-language-models","llm","nlp","survey"],"updated_at":"2026-09-27T12:34:04Z","url":"https://github.com/WooooDyy/LLM-Agent-Paper-List"}
|
| 46 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2023-12-24T22:09:14Z","description":"The open source research environment for AI researchers to seamlessly train, evaluate, and scale models from local hardware to GPU clusters.","domains":["generative-modeling","inference-systems","ml-systems"],"fork":false,"forks":551,"github_id":735425203,"homepage":"https://lab.cloud","language":"Python","license":"AGPL-3.0","methods":["diffusion","inference-optimization","inference-serving","kernel-optimization","lora"],"name":"transformerlab/transformerlab-app","novelty_signals":["description","github-topics","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2026-09-18T21:55:10Z","query_ids":["efficiency.inference-systems"],"stars":5197,"topics":["diffusion","diffusion-models","electron","llama","llms","lora","mlx","rlhf","stability-diffusion","transformers"],"updated_at":"2026-09-28T02:31:32Z","url":"https://github.com/transformerlab/transformerlab-app"}
|
| 47 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2024-01-19T15:15:10Z","description":"Curated list of chatgpt prompts from the top-rated GPTs in the GPTs Store. Prompt Engineering, prompt attack & prompt protect. Advanced Prompt Engineering papers.","domains":["biology","computational-biology","inference-systems","ml-systems","prompt-learning","transcriptomics","vision-language"],"fork":false,"forks":872,"github_id":745546184,"homepage":"https://awesomegpt.vip","language":null,"license":"GPL-3.0","methods":["gene-expression-modeling","inference-optimization","inference-serving","kernel-optimization","representation-learning","spatial-modeling","visual-prompting"],"name":"ai-boost/awesome-prompts","novelty_signals":["description","github-topics","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2026-09-24T08:35:01Z","query_ids":["bio.spatial-transcriptomics","cross27.vision.visual-prompting","efficiency.inference-systems"],"stars":8953,"topics":["awesome","awesome-list","chatgpt","gpt4","gpts","gptstore","papers","prompt","prompt-engineering"],"updated_at":"2026-09-28T00:18:22Z","url":"https://github.com/ai-boost/awesome-prompts"}
|
| 48 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2024-04-19T03:31:13Z","description":"🧑🚀 全世界最好的LLM资料总结（多模态生成、Agent、辅助编程、AI审稿、数据处理、模型训练、模型推理、o1 模型、MCP、小语言模型、视觉语言模型） | Summary of the world's best LLM resources.","domains":["inference-systems","ml-systems","natural-language-processing"],"fork":false,"forks":1004,"github_id":788768107,"homepage":null,"language":null,"license":"Apache-2.0","methods":["inference-optimization","inference-serving","kernel-optimization","rag","retrieval-augmented-generation"],"name":"WangRongsheng/awesome-LLM-resources","novelty_signals":["description","github-topics","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2026-09-21T01:46:58Z","query_ids":["efficiency.inference-systems"],"stars":8988,"topics":["awesome-list","book","course","large-language-models","llama","llm","mistral","openai","qwen","rag","retrieval-augmented-generation","webui"],"updated_at":"2026-09-28T03:00:49Z","url":"https://github.com/WangRongsheng/awesome-LLM-resources"}
|
| 49 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2024-04-22T06:55:47Z","description":"DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model","domains":["deep-learning","efficient-ml","language","natural-language-processing"],"fork":false,"forks":551,"github_id":790044051,"homepage":null,"language":null,"license":"MIT","methods":["conditional-computation","mixture-of-experts","sparse-activation"],"name":"deepseek-ai/DeepSeek-V2","novelty_signals":["description","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2024-09-25T10:23:55Z","query_ids":["efficiency.mixture-of-experts"],"stars":5041,"topics":[],"updated_at":"2026-09-24T15:49:42Z","url":"https://github.com/deepseek-ai/DeepSeek-V2"}
|
| 50 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2024-06-25T05:21:05Z","description":"Mooncake is the serving platform for Kimi, a leading LLM service provided by Moonshot AI.","domains":["inference-systems","ml-systems","natural-language-processing","reinforcement-learning"],"fork":false,"forks":1275,"github_id":819733173,"homepage":"https://kvcache-ai.github.io/Mooncake/","language":"C++","license":"Apache-2.0","methods":["inference-optimization","inference-serving","kernel-optimization","reinforcement-learning"],"name":"kvcache-ai/Mooncake","novelty_signals":["description","github-topics","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2026-09-28T04:04:25Z","query_ids":["efficiency.inference-systems"],"stars":6671,"topics":["disaggregation","inference","kvcache","llm","rdma","reinforcement-learning","sglang","tokenspeed","trt-llm","vllm"],"updated_at":"2026-09-28T04:04:31Z","url":"https://github.com/kvcache-ai/Mooncake"}
|
| 51 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2024-11-09T17:37:17Z","description":"My learning notes for ML SYS.","domains":["inference-systems","ml-systems"],"fork":false,"forks":511,"github_id":885902812,"homepage":null,"language":"HTML","license":"Apache-2.0","methods":["inference-optimization","inference-serving","kernel-optimization"],"name":"zhaochenyang20/Awesome-ML-SYS-Tutorial","novelty_signals":["description","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2026-09-20T21:10:50Z","query_ids":["efficiency.inference-systems"],"stars":7408,"topics":[],"updated_at":"2026-09-27T23:11:59Z","url":"https://github.com/zhaochenyang20/Awesome-ML-SYS-Tutorial"}
|
| 52 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2024-12-13T04:55:41Z","description":"DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding","domains":["computer-vision","deep-learning","efficient-ml","language","multimodal-learning","natural-language-processing"],"fork":false,"forks":1809,"github_id":902697574,"homepage":null,"language":"Python","license":"MIT","methods":["conditional-computation","mixture-of-experts","sparse-activation"],"name":"deepseek-ai/DeepSeek-VL2","novelty_signals":["description","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2025-02-26T05:03:42Z","query_ids":["efficiency.mixture-of-experts"],"stars":5373,"topics":[],"updated_at":"2026-09-26T23:45:42Z","url":"https://github.com/deepseek-ai/DeepSeek-VL2"}
|
| 53 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2026-01-03T13:16:29Z","description":"Official Python inference and LoRA trainer package for the LTX-2 audio–video generative model.","domains":["efficient-ml","generative-modeling","language","speech-and-audio"],"fork":false,"forks":1504,"github_id":1127234517,"homepage":"https://ltx.io","language":"Python","license":"NOASSERTION","methods":["lora","low-rank-adaptation","parameter-efficient-fine-tuning"],"name":"Lightricks/LTX-2","novelty_signals":["description","github-topics","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-28T05:29:09Z","pushed_at":"2026-08-26T10:47:10Z","query_ids":["efficiency.lora"],"stars":9540,"topics":["generative-ai","ltx","ltx-2"],"updated_at":"2026-09-28T02:22:40Z","url":"https://github.com/Lightricks/LTX-2"}
|
state/star-crawl.json
CHANGED
|
@@ -545,502 +545,502 @@
|
|
| 545 |
"query": "\"protein folding\" in:readme stars:5000..9999"
|
| 546 |
},
|
| 547 |
"bio.protein-interactions": {
|
| 548 |
-
"complete":
|
| 549 |
"cursor": null,
|
| 550 |
"query": "\"protein-protein interaction prediction\" in:readme stars:5000..9999"
|
| 551 |
},
|
| 552 |
"bio.protein-structure": {
|
| 553 |
-
"complete":
|
| 554 |
"cursor": null,
|
| 555 |
"query": "\"protein structure prediction\" in:description stars:5000..9999"
|
| 556 |
},
|
| 557 |
"bio.regulatory-sequence-modeling": {
|
| 558 |
-
"complete":
|
| 559 |
"cursor": null,
|
| 560 |
"query": "enhancer prediction genomics in:description,readme stars:5000..9999"
|
| 561 |
},
|
| 562 |
"bio.rna-language-models": {
|
| 563 |
-
"complete":
|
| 564 |
"cursor": null,
|
| 565 |
"query": "\"RNA language model\" in:description stars:5000..9999"
|
| 566 |
},
|
| 567 |
"bio.rna-structure": {
|
| 568 |
-
"complete":
|
| 569 |
"cursor": null,
|
| 570 |
"query": "\"RNA structure prediction\" in:description,readme stars:5000..9999"
|
| 571 |
},
|
| 572 |
"bio.scrna-seq": {
|
| 573 |
-
"complete":
|
| 574 |
"cursor": null,
|
| 575 |
"query": "\"scRNA-seq\" in:readme stars:5000..9999"
|
| 576 |
},
|
| 577 |
"bio.single-cell": {
|
| 578 |
-
"complete":
|
| 579 |
"cursor": null,
|
| 580 |
"query": "\"single-cell\" in:description stars:5000..9999"
|
| 581 |
},
|
| 582 |
"bio.spatial-transcriptomics": {
|
| 583 |
-
"complete":
|
| 584 |
"cursor": null,
|
| 585 |
"query": "\"spatial transcriptomics\" in:description,readme stars:5000..9999"
|
| 586 |
},
|
| 587 |
"bio.transcriptomics": {
|
| 588 |
-
"complete":
|
| 589 |
"cursor": null,
|
| 590 |
"query": "\"transcriptomics\" in:description stars:5000..9999"
|
| 591 |
},
|
| 592 |
"bio.variant-effects": {
|
| 593 |
-
"complete":
|
| 594 |
"cursor": null,
|
| 595 |
"query": "\"variant effect prediction\" in:readme stars:5000..9999"
|
| 596 |
},
|
| 597 |
"chem-admet": {
|
| 598 |
-
"complete":
|
| 599 |
"cursor": null,
|
| 600 |
"query": "\"ADMET prediction\" in:description,readme stars:5000..9999"
|
| 601 |
},
|
| 602 |
"chem-binding-affinity": {
|
| 603 |
-
"complete":
|
| 604 |
"cursor": null,
|
| 605 |
"query": "\"binding affinity prediction\" in:description,readme stars:5000..9999"
|
| 606 |
},
|
| 607 |
"chem-catalyst-discovery": {
|
| 608 |
-
"complete":
|
| 609 |
"cursor": null,
|
| 610 |
"query": "\"catalyst discovery\" in:description,readme stars:5000..9999"
|
| 611 |
},
|
| 612 |
"chem-cheminformatics": {
|
| 613 |
-
"complete":
|
| 614 |
"cursor": null,
|
| 615 |
"query": "topic:cheminformatics stars:5000..9999"
|
| 616 |
},
|
| 617 |
"chem-drug-discovery": {
|
| 618 |
-
"complete":
|
| 619 |
"cursor": null,
|
| 620 |
"query": "\"drug discovery\" in:description,readme stars:5000..9999"
|
| 621 |
},
|
| 622 |
"chem-interatomic-potentials": {
|
| 623 |
-
"complete":
|
| 624 |
"cursor": null,
|
| 625 |
"query": "\"machine learning interatomic potential\" in:description,readme stars:5000..9999"
|
| 626 |
},
|
| 627 |
"chem-molecular-docking": {
|
| 628 |
-
"complete":
|
| 629 |
"cursor": null,
|
| 630 |
"query": "\"molecular docking\" in:description,readme stars:5000..9999"
|
| 631 |
},
|
| 632 |
"chem-molecular-dynamics": {
|
| 633 |
-
"complete":
|
| 634 |
"cursor": null,
|
| 635 |
"query": "\"molecular dynamics\" \"machine learning\" in:description,readme stars:5000..9999"
|
| 636 |
},
|
| 637 |
"chem-molecular-generation": {
|
| 638 |
-
"complete":
|
| 639 |
"cursor": null,
|
| 640 |
"query": "\"molecular generation\" in:description,readme stars:5000..9999"
|
| 641 |
},
|
| 642 |
"chem-molecular-property-benchmark": {
|
| 643 |
-
"complete":
|
| 644 |
"cursor": null,
|
| 645 |
"query": "MoleculeNet in:description,readme stars:5000..9999"
|
| 646 |
},
|
| 647 |
"chem-molecular-spectroscopy": {
|
| 648 |
-
"complete":
|
| 649 |
"cursor": null,
|
| 650 |
"query": "molecular spectroscopy \"machine learning\" in:description,readme stars:5000..9999"
|
| 651 |
},
|
| 652 |
"chem-property-prediction": {
|
| 653 |
-
"complete":
|
| 654 |
"cursor": null,
|
| 655 |
"query": "\"molecular property prediction\" in:description,readme stars:5000..9999"
|
| 656 |
},
|
| 657 |
"chem-protein-ligand-docking": {
|
| 658 |
-
"complete":
|
| 659 |
"cursor": null,
|
| 660 |
"query": "\"protein-ligand docking\" in:description,readme stars:5000..9999"
|
| 661 |
},
|
| 662 |
"chem-quantum-chemistry": {
|
| 663 |
-
"complete":
|
| 664 |
"cursor": null,
|
| 665 |
"query": "\"quantum chemistry\" \"machine learning\" in:description,readme stars:5000..9999"
|
| 666 |
},
|
| 667 |
"chem-reaction-conditions": {
|
| 668 |
-
"complete":
|
| 669 |
"cursor": null,
|
| 670 |
"query": "\"reaction condition prediction\" in:description,readme stars:5000..9999"
|
| 671 |
},
|
| 672 |
"chem-reaction-prediction": {
|
| 673 |
-
"complete":
|
| 674 |
"cursor": null,
|
| 675 |
"query": "\"reaction prediction\" in:description,readme stars:5000..9999"
|
| 676 |
},
|
| 677 |
"chem-reaction-yield": {
|
| 678 |
-
"complete":
|
| 679 |
"cursor": null,
|
| 680 |
"query": "\"reaction yield prediction\" in:description,readme stars:5000..9999"
|
| 681 |
},
|
| 682 |
"chem-retrosynthesis": {
|
| 683 |
-
"complete":
|
| 684 |
"cursor": null,
|
| 685 |
"query": "\"computer aided synthesis planning\" in:description,readme stars:5000..9999"
|
| 686 |
},
|
| 687 |
"chem-retrosynthesis-planning": {
|
| 688 |
-
"complete":
|
| 689 |
"cursor": null,
|
| 690 |
"query": "retrosynthesis in:description,readme stars:5000..9999"
|
| 691 |
},
|
| 692 |
"chem-virtual-screening": {
|
| 693 |
-
"complete":
|
| 694 |
"cursor": null,
|
| 695 |
"query": "\"virtual screening\" in:description,readme stars:5000..9999"
|
| 696 |
},
|
| 697 |
"cross26.audio-diffusion": {
|
| 698 |
-
"complete":
|
| 699 |
"cursor": null,
|
| 700 |
"query": "\"audio diffusion\" generative model in:description,readme stars:5000..9999"
|
| 701 |
},
|
| 702 |
"cross26.audio-neural-vocoder": {
|
| 703 |
-
"complete":
|
| 704 |
"cursor": null,
|
| 705 |
"query": "\"neural vocoder\" in:description,readme stars:5000..9999"
|
| 706 |
},
|
| 707 |
"cross26.audio-self-supervised-speech": {
|
| 708 |
-
"complete":
|
| 709 |
"cursor": null,
|
| 710 |
"query": "\"self-supervised speech representation\" in:description,readme stars:5000..9999"
|
| 711 |
},
|
| 712 |
"cross26.audio-singing-synthesis": {
|
| 713 |
-
"complete":
|
| 714 |
"cursor": null,
|
| 715 |
"query": "\"singing voice synthesis\" in:description,readme stars:5000..9999"
|
| 716 |
},
|
| 717 |
"cross26.audio-speech-enhancement-transformer": {
|
| 718 |
-
"complete":
|
| 719 |
"cursor": null,
|
| 720 |
"query": "\"speech enhancement transformer\" in:description,readme stars:5000..9999"
|
| 721 |
},
|
| 722 |
"cross26.biomed-antibody-design": {
|
| 723 |
-
"complete":
|
| 724 |
"cursor": null,
|
| 725 |
"query": "\"antibody design\" machine learning in:description,readme stars:5000..9999"
|
| 726 |
},
|
| 727 |
"cross26.biomed-drug-response": {
|
| 728 |
-
"complete":
|
| 729 |
"cursor": null,
|
| 730 |
"query": "\"drug response prediction\" machine learning in:description,readme stars:5000..9999"
|
| 731 |
},
|
| 732 |
"cross26.biomed-peptide-design": {
|
| 733 |
-
"complete":
|
| 734 |
"cursor": null,
|
| 735 |
"query": "\"peptide design\" generative model in:description,readme stars:5000..9999"
|
| 736 |
},
|
| 737 |
"cross26.biomed-retinal-vessel-segmentation": {
|
| 738 |
-
"complete":
|
| 739 |
"cursor": null,
|
| 740 |
"query": "\"retinal vessel segmentation\" deep learning in:description,readme stars:5000..9999"
|
| 741 |
},
|
| 742 |
"cross26.biomed-whole-slide-analysis": {
|
| 743 |
-
"complete":
|
| 744 |
"cursor": null,
|
| 745 |
"query": "\"whole slide image\" deep learning in:description,readme stars:5000..9999"
|
| 746 |
},
|
| 747 |
"cross26.causal-bandits": {
|
| 748 |
-
"complete":
|
| 749 |
"cursor": null,
|
| 750 |
"query": "\"causal bandit\" machine learning in:description,readme stars:5000..9999"
|
| 751 |
},
|
| 752 |
"cross26.causal-bayesian-structural-time-series": {
|
| 753 |
-
"complete":
|
| 754 |
"cursor": null,
|
| 755 |
"query": "\"Bayesian structural time series\" causal in:description,readme stars:5000..9999"
|
| 756 |
},
|
| 757 |
"cross26.causal-doubly-robust": {
|
| 758 |
-
"complete":
|
| 759 |
"cursor": null,
|
| 760 |
"query": "\"doubly robust\" treatment effect machine learning in:description,readme stars:5000..9999"
|
| 761 |
},
|
| 762 |
"cross26.causal-instrumental-variable": {
|
| 763 |
-
"complete":
|
| 764 |
"cursor": null,
|
| 765 |
"query": "\"instrumental variable\" machine learning in:description,readme stars:5000..9999"
|
| 766 |
},
|
| 767 |
"cross26.causal-synthetic-control": {
|
| 768 |
-
"complete":
|
| 769 |
"cursor": null,
|
| 770 |
"query": "\"synthetic control\" machine learning in:description,readme stars:5000..9999"
|
| 771 |
},
|
| 772 |
"cross26.chem-battery-degradation": {
|
| 773 |
-
"complete":
|
| 774 |
"cursor": null,
|
| 775 |
"query": "\"battery degradation prediction\" machine learning in:description,readme stars:5000..9999"
|
| 776 |
},
|
| 777 |
"cross26.chem-catalyst-activity": {
|
| 778 |
-
"complete":
|
| 779 |
"cursor": null,
|
| 780 |
"query": "\"catalyst activity prediction\" machine learning in:description,readme stars:5000..9999"
|
| 781 |
},
|
| 782 |
"cross26.chem-crystal-property": {
|
| 783 |
-
"complete":
|
| 784 |
"cursor": null,
|
| 785 |
"query": "\"crystal property prediction\" machine learning in:description,readme stars:5000..9999"
|
| 786 |
},
|
| 787 |
"cross26.chem-polymer-generation": {
|
| 788 |
-
"complete":
|
| 789 |
"cursor": null,
|
| 790 |
"query": "\"polymer generation\" machine learning in:description,readme stars:5000..9999"
|
| 791 |
},
|
| 792 |
"cross26.chem-reaction-center": {
|
| 793 |
-
"complete":
|
| 794 |
"cursor": null,
|
| 795 |
"query": "\"reaction center prediction\" machine learning in:description,readme stars:5000..9999"
|
| 796 |
},
|
| 797 |
"cross26.geo-air-quality-forecasting": {
|
| 798 |
-
"complete":
|
| 799 |
"cursor": null,
|
| 800 |
"query": "\"air quality forecasting\" machine learning in:description,readme stars:5000..9999"
|
| 801 |
},
|
| 802 |
"cross26.geo-sea-level-prediction": {
|
| 803 |
-
"complete":
|
| 804 |
"cursor": null,
|
| 805 |
"query": "\"sea level prediction\" machine learning in:description,readme stars:5000..9999"
|
| 806 |
},
|
| 807 |
"cross26.geo-soil-moisture-downscaling": {
|
| 808 |
-
"complete":
|
| 809 |
"cursor": null,
|
| 810 |
"query": "\"soil moisture downscaling\" machine learning in:description,readme stars:5000..9999"
|
| 811 |
},
|
| 812 |
"cross26.geo-urban-heat-prediction": {
|
| 813 |
-
"complete":
|
| 814 |
"cursor": null,
|
| 815 |
"query": "\"urban heat island prediction\" machine learning in:description,readme stars:5000..9999"
|
| 816 |
},
|
| 817 |
"cross26.geo-wildfire-spread": {
|
| 818 |
-
"complete":
|
| 819 |
"cursor": null,
|
| 820 |
"query": "\"wildfire spread prediction\" machine learning in:description,readme stars:5000..9999"
|
| 821 |
},
|
| 822 |
"cross26.graphics-differentiable-rendering": {
|
| 823 |
-
"complete":
|
| 824 |
"cursor": null,
|
| 825 |
"query": "\"differentiable rendering\" in:description,readme stars:5000..9999"
|
| 826 |
},
|
| 827 |
"cross26.graphics-implicit-neural-representation": {
|
| 828 |
-
"complete":
|
| 829 |
"cursor": null,
|
| 830 |
"query": "\"implicit neural representation\" in:description,readme stars:5000..9999"
|
| 831 |
},
|
| 832 |
"cross26.graphics-neural-radiance-field": {
|
| 833 |
-
"complete":
|
| 834 |
"cursor": null,
|
| 835 |
"query": "\"neural radiance field\" in:description,readme stars:5000..9999"
|
| 836 |
},
|
| 837 |
"cross26.graphics-occupancy-network": {
|
| 838 |
-
"complete":
|
| 839 |
"cursor": null,
|
| 840 |
"query": "\"occupancy network\" 3D in:description,readme stars:5000..9999"
|
| 841 |
},
|
| 842 |
"cross26.graphics-sixd-pose": {
|
| 843 |
-
"complete":
|
| 844 |
"cursor": null,
|
| 845 |
"query": "\"6D pose estimation\" in:description,readme stars:5000..9999"
|
| 846 |
},
|
| 847 |
"cross26.robotics-contact-rich-manipulation": {
|
| 848 |
-
"complete":
|
| 849 |
"cursor": null,
|
| 850 |
"query": "\"contact-rich manipulation\" learning in:description,readme stars:5000..9999"
|
| 851 |
},
|
| 852 |
"cross26.robotics-model-predictive-control": {
|
| 853 |
-
"complete":
|
| 854 |
"cursor": null,
|
| 855 |
"query": "\"model predictive control\" robot learning in:description,readme stars:5000..9999"
|
| 856 |
},
|
| 857 |
"cross26.robotics-tactile-policy": {
|
| 858 |
-
"complete":
|
| 859 |
"cursor": null,
|
| 860 |
"query": "\"tactile policy learning\" in:description,readme stars:5000..9999"
|
| 861 |
},
|
| 862 |
"cross26.robotics-trajectory-optimization": {
|
| 863 |
-
"complete":
|
| 864 |
"cursor": null,
|
| 865 |
"query": "\"trajectory optimization\" robot learning in:description,readme stars:5000..9999"
|
| 866 |
},
|
| 867 |
"cross26.robotics-visual-servoing": {
|
| 868 |
-
"complete":
|
| 869 |
"cursor": null,
|
| 870 |
"query": "\"visual servoing\" deep learning in:description,readme stars:5000..9999"
|
| 871 |
},
|
| 872 |
"cross26.science-learned-turbulence-closure": {
|
| 873 |
-
"complete":
|
| 874 |
"cursor": null,
|
| 875 |
"query": "\"learned turbulence closure\" in:description,readme stars:5000..9999"
|
| 876 |
},
|
| 877 |
"cross26.science-neural-weather-model": {
|
| 878 |
-
"complete":
|
| 879 |
"cursor": null,
|
| 880 |
"query": "\"neural weather model\" in:description,readme stars:5000..9999"
|
| 881 |
},
|
| 882 |
"cross26.science-ocean-forecasting": {
|
| 883 |
-
"complete":
|
| 884 |
"cursor": null,
|
| 885 |
"query": "\"ocean forecasting\" deep learning in:description,readme stars:5000..9999"
|
| 886 |
},
|
| 887 |
"cross26.science-physics-informed-operator": {
|
| 888 |
-
"complete":
|
| 889 |
"cursor": null,
|
| 890 |
"query": "\"physics-informed operator learning\" in:description,readme stars:5000..9999"
|
| 891 |
},
|
| 892 |
"cross26.science-quantum-error-correction": {
|
| 893 |
-
"complete":
|
| 894 |
"cursor": null,
|
| 895 |
"query": "\"quantum error correction\" machine learning in:description,readme stars:5000..9999"
|
| 896 |
},
|
| 897 |
"cross27.vision.hand-object-interaction": {
|
| 898 |
-
"complete":
|
| 899 |
"cursor": null,
|
| 900 |
"query": "\"hand-object interaction\" deep learning in:description,readme stars:5000..9999"
|
| 901 |
},
|
| 902 |
"cross27.vision.neural-surface-reconstruction": {
|
| 903 |
-
"complete":
|
| 904 |
"cursor": null,
|
| 905 |
"query": "\"neural surface reconstruction\" in:description,readme stars:5000..9999"
|
| 906 |
},
|
| 907 |
"cross27.vision.referring-segmentation": {
|
| 908 |
-
"complete":
|
| 909 |
"cursor": null,
|
| 910 |
"query": "\"referring image segmentation\" in:description,readme stars:5000..9999"
|
| 911 |
},
|
| 912 |
"cross27.vision.semantic-correspondence": {
|
| 913 |
-
"complete":
|
| 914 |
"cursor": null,
|
| 915 |
"query": "\"semantic correspondence\" image matching in:description,readme stars:5000..9999"
|
| 916 |
},
|
| 917 |
"cross27.vision.video-object-segmentation": {
|
| 918 |
-
"complete":
|
| 919 |
"cursor": null,
|
| 920 |
"query": "\"video object segmentation\" deep learning in:description,readme stars:5000..9999"
|
| 921 |
},
|
| 922 |
"cross27.vision.visual-prompting": {
|
| 923 |
-
"complete":
|
| 924 |
"cursor": null,
|
| 925 |
"query": "\"visual prompt\" segmentation model in:description,readme stars:5000..9999"
|
| 926 |
},
|
| 927 |
"efficiency.activation-checkpointing": {
|
| 928 |
-
"complete":
|
| 929 |
"cursor": null,
|
| 930 |
"query": "\"activation checkpointing\" in:description stars:5000..9999"
|
| 931 |
},
|
| 932 |
"efficiency.automl": {
|
| 933 |
-
"complete":
|
| 934 |
"cursor": null,
|
| 935 |
"query": "AutoML in:description stars:5000..9999"
|
| 936 |
},
|
| 937 |
"efficiency.communication-efficient-training": {
|
| 938 |
-
"complete":
|
| 939 |
"cursor": null,
|
| 940 |
"query": "\"communication efficient training\" in:description stars:5000..9999"
|
| 941 |
},
|
| 942 |
"efficiency.compiler-optimization": {
|
| 943 |
-
"complete":
|
| 944 |
"cursor": null,
|
| 945 |
"query": "\"machine learning compiler\" in:description stars:5000..9999"
|
| 946 |
},
|
| 947 |
"efficiency.distributed-training": {
|
| 948 |
-
"complete":
|
| 949 |
"cursor": null,
|
| 950 |
"query": "\"distributed training\" in:description stars:5000..9999"
|
| 951 |
},
|
| 952 |
"efficiency.edge-ai": {
|
| 953 |
-
"complete":
|
| 954 |
"cursor": null,
|
| 955 |
"query": "\"edge AI\" in:description stars:5000..9999"
|
| 956 |
},
|
| 957 |
"efficiency.energy-efficient-ml": {
|
| 958 |
-
"complete":
|
| 959 |
"cursor": null,
|
| 960 |
"query": "\"energy efficient machine learning\" in:description stars:5000..9999"
|
| 961 |
},
|
| 962 |
"efficiency.flash-attention": {
|
| 963 |
-
"complete":
|
| 964 |
"cursor": null,
|
| 965 |
"query": "\"FlashAttention\" in:description stars:5000..9999"
|
| 966 |
},
|
| 967 |
"efficiency.gpu-kernels": {
|
| 968 |
-
"complete":
|
| 969 |
"cursor": null,
|
| 970 |
"query": "\"GPU kernel\" in:description stars:5000..9999"
|
| 971 |
},
|
| 972 |
"efficiency.gradient-optimization": {
|
| 973 |
-
"complete":
|
| 974 |
"cursor": null,
|
| 975 |
"query": "\"gradient-based optimization\" in:description stars:5000..9999"
|
| 976 |
},
|
| 977 |
"efficiency.hyperparameter-optimization": {
|
| 978 |
-
"complete":
|
| 979 |
"cursor": null,
|
| 980 |
"query": "\"hyperparameter optimization\" in:description stars:5000..9999"
|
| 981 |
},
|
| 982 |
"efficiency.inference-systems": {
|
| 983 |
-
"complete":
|
| 984 |
"cursor": null,
|
| 985 |
"query": "\"inference engine\" \"machine learning\" in:description,readme stars:5000..9999"
|
| 986 |
},
|
| 987 |
"efficiency.knowledge-distillation": {
|
| 988 |
-
"complete":
|
| 989 |
"cursor": null,
|
| 990 |
"query": "\"knowledge distillation\" in:description stars:5000..9999"
|
| 991 |
},
|
| 992 |
"efficiency.kv-cache": {
|
| 993 |
-
"complete":
|
| 994 |
"cursor": null,
|
| 995 |
"query": "\"KV cache\" in:description stars:5000..9999"
|
| 996 |
},
|
| 997 |
"efficiency.lora": {
|
| 998 |
-
"complete":
|
| 999 |
"cursor": null,
|
| 1000 |
"query": "LoRA in:description stars:5000..9999"
|
| 1001 |
},
|
| 1002 |
"efficiency.low-rank-factorization": {
|
| 1003 |
-
"complete":
|
| 1004 |
"cursor": null,
|
| 1005 |
"query": "\"low-rank factorization\" in:description stars:5000..9999"
|
| 1006 |
},
|
| 1007 |
"efficiency.mixed-precision-training": {
|
| 1008 |
-
"complete":
|
| 1009 |
"cursor": null,
|
| 1010 |
"query": "\"mixed precision training\" in:description stars:5000..9999"
|
| 1011 |
},
|
| 1012 |
"efficiency.mixture-of-experts": {
|
| 1013 |
-
"complete":
|
| 1014 |
"cursor": null,
|
| 1015 |
"query": "\"mixture of experts\" in:description stars:5000..9999"
|
| 1016 |
},
|
| 1017 |
"efficiency.model-compression": {
|
| 1018 |
-
"complete":
|
| 1019 |
"cursor": null,
|
| 1020 |
"query": "\"model compression\" in:description stars:5000..9999"
|
| 1021 |
},
|
| 1022 |
"efficiency.neural-architecture-search": {
|
| 1023 |
-
"complete":
|
| 1024 |
"cursor": null,
|
| 1025 |
"query": "\"neural architecture search\" in:description stars:5000..9999"
|
| 1026 |
},
|
| 1027 |
"efficiency.optimizer-state-sharding": {
|
| 1028 |
-
"complete":
|
| 1029 |
"cursor": null,
|
| 1030 |
"query": "\"optimizer state sharding\" in:description stars:5000..9999"
|
| 1031 |
},
|
| 1032 |
"efficiency.parameter-efficient-finetuning": {
|
| 1033 |
-
"complete":
|
| 1034 |
"cursor": null,
|
| 1035 |
"query": "\"parameter-efficient fine-tuning\" in:description stars:5000..9999"
|
| 1036 |
},
|
| 1037 |
"efficiency.post-training-quantization": {
|
| 1038 |
-
"complete":
|
| 1039 |
"cursor": null,
|
| 1040 |
"query": "\"post-training quantization\" in:description stars:5000..9999"
|
| 1041 |
},
|
| 1042 |
"efficiency.pruning": {
|
| 1043 |
-
"complete":
|
| 1044 |
"cursor": null,
|
| 1045 |
"query": "pruning in:description stars:5000..9999"
|
| 1046 |
},
|
|
@@ -4105,7 +4105,7 @@
|
|
| 4105 |
"query": "\"visual localization\" in:readme stars:5000..9999"
|
| 4106 |
}
|
| 4107 |
},
|
| 4108 |
-
"next_index":
|
| 4109 |
"per_page": 100,
|
| 4110 |
"start": "2008-01-01",
|
| 4111 |
"version": 1
|
|
|
|
| 545 |
"query": "\"protein folding\" in:readme stars:5000..9999"
|
| 546 |
},
|
| 547 |
"bio.protein-interactions": {
|
| 548 |
+
"complete": true,
|
| 549 |
"cursor": null,
|
| 550 |
"query": "\"protein-protein interaction prediction\" in:readme stars:5000..9999"
|
| 551 |
},
|
| 552 |
"bio.protein-structure": {
|
| 553 |
+
"complete": true,
|
| 554 |
"cursor": null,
|
| 555 |
"query": "\"protein structure prediction\" in:description stars:5000..9999"
|
| 556 |
},
|
| 557 |
"bio.regulatory-sequence-modeling": {
|
| 558 |
+
"complete": true,
|
| 559 |
"cursor": null,
|
| 560 |
"query": "enhancer prediction genomics in:description,readme stars:5000..9999"
|
| 561 |
},
|
| 562 |
"bio.rna-language-models": {
|
| 563 |
+
"complete": true,
|
| 564 |
"cursor": null,
|
| 565 |
"query": "\"RNA language model\" in:description stars:5000..9999"
|
| 566 |
},
|
| 567 |
"bio.rna-structure": {
|
| 568 |
+
"complete": true,
|
| 569 |
"cursor": null,
|
| 570 |
"query": "\"RNA structure prediction\" in:description,readme stars:5000..9999"
|
| 571 |
},
|
| 572 |
"bio.scrna-seq": {
|
| 573 |
+
"complete": true,
|
| 574 |
"cursor": null,
|
| 575 |
"query": "\"scRNA-seq\" in:readme stars:5000..9999"
|
| 576 |
},
|
| 577 |
"bio.single-cell": {
|
| 578 |
+
"complete": true,
|
| 579 |
"cursor": null,
|
| 580 |
"query": "\"single-cell\" in:description stars:5000..9999"
|
| 581 |
},
|
| 582 |
"bio.spatial-transcriptomics": {
|
| 583 |
+
"complete": true,
|
| 584 |
"cursor": null,
|
| 585 |
"query": "\"spatial transcriptomics\" in:description,readme stars:5000..9999"
|
| 586 |
},
|
| 587 |
"bio.transcriptomics": {
|
| 588 |
+
"complete": true,
|
| 589 |
"cursor": null,
|
| 590 |
"query": "\"transcriptomics\" in:description stars:5000..9999"
|
| 591 |
},
|
| 592 |
"bio.variant-effects": {
|
| 593 |
+
"complete": true,
|
| 594 |
"cursor": null,
|
| 595 |
"query": "\"variant effect prediction\" in:readme stars:5000..9999"
|
| 596 |
},
|
| 597 |
"chem-admet": {
|
| 598 |
+
"complete": true,
|
| 599 |
"cursor": null,
|
| 600 |
"query": "\"ADMET prediction\" in:description,readme stars:5000..9999"
|
| 601 |
},
|
| 602 |
"chem-binding-affinity": {
|
| 603 |
+
"complete": true,
|
| 604 |
"cursor": null,
|
| 605 |
"query": "\"binding affinity prediction\" in:description,readme stars:5000..9999"
|
| 606 |
},
|
| 607 |
"chem-catalyst-discovery": {
|
| 608 |
+
"complete": true,
|
| 609 |
"cursor": null,
|
| 610 |
"query": "\"catalyst discovery\" in:description,readme stars:5000..9999"
|
| 611 |
},
|
| 612 |
"chem-cheminformatics": {
|
| 613 |
+
"complete": true,
|
| 614 |
"cursor": null,
|
| 615 |
"query": "topic:cheminformatics stars:5000..9999"
|
| 616 |
},
|
| 617 |
"chem-drug-discovery": {
|
| 618 |
+
"complete": true,
|
| 619 |
"cursor": null,
|
| 620 |
"query": "\"drug discovery\" in:description,readme stars:5000..9999"
|
| 621 |
},
|
| 622 |
"chem-interatomic-potentials": {
|
| 623 |
+
"complete": true,
|
| 624 |
"cursor": null,
|
| 625 |
"query": "\"machine learning interatomic potential\" in:description,readme stars:5000..9999"
|
| 626 |
},
|
| 627 |
"chem-molecular-docking": {
|
| 628 |
+
"complete": true,
|
| 629 |
"cursor": null,
|
| 630 |
"query": "\"molecular docking\" in:description,readme stars:5000..9999"
|
| 631 |
},
|
| 632 |
"chem-molecular-dynamics": {
|
| 633 |
+
"complete": true,
|
| 634 |
"cursor": null,
|
| 635 |
"query": "\"molecular dynamics\" \"machine learning\" in:description,readme stars:5000..9999"
|
| 636 |
},
|
| 637 |
"chem-molecular-generation": {
|
| 638 |
+
"complete": true,
|
| 639 |
"cursor": null,
|
| 640 |
"query": "\"molecular generation\" in:description,readme stars:5000..9999"
|
| 641 |
},
|
| 642 |
"chem-molecular-property-benchmark": {
|
| 643 |
+
"complete": true,
|
| 644 |
"cursor": null,
|
| 645 |
"query": "MoleculeNet in:description,readme stars:5000..9999"
|
| 646 |
},
|
| 647 |
"chem-molecular-spectroscopy": {
|
| 648 |
+
"complete": true,
|
| 649 |
"cursor": null,
|
| 650 |
"query": "molecular spectroscopy \"machine learning\" in:description,readme stars:5000..9999"
|
| 651 |
},
|
| 652 |
"chem-property-prediction": {
|
| 653 |
+
"complete": true,
|
| 654 |
"cursor": null,
|
| 655 |
"query": "\"molecular property prediction\" in:description,readme stars:5000..9999"
|
| 656 |
},
|
| 657 |
"chem-protein-ligand-docking": {
|
| 658 |
+
"complete": true,
|
| 659 |
"cursor": null,
|
| 660 |
"query": "\"protein-ligand docking\" in:description,readme stars:5000..9999"
|
| 661 |
},
|
| 662 |
"chem-quantum-chemistry": {
|
| 663 |
+
"complete": true,
|
| 664 |
"cursor": null,
|
| 665 |
"query": "\"quantum chemistry\" \"machine learning\" in:description,readme stars:5000..9999"
|
| 666 |
},
|
| 667 |
"chem-reaction-conditions": {
|
| 668 |
+
"complete": true,
|
| 669 |
"cursor": null,
|
| 670 |
"query": "\"reaction condition prediction\" in:description,readme stars:5000..9999"
|
| 671 |
},
|
| 672 |
"chem-reaction-prediction": {
|
| 673 |
+
"complete": true,
|
| 674 |
"cursor": null,
|
| 675 |
"query": "\"reaction prediction\" in:description,readme stars:5000..9999"
|
| 676 |
},
|
| 677 |
"chem-reaction-yield": {
|
| 678 |
+
"complete": true,
|
| 679 |
"cursor": null,
|
| 680 |
"query": "\"reaction yield prediction\" in:description,readme stars:5000..9999"
|
| 681 |
},
|
| 682 |
"chem-retrosynthesis": {
|
| 683 |
+
"complete": true,
|
| 684 |
"cursor": null,
|
| 685 |
"query": "\"computer aided synthesis planning\" in:description,readme stars:5000..9999"
|
| 686 |
},
|
| 687 |
"chem-retrosynthesis-planning": {
|
| 688 |
+
"complete": true,
|
| 689 |
"cursor": null,
|
| 690 |
"query": "retrosynthesis in:description,readme stars:5000..9999"
|
| 691 |
},
|
| 692 |
"chem-virtual-screening": {
|
| 693 |
+
"complete": true,
|
| 694 |
"cursor": null,
|
| 695 |
"query": "\"virtual screening\" in:description,readme stars:5000..9999"
|
| 696 |
},
|
| 697 |
"cross26.audio-diffusion": {
|
| 698 |
+
"complete": true,
|
| 699 |
"cursor": null,
|
| 700 |
"query": "\"audio diffusion\" generative model in:description,readme stars:5000..9999"
|
| 701 |
},
|
| 702 |
"cross26.audio-neural-vocoder": {
|
| 703 |
+
"complete": true,
|
| 704 |
"cursor": null,
|
| 705 |
"query": "\"neural vocoder\" in:description,readme stars:5000..9999"
|
| 706 |
},
|
| 707 |
"cross26.audio-self-supervised-speech": {
|
| 708 |
+
"complete": true,
|
| 709 |
"cursor": null,
|
| 710 |
"query": "\"self-supervised speech representation\" in:description,readme stars:5000..9999"
|
| 711 |
},
|
| 712 |
"cross26.audio-singing-synthesis": {
|
| 713 |
+
"complete": true,
|
| 714 |
"cursor": null,
|
| 715 |
"query": "\"singing voice synthesis\" in:description,readme stars:5000..9999"
|
| 716 |
},
|
| 717 |
"cross26.audio-speech-enhancement-transformer": {
|
| 718 |
+
"complete": true,
|
| 719 |
"cursor": null,
|
| 720 |
"query": "\"speech enhancement transformer\" in:description,readme stars:5000..9999"
|
| 721 |
},
|
| 722 |
"cross26.biomed-antibody-design": {
|
| 723 |
+
"complete": true,
|
| 724 |
"cursor": null,
|
| 725 |
"query": "\"antibody design\" machine learning in:description,readme stars:5000..9999"
|
| 726 |
},
|
| 727 |
"cross26.biomed-drug-response": {
|
| 728 |
+
"complete": true,
|
| 729 |
"cursor": null,
|
| 730 |
"query": "\"drug response prediction\" machine learning in:description,readme stars:5000..9999"
|
| 731 |
},
|
| 732 |
"cross26.biomed-peptide-design": {
|
| 733 |
+
"complete": true,
|
| 734 |
"cursor": null,
|
| 735 |
"query": "\"peptide design\" generative model in:description,readme stars:5000..9999"
|
| 736 |
},
|
| 737 |
"cross26.biomed-retinal-vessel-segmentation": {
|
| 738 |
+
"complete": true,
|
| 739 |
"cursor": null,
|
| 740 |
"query": "\"retinal vessel segmentation\" deep learning in:description,readme stars:5000..9999"
|
| 741 |
},
|
| 742 |
"cross26.biomed-whole-slide-analysis": {
|
| 743 |
+
"complete": true,
|
| 744 |
"cursor": null,
|
| 745 |
"query": "\"whole slide image\" deep learning in:description,readme stars:5000..9999"
|
| 746 |
},
|
| 747 |
"cross26.causal-bandits": {
|
| 748 |
+
"complete": true,
|
| 749 |
"cursor": null,
|
| 750 |
"query": "\"causal bandit\" machine learning in:description,readme stars:5000..9999"
|
| 751 |
},
|
| 752 |
"cross26.causal-bayesian-structural-time-series": {
|
| 753 |
+
"complete": true,
|
| 754 |
"cursor": null,
|
| 755 |
"query": "\"Bayesian structural time series\" causal in:description,readme stars:5000..9999"
|
| 756 |
},
|
| 757 |
"cross26.causal-doubly-robust": {
|
| 758 |
+
"complete": true,
|
| 759 |
"cursor": null,
|
| 760 |
"query": "\"doubly robust\" treatment effect machine learning in:description,readme stars:5000..9999"
|
| 761 |
},
|
| 762 |
"cross26.causal-instrumental-variable": {
|
| 763 |
+
"complete": true,
|
| 764 |
"cursor": null,
|
| 765 |
"query": "\"instrumental variable\" machine learning in:description,readme stars:5000..9999"
|
| 766 |
},
|
| 767 |
"cross26.causal-synthetic-control": {
|
| 768 |
+
"complete": true,
|
| 769 |
"cursor": null,
|
| 770 |
"query": "\"synthetic control\" machine learning in:description,readme stars:5000..9999"
|
| 771 |
},
|
| 772 |
"cross26.chem-battery-degradation": {
|
| 773 |
+
"complete": true,
|
| 774 |
"cursor": null,
|
| 775 |
"query": "\"battery degradation prediction\" machine learning in:description,readme stars:5000..9999"
|
| 776 |
},
|
| 777 |
"cross26.chem-catalyst-activity": {
|
| 778 |
+
"complete": true,
|
| 779 |
"cursor": null,
|
| 780 |
"query": "\"catalyst activity prediction\" machine learning in:description,readme stars:5000..9999"
|
| 781 |
},
|
| 782 |
"cross26.chem-crystal-property": {
|
| 783 |
+
"complete": true,
|
| 784 |
"cursor": null,
|
| 785 |
"query": "\"crystal property prediction\" machine learning in:description,readme stars:5000..9999"
|
| 786 |
},
|
| 787 |
"cross26.chem-polymer-generation": {
|
| 788 |
+
"complete": true,
|
| 789 |
"cursor": null,
|
| 790 |
"query": "\"polymer generation\" machine learning in:description,readme stars:5000..9999"
|
| 791 |
},
|
| 792 |
"cross26.chem-reaction-center": {
|
| 793 |
+
"complete": true,
|
| 794 |
"cursor": null,
|
| 795 |
"query": "\"reaction center prediction\" machine learning in:description,readme stars:5000..9999"
|
| 796 |
},
|
| 797 |
"cross26.geo-air-quality-forecasting": {
|
| 798 |
+
"complete": true,
|
| 799 |
"cursor": null,
|
| 800 |
"query": "\"air quality forecasting\" machine learning in:description,readme stars:5000..9999"
|
| 801 |
},
|
| 802 |
"cross26.geo-sea-level-prediction": {
|
| 803 |
+
"complete": true,
|
| 804 |
"cursor": null,
|
| 805 |
"query": "\"sea level prediction\" machine learning in:description,readme stars:5000..9999"
|
| 806 |
},
|
| 807 |
"cross26.geo-soil-moisture-downscaling": {
|
| 808 |
+
"complete": true,
|
| 809 |
"cursor": null,
|
| 810 |
"query": "\"soil moisture downscaling\" machine learning in:description,readme stars:5000..9999"
|
| 811 |
},
|
| 812 |
"cross26.geo-urban-heat-prediction": {
|
| 813 |
+
"complete": true,
|
| 814 |
"cursor": null,
|
| 815 |
"query": "\"urban heat island prediction\" machine learning in:description,readme stars:5000..9999"
|
| 816 |
},
|
| 817 |
"cross26.geo-wildfire-spread": {
|
| 818 |
+
"complete": true,
|
| 819 |
"cursor": null,
|
| 820 |
"query": "\"wildfire spread prediction\" machine learning in:description,readme stars:5000..9999"
|
| 821 |
},
|
| 822 |
"cross26.graphics-differentiable-rendering": {
|
| 823 |
+
"complete": true,
|
| 824 |
"cursor": null,
|
| 825 |
"query": "\"differentiable rendering\" in:description,readme stars:5000..9999"
|
| 826 |
},
|
| 827 |
"cross26.graphics-implicit-neural-representation": {
|
| 828 |
+
"complete": true,
|
| 829 |
"cursor": null,
|
| 830 |
"query": "\"implicit neural representation\" in:description,readme stars:5000..9999"
|
| 831 |
},
|
| 832 |
"cross26.graphics-neural-radiance-field": {
|
| 833 |
+
"complete": true,
|
| 834 |
"cursor": null,
|
| 835 |
"query": "\"neural radiance field\" in:description,readme stars:5000..9999"
|
| 836 |
},
|
| 837 |
"cross26.graphics-occupancy-network": {
|
| 838 |
+
"complete": true,
|
| 839 |
"cursor": null,
|
| 840 |
"query": "\"occupancy network\" 3D in:description,readme stars:5000..9999"
|
| 841 |
},
|
| 842 |
"cross26.graphics-sixd-pose": {
|
| 843 |
+
"complete": true,
|
| 844 |
"cursor": null,
|
| 845 |
"query": "\"6D pose estimation\" in:description,readme stars:5000..9999"
|
| 846 |
},
|
| 847 |
"cross26.robotics-contact-rich-manipulation": {
|
| 848 |
+
"complete": true,
|
| 849 |
"cursor": null,
|
| 850 |
"query": "\"contact-rich manipulation\" learning in:description,readme stars:5000..9999"
|
| 851 |
},
|
| 852 |
"cross26.robotics-model-predictive-control": {
|
| 853 |
+
"complete": true,
|
| 854 |
"cursor": null,
|
| 855 |
"query": "\"model predictive control\" robot learning in:description,readme stars:5000..9999"
|
| 856 |
},
|
| 857 |
"cross26.robotics-tactile-policy": {
|
| 858 |
+
"complete": true,
|
| 859 |
"cursor": null,
|
| 860 |
"query": "\"tactile policy learning\" in:description,readme stars:5000..9999"
|
| 861 |
},
|
| 862 |
"cross26.robotics-trajectory-optimization": {
|
| 863 |
+
"complete": true,
|
| 864 |
"cursor": null,
|
| 865 |
"query": "\"trajectory optimization\" robot learning in:description,readme stars:5000..9999"
|
| 866 |
},
|
| 867 |
"cross26.robotics-visual-servoing": {
|
| 868 |
+
"complete": true,
|
| 869 |
"cursor": null,
|
| 870 |
"query": "\"visual servoing\" deep learning in:description,readme stars:5000..9999"
|
| 871 |
},
|
| 872 |
"cross26.science-learned-turbulence-closure": {
|
| 873 |
+
"complete": true,
|
| 874 |
"cursor": null,
|
| 875 |
"query": "\"learned turbulence closure\" in:description,readme stars:5000..9999"
|
| 876 |
},
|
| 877 |
"cross26.science-neural-weather-model": {
|
| 878 |
+
"complete": true,
|
| 879 |
"cursor": null,
|
| 880 |
"query": "\"neural weather model\" in:description,readme stars:5000..9999"
|
| 881 |
},
|
| 882 |
"cross26.science-ocean-forecasting": {
|
| 883 |
+
"complete": true,
|
| 884 |
"cursor": null,
|
| 885 |
"query": "\"ocean forecasting\" deep learning in:description,readme stars:5000..9999"
|
| 886 |
},
|
| 887 |
"cross26.science-physics-informed-operator": {
|
| 888 |
+
"complete": true,
|
| 889 |
"cursor": null,
|
| 890 |
"query": "\"physics-informed operator learning\" in:description,readme stars:5000..9999"
|
| 891 |
},
|
| 892 |
"cross26.science-quantum-error-correction": {
|
| 893 |
+
"complete": true,
|
| 894 |
"cursor": null,
|
| 895 |
"query": "\"quantum error correction\" machine learning in:description,readme stars:5000..9999"
|
| 896 |
},
|
| 897 |
"cross27.vision.hand-object-interaction": {
|
| 898 |
+
"complete": true,
|
| 899 |
"cursor": null,
|
| 900 |
"query": "\"hand-object interaction\" deep learning in:description,readme stars:5000..9999"
|
| 901 |
},
|
| 902 |
"cross27.vision.neural-surface-reconstruction": {
|
| 903 |
+
"complete": true,
|
| 904 |
"cursor": null,
|
| 905 |
"query": "\"neural surface reconstruction\" in:description,readme stars:5000..9999"
|
| 906 |
},
|
| 907 |
"cross27.vision.referring-segmentation": {
|
| 908 |
+
"complete": true,
|
| 909 |
"cursor": null,
|
| 910 |
"query": "\"referring image segmentation\" in:description,readme stars:5000..9999"
|
| 911 |
},
|
| 912 |
"cross27.vision.semantic-correspondence": {
|
| 913 |
+
"complete": true,
|
| 914 |
"cursor": null,
|
| 915 |
"query": "\"semantic correspondence\" image matching in:description,readme stars:5000..9999"
|
| 916 |
},
|
| 917 |
"cross27.vision.video-object-segmentation": {
|
| 918 |
+
"complete": true,
|
| 919 |
"cursor": null,
|
| 920 |
"query": "\"video object segmentation\" deep learning in:description,readme stars:5000..9999"
|
| 921 |
},
|
| 922 |
"cross27.vision.visual-prompting": {
|
| 923 |
+
"complete": true,
|
| 924 |
"cursor": null,
|
| 925 |
"query": "\"visual prompt\" segmentation model in:description,readme stars:5000..9999"
|
| 926 |
},
|
| 927 |
"efficiency.activation-checkpointing": {
|
| 928 |
+
"complete": true,
|
| 929 |
"cursor": null,
|
| 930 |
"query": "\"activation checkpointing\" in:description stars:5000..9999"
|
| 931 |
},
|
| 932 |
"efficiency.automl": {
|
| 933 |
+
"complete": true,
|
| 934 |
"cursor": null,
|
| 935 |
"query": "AutoML in:description stars:5000..9999"
|
| 936 |
},
|
| 937 |
"efficiency.communication-efficient-training": {
|
| 938 |
+
"complete": true,
|
| 939 |
"cursor": null,
|
| 940 |
"query": "\"communication efficient training\" in:description stars:5000..9999"
|
| 941 |
},
|
| 942 |
"efficiency.compiler-optimization": {
|
| 943 |
+
"complete": true,
|
| 944 |
"cursor": null,
|
| 945 |
"query": "\"machine learning compiler\" in:description stars:5000..9999"
|
| 946 |
},
|
| 947 |
"efficiency.distributed-training": {
|
| 948 |
+
"complete": true,
|
| 949 |
"cursor": null,
|
| 950 |
"query": "\"distributed training\" in:description stars:5000..9999"
|
| 951 |
},
|
| 952 |
"efficiency.edge-ai": {
|
| 953 |
+
"complete": true,
|
| 954 |
"cursor": null,
|
| 955 |
"query": "\"edge AI\" in:description stars:5000..9999"
|
| 956 |
},
|
| 957 |
"efficiency.energy-efficient-ml": {
|
| 958 |
+
"complete": true,
|
| 959 |
"cursor": null,
|
| 960 |
"query": "\"energy efficient machine learning\" in:description stars:5000..9999"
|
| 961 |
},
|
| 962 |
"efficiency.flash-attention": {
|
| 963 |
+
"complete": true,
|
| 964 |
"cursor": null,
|
| 965 |
"query": "\"FlashAttention\" in:description stars:5000..9999"
|
| 966 |
},
|
| 967 |
"efficiency.gpu-kernels": {
|
| 968 |
+
"complete": true,
|
| 969 |
"cursor": null,
|
| 970 |
"query": "\"GPU kernel\" in:description stars:5000..9999"
|
| 971 |
},
|
| 972 |
"efficiency.gradient-optimization": {
|
| 973 |
+
"complete": true,
|
| 974 |
"cursor": null,
|
| 975 |
"query": "\"gradient-based optimization\" in:description stars:5000..9999"
|
| 976 |
},
|
| 977 |
"efficiency.hyperparameter-optimization": {
|
| 978 |
+
"complete": true,
|
| 979 |
"cursor": null,
|
| 980 |
"query": "\"hyperparameter optimization\" in:description stars:5000..9999"
|
| 981 |
},
|
| 982 |
"efficiency.inference-systems": {
|
| 983 |
+
"complete": true,
|
| 984 |
"cursor": null,
|
| 985 |
"query": "\"inference engine\" \"machine learning\" in:description,readme stars:5000..9999"
|
| 986 |
},
|
| 987 |
"efficiency.knowledge-distillation": {
|
| 988 |
+
"complete": true,
|
| 989 |
"cursor": null,
|
| 990 |
"query": "\"knowledge distillation\" in:description stars:5000..9999"
|
| 991 |
},
|
| 992 |
"efficiency.kv-cache": {
|
| 993 |
+
"complete": true,
|
| 994 |
"cursor": null,
|
| 995 |
"query": "\"KV cache\" in:description stars:5000..9999"
|
| 996 |
},
|
| 997 |
"efficiency.lora": {
|
| 998 |
+
"complete": true,
|
| 999 |
"cursor": null,
|
| 1000 |
"query": "LoRA in:description stars:5000..9999"
|
| 1001 |
},
|
| 1002 |
"efficiency.low-rank-factorization": {
|
| 1003 |
+
"complete": true,
|
| 1004 |
"cursor": null,
|
| 1005 |
"query": "\"low-rank factorization\" in:description stars:5000..9999"
|
| 1006 |
},
|
| 1007 |
"efficiency.mixed-precision-training": {
|
| 1008 |
+
"complete": true,
|
| 1009 |
"cursor": null,
|
| 1010 |
"query": "\"mixed precision training\" in:description stars:5000..9999"
|
| 1011 |
},
|
| 1012 |
"efficiency.mixture-of-experts": {
|
| 1013 |
+
"complete": true,
|
| 1014 |
"cursor": null,
|
| 1015 |
"query": "\"mixture of experts\" in:description stars:5000..9999"
|
| 1016 |
},
|
| 1017 |
"efficiency.model-compression": {
|
| 1018 |
+
"complete": true,
|
| 1019 |
"cursor": null,
|
| 1020 |
"query": "\"model compression\" in:description stars:5000..9999"
|
| 1021 |
},
|
| 1022 |
"efficiency.neural-architecture-search": {
|
| 1023 |
+
"complete": true,
|
| 1024 |
"cursor": null,
|
| 1025 |
"query": "\"neural architecture search\" in:description stars:5000..9999"
|
| 1026 |
},
|
| 1027 |
"efficiency.optimizer-state-sharding": {
|
| 1028 |
+
"complete": true,
|
| 1029 |
"cursor": null,
|
| 1030 |
"query": "\"optimizer state sharding\" in:description stars:5000..9999"
|
| 1031 |
},
|
| 1032 |
"efficiency.parameter-efficient-finetuning": {
|
| 1033 |
+
"complete": true,
|
| 1034 |
"cursor": null,
|
| 1035 |
"query": "\"parameter-efficient fine-tuning\" in:description stars:5000..9999"
|
| 1036 |
},
|
| 1037 |
"efficiency.post-training-quantization": {
|
| 1038 |
+
"complete": true,
|
| 1039 |
"cursor": null,
|
| 1040 |
"query": "\"post-training quantization\" in:description stars:5000..9999"
|
| 1041 |
},
|
| 1042 |
"efficiency.pruning": {
|
| 1043 |
+
"complete": true,
|
| 1044 |
"cursor": null,
|
| 1045 |
"query": "pruning in:description stars:5000..9999"
|
| 1046 |
},
|
|
|
|
| 4105 |
"query": "\"visual localization\" in:readme stars:5000..9999"
|
| 4106 |
}
|
| 4107 |
},
|
| 4108 |
+
"next_index": 200,
|
| 4109 |
"per_page": 100,
|
| 4110 |
"start": "2008-01-01",
|
| 4111 |
"version": 1
|