Add GitHub ML run 20260924T200837000000Z-63e95503
Browse files
coverage/20260924T200837000000Z-63e95503.json
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{
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"complete_sweep": false,
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"date_field": "created",
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"end": "2026-09-24",
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"mode": "historical-sample",
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"queries": [
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{
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"coverage_gap": false,
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"coverage_gap_reason": null,
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"incomplete_results": false,
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"pages_scanned": 1,
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"query": "\"ad ranking\" in:description created:2026-01-01..2026-09-24",
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"query_id": "applied.ad-ranking",
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"repositories_observed_on_last_page": 21,
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"sampled_first_page": true,
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"search_limit_reached": false,
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"status": "complete",
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"total_count": 21,
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"year": 2026
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}
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],
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"requests_used": 1,
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"run_id": "20260924T200837000000Z-63e95503",
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"start_year": 2008,
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"started_at": "2026-09-24T20:08:37Z"
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}
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data/observations/2026/09/24/20260924T200837000000Z-63e95503.jsonl
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{"archived":false,"candidate_status":"candidate","created_at":"2026-02-06T03:26:12Z","description":"Super Bowl Ad Ranking Matrix","domains":["ad-ranking","advertising"],"fork":false,"forks":0,"github_id":1151090228,"homepage":null,"language":"HTML","license":null,"methods":[],"name":"ehurtamplified-ai/superbowl-ad-ranker","novelty_signals":["description","query-match","repository-metadata"],"observed_at":"2026-09-24T20:08:37Z","pushed_at":"2026-02-08T20:08:01Z","query_ids":["applied.ad-ranking"],"stars":0,"topics":[],"updated_at":"2026-02-08T20:08:04Z","url":"https://github.com/ehurtamplified-ai/superbowl-ad-ranker"}
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{"archived":false,"candidate_status":"candidate","created_at":"2026-02-19T23:01:50Z","description":"Two-stage ad ranking pipeline with FAISS candidate retrieval, LightGBM scoring, and a second-price auction with position bias correction.","domains":["ad-ranking","advertising"],"fork":false,"forks":0,"github_id":1162138878,"homepage":null,"language":"Python","license":null,"methods":[],"name":"Aman12x/RetailRank","novelty_signals":["description","query-match","repository-metadata"],"observed_at":"2026-09-24T20:08:37Z","pushed_at":"2026-08-04T01:46:41Z","query_ids":["applied.ad-ranking"],"stars":0,"topics":[],"updated_at":"2026-08-04T01:46:51Z","url":"https://github.com/Aman12x/RetailRank"}
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| 3 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2026-03-04T17:27:22Z","description":"algorithmic-problem-solving-js │ ├── README.md │ ├── concurrent-requests │ └── solution.js │ ├── taxi-ride-chaining │ └── solution.js │ ├── geo-targeted-ad-ranking │ └── solution.js │ └── service-time-window └── solution.js","domains":["ad-ranking","advertising"],"fork":false,"forks":0,"github_id":1172785206,"homepage":null,"language":null,"license":null,"methods":[],"name":"DiegoKazadi/algorithmic-problem-solving-js","novelty_signals":["description","query-match","repository-metadata"],"observed_at":"2026-09-24T20:08:37Z","pushed_at":"2026-03-04T17:27:22Z","query_ids":["applied.ad-ranking"],"stars":0,"topics":[],"updated_at":"2026-03-04T17:27:22Z","url":"https://github.com/DiegoKazadi/algorithmic-problem-solving-js"}
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| 4 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2026-03-09T06:06:52Z","description":"Ad ranking forecasting platform","domains":["ad-ranking","advertising","time-series-and-forecasting"],"fork":false,"forks":0,"github_id":1176551045,"homepage":null,"language":"Python","license":null,"methods":[],"name":"narendersurabhi/ad-ranking-forecasting-platform","novelty_signals":["description","query-match","repository-metadata"],"observed_at":"2026-09-24T20:08:37Z","pushed_at":"2026-03-19T20:20:48Z","query_ids":["applied.ad-ranking"],"stars":0,"topics":[],"updated_at":"2026-03-19T20:20:54Z","url":"https://github.com/narendersurabhi/ad-ranking-forecasting-platform"}
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| 5 |
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{"archived":false,"candidate_status":"candidate","created_at":"2026-03-15T19:25:21Z","description":"End‑to‑end ad ranking engine that serves ads via a Go HTTP ad server, scores candidates with a Python/FastAPI CTR model, logs impressions and clicks to Kafka + SQLite, and exposes a React dashboard for campaigns, ads, and optimization suggestions.","domains":["ad-ranking","advertising"],"fork":false,"forks":0,"github_id":1182635667,"homepage":null,"language":"Go","license":null,"methods":[],"name":"vanshika5605/ad-ranking-engine","novelty_signals":["description","query-match","repository-metadata"],"observed_at":"2026-09-24T20:08:37Z","pushed_at":"2026-03-16T14:44:22Z","query_ids":["applied.ad-ranking"],"stars":0,"topics":[],"updated_at":"2026-03-16T14:44:30Z","url":"https://github.com/vanshika5605/ad-ranking-engine"}
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| 6 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2026-04-08T19:52:00Z","description":"CTR prediction on Criteo dataset — LightGBM, ad ranking, SHAP explainability","domains":["ad-ranking","advertising"],"fork":false,"forks":0,"github_id":1205264121,"homepage":null,"language":"Python","license":null,"methods":[],"name":"shanmukhi-developer/ads-ctr-prediction","novelty_signals":["description","query-match","repository-metadata"],"observed_at":"2026-09-24T20:08:37Z","pushed_at":"2026-04-08T20:06:59Z","query_ids":["applied.ad-ranking"],"stars":0,"topics":[],"updated_at":"2026-04-08T20:07:04Z","url":"https://github.com/shanmukhi-developer/ads-ctr-prediction"}
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| 7 |
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{"archived":false,"candidate_status":"candidate","created_at":"2026-04-23T23:01:34Z","description":"Distributed ad ranking and recommendation system using machine learning, personalized click prediction, and large-scale user-ad interaction modeling","domains":["ad-ranking","advertising","recommender-systems"],"fork":false,"forks":0,"github_id":1219469985,"homepage":null,"language":"Python","license":null,"methods":[],"name":"Kaushal1025/distributed-ads-ranking-system","novelty_signals":["description","query-match","repository-metadata"],"observed_at":"2026-09-24T20:08:37Z","pushed_at":"2026-04-23T23:10:28Z","query_ids":["applied.ad-ranking"],"stars":0,"topics":[],"updated_at":"2026-04-23T23:10:31Z","url":"https://github.com/Kaushal1025/distributed-ads-ranking-system"}
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| 8 |
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{"archived":false,"candidate_status":"candidate","created_at":"2026-04-27T06:40:21Z","description":"CTR prediction and ad ranking pipeline for performance advertising","domains":["ad-ranking","advertising"],"fork":false,"forks":0,"github_id":1222215268,"homepage":null,"language":null,"license":null,"methods":[],"name":"parkjuwon-ai/ad-ctr-ranking-pipeline","novelty_signals":["description","query-match","repository-metadata"],"observed_at":"2026-09-24T20:08:37Z","pushed_at":"2026-04-27T06:40:21Z","query_ids":["applied.ad-ranking"],"stars":0,"topics":[],"updated_at":"2026-04-27T06:40:21Z","url":"https://github.com/parkjuwon-ai/ad-ctr-ranking-pipeline"}
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| 9 |
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{"archived":false,"candidate_status":"candidate","created_at":"2026-04-28T02:07:09Z","description":"ad ranking related repo","domains":["ad-ranking","advertising"],"fork":false,"forks":0,"github_id":1223090347,"homepage":null,"language":"Python","license":null,"methods":[],"name":"doogkong/ad_ranking","novelty_signals":["description","query-match","repository-metadata"],"observed_at":"2026-09-24T20:08:37Z","pushed_at":"2026-09-13T21:23:20Z","query_ids":["applied.ad-ranking"],"stars":0,"topics":[],"updated_at":"2026-09-13T21:23:24Z","url":"https://github.com/doogkong/ad_ranking"}
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| 10 |
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{"archived":false,"candidate_status":"candidate","created_at":"2026-04-29T00:54:27Z","description":"Utility-aware ad ranking pipeline for the Criteo attribution dataset","domains":["ad-ranking","advertising"],"fork":false,"forks":0,"github_id":1224102353,"homepage":null,"language":"Python","license":null,"methods":[],"name":"cyrilllau/criteo-utility-ranking-public","novelty_signals":["description","query-match","repository-metadata"],"observed_at":"2026-09-24T20:08:37Z","pushed_at":"2026-04-29T00:54:29Z","query_ids":["applied.ad-ranking"],"stars":0,"topics":[],"updated_at":"2026-04-29T00:54:33Z","url":"https://github.com/cyrilllau/criteo-utility-ranking-public"}
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| 11 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2026-05-23T17:48:21Z","description":"CTR prediction, candidate ad ranking, cold-start handling, drift monitoring, and low-latency ML serving for contextual ad systems.","domains":["ad-ranking","advertising"],"fork":false,"forks":0,"github_id":1247731146,"homepage":null,"language":"Python","license":null,"methods":[],"name":"SanthoshBotcha/contextual-ctr-ranking-system","novelty_signals":["description","github-topics","query-match","repository-metadata"],"observed_at":"2026-09-24T20:08:37Z","pushed_at":"2026-07-08T00:00:17Z","query_ids":["applied.ad-ranking"],"stars":0,"topics":["ad-ranking","cold-start","ctr-prediction","drift-detection","lightgbm","machine-learning","mlops","ranking-systems"],"updated_at":"2026-07-08T00:03:20Z","url":"https://github.com/SanthoshBotcha/contextual-ctr-ranking-system"}
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| 12 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2026-05-24T07:01:05Z","description":"Flutter ad-ranking + delivery prototype with interest learning. Anaheim Technologies Full Stack Developer assessment.","domains":["ad-ranking","advertising"],"fork":false,"forks":0,"github_id":1248085046,"homepage":null,"language":"Dart","license":null,"methods":[],"name":"deibeeed/assessment-fsd-kharlyn-huntst","novelty_signals":["description","query-match","repository-metadata"],"observed_at":"2026-09-24T20:08:37Z","pushed_at":"2026-05-24T07:28:08Z","query_ids":["applied.ad-ranking"],"stars":0,"topics":[],"updated_at":"2026-05-24T07:28:12Z","url":"https://github.com/deibeeed/assessment-fsd-kharlyn-huntst"}
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| 13 |
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{"archived":false,"candidate_status":"candidate","created_at":"2026-05-26T20:19:34Z","description":"CTR Prediction & Ad Ranking System built with Python and ML models (Logistic Regression, Gradient Boosting, and Neural Networks). Generates synthetic user engagement data, predicts click-through probability, and ranks ad impressions using AUC-ROC and log loss evaluation.","domains":["ad-ranking","advertising","tabular-and-structured-data"],"fork":false,"forks":0,"github_id":1250624026,"homepage":null,"language":"Python","license":null,"methods":["gradient-boosting","neural-network"],"name":"sruthi-va/ctr-prediction-project","novelty_signals":["description","query-match","repository-metadata"],"observed_at":"2026-09-24T20:08:37Z","pushed_at":"2026-05-29T20:03:33Z","query_ids":["applied.ad-ranking"],"stars":0,"topics":[],"updated_at":"2026-05-29T20:03:37Z","url":"https://github.com/sruthi-va/ctr-prediction-project"}
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| 14 |
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{"archived":false,"candidate_status":"candidate","created_at":"2026-05-27T23:21:46Z","description":"Open Ranker Protocol — an open spec for transparent, interoperable ad ranking. Public draft for community review and comment.","domains":["ad-ranking","advertising"],"fork":false,"forks":0,"github_id":1251793737,"homepage":null,"language":null,"license":"Apache-2.0","methods":[],"name":"ether-data/open-ranker-protocol","novelty_signals":["description","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-24T20:08:37Z","pushed_at":"2026-05-27T23:24:18Z","query_ids":["applied.ad-ranking"],"stars":0,"topics":[],"updated_at":"2026-05-27T23:24:21Z","url":"https://github.com/ether-data/open-ranker-protocol"}
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| 15 |
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{"archived":false,"candidate_status":"candidate","created_at":"2026-05-28T20:05:53Z","description":"ML-powered ad ranking and relevance system with CTR prediction, feature pipelines, and production-style API serving.","domains":["ad-ranking","advertising"],"fork":false,"forks":0,"github_id":1252725653,"homepage":null,"language":"Python","license":null,"methods":[],"name":"ericlewisX/adrank-studio","novelty_signals":["description","query-match","repository-metadata"],"observed_at":"2026-09-24T20:08:37Z","pushed_at":"2026-06-04T22:51:30Z","query_ids":["applied.ad-ranking"],"stars":0,"topics":[],"updated_at":"2026-05-31T06:22:58Z","url":"https://github.com/ericlewisX/adrank-studio"}
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| 16 |
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{"archived":false,"candidate_status":"candidate","created_at":"2026-07-08T22:06:17Z","description":"YieldGuard: two-stage ad ranking with two-tower retrieval, GBDT reranking, and isotonic calibration -- includes an auction simulation quantifying calibration's dollar impact on yield. Offline eval harness reproducible from one command.","domains":["ad-ranking","advertising"],"fork":false,"forks":0,"github_id":1294311368,"homepage":null,"language":"Python","license":"MIT","methods":[],"name":"Abhics8/YieldGuard","novelty_signals":["description","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-24T20:08:37Z","pushed_at":"2026-07-08T22:25:07Z","query_ids":["applied.ad-ranking"],"stars":0,"topics":[],"updated_at":"2026-07-08T22:25:14Z","url":"https://github.com/Abhics8/YieldGuard"}
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| 17 |
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{"archived":false,"candidate_status":"candidate","created_at":"2026-07-17T11:02:15Z","description":"CTR prediction estimates the probability that a user clicks an advertisement, directly influencing ad ranking and revenue. The task is challenging due to massive-scale datasets, high-dimensional sparse features, and complex nonlinear interactions that cannot be effectively captured by traditional linear models such as Logistic Regression.","domains":["ad-ranking","advertising","recommender-systems"],"fork":false,"forks":0,"github_id":1303896557,"homepage":null,"language":"Jupyter Notebook","license":null,"methods":[],"name":"AIVIETNAM-AIO-TommyPhan/Recommendation-System-Taobao","novelty_signals":["description","query-match","repository-metadata"],"observed_at":"2026-09-24T20:08:37Z","pushed_at":"2026-08-04T07:30:16Z","query_ids":["applied.ad-ranking"],"stars":0,"topics":[],"updated_at":"2026-08-04T07:31:03Z","url":"https://github.com/AIVIETNAM-AIO-TommyPhan/Recommendation-System-Taobao"}
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| 18 |
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{"archived":false,"candidate_status":"candidate","created_at":"2026-07-30T04:07:57Z","description":"AI-Powered Ad Ranking & Auction Engine","domains":["ad-ranking","advertising"],"fork":false,"forks":0,"github_id":1316811696,"homepage":null,"language":"Python","license":null,"methods":[],"name":"rao305/ai-ad-ranking","novelty_signals":["description","query-match","repository-metadata"],"observed_at":"2026-09-24T20:08:37Z","pushed_at":"2026-07-30T15:30:39Z","query_ids":["applied.ad-ranking"],"stars":0,"topics":[],"updated_at":"2026-07-30T15:36:52Z","url":"https://github.com/rao305/ai-ad-ranking"}
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| 19 |
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{"archived":false,"candidate_status":"candidate","created_at":"2026-09-20T17:05:17Z","description":"Real-time ad ranking and creative review on a typed evaluation model — one call prices a whole auction, no trained CTR model, no logged clicks.","domains":["ad-ranking","advertising","recommender-systems"],"fork":false,"forks":1,"github_id":1378581657,"homepage":null,"language":"JavaScript","license":"MIT","methods":[],"name":"leepokai/jev-adrank","novelty_signals":["description","github-topics","license-metadata","query-match","repository-metadata"],"observed_at":"2026-09-24T20:08:37Z","pushed_at":"2026-09-20T18:19:11Z","query_ids":["applied.ad-ranking"],"stars":0,"topics":["ad-ranking","ads","auction","brand-safety","ecpm","jev","prompt-injection","real-time","recommender-system","typesafe"],"updated_at":"2026-09-20T18:19:15Z","url":"https://github.com/leepokai/jev-adrank"}
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| 20 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2026-09-23T07:31:22Z","description":"Built a distributed Go ad platform with separate PostgreSQL-backed serving/analytics services using RabbitMQ. Implemented bid-based ad ranking, concurrency-safe Redis Lua budget enforcement, idempotent event processing, privacy suppression, and 31 full-stack tests via Docker Compose.","domains":["ad-ranking","advertising"],"fork":false,"forks":0,"github_id":1382973246,"homepage":null,"language":"Go","license":null,"methods":[],"name":"DranzerD/CampaignFlow","novelty_signals":["description","query-match","repository-metadata"],"observed_at":"2026-09-24T20:08:37Z","pushed_at":"2026-09-24T06:33:18Z","query_ids":["applied.ad-ranking"],"stars":0,"topics":[],"updated_at":"2026-09-24T06:33:22Z","url":"https://github.com/DranzerD/CampaignFlow"}
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| 21 |
+
{"archived":false,"candidate_status":"candidate","created_at":"2026-09-24T16:48:40Z","description":"Beyond Keywords: does emotional congruence improve ad ranking over TF-IDF, Sentence-BERT, LightGBM and NRMS? An 8-model ablation on MIND click logs (MSc Research Practicum, NCI).","domains":["ad-ranking","advertising"],"fork":false,"forks":0,"github_id":1385963635,"homepage":null,"language":null,"license":null,"methods":[],"name":"smit-savaliya-dev/beyond-keywords-emotion-aware-ad-ranking","novelty_signals":["description","query-match","repository-metadata"],"observed_at":"2026-09-24T20:08:37Z","pushed_at":"2026-09-24T16:48:42Z","query_ids":["applied.ad-ranking"],"stars":0,"topics":[],"updated_at":"2026-09-24T16:48:46Z","url":"https://github.com/smit-savaliya-dev/beyond-keywords-emotion-aware-ad-ranking"}
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state/historical-sample.json
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},
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{
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"id": "applied.algorithmic-trading",
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-
"query": "\"algorithmic trading\" in:
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},
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{
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"id": "applied.causal-inference",
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@@ -38,7 +38,7 @@
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},
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{
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"id": "applied.customer-segmentation",
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-
"query": "\"customer segmentation\" in:readme"
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},
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{
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"id": "applied.cybersecurity-ml",
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@@ -50,7 +50,7 @@
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},
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{
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"id": "applied.dynamic-pricing",
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-
"query": "\"dynamic pricing\" in:readme"
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},
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{
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"id": "applied.ecological-forecasting",
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@@ -66,7 +66,7 @@
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},
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{
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"id": "applied.fraud-detection",
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-
"query": "\"fraud detection\" in:readme"
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},
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{
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"id": "applied.industrial-defect-detection",
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@@ -482,7 +482,7 @@
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},
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{
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"id": "efficiency.inference-systems",
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-
"query": "\"inference engine\" in:description"
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},
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{
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"id": "efficiency.knowledge-distillation",
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@@ -548,6 +548,122 @@
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"id": "efficiency.speculative-decoding",
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"query": "\"speculative decoding\" in:description"
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},
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| 551 |
{
|
| 552 |
"id": "general.active-learning",
|
| 553 |
"query": "\"active learning\" in:description"
|
|
@@ -558,7 +674,7 @@
|
|
| 558 |
},
|
| 559 |
{
|
| 560 |
"id": "general.arxiv",
|
| 561 |
-
"query": "arxiv in:description"
|
| 562 |
},
|
| 563 |
{
|
| 564 |
"id": "general.automated-machine-learning",
|
|
@@ -574,7 +690,7 @@
|
|
| 574 |
},
|
| 575 |
{
|
| 576 |
"id": "general.clustering",
|
| 577 |
-
"query": "clustering in:description"
|
| 578 |
},
|
| 579 |
{
|
| 580 |
"id": "general.continual-learning",
|
|
@@ -690,7 +806,7 @@
|
|
| 690 |
},
|
| 691 |
{
|
| 692 |
"id": "general.research-code",
|
| 693 |
-
"query": "\"research code\" in:description"
|
| 694 |
},
|
| 695 |
{
|
| 696 |
"id": "general.self-supervised-learning",
|
|
@@ -2136,3506 +2252,9 @@
|
|
| 2136 |
"completed": {
|
| 2137 |
"applied.ad-ranking": {
|
| 2138 |
"query": "\"ad ranking\" in:description",
|
| 2139 |
-
"years": [
|
| 2140 |
-
2026,
|
| 2141 |
-
2025
|
| 2142 |
-
]
|
| 2143 |
-
},
|
| 2144 |
-
"applied.agricultural-ml": {
|
| 2145 |
-
"query": "\"agricultural machine learning\" in:description,readme",
|
| 2146 |
-
"years": [
|
| 2147 |
-
2026,
|
| 2148 |
-
2025
|
| 2149 |
-
]
|
| 2150 |
-
},
|
| 2151 |
-
"applied.algorithmic-trading": {
|
| 2152 |
-
"query": "\"algorithmic trading\" in:description",
|
| 2153 |
-
"years": [
|
| 2154 |
-
2026,
|
| 2155 |
-
2025
|
| 2156 |
-
]
|
| 2157 |
-
},
|
| 2158 |
-
"applied.causal-inference": {
|
| 2159 |
-
"query": "\"causal inference\" in:description",
|
| 2160 |
-
"years": [
|
| 2161 |
-
2026,
|
| 2162 |
-
2025
|
| 2163 |
-
]
|
| 2164 |
-
},
|
| 2165 |
-
"applied.churn-prediction": {
|
| 2166 |
-
"query": "\"customer churn prediction\" in:description",
|
| 2167 |
-
"years": [
|
| 2168 |
-
2026,
|
| 2169 |
-
2025
|
| 2170 |
-
]
|
| 2171 |
-
},
|
| 2172 |
-
"applied.click-through-rate": {
|
| 2173 |
-
"query": "\"click-through rate prediction\" in:readme",
|
| 2174 |
-
"years": [
|
| 2175 |
-
2026,
|
| 2176 |
-
2025
|
| 2177 |
-
]
|
| 2178 |
-
},
|
| 2179 |
-
"applied.collaborative-filtering": {
|
| 2180 |
-
"query": "\"collaborative filtering\" in:description",
|
| 2181 |
-
"years": [
|
| 2182 |
-
2026,
|
| 2183 |
-
2025
|
| 2184 |
-
]
|
| 2185 |
-
},
|
| 2186 |
-
"applied.credit-scoring": {
|
| 2187 |
-
"query": "\"credit scoring\" in:description",
|
| 2188 |
-
"years": [
|
| 2189 |
-
2026,
|
| 2190 |
-
2025
|
| 2191 |
-
]
|
| 2192 |
-
},
|
| 2193 |
-
"applied.customer-lifetime-value": {
|
| 2194 |
-
"query": "\"customer lifetime value\" in:description",
|
| 2195 |
-
"years": [
|
| 2196 |
-
2026,
|
| 2197 |
-
2025
|
| 2198 |
-
]
|
| 2199 |
-
},
|
| 2200 |
-
"applied.customer-segmentation": {
|
| 2201 |
-
"query": "\"customer segmentation\" in:readme",
|
| 2202 |
-
"years": [
|
| 2203 |
-
2026,
|
| 2204 |
-
2025
|
| 2205 |
-
]
|
| 2206 |
-
},
|
| 2207 |
-
"applied.cybersecurity-ml": {
|
| 2208 |
-
"query": "\"machine learning\" cybersecurity in:description,readme",
|
| 2209 |
-
"years": [
|
| 2210 |
-
2026,
|
| 2211 |
-
2025
|
| 2212 |
-
]
|
| 2213 |
-
},
|
| 2214 |
-
"applied.demand-forecasting": {
|
| 2215 |
-
"query": "\"demand forecasting\" in:description",
|
| 2216 |
-
"years": [
|
| 2217 |
-
2026,
|
| 2218 |
-
2025
|
| 2219 |
-
]
|
| 2220 |
-
},
|
| 2221 |
-
"applied.dynamic-pricing": {
|
| 2222 |
-
"query": "\"dynamic pricing\" in:readme",
|
| 2223 |
-
"years": [
|
| 2224 |
-
2026,
|
| 2225 |
-
2025
|
| 2226 |
-
]
|
| 2227 |
-
},
|
| 2228 |
-
"applied.ecological-forecasting": {
|
| 2229 |
-
"query": "\"ecological forecasting\" \"machine learning\" in:description,readme",
|
| 2230 |
-
"years": [
|
| 2231 |
-
2026,
|
| 2232 |
-
2025
|
| 2233 |
-
]
|
| 2234 |
-
},
|
| 2235 |
-
"applied.educational-data-mining": {
|
| 2236 |
-
"query": "\"educational data mining\" in:description,readme",
|
| 2237 |
-
"years": [
|
| 2238 |
-
2026,
|
| 2239 |
-
2025
|
| 2240 |
-
]
|
| 2241 |
-
},
|
| 2242 |
-
"applied.energy-load-forecasting": {
|
| 2243 |
-
"query": "\"load forecasting\" energy in:description,readme",
|
| 2244 |
-
"years": [
|
| 2245 |
-
2026,
|
| 2246 |
-
2025
|
| 2247 |
-
]
|
| 2248 |
-
},
|
| 2249 |
-
"applied.fraud-detection": {
|
| 2250 |
-
"query": "\"fraud detection\" in:readme",
|
| 2251 |
-
"years": [
|
| 2252 |
-
2026,
|
| 2253 |
-
2025
|
| 2254 |
-
]
|
| 2255 |
-
},
|
| 2256 |
-
"applied.industrial-defect-detection": {
|
| 2257 |
-
"query": "\"industrial defect detection\" \"deep learning\" in:description,readme",
|
| 2258 |
-
"years": [
|
| 2259 |
-
2026,
|
| 2260 |
-
2025
|
| 2261 |
-
]
|
| 2262 |
-
},
|
| 2263 |
-
"applied.intrusion-detection": {
|
| 2264 |
-
"query": "\"intrusion detection\" \"machine learning\" in:description,readme",
|
| 2265 |
-
"years": [
|
| 2266 |
-
2026,
|
| 2267 |
-
2025
|
| 2268 |
-
]
|
| 2269 |
-
},
|
| 2270 |
-
"applied.inventory-optimization": {
|
| 2271 |
-
"query": "\"inventory optimization\" in:readme",
|
| 2272 |
-
"years": [
|
| 2273 |
-
2026,
|
| 2274 |
-
2025
|
| 2275 |
-
]
|
| 2276 |
-
},
|
| 2277 |
-
"applied.learning-to-rank": {
|
| 2278 |
-
"query": "\"learning to rank\" in:description",
|
| 2279 |
-
"years": [
|
| 2280 |
-
2026,
|
| 2281 |
-
2025
|
| 2282 |
-
]
|
| 2283 |
-
},
|
| 2284 |
-
"applied.malware-detection": {
|
| 2285 |
-
"query": "\"malware detection\" \"machine learning\" in:description,readme",
|
| 2286 |
-
"years": [
|
| 2287 |
-
2026,
|
| 2288 |
-
2025
|
| 2289 |
-
]
|
| 2290 |
-
},
|
| 2291 |
-
"applied.ml-econometrics": {
|
| 2292 |
-
"query": "\"machine learning econometrics\" in:readme",
|
| 2293 |
-
"years": [
|
| 2294 |
-
2026,
|
| 2295 |
-
2025
|
| 2296 |
-
]
|
| 2297 |
-
},
|
| 2298 |
-
"applied.plant-disease-detection": {
|
| 2299 |
-
"query": "\"plant disease detection\" \"deep learning\" in:description,readme",
|
| 2300 |
-
"years": [
|
| 2301 |
-
2026,
|
| 2302 |
-
2025
|
| 2303 |
-
]
|
| 2304 |
-
},
|
| 2305 |
-
"applied.portfolio-optimization": {
|
| 2306 |
-
"query": "\"portfolio optimization\" in:readme",
|
| 2307 |
-
"years": [
|
| 2308 |
-
2026,
|
| 2309 |
-
2025
|
| 2310 |
-
]
|
| 2311 |
-
},
|
| 2312 |
-
"applied.predictive-maintenance": {
|
| 2313 |
-
"query": "\"predictive maintenance\" industrial in:description",
|
| 2314 |
-
"years": [
|
| 2315 |
-
2026,
|
| 2316 |
-
2025
|
| 2317 |
-
]
|
| 2318 |
-
},
|
| 2319 |
-
"applied.process-mining-prediction": {
|
| 2320 |
-
"query": "\"predictive process monitoring\" in:description,readme",
|
| 2321 |
-
"years": [
|
| 2322 |
-
2026,
|
| 2323 |
-
2025
|
| 2324 |
-
]
|
| 2325 |
-
},
|
| 2326 |
-
"applied.quantitative-finance": {
|
| 2327 |
-
"query": "\"quantitative finance\" in:readme",
|
| 2328 |
-
"years": [
|
| 2329 |
-
2026,
|
| 2330 |
-
2025
|
| 2331 |
-
]
|
| 2332 |
-
},
|
| 2333 |
-
"applied.recommender-systems": {
|
| 2334 |
-
"query": "\"recommender system\" in:readme",
|
| 2335 |
-
"years": [
|
| 2336 |
-
2026,
|
| 2337 |
-
2025
|
| 2338 |
-
]
|
| 2339 |
-
},
|
| 2340 |
-
"applied.search-ranking": {
|
| 2341 |
-
"query": "\"search ranking\" in:readme",
|
| 2342 |
-
"years": [
|
| 2343 |
-
2026,
|
| 2344 |
-
2025
|
| 2345 |
-
]
|
| 2346 |
-
},
|
| 2347 |
-
"applied.species-distribution-modeling": {
|
| 2348 |
-
"query": "\"species distribution modeling\" in:description,readme",
|
| 2349 |
-
"years": [
|
| 2350 |
-
2026,
|
| 2351 |
-
2025
|
| 2352 |
-
]
|
| 2353 |
-
},
|
| 2354 |
-
"applied.supply-chain-optimization": {
|
| 2355 |
-
"query": "\"supply chain optimization\" in:readme",
|
| 2356 |
-
"years": [
|
| 2357 |
-
2026,
|
| 2358 |
-
2025
|
| 2359 |
-
]
|
| 2360 |
-
},
|
| 2361 |
-
"applied.tabular-ml": {
|
| 2362 |
-
"query": "\"tabular data\" in:readme",
|
| 2363 |
-
"years": [
|
| 2364 |
-
2026,
|
| 2365 |
-
2025
|
| 2366 |
-
]
|
| 2367 |
-
},
|
| 2368 |
-
"applied.time-series-forecasting": {
|
| 2369 |
-
"query": "\"time series forecasting\" manufacturing in:description",
|
| 2370 |
-
"years": [
|
| 2371 |
-
2026,
|
| 2372 |
-
2025
|
| 2373 |
-
]
|
| 2374 |
-
},
|
| 2375 |
-
"applied.traffic-prediction": {
|
| 2376 |
-
"query": "\"traffic prediction\" \"machine learning\" in:description,readme",
|
| 2377 |
-
"years": [
|
| 2378 |
-
2026,
|
| 2379 |
-
2025
|
| 2380 |
-
]
|
| 2381 |
-
},
|
| 2382 |
-
"applied.uplift-modeling": {
|
| 2383 |
-
"query": "\"uplift modeling\" in:description",
|
| 2384 |
-
"years": [
|
| 2385 |
-
2026,
|
| 2386 |
-
2025
|
| 2387 |
-
]
|
| 2388 |
-
},
|
| 2389 |
-
"audio-acoustic-event-classification": {
|
| 2390 |
-
"query": "\"acoustic scene classification\" in:readme",
|
| 2391 |
-
"years": [
|
| 2392 |
-
2026,
|
| 2393 |
-
2025
|
| 2394 |
-
]
|
| 2395 |
-
},
|
| 2396 |
-
"audio-anomaly-detection": {
|
| 2397 |
-
"query": "\"audio anomaly detection\" in:readme",
|
| 2398 |
-
"years": [
|
| 2399 |
-
2026,
|
| 2400 |
-
2025
|
| 2401 |
-
]
|
| 2402 |
-
},
|
| 2403 |
-
"audio-asr": {
|
| 2404 |
-
"query": "\"automatic speech recognition\" in:readme",
|
| 2405 |
-
"years": [
|
| 2406 |
-
2026,
|
| 2407 |
-
2025
|
| 2408 |
-
]
|
| 2409 |
-
},
|
| 2410 |
-
"audio-audio-captioning": {
|
| 2411 |
-
"query": "\"audio captioning\" in:readme",
|
| 2412 |
-
"years": [
|
| 2413 |
-
2026,
|
| 2414 |
-
2025
|
| 2415 |
-
]
|
| 2416 |
-
},
|
| 2417 |
-
"audio-audio-classification": {
|
| 2418 |
-
"query": "\"audio classification\" in:readme",
|
| 2419 |
-
"years": [
|
| 2420 |
-
2026,
|
| 2421 |
-
2025
|
| 2422 |
-
]
|
| 2423 |
-
},
|
| 2424 |
-
"audio-audio-question-answering": {
|
| 2425 |
-
"query": "\"audio question answering\" in:readme",
|
| 2426 |
-
"years": [
|
| 2427 |
-
2026,
|
| 2428 |
-
2025
|
| 2429 |
-
]
|
| 2430 |
-
},
|
| 2431 |
-
"audio-audio-super-resolution": {
|
| 2432 |
-
"query": "\"audio super-resolution\" in:readme",
|
| 2433 |
-
"years": [
|
| 2434 |
-
2026,
|
| 2435 |
-
2025
|
| 2436 |
-
]
|
| 2437 |
-
},
|
| 2438 |
-
"audio-generation": {
|
| 2439 |
-
"query": "\"audio generation\" in:readme",
|
| 2440 |
-
"years": [
|
| 2441 |
-
2026,
|
| 2442 |
-
2025
|
| 2443 |
-
]
|
| 2444 |
-
},
|
| 2445 |
-
"audio-keyword-spotting": {
|
| 2446 |
-
"query": "\"keyword spotting\" audio in:readme",
|
| 2447 |
-
"years": [
|
| 2448 |
-
2026,
|
| 2449 |
-
2025
|
| 2450 |
-
]
|
| 2451 |
-
},
|
| 2452 |
-
"audio-music-generation": {
|
| 2453 |
-
"query": "\"music generation\" in:readme",
|
| 2454 |
-
"years": [
|
| 2455 |
-
2026,
|
| 2456 |
-
2025
|
| 2457 |
-
]
|
| 2458 |
-
},
|
| 2459 |
-
"audio-music-information-retrieval": {
|
| 2460 |
-
"query": "\"music information retrieval\" in:readme",
|
| 2461 |
-
"years": [
|
| 2462 |
-
2026,
|
| 2463 |
-
2025
|
| 2464 |
-
]
|
| 2465 |
-
},
|
| 2466 |
-
"audio-music-recommendation": {
|
| 2467 |
-
"query": "\"music recommendation\" in:readme",
|
| 2468 |
-
"years": [
|
| 2469 |
-
2026,
|
| 2470 |
-
2025
|
| 2471 |
-
]
|
| 2472 |
-
},
|
| 2473 |
-
"audio-music-source-separation": {
|
| 2474 |
-
"query": "\"music source separation\" in:readme",
|
| 2475 |
-
"years": [
|
| 2476 |
-
2026,
|
| 2477 |
-
2025
|
| 2478 |
-
]
|
| 2479 |
-
},
|
| 2480 |
-
"audio-music-transcription": {
|
| 2481 |
-
"query": "\"automatic music transcription\" in:readme",
|
| 2482 |
-
"years": [
|
| 2483 |
-
2026,
|
| 2484 |
-
2025
|
| 2485 |
-
]
|
| 2486 |
-
},
|
| 2487 |
-
"audio-neural-codec": {
|
| 2488 |
-
"query": "\"neural audio codec\" in:readme",
|
| 2489 |
-
"years": [
|
| 2490 |
-
2026,
|
| 2491 |
-
2025
|
| 2492 |
-
]
|
| 2493 |
-
},
|
| 2494 |
-
"audio-sound-event-detection": {
|
| 2495 |
-
"query": "\"sound event detection\" in:readme",
|
| 2496 |
-
"years": [
|
| 2497 |
-
2026,
|
| 2498 |
-
2025
|
| 2499 |
-
]
|
| 2500 |
-
},
|
| 2501 |
-
"audio-source-separation": {
|
| 2502 |
-
"query": "\"audio source separation\" in:readme",
|
| 2503 |
-
"years": [
|
| 2504 |
-
2026,
|
| 2505 |
-
2025
|
| 2506 |
-
]
|
| 2507 |
-
},
|
| 2508 |
-
"audio-speaker-diarization": {
|
| 2509 |
-
"query": "\"speaker diarization\" in:readme",
|
| 2510 |
-
"years": [
|
| 2511 |
-
2026,
|
| 2512 |
-
2025
|
| 2513 |
-
]
|
| 2514 |
-
},
|
| 2515 |
-
"audio-speaker-verification": {
|
| 2516 |
-
"query": "\"speaker verification\" in:readme",
|
| 2517 |
-
"years": [
|
| 2518 |
-
2026,
|
| 2519 |
-
2025
|
| 2520 |
-
]
|
| 2521 |
-
},
|
| 2522 |
-
"audio-speech-emotion-recognition": {
|
| 2523 |
-
"query": "\"speech emotion recognition\" in:readme",
|
| 2524 |
-
"years": [
|
| 2525 |
-
2026,
|
| 2526 |
-
2025
|
| 2527 |
-
]
|
| 2528 |
-
},
|
| 2529 |
-
"audio-speech-enhancement": {
|
| 2530 |
-
"query": "\"speech enhancement\" in:readme",
|
| 2531 |
-
"years": [
|
| 2532 |
-
2026,
|
| 2533 |
-
2025
|
| 2534 |
-
]
|
| 2535 |
-
},
|
| 2536 |
-
"audio-speech-synthesis": {
|
| 2537 |
-
"query": "\"speech synthesis\" in:readme",
|
| 2538 |
-
"years": [
|
| 2539 |
-
2026,
|
| 2540 |
-
2025
|
| 2541 |
-
]
|
| 2542 |
-
},
|
| 2543 |
-
"audio-speech-to-text": {
|
| 2544 |
-
"query": "\"speech to text\" in:readme",
|
| 2545 |
-
"years": [
|
| 2546 |
-
2026,
|
| 2547 |
-
2025
|
| 2548 |
-
]
|
| 2549 |
-
},
|
| 2550 |
-
"audio-speech-translation": {
|
| 2551 |
-
"query": "\"speech translation\" in:readme",
|
| 2552 |
-
"years": [
|
| 2553 |
-
2026,
|
| 2554 |
-
2025
|
| 2555 |
-
]
|
| 2556 |
-
},
|
| 2557 |
-
"audio-text-to-audio": {
|
| 2558 |
-
"query": "\"text to audio\" in:readme",
|
| 2559 |
-
"years": [
|
| 2560 |
-
2026,
|
| 2561 |
-
2025
|
| 2562 |
-
]
|
| 2563 |
-
},
|
| 2564 |
-
"audio-text-to-speech": {
|
| 2565 |
-
"query": "\"text to speech\" in:readme",
|
| 2566 |
-
"years": [
|
| 2567 |
-
2026,
|
| 2568 |
-
2025
|
| 2569 |
-
]
|
| 2570 |
-
},
|
| 2571 |
-
"audio-voice-conversion": {
|
| 2572 |
-
"query": "\"voice conversion\" in:readme",
|
| 2573 |
-
"years": [
|
| 2574 |
-
2026,
|
| 2575 |
-
2025
|
| 2576 |
-
]
|
| 2577 |
-
},
|
| 2578 |
-
"bio.cell-atlas": {
|
| 2579 |
-
"query": "\"cell atlas\" in:description,readme",
|
| 2580 |
-
"years": [
|
| 2581 |
-
2026,
|
| 2582 |
-
2025
|
| 2583 |
-
]
|
| 2584 |
-
},
|
| 2585 |
-
"bio.chemical-biology": {
|
| 2586 |
-
"query": "\"protein function prediction\" in:description,readme",
|
| 2587 |
-
"years": [
|
| 2588 |
-
2026,
|
| 2589 |
-
2025
|
| 2590 |
-
]
|
| 2591 |
-
},
|
| 2592 |
-
"bio.crispr": {
|
| 2593 |
-
"query": "\"CRISPR screen\" in:description,readme",
|
| 2594 |
-
"years": [
|
| 2595 |
-
2026,
|
| 2596 |
-
2025
|
| 2597 |
-
]
|
| 2598 |
-
},
|
| 2599 |
-
"bio.dna-language-models": {
|
| 2600 |
-
"query": "\"DNA language model\" in:description",
|
| 2601 |
-
"years": [
|
| 2602 |
-
2026,
|
| 2603 |
-
2025
|
| 2604 |
-
]
|
| 2605 |
-
},
|
| 2606 |
-
"bio.gene-regulatory-networks": {
|
| 2607 |
-
"query": "\"gene regulatory network\" in:description,readme",
|
| 2608 |
-
"years": [
|
| 2609 |
-
2026,
|
| 2610 |
-
2025
|
| 2611 |
-
]
|
| 2612 |
-
},
|
| 2613 |
-
"bio.genome-assembly": {
|
| 2614 |
-
"query": "\"genome assembly\" \"neural network\" in:description,readme",
|
| 2615 |
-
"years": [
|
| 2616 |
-
2026,
|
| 2617 |
-
2025
|
| 2618 |
-
]
|
| 2619 |
-
},
|
| 2620 |
-
"bio.genomics": {
|
| 2621 |
-
"query": "\"genomics\" in:description",
|
| 2622 |
-
"years": [
|
| 2623 |
-
2026,
|
| 2624 |
-
2025
|
| 2625 |
-
]
|
| 2626 |
-
},
|
| 2627 |
-
"bio.inverse-folding": {
|
| 2628 |
-
"query": "\"inverse folding\" in:readme",
|
| 2629 |
-
"years": [
|
| 2630 |
-
2026,
|
| 2631 |
-
2025
|
| 2632 |
-
]
|
| 2633 |
-
},
|
| 2634 |
-
"bio.metagenomics": {
|
| 2635 |
-
"query": "\"metagenomic classification\" in:description,readme",
|
| 2636 |
-
"years": [
|
| 2637 |
-
2026,
|
| 2638 |
-
2025
|
| 2639 |
-
]
|
| 2640 |
-
},
|
| 2641 |
-
"bio.microbiome": {
|
| 2642 |
-
"query": "\"microbiome\" \"machine learning\" in:description,readme",
|
| 2643 |
-
"years": [
|
| 2644 |
-
2026,
|
| 2645 |
-
2025
|
| 2646 |
-
]
|
| 2647 |
-
},
|
| 2648 |
-
"bio.microscopy-cell-analysis": {
|
| 2649 |
-
"query": "\"cell segmentation\" microscopy in:description,readme",
|
| 2650 |
-
"years": [
|
| 2651 |
-
2026,
|
| 2652 |
-
2025
|
| 2653 |
-
]
|
| 2654 |
-
},
|
| 2655 |
-
"bio.molecular-dynamics": {
|
| 2656 |
-
"query": "\"molecular dynamics\" in:description",
|
| 2657 |
-
"years": [
|
| 2658 |
-
2026,
|
| 2659 |
-
2025
|
| 2660 |
-
]
|
| 2661 |
-
},
|
| 2662 |
-
"bio.molecular-force-fields": {
|
| 2663 |
-
"query": "\"machine learning force field\" in:readme",
|
| 2664 |
-
"years": [
|
| 2665 |
-
2026,
|
| 2666 |
-
2025
|
| 2667 |
-
]
|
| 2668 |
-
},
|
| 2669 |
-
"bio.phylogenetics": {
|
| 2670 |
-
"query": "\"phylogenetics\" in:description",
|
| 2671 |
-
"years": [
|
| 2672 |
-
2026,
|
| 2673 |
-
2025
|
| 2674 |
-
]
|
| 2675 |
-
},
|
| 2676 |
-
"bio.protein-design": {
|
| 2677 |
-
"query": "\"protein design\" in:description",
|
| 2678 |
-
"years": [
|
| 2679 |
-
2026,
|
| 2680 |
-
2025
|
| 2681 |
-
]
|
| 2682 |
-
},
|
| 2683 |
-
"bio.protein-folding": {
|
| 2684 |
-
"query": "\"protein folding\" in:readme",
|
| 2685 |
-
"years": [
|
| 2686 |
-
2026,
|
| 2687 |
-
2025
|
| 2688 |
-
]
|
| 2689 |
-
},
|
| 2690 |
-
"bio.protein-interactions": {
|
| 2691 |
-
"query": "\"protein-protein interaction prediction\" in:readme",
|
| 2692 |
-
"years": [
|
| 2693 |
-
2026,
|
| 2694 |
-
2025
|
| 2695 |
-
]
|
| 2696 |
-
},
|
| 2697 |
-
"bio.protein-structure": {
|
| 2698 |
-
"query": "\"protein structure prediction\" in:description",
|
| 2699 |
-
"years": [
|
| 2700 |
-
2026,
|
| 2701 |
-
2025
|
| 2702 |
-
]
|
| 2703 |
-
},
|
| 2704 |
-
"bio.regulatory-sequence-modeling": {
|
| 2705 |
-
"query": "enhancer prediction genomics in:description,readme",
|
| 2706 |
-
"years": [
|
| 2707 |
-
2026,
|
| 2708 |
-
2025
|
| 2709 |
-
]
|
| 2710 |
-
},
|
| 2711 |
-
"bio.rna-language-models": {
|
| 2712 |
-
"query": "\"RNA language model\" in:description",
|
| 2713 |
-
"years": [
|
| 2714 |
-
2026,
|
| 2715 |
-
2025
|
| 2716 |
-
]
|
| 2717 |
-
},
|
| 2718 |
-
"bio.rna-structure": {
|
| 2719 |
-
"query": "\"RNA structure prediction\" in:description,readme",
|
| 2720 |
-
"years": [
|
| 2721 |
-
2026,
|
| 2722 |
-
2025
|
| 2723 |
-
]
|
| 2724 |
-
},
|
| 2725 |
-
"bio.scrna-seq": {
|
| 2726 |
-
"query": "\"scRNA-seq\" in:readme",
|
| 2727 |
-
"years": [
|
| 2728 |
-
2026,
|
| 2729 |
-
2025
|
| 2730 |
-
]
|
| 2731 |
-
},
|
| 2732 |
-
"bio.single-cell": {
|
| 2733 |
-
"query": "\"single-cell\" in:description",
|
| 2734 |
-
"years": [
|
| 2735 |
-
2026,
|
| 2736 |
-
2025
|
| 2737 |
-
]
|
| 2738 |
-
},
|
| 2739 |
-
"bio.spatial-transcriptomics": {
|
| 2740 |
-
"query": "\"spatial transcriptomics\" in:description,readme",
|
| 2741 |
-
"years": [
|
| 2742 |
-
2026,
|
| 2743 |
-
2025
|
| 2744 |
-
]
|
| 2745 |
-
},
|
| 2746 |
-
"bio.transcriptomics": {
|
| 2747 |
-
"query": "\"transcriptomics\" in:description",
|
| 2748 |
-
"years": [
|
| 2749 |
-
2026,
|
| 2750 |
-
2025
|
| 2751 |
-
]
|
| 2752 |
-
},
|
| 2753 |
-
"bio.variant-effects": {
|
| 2754 |
-
"query": "\"variant effect prediction\" in:readme",
|
| 2755 |
-
"years": [
|
| 2756 |
-
2026,
|
| 2757 |
-
2025
|
| 2758 |
-
]
|
| 2759 |
-
},
|
| 2760 |
-
"chem-admet": {
|
| 2761 |
-
"query": "\"ADMET prediction\" in:description,readme",
|
| 2762 |
-
"years": [
|
| 2763 |
-
2026,
|
| 2764 |
-
2025
|
| 2765 |
-
]
|
| 2766 |
-
},
|
| 2767 |
-
"chem-binding-affinity": {
|
| 2768 |
-
"query": "\"binding affinity prediction\" in:description,readme",
|
| 2769 |
-
"years": [
|
| 2770 |
-
2026,
|
| 2771 |
-
2025
|
| 2772 |
-
]
|
| 2773 |
-
},
|
| 2774 |
-
"chem-catalyst-discovery": {
|
| 2775 |
-
"query": "\"catalyst discovery\" in:description,readme",
|
| 2776 |
-
"years": [
|
| 2777 |
-
2026,
|
| 2778 |
-
2025
|
| 2779 |
-
]
|
| 2780 |
-
},
|
| 2781 |
-
"chem-cheminformatics": {
|
| 2782 |
-
"query": "topic:cheminformatics",
|
| 2783 |
-
"years": [
|
| 2784 |
-
2026,
|
| 2785 |
-
2025
|
| 2786 |
-
]
|
| 2787 |
-
},
|
| 2788 |
-
"chem-drug-discovery": {
|
| 2789 |
-
"query": "\"drug discovery\" in:description,readme",
|
| 2790 |
-
"years": [
|
| 2791 |
-
2026,
|
| 2792 |
-
2025
|
| 2793 |
-
]
|
| 2794 |
-
},
|
| 2795 |
-
"chem-interatomic-potentials": {
|
| 2796 |
-
"query": "\"machine learning interatomic potential\" in:description,readme",
|
| 2797 |
-
"years": [
|
| 2798 |
-
2026,
|
| 2799 |
-
2025
|
| 2800 |
-
]
|
| 2801 |
-
},
|
| 2802 |
-
"chem-molecular-docking": {
|
| 2803 |
-
"query": "\"molecular docking\" in:description,readme",
|
| 2804 |
-
"years": [
|
| 2805 |
-
2026,
|
| 2806 |
-
2025
|
| 2807 |
-
]
|
| 2808 |
-
},
|
| 2809 |
-
"chem-molecular-dynamics": {
|
| 2810 |
-
"query": "\"molecular dynamics\" \"machine learning\" in:description,readme",
|
| 2811 |
-
"years": [
|
| 2812 |
-
2026,
|
| 2813 |
-
2025
|
| 2814 |
-
]
|
| 2815 |
-
},
|
| 2816 |
-
"chem-molecular-generation": {
|
| 2817 |
-
"query": "\"molecular generation\" in:description,readme",
|
| 2818 |
-
"years": [
|
| 2819 |
-
2026,
|
| 2820 |
-
2025
|
| 2821 |
-
]
|
| 2822 |
-
},
|
| 2823 |
-
"chem-molecular-property-benchmark": {
|
| 2824 |
-
"query": "MoleculeNet in:description,readme",
|
| 2825 |
-
"years": [
|
| 2826 |
-
2026,
|
| 2827 |
-
2025
|
| 2828 |
-
]
|
| 2829 |
-
},
|
| 2830 |
-
"chem-molecular-spectroscopy": {
|
| 2831 |
-
"query": "molecular spectroscopy \"machine learning\" in:description,readme",
|
| 2832 |
-
"years": [
|
| 2833 |
-
2026,
|
| 2834 |
-
2025
|
| 2835 |
-
]
|
| 2836 |
-
},
|
| 2837 |
-
"chem-property-prediction": {
|
| 2838 |
-
"query": "\"molecular property prediction\" in:description,readme",
|
| 2839 |
-
"years": [
|
| 2840 |
-
2026,
|
| 2841 |
-
2025
|
| 2842 |
-
]
|
| 2843 |
-
},
|
| 2844 |
-
"chem-protein-ligand-docking": {
|
| 2845 |
-
"query": "\"protein-ligand docking\" in:description,readme",
|
| 2846 |
-
"years": [
|
| 2847 |
-
2026,
|
| 2848 |
-
2025
|
| 2849 |
-
]
|
| 2850 |
-
},
|
| 2851 |
-
"chem-quantum-chemistry": {
|
| 2852 |
-
"query": "\"quantum chemistry\" \"machine learning\" in:description,readme",
|
| 2853 |
-
"years": [
|
| 2854 |
-
2026,
|
| 2855 |
-
2025
|
| 2856 |
-
]
|
| 2857 |
-
},
|
| 2858 |
-
"chem-reaction-conditions": {
|
| 2859 |
-
"query": "\"reaction condition prediction\" in:description,readme",
|
| 2860 |
-
"years": [
|
| 2861 |
-
2026,
|
| 2862 |
-
2025
|
| 2863 |
-
]
|
| 2864 |
-
},
|
| 2865 |
-
"chem-reaction-prediction": {
|
| 2866 |
-
"query": "\"reaction prediction\" in:description,readme",
|
| 2867 |
-
"years": [
|
| 2868 |
-
2026,
|
| 2869 |
-
2025
|
| 2870 |
-
]
|
| 2871 |
-
},
|
| 2872 |
-
"chem-reaction-yield": {
|
| 2873 |
-
"query": "\"reaction yield prediction\" in:description,readme",
|
| 2874 |
-
"years": [
|
| 2875 |
-
2026,
|
| 2876 |
-
2025
|
| 2877 |
-
]
|
| 2878 |
-
},
|
| 2879 |
-
"chem-retrosynthesis": {
|
| 2880 |
-
"query": "\"computer aided synthesis planning\" in:description,readme",
|
| 2881 |
-
"years": [
|
| 2882 |
-
2026,
|
| 2883 |
-
2025
|
| 2884 |
-
]
|
| 2885 |
-
},
|
| 2886 |
-
"chem-retrosynthesis-planning": {
|
| 2887 |
-
"query": "retrosynthesis in:description,readme",
|
| 2888 |
-
"years": [
|
| 2889 |
-
2026,
|
| 2890 |
-
2025
|
| 2891 |
-
]
|
| 2892 |
-
},
|
| 2893 |
-
"chem-virtual-screening": {
|
| 2894 |
-
"query": "\"virtual screening\" in:description,readme",
|
| 2895 |
-
"years": [
|
| 2896 |
-
2026,
|
| 2897 |
-
2025
|
| 2898 |
-
]
|
| 2899 |
-
},
|
| 2900 |
-
"efficiency.activation-checkpointing": {
|
| 2901 |
-
"query": "\"activation checkpointing\" in:description",
|
| 2902 |
-
"years": [
|
| 2903 |
-
2026,
|
| 2904 |
-
2025
|
| 2905 |
-
]
|
| 2906 |
-
},
|
| 2907 |
-
"efficiency.automl": {
|
| 2908 |
-
"query": "AutoML in:description",
|
| 2909 |
-
"years": [
|
| 2910 |
-
2026,
|
| 2911 |
-
2025
|
| 2912 |
-
]
|
| 2913 |
-
},
|
| 2914 |
-
"efficiency.communication-efficient-training": {
|
| 2915 |
-
"query": "\"communication efficient training\" in:description",
|
| 2916 |
-
"years": [
|
| 2917 |
-
2026,
|
| 2918 |
-
2025
|
| 2919 |
-
]
|
| 2920 |
-
},
|
| 2921 |
-
"efficiency.compiler-optimization": {
|
| 2922 |
-
"query": "\"machine learning compiler\" in:description",
|
| 2923 |
-
"years": [
|
| 2924 |
-
2026,
|
| 2925 |
-
2025
|
| 2926 |
-
]
|
| 2927 |
-
},
|
| 2928 |
-
"efficiency.distributed-training": {
|
| 2929 |
-
"query": "\"distributed training\" in:description",
|
| 2930 |
-
"years": [
|
| 2931 |
-
2026,
|
| 2932 |
-
2025
|
| 2933 |
-
]
|
| 2934 |
-
},
|
| 2935 |
-
"efficiency.edge-ai": {
|
| 2936 |
-
"query": "\"edge AI\" in:description",
|
| 2937 |
-
"years": [
|
| 2938 |
-
2026,
|
| 2939 |
-
2025
|
| 2940 |
-
]
|
| 2941 |
-
},
|
| 2942 |
-
"efficiency.energy-efficient-ml": {
|
| 2943 |
-
"query": "\"energy efficient machine learning\" in:description",
|
| 2944 |
-
"years": [
|
| 2945 |
-
2026,
|
| 2946 |
-
2025
|
| 2947 |
-
]
|
| 2948 |
-
},
|
| 2949 |
-
"efficiency.flash-attention": {
|
| 2950 |
-
"query": "\"FlashAttention\" in:description",
|
| 2951 |
-
"years": [
|
| 2952 |
-
2026,
|
| 2953 |
-
2025
|
| 2954 |
-
]
|
| 2955 |
-
},
|
| 2956 |
-
"efficiency.gpu-kernels": {
|
| 2957 |
-
"query": "\"GPU kernel\" in:description",
|
| 2958 |
-
"years": [
|
| 2959 |
-
2026,
|
| 2960 |
-
2025
|
| 2961 |
-
]
|
| 2962 |
-
},
|
| 2963 |
-
"efficiency.gradient-optimization": {
|
| 2964 |
-
"query": "\"gradient-based optimization\" in:description",
|
| 2965 |
-
"years": [
|
| 2966 |
-
2026,
|
| 2967 |
-
2025
|
| 2968 |
-
]
|
| 2969 |
-
},
|
| 2970 |
-
"efficiency.hyperparameter-optimization": {
|
| 2971 |
-
"query": "\"hyperparameter optimization\" in:description",
|
| 2972 |
-
"years": [
|
| 2973 |
-
2026,
|
| 2974 |
-
2025
|
| 2975 |
-
]
|
| 2976 |
-
},
|
| 2977 |
-
"efficiency.inference-systems": {
|
| 2978 |
-
"query": "\"inference engine\" in:description",
|
| 2979 |
-
"years": [
|
| 2980 |
-
2026,
|
| 2981 |
-
2025
|
| 2982 |
-
]
|
| 2983 |
-
},
|
| 2984 |
-
"efficiency.knowledge-distillation": {
|
| 2985 |
-
"query": "\"knowledge distillation\" in:description",
|
| 2986 |
-
"years": [
|
| 2987 |
-
2026,
|
| 2988 |
-
2025
|
| 2989 |
-
]
|
| 2990 |
-
},
|
| 2991 |
-
"efficiency.kv-cache": {
|
| 2992 |
-
"query": "\"KV cache\" in:description",
|
| 2993 |
-
"years": [
|
| 2994 |
-
2026,
|
| 2995 |
-
2025
|
| 2996 |
-
]
|
| 2997 |
-
},
|
| 2998 |
-
"efficiency.lora": {
|
| 2999 |
-
"query": "LoRA in:description",
|
| 3000 |
-
"years": [
|
| 3001 |
-
2026,
|
| 3002 |
-
2025
|
| 3003 |
-
]
|
| 3004 |
-
},
|
| 3005 |
-
"efficiency.low-rank-factorization": {
|
| 3006 |
-
"query": "\"low-rank factorization\" in:description",
|
| 3007 |
-
"years": [
|
| 3008 |
-
2026,
|
| 3009 |
-
2025
|
| 3010 |
-
]
|
| 3011 |
-
},
|
| 3012 |
-
"efficiency.mixed-precision-training": {
|
| 3013 |
-
"query": "\"mixed precision training\" in:description",
|
| 3014 |
-
"years": [
|
| 3015 |
-
2026,
|
| 3016 |
-
2025
|
| 3017 |
-
]
|
| 3018 |
-
},
|
| 3019 |
-
"efficiency.mixture-of-experts": {
|
| 3020 |
-
"query": "\"mixture of experts\" in:description",
|
| 3021 |
-
"years": [
|
| 3022 |
-
2026,
|
| 3023 |
-
2025
|
| 3024 |
-
]
|
| 3025 |
-
},
|
| 3026 |
-
"efficiency.model-compression": {
|
| 3027 |
-
"query": "\"model compression\" in:description",
|
| 3028 |
-
"years": [
|
| 3029 |
-
2026,
|
| 3030 |
-
2025
|
| 3031 |
-
]
|
| 3032 |
-
},
|
| 3033 |
-
"efficiency.neural-architecture-search": {
|
| 3034 |
-
"query": "\"neural architecture search\" in:description",
|
| 3035 |
-
"years": [
|
| 3036 |
-
2026,
|
| 3037 |
-
2025
|
| 3038 |
-
]
|
| 3039 |
-
},
|
| 3040 |
-
"efficiency.optimizer-state-sharding": {
|
| 3041 |
-
"query": "\"optimizer state sharding\" in:description",
|
| 3042 |
-
"years": [
|
| 3043 |
-
2026,
|
| 3044 |
-
2025
|
| 3045 |
-
]
|
| 3046 |
-
},
|
| 3047 |
-
"efficiency.parameter-efficient-finetuning": {
|
| 3048 |
-
"query": "\"parameter-efficient fine-tuning\" in:description",
|
| 3049 |
-
"years": [
|
| 3050 |
-
2026,
|
| 3051 |
-
2025
|
| 3052 |
-
]
|
| 3053 |
-
},
|
| 3054 |
-
"efficiency.post-training-quantization": {
|
| 3055 |
-
"query": "\"post-training quantization\" in:description",
|
| 3056 |
-
"years": [
|
| 3057 |
-
2026,
|
| 3058 |
-
2025
|
| 3059 |
-
]
|
| 3060 |
-
},
|
| 3061 |
-
"efficiency.pruning": {
|
| 3062 |
-
"query": "pruning in:description",
|
| 3063 |
-
"years": [
|
| 3064 |
-
2026,
|
| 3065 |
-
2025
|
| 3066 |
-
]
|
| 3067 |
-
},
|
| 3068 |
-
"efficiency.quantization": {
|
| 3069 |
-
"query": "quantization in:description",
|
| 3070 |
-
"years": [
|
| 3071 |
-
2026,
|
| 3072 |
-
2025
|
| 3073 |
-
]
|
| 3074 |
-
},
|
| 3075 |
-
"efficiency.quantization-aware-training": {
|
| 3076 |
-
"query": "\"quantization-aware training\" in:description",
|
| 3077 |
-
"years": [
|
| 3078 |
-
2026,
|
| 3079 |
-
2025
|
| 3080 |
-
]
|
| 3081 |
-
},
|
| 3082 |
-
"efficiency.sparse-neural-networks": {
|
| 3083 |
-
"query": "\"sparse neural network\" in:description",
|
| 3084 |
-
"years": [
|
| 3085 |
-
2026,
|
| 3086 |
-
2025
|
| 3087 |
-
]
|
| 3088 |
-
},
|
| 3089 |
-
"efficiency.speculative-decoding": {
|
| 3090 |
-
"query": "\"speculative decoding\" in:description",
|
| 3091 |
-
"years": [
|
| 3092 |
-
2026,
|
| 3093 |
-
2025
|
| 3094 |
-
]
|
| 3095 |
-
},
|
| 3096 |
-
"general.active-learning": {
|
| 3097 |
-
"query": "\"active learning\" in:description",
|
| 3098 |
-
"years": [
|
| 3099 |
-
2026,
|
| 3100 |
-
2025
|
| 3101 |
-
]
|
| 3102 |
-
},
|
| 3103 |
-
"general.artificial-intelligence-description": {
|
| 3104 |
-
"query": "\"artificial intelligence\" in:description",
|
| 3105 |
-
"years": [
|
| 3106 |
-
2026,
|
| 3107 |
-
2025
|
| 3108 |
-
]
|
| 3109 |
-
},
|
| 3110 |
-
"general.arxiv": {
|
| 3111 |
-
"query": "arxiv in:description",
|
| 3112 |
-
"years": [
|
| 3113 |
-
2026,
|
| 3114 |
-
2025
|
| 3115 |
-
]
|
| 3116 |
-
},
|
| 3117 |
-
"general.automated-machine-learning": {
|
| 3118 |
-
"query": "\"automated machine learning\" in:readme",
|
| 3119 |
-
"years": [
|
| 3120 |
-
2026,
|
| 3121 |
-
2025
|
| 3122 |
-
]
|
| 3123 |
-
},
|
| 3124 |
-
"general.bayesian-deep-learning": {
|
| 3125 |
-
"query": "\"Bayesian deep learning\" in:description,readme",
|
| 3126 |
-
"years": [
|
| 3127 |
-
2026,
|
| 3128 |
-
2025
|
| 3129 |
-
]
|
| 3130 |
-
},
|
| 3131 |
-
"general.bayesian-optimization": {
|
| 3132 |
-
"query": "\"Bayesian optimization\" in:description",
|
| 3133 |
-
"years": [
|
| 3134 |
-
2026,
|
| 3135 |
-
2025
|
| 3136 |
-
]
|
| 3137 |
-
},
|
| 3138 |
-
"general.clustering": {
|
| 3139 |
-
"query": "clustering in:description",
|
| 3140 |
-
"years": [
|
| 3141 |
-
2026,
|
| 3142 |
-
2025
|
| 3143 |
-
]
|
| 3144 |
-
},
|
| 3145 |
-
"general.continual-learning": {
|
| 3146 |
-
"query": "\"continual learning\" in:description,readme",
|
| 3147 |
-
"years": [
|
| 3148 |
-
2026,
|
| 3149 |
-
2025
|
| 3150 |
-
]
|
| 3151 |
-
},
|
| 3152 |
-
"general.contrastive-learning": {
|
| 3153 |
-
"query": "\"contrastive learning\" in:description,readme",
|
| 3154 |
-
"years": [
|
| 3155 |
-
2026,
|
| 3156 |
-
2025
|
| 3157 |
-
]
|
| 3158 |
-
},
|
| 3159 |
-
"general.decision-trees": {
|
| 3160 |
-
"query": "\"decision tree\" \"machine learning\" in:description,readme",
|
| 3161 |
-
"years": [
|
| 3162 |
-
2026,
|
| 3163 |
-
2025
|
| 3164 |
-
]
|
| 3165 |
-
},
|
| 3166 |
-
"general.deep-learning-description": {
|
| 3167 |
-
"query": "\"deep learning\" in:description",
|
| 3168 |
-
"years": [
|
| 3169 |
-
2026,
|
| 3170 |
-
2025
|
| 3171 |
-
]
|
| 3172 |
-
},
|
| 3173 |
-
"general.deep-learning-readme": {
|
| 3174 |
-
"query": "\"deep learning\" in:readme",
|
| 3175 |
-
"years": [
|
| 3176 |
-
2026,
|
| 3177 |
-
2025
|
| 3178 |
-
]
|
| 3179 |
-
},
|
| 3180 |
-
"general.dimensionality-reduction": {
|
| 3181 |
-
"query": "\"dimensionality reduction\" in:description,readme",
|
| 3182 |
-
"years": [
|
| 3183 |
-
2026,
|
| 3184 |
-
2025
|
| 3185 |
-
]
|
| 3186 |
-
},
|
| 3187 |
-
"general.domain-adaptation": {
|
| 3188 |
-
"query": "\"domain adaptation\" \"machine learning\" in:description,readme",
|
| 3189 |
-
"years": [
|
| 3190 |
-
2026,
|
| 3191 |
-
2025
|
| 3192 |
-
]
|
| 3193 |
-
},
|
| 3194 |
-
"general.gaussian-mixture-models": {
|
| 3195 |
-
"query": "\"Gaussian mixture model\" in:description,readme",
|
| 3196 |
-
"years": [
|
| 3197 |
-
2026,
|
| 3198 |
-
2025
|
| 3199 |
-
]
|
| 3200 |
-
},
|
| 3201 |
-
"general.gaussian-process": {
|
| 3202 |
-
"query": "\"Gaussian process\" in:description",
|
| 3203 |
-
"years": [
|
| 3204 |
-
2026,
|
| 3205 |
-
2025
|
| 3206 |
-
]
|
| 3207 |
-
},
|
| 3208 |
-
"general.gradient-boosting": {
|
| 3209 |
-
"query": "\"gradient boosting\" in:description",
|
| 3210 |
-
"years": [
|
| 3211 |
-
2026,
|
| 3212 |
-
2025
|
| 3213 |
-
]
|
| 3214 |
-
},
|
| 3215 |
-
"general.hidden-markov-models": {
|
| 3216 |
-
"query": "\"hidden Markov model\" \"machine learning\" in:description,readme",
|
| 3217 |
-
"years": [
|
| 3218 |
-
2026,
|
| 3219 |
-
2025
|
| 3220 |
-
]
|
| 3221 |
-
},
|
| 3222 |
-
"general.logistic-regression": {
|
| 3223 |
-
"query": "\"logistic regression\" in:description,readme",
|
| 3224 |
-
"years": [
|
| 3225 |
-
2026,
|
| 3226 |
-
2025
|
| 3227 |
-
]
|
| 3228 |
-
},
|
| 3229 |
-
"general.machine-learning-description": {
|
| 3230 |
-
"query": "\"machine learning\" in:description",
|
| 3231 |
-
"years": [
|
| 3232 |
-
2026,
|
| 3233 |
-
2025
|
| 3234 |
-
]
|
| 3235 |
-
},
|
| 3236 |
-
"general.machine-learning-readme": {
|
| 3237 |
-
"query": "\"machine learning\" in:readme",
|
| 3238 |
-
"years": [
|
| 3239 |
-
2026,
|
| 3240 |
-
2025
|
| 3241 |
-
]
|
| 3242 |
-
},
|
| 3243 |
-
"general.matrix-factorization": {
|
| 3244 |
-
"query": "\"matrix factorization\" \"machine learning\" in:description,readme",
|
| 3245 |
-
"years": [
|
| 3246 |
-
2026,
|
| 3247 |
-
2025
|
| 3248 |
-
]
|
| 3249 |
-
},
|
| 3250 |
-
"general.meta-learning": {
|
| 3251 |
-
"query": "\"meta-learning\" in:description,readme",
|
| 3252 |
-
"years": [
|
| 3253 |
-
2026,
|
| 3254 |
-
2025
|
| 3255 |
-
]
|
| 3256 |
-
},
|
| 3257 |
-
"general.metric-learning": {
|
| 3258 |
-
"query": "\"metric learning\" in:description,readme",
|
| 3259 |
-
"years": [
|
| 3260 |
-
2026,
|
| 3261 |
-
2025
|
| 3262 |
-
]
|
| 3263 |
-
},
|
| 3264 |
-
"general.model-architecture": {
|
| 3265 |
-
"query": "\"model architecture\" in:description",
|
| 3266 |
-
"years": [
|
| 3267 |
-
2026,
|
| 3268 |
-
2025
|
| 3269 |
-
]
|
| 3270 |
-
},
|
| 3271 |
-
"general.multi-task-learning": {
|
| 3272 |
-
"query": "\"multi-task learning\" in:description,readme",
|
| 3273 |
-
"years": [
|
| 3274 |
-
2026,
|
| 3275 |
-
2025
|
| 3276 |
-
]
|
| 3277 |
-
},
|
| 3278 |
-
"general.naive-bayes": {
|
| 3279 |
-
"query": "\"naive Bayes\" in:description,readme",
|
| 3280 |
-
"years": [
|
| 3281 |
-
2026,
|
| 3282 |
-
2025
|
| 3283 |
-
]
|
| 3284 |
-
},
|
| 3285 |
-
"general.nearest-neighbors": {
|
| 3286 |
-
"query": "\"nearest neighbors\" \"machine learning\" in:description,readme",
|
| 3287 |
-
"years": [
|
| 3288 |
-
2026,
|
| 3289 |
-
2025
|
| 3290 |
-
]
|
| 3291 |
-
},
|
| 3292 |
-
"general.neural-network": {
|
| 3293 |
-
"query": "\"neural network\" in:description",
|
| 3294 |
-
"years": [
|
| 3295 |
-
2026,
|
| 3296 |
-
2025
|
| 3297 |
-
]
|
| 3298 |
-
},
|
| 3299 |
-
"general.neural-processes": {
|
| 3300 |
-
"query": "\"neural processes\" in:description,readme",
|
| 3301 |
-
"years": [
|
| 3302 |
-
2026,
|
| 3303 |
-
2025
|
| 3304 |
-
]
|
| 3305 |
-
},
|
| 3306 |
-
"general.normalizing-flows": {
|
| 3307 |
-
"query": "\"normalizing flows\" \"machine learning\" in:description,readme",
|
| 3308 |
-
"years": [
|
| 3309 |
-
2026,
|
| 3310 |
-
2025
|
| 3311 |
-
]
|
| 3312 |
-
},
|
| 3313 |
-
"general.novel-method": {
|
| 3314 |
-
"query": "\"novel method\" in:description",
|
| 3315 |
-
"years": [
|
| 3316 |
-
2026,
|
| 3317 |
-
2025
|
| 3318 |
-
]
|
| 3319 |
-
},
|
| 3320 |
-
"general.paper-implementation": {
|
| 3321 |
-
"query": "\"paper implementation\" in:description",
|
| 3322 |
-
"years": [
|
| 3323 |
-
2026,
|
| 3324 |
-
2025
|
| 3325 |
-
]
|
| 3326 |
-
},
|
| 3327 |
-
"general.probabilistic-programming": {
|
| 3328 |
-
"query": "\"probabilistic programming\" in:description,readme",
|
| 3329 |
-
"years": [
|
| 3330 |
-
2026,
|
| 3331 |
-
2025
|
| 3332 |
-
]
|
| 3333 |
-
},
|
| 3334 |
-
"general.random-forest": {
|
| 3335 |
-
"query": "\"random forest\" in:description",
|
| 3336 |
-
"years": [
|
| 3337 |
-
2026,
|
| 3338 |
-
2025
|
| 3339 |
-
]
|
| 3340 |
-
},
|
| 3341 |
-
"general.research-code": {
|
| 3342 |
-
"query": "\"research code\" in:description",
|
| 3343 |
-
"years": [
|
| 3344 |
-
2026,
|
| 3345 |
-
2025
|
| 3346 |
-
]
|
| 3347 |
-
},
|
| 3348 |
-
"general.self-supervised-learning": {
|
| 3349 |
-
"query": "\"self-supervised learning\" in:description",
|
| 3350 |
-
"years": [
|
| 3351 |
-
2026,
|
| 3352 |
-
2025
|
| 3353 |
-
]
|
| 3354 |
-
},
|
| 3355 |
-
"general.semi-supervised-learning": {
|
| 3356 |
-
"query": "\"semi-supervised learning\" in:description,readme",
|
| 3357 |
-
"years": [
|
| 3358 |
-
2026,
|
| 3359 |
-
2025
|
| 3360 |
-
]
|
| 3361 |
-
},
|
| 3362 |
-
"general.support-vector-machine": {
|
| 3363 |
-
"query": "\"support vector machine\" in:description",
|
| 3364 |
-
"years": [
|
| 3365 |
-
2026,
|
| 3366 |
-
2025
|
| 3367 |
-
]
|
| 3368 |
-
},
|
| 3369 |
-
"general.topic-artificial-intelligence": {
|
| 3370 |
-
"query": "topic:artificial-intelligence",
|
| 3371 |
-
"years": [
|
| 3372 |
-
2026,
|
| 3373 |
-
2025
|
| 3374 |
-
]
|
| 3375 |
-
},
|
| 3376 |
-
"general.topic-deep-learning": {
|
| 3377 |
-
"query": "topic:deep-learning",
|
| 3378 |
-
"years": [
|
| 3379 |
-
2026,
|
| 3380 |
-
2025
|
| 3381 |
-
]
|
| 3382 |
-
},
|
| 3383 |
-
"general.topic-machine-learning": {
|
| 3384 |
-
"query": "topic:machine-learning",
|
| 3385 |
-
"years": [
|
| 3386 |
-
2026
|
| 3387 |
-
]
|
| 3388 |
-
},
|
| 3389 |
-
"general.transfer-learning": {
|
| 3390 |
-
"query": "\"transfer learning\" in:description,readme",
|
| 3391 |
-
"years": [
|
| 3392 |
-
2026
|
| 3393 |
-
]
|
| 3394 |
-
},
|
| 3395 |
-
"general.weak-supervision": {
|
| 3396 |
-
"query": "\"weak supervision\" \"machine learning\" in:description,readme",
|
| 3397 |
"years": [
|
| 3398 |
2026
|
| 3399 |
]
|
| 3400 |
-
},
|
| 3401 |
-
"geo.building-footprints": {
|
| 3402 |
-
"query": "\"building footprint extraction\" in:description,readme",
|
| 3403 |
-
"years": [
|
| 3404 |
-
2026,
|
| 3405 |
-
2025
|
| 3406 |
-
]
|
| 3407 |
-
},
|
| 3408 |
-
"geo.change-detection": {
|
| 3409 |
-
"query": "\"remote sensing change detection\" in:description,readme",
|
| 3410 |
-
"years": [
|
| 3411 |
-
2026,
|
| 3412 |
-
2025
|
| 3413 |
-
]
|
| 3414 |
-
},
|
| 3415 |
-
"geo.climate-mapping": {
|
| 3416 |
-
"query": "\"climate downscaling\" in:readme",
|
| 3417 |
-
"years": [
|
| 3418 |
-
2026,
|
| 3419 |
-
2025
|
| 3420 |
-
]
|
| 3421 |
-
},
|
| 3422 |
-
"geo.crop-monitoring": {
|
| 3423 |
-
"query": "\"crop yield prediction\" in:description,readme",
|
| 3424 |
-
"years": [
|
| 3425 |
-
2026,
|
| 3426 |
-
2025
|
| 3427 |
-
]
|
| 3428 |
-
},
|
| 3429 |
-
"geo.earth-observation-models": {
|
| 3430 |
-
"query": "\"Earth observation model\" in:description",
|
| 3431 |
-
"years": [
|
| 3432 |
-
2026,
|
| 3433 |
-
2025
|
| 3434 |
-
]
|
| 3435 |
-
},
|
| 3436 |
-
"geo.environmental-monitoring": {
|
| 3437 |
-
"query": "\"environmental monitoring\" in:readme",
|
| 3438 |
-
"years": [
|
| 3439 |
-
2026,
|
| 3440 |
-
2025
|
| 3441 |
-
]
|
| 3442 |
-
},
|
| 3443 |
-
"geo.flood-mapping": {
|
| 3444 |
-
"query": "\"flood mapping\" in:readme",
|
| 3445 |
-
"years": [
|
| 3446 |
-
2026,
|
| 3447 |
-
2025
|
| 3448 |
-
]
|
| 3449 |
-
},
|
| 3450 |
-
"geo.foundation-models-earth": {
|
| 3451 |
-
"query": "\"Earth observation foundation model\" in:description,readme",
|
| 3452 |
-
"years": [
|
| 3453 |
-
2026,
|
| 3454 |
-
2025
|
| 3455 |
-
]
|
| 3456 |
-
},
|
| 3457 |
-
"geo.geospatial-foundation-models": {
|
| 3458 |
-
"query": "\"geospatial foundation model\" in:readme",
|
| 3459 |
-
"years": [
|
| 3460 |
-
2026,
|
| 3461 |
-
2025
|
| 3462 |
-
]
|
| 3463 |
-
},
|
| 3464 |
-
"geo.geospatial-knowledge-graph": {
|
| 3465 |
-
"query": "\"geospatial knowledge graph\" in:description,readme",
|
| 3466 |
-
"years": [
|
| 3467 |
-
2026,
|
| 3468 |
-
2025
|
| 3469 |
-
]
|
| 3470 |
-
},
|
| 3471 |
-
"geo.hydrology": {
|
| 3472 |
-
"query": "\"hydrology machine learning\" in:readme",
|
| 3473 |
-
"years": [
|
| 3474 |
-
2026,
|
| 3475 |
-
2025
|
| 3476 |
-
]
|
| 3477 |
-
},
|
| 3478 |
-
"geo.land-cover": {
|
| 3479 |
-
"query": "\"land cover\" in:readme",
|
| 3480 |
-
"years": [
|
| 3481 |
-
2026,
|
| 3482 |
-
2025
|
| 3483 |
-
]
|
| 3484 |
-
},
|
| 3485 |
-
"geo.land-use": {
|
| 3486 |
-
"query": "\"land use\" in:readme",
|
| 3487 |
-
"years": [
|
| 3488 |
-
2026,
|
| 3489 |
-
2025
|
| 3490 |
-
]
|
| 3491 |
-
},
|
| 3492 |
-
"geo.lidar": {
|
| 3493 |
-
"query": "LiDAR in:description \"deep learning\"",
|
| 3494 |
-
"years": [
|
| 3495 |
-
2026,
|
| 3496 |
-
2025
|
| 3497 |
-
]
|
| 3498 |
-
},
|
| 3499 |
-
"geo.mobility-modeling": {
|
| 3500 |
-
"query": "\"human mobility\" \"machine learning\" in:description,readme",
|
| 3501 |
-
"years": [
|
| 3502 |
-
2026,
|
| 3503 |
-
2025
|
| 3504 |
-
]
|
| 3505 |
-
},
|
| 3506 |
-
"geo.object-detection": {
|
| 3507 |
-
"query": "\"object detection\" \"remote sensing\" in:description,readme",
|
| 3508 |
-
"years": [
|
| 3509 |
-
2026,
|
| 3510 |
-
2025
|
| 3511 |
-
]
|
| 3512 |
-
},
|
| 3513 |
-
"geo.ocean-remote-sensing": {
|
| 3514 |
-
"query": "\"ocean remote sensing\" in:description,readme",
|
| 3515 |
-
"years": [
|
| 3516 |
-
2026,
|
| 3517 |
-
2025
|
| 3518 |
-
]
|
| 3519 |
-
},
|
| 3520 |
-
"geo.precipitation-forecasting": {
|
| 3521 |
-
"query": "\"precipitation forecasting\" in:description,readme",
|
| 3522 |
-
"years": [
|
| 3523 |
-
2026,
|
| 3524 |
-
2025
|
| 3525 |
-
]
|
| 3526 |
-
},
|
| 3527 |
-
"geo.sar-imagery": {
|
| 3528 |
-
"query": "\"synthetic aperture radar\" in:readme",
|
| 3529 |
-
"years": [
|
| 3530 |
-
2026,
|
| 3531 |
-
2025
|
| 3532 |
-
]
|
| 3533 |
-
},
|
| 3534 |
-
"geo.satellite-imagery": {
|
| 3535 |
-
"query": "\"satellite imagery\" in:readme",
|
| 3536 |
-
"years": [
|
| 3537 |
-
2026,
|
| 3538 |
-
2025
|
| 3539 |
-
]
|
| 3540 |
-
},
|
| 3541 |
-
"geo.topic-earth-observation": {
|
| 3542 |
-
"query": "topic:earth-observation",
|
| 3543 |
-
"years": [
|
| 3544 |
-
2026,
|
| 3545 |
-
2025
|
| 3546 |
-
]
|
| 3547 |
-
},
|
| 3548 |
-
"geo.topic-geospatial": {
|
| 3549 |
-
"query": "topic:geospatial",
|
| 3550 |
-
"years": [
|
| 3551 |
-
2026,
|
| 3552 |
-
2025
|
| 3553 |
-
]
|
| 3554 |
-
},
|
| 3555 |
-
"geo.topic-gis": {
|
| 3556 |
-
"query": "topic:gis",
|
| 3557 |
-
"years": [
|
| 3558 |
-
2026,
|
| 3559 |
-
2025
|
| 3560 |
-
]
|
| 3561 |
-
},
|
| 3562 |
-
"geo.topic-remote-sensing": {
|
| 3563 |
-
"query": "topic:remote-sensing",
|
| 3564 |
-
"years": [
|
| 3565 |
-
2026,
|
| 3566 |
-
2025
|
| 3567 |
-
]
|
| 3568 |
-
},
|
| 3569 |
-
"geo.trajectory-prediction": {
|
| 3570 |
-
"query": "\"trajectory prediction\" geospatial in:description,readme",
|
| 3571 |
-
"years": [
|
| 3572 |
-
2026,
|
| 3573 |
-
2025
|
| 3574 |
-
]
|
| 3575 |
-
},
|
| 3576 |
-
"geo.urban-sensing": {
|
| 3577 |
-
"query": "\"urban remote sensing\" in:readme",
|
| 3578 |
-
"years": [
|
| 3579 |
-
2026,
|
| 3580 |
-
2025
|
| 3581 |
-
]
|
| 3582 |
-
},
|
| 3583 |
-
"geo.wildfire-mapping": {
|
| 3584 |
-
"query": "\"wildfire detection\" in:description,readme",
|
| 3585 |
-
"years": [
|
| 3586 |
-
2026,
|
| 3587 |
-
2025
|
| 3588 |
-
]
|
| 3589 |
-
},
|
| 3590 |
-
"graph.e3-equivariance": {
|
| 3591 |
-
"query": "\"E(3)-equivariant\" in:readme",
|
| 3592 |
-
"years": [
|
| 3593 |
-
2026,
|
| 3594 |
-
2025
|
| 3595 |
-
]
|
| 3596 |
-
},
|
| 3597 |
-
"graph.equivariant-networks": {
|
| 3598 |
-
"query": "\"equivariant neural network\" in:description",
|
| 3599 |
-
"years": [
|
| 3600 |
-
2026,
|
| 3601 |
-
2025
|
| 3602 |
-
]
|
| 3603 |
-
},
|
| 3604 |
-
"graph.geometric-learning": {
|
| 3605 |
-
"query": "\"geometric deep learning\" in:readme",
|
| 3606 |
-
"years": [
|
| 3607 |
-
2026,
|
| 3608 |
-
2025
|
| 3609 |
-
]
|
| 3610 |
-
},
|
| 3611 |
-
"graph.gnn-description": {
|
| 3612 |
-
"query": "\"graph neural network\" in:description",
|
| 3613 |
-
"years": [
|
| 3614 |
-
2026,
|
| 3615 |
-
2025
|
| 3616 |
-
]
|
| 3617 |
-
},
|
| 3618 |
-
"graph.graph-anomaly-detection": {
|
| 3619 |
-
"query": "\"graph anomaly detection\" in:description,readme",
|
| 3620 |
-
"years": [
|
| 3621 |
-
2026,
|
| 3622 |
-
2025
|
| 3623 |
-
]
|
| 3624 |
-
},
|
| 3625 |
-
"graph.graph-classification": {
|
| 3626 |
-
"query": "\"graph classification\" in:description",
|
| 3627 |
-
"years": [
|
| 3628 |
-
2026,
|
| 3629 |
-
2025
|
| 3630 |
-
]
|
| 3631 |
-
},
|
| 3632 |
-
"graph.graph-contrastive-learning": {
|
| 3633 |
-
"query": "\"graph contrastive learning\" in:description",
|
| 3634 |
-
"years": [
|
| 3635 |
-
2026,
|
| 3636 |
-
2025
|
| 3637 |
-
]
|
| 3638 |
-
},
|
| 3639 |
-
"graph.graph-diffusion": {
|
| 3640 |
-
"query": "\"graph diffusion model\" in:description",
|
| 3641 |
-
"years": [
|
| 3642 |
-
2026,
|
| 3643 |
-
2025
|
| 3644 |
-
]
|
| 3645 |
-
},
|
| 3646 |
-
"graph.graph-foundation-model": {
|
| 3647 |
-
"query": "\"graph foundation model\" in:description",
|
| 3648 |
-
"years": [
|
| 3649 |
-
2026,
|
| 3650 |
-
2025
|
| 3651 |
-
]
|
| 3652 |
-
},
|
| 3653 |
-
"graph.graph-generation": {
|
| 3654 |
-
"query": "\"graph generation\" in:readme",
|
| 3655 |
-
"years": [
|
| 3656 |
-
2026,
|
| 3657 |
-
2025
|
| 3658 |
-
]
|
| 3659 |
-
},
|
| 3660 |
-
"graph.graph-transformer": {
|
| 3661 |
-
"query": "\"graph transformer\" in:description",
|
| 3662 |
-
"years": [
|
| 3663 |
-
2026,
|
| 3664 |
-
2025
|
| 3665 |
-
]
|
| 3666 |
-
},
|
| 3667 |
-
"graph.hypergraph-neural-network": {
|
| 3668 |
-
"query": "\"hypergraph neural network\" in:description",
|
| 3669 |
-
"years": [
|
| 3670 |
-
2026,
|
| 3671 |
-
2025
|
| 3672 |
-
]
|
| 3673 |
-
},
|
| 3674 |
-
"graph.knowledge-graph-embedding": {
|
| 3675 |
-
"query": "\"knowledge graph embedding\" in:description",
|
| 3676 |
-
"years": [
|
| 3677 |
-
2026,
|
| 3678 |
-
2025
|
| 3679 |
-
]
|
| 3680 |
-
},
|
| 3681 |
-
"graph.knowledge-graphs": {
|
| 3682 |
-
"query": "topic:knowledge-graph",
|
| 3683 |
-
"years": [
|
| 3684 |
-
2026,
|
| 3685 |
-
2025
|
| 3686 |
-
]
|
| 3687 |
-
},
|
| 3688 |
-
"graph.link-prediction": {
|
| 3689 |
-
"query": "\"link prediction\" in:description",
|
| 3690 |
-
"years": [
|
| 3691 |
-
2026,
|
| 3692 |
-
2025
|
| 3693 |
-
]
|
| 3694 |
-
},
|
| 3695 |
-
"graph.manifold-learning": {
|
| 3696 |
-
"query": "\"manifold learning\" in:description",
|
| 3697 |
-
"years": [
|
| 3698 |
-
2026,
|
| 3699 |
-
2025
|
| 3700 |
-
]
|
| 3701 |
-
},
|
| 3702 |
-
"graph.mesh-graph-learning": {
|
| 3703 |
-
"query": "\"mesh graph neural network\" in:description",
|
| 3704 |
-
"years": [
|
| 3705 |
-
2026,
|
| 3706 |
-
2025
|
| 3707 |
-
]
|
| 3708 |
-
},
|
| 3709 |
-
"graph.molecular-graphs": {
|
| 3710 |
-
"query": "\"molecular graph neural network\" in:readme",
|
| 3711 |
-
"years": [
|
| 3712 |
-
2026,
|
| 3713 |
-
2025
|
| 3714 |
-
]
|
| 3715 |
-
},
|
| 3716 |
-
"graph.neural-ode-graph": {
|
| 3717 |
-
"query": "\"graph neural ODE\" in:description",
|
| 3718 |
-
"years": [
|
| 3719 |
-
2026,
|
| 3720 |
-
2025
|
| 3721 |
-
]
|
| 3722 |
-
},
|
| 3723 |
-
"graph.persistent-homology": {
|
| 3724 |
-
"query": "\"persistent homology\" in:description",
|
| 3725 |
-
"years": [
|
| 3726 |
-
2026,
|
| 3727 |
-
2025
|
| 3728 |
-
]
|
| 3729 |
-
},
|
| 3730 |
-
"graph.point-cloud-learning": {
|
| 3731 |
-
"query": "\"point cloud deep learning\" in:description",
|
| 3732 |
-
"years": [
|
| 3733 |
-
2026,
|
| 3734 |
-
2025
|
| 3735 |
-
]
|
| 3736 |
-
},
|
| 3737 |
-
"graph.point-clouds": {
|
| 3738 |
-
"query": "topic:point-cloud",
|
| 3739 |
-
"years": [
|
| 3740 |
-
2026,
|
| 3741 |
-
2025
|
| 3742 |
-
]
|
| 3743 |
-
},
|
| 3744 |
-
"graph.representation-learning": {
|
| 3745 |
-
"query": "\"graph representation learning\" in:readme",
|
| 3746 |
-
"years": [
|
| 3747 |
-
2026,
|
| 3748 |
-
2025
|
| 3749 |
-
]
|
| 3750 |
-
},
|
| 3751 |
-
"graph.signal-processing": {
|
| 3752 |
-
"query": "\"graph signal processing\" in:description,readme",
|
| 3753 |
-
"years": [
|
| 3754 |
-
2026,
|
| 3755 |
-
2025
|
| 3756 |
-
]
|
| 3757 |
-
},
|
| 3758 |
-
"graph.temporal-learning": {
|
| 3759 |
-
"query": "\"temporal graph\" in:description,readme",
|
| 3760 |
-
"years": [
|
| 3761 |
-
2026,
|
| 3762 |
-
2025
|
| 3763 |
-
]
|
| 3764 |
-
},
|
| 3765 |
-
"graph.topic-gnn": {
|
| 3766 |
-
"query": "topic:graph-neural-network",
|
| 3767 |
-
"years": [
|
| 3768 |
-
2026,
|
| 3769 |
-
2025
|
| 3770 |
-
]
|
| 3771 |
-
},
|
| 3772 |
-
"graph.topological-ml": {
|
| 3773 |
-
"query": "\"topological machine learning\" in:readme",
|
| 3774 |
-
"years": [
|
| 3775 |
-
2026,
|
| 3776 |
-
2025
|
| 3777 |
-
]
|
| 3778 |
-
},
|
| 3779 |
-
"llm-code-generation": {
|
| 3780 |
-
"query": "\"code generation\" \"language model\" in:readme",
|
| 3781 |
-
"years": [
|
| 3782 |
-
2026,
|
| 3783 |
-
2025
|
| 3784 |
-
]
|
| 3785 |
-
},
|
| 3786 |
-
"llm-reasoning": {
|
| 3787 |
-
"query": "\"reasoning model\" in:readme",
|
| 3788 |
-
"years": [
|
| 3789 |
-
2026,
|
| 3790 |
-
2025
|
| 3791 |
-
]
|
| 3792 |
-
},
|
| 3793 |
-
"llm-retrieval-augmented-generation": {
|
| 3794 |
-
"query": "\"retrieval-augmented generation\" in:readme",
|
| 3795 |
-
"years": [
|
| 3796 |
-
2026,
|
| 3797 |
-
2025
|
| 3798 |
-
]
|
| 3799 |
-
},
|
| 3800 |
-
"llm-text-to-speech": {
|
| 3801 |
-
"query": "\"text-to-speech\" \"language model\" in:readme",
|
| 3802 |
-
"years": [
|
| 3803 |
-
2026,
|
| 3804 |
-
2025
|
| 3805 |
-
]
|
| 3806 |
-
},
|
| 3807 |
-
"llm.agents": {
|
| 3808 |
-
"query": "topic:ai-agents",
|
| 3809 |
-
"years": [
|
| 3810 |
-
2026,
|
| 3811 |
-
2025
|
| 3812 |
-
]
|
| 3813 |
-
},
|
| 3814 |
-
"llm.ai-agent": {
|
| 3815 |
-
"query": "\"AI agent\" in:description",
|
| 3816 |
-
"years": [
|
| 3817 |
-
2026,
|
| 3818 |
-
2025
|
| 3819 |
-
]
|
| 3820 |
-
},
|
| 3821 |
-
"llm.alignment": {
|
| 3822 |
-
"query": "\"alignment\" in:description",
|
| 3823 |
-
"years": [
|
| 3824 |
-
2026,
|
| 3825 |
-
2025
|
| 3826 |
-
]
|
| 3827 |
-
},
|
| 3828 |
-
"llm.diffusion": {
|
| 3829 |
-
"query": "topic:diffusion-model",
|
| 3830 |
-
"years": [
|
| 3831 |
-
2026,
|
| 3832 |
-
2025
|
| 3833 |
-
]
|
| 3834 |
-
},
|
| 3835 |
-
"llm.diffusion-phrase": {
|
| 3836 |
-
"query": "\"diffusion model\" in:description",
|
| 3837 |
-
"years": [
|
| 3838 |
-
2026,
|
| 3839 |
-
2025
|
| 3840 |
-
]
|
| 3841 |
-
},
|
| 3842 |
-
"llm.evaluation": {
|
| 3843 |
-
"query": "\"LLM evaluation\" in:readme",
|
| 3844 |
-
"years": [
|
| 3845 |
-
2026,
|
| 3846 |
-
2025
|
| 3847 |
-
]
|
| 3848 |
-
},
|
| 3849 |
-
"llm.finetuning": {
|
| 3850 |
-
"query": "\"fine-tuning\" in:description",
|
| 3851 |
-
"years": [
|
| 3852 |
-
2026,
|
| 3853 |
-
2025
|
| 3854 |
-
]
|
| 3855 |
-
},
|
| 3856 |
-
"llm.foundation-models": {
|
| 3857 |
-
"query": "\"foundation model\" in:description",
|
| 3858 |
-
"years": [
|
| 3859 |
-
2026,
|
| 3860 |
-
2025
|
| 3861 |
-
]
|
| 3862 |
-
},
|
| 3863 |
-
"llm.generative-ai": {
|
| 3864 |
-
"query": "\"generative AI\" in:description",
|
| 3865 |
-
"years": [
|
| 3866 |
-
2026,
|
| 3867 |
-
2025
|
| 3868 |
-
]
|
| 3869 |
-
},
|
| 3870 |
-
"llm.instruction-tuning": {
|
| 3871 |
-
"query": "\"instruction tuning\" in:readme",
|
| 3872 |
-
"years": [
|
| 3873 |
-
2026,
|
| 3874 |
-
2025
|
| 3875 |
-
]
|
| 3876 |
-
},
|
| 3877 |
-
"llm.language-model": {
|
| 3878 |
-
"query": "\"language model\" in:description",
|
| 3879 |
-
"years": [
|
| 3880 |
-
2026,
|
| 3881 |
-
2025
|
| 3882 |
-
]
|
| 3883 |
-
},
|
| 3884 |
-
"llm.language-models": {
|
| 3885 |
-
"query": "topic:large-language-models",
|
| 3886 |
-
"years": [
|
| 3887 |
-
2026,
|
| 3888 |
-
2025
|
| 3889 |
-
]
|
| 3890 |
-
},
|
| 3891 |
-
"llm.large-language-model": {
|
| 3892 |
-
"query": "\"large language model\" in:description",
|
| 3893 |
-
"years": [
|
| 3894 |
-
2026,
|
| 3895 |
-
2025
|
| 3896 |
-
]
|
| 3897 |
-
},
|
| 3898 |
-
"llm.long-context": {
|
| 3899 |
-
"query": "\"long context\" \"language model\" in:readme",
|
| 3900 |
-
"years": [
|
| 3901 |
-
2026,
|
| 3902 |
-
2025
|
| 3903 |
-
]
|
| 3904 |
-
},
|
| 3905 |
-
"llm.multimodal": {
|
| 3906 |
-
"query": "\"multimodal language model\" in:description",
|
| 3907 |
-
"years": [
|
| 3908 |
-
2026,
|
| 3909 |
-
2025
|
| 3910 |
-
]
|
| 3911 |
-
},
|
| 3912 |
-
"llm.parameter-efficient-finetuning": {
|
| 3913 |
-
"query": "\"parameter-efficient fine-tuning\" in:readme",
|
| 3914 |
-
"years": [
|
| 3915 |
-
2026,
|
| 3916 |
-
2025
|
| 3917 |
-
]
|
| 3918 |
-
},
|
| 3919 |
-
"llm.preference-optimization": {
|
| 3920 |
-
"query": "\"direct preference optimization\" in:readme",
|
| 3921 |
-
"years": [
|
| 3922 |
-
2026,
|
| 3923 |
-
2025
|
| 3924 |
-
]
|
| 3925 |
-
},
|
| 3926 |
-
"llm.quantization": {
|
| 3927 |
-
"query": "\"LLM quantization\" in:readme",
|
| 3928 |
-
"years": [
|
| 3929 |
-
2026,
|
| 3930 |
-
2025
|
| 3931 |
-
]
|
| 3932 |
-
},
|
| 3933 |
-
"llm.speculative-decoding": {
|
| 3934 |
-
"query": "\"speculative decoding\" in:readme",
|
| 3935 |
-
"years": [
|
| 3936 |
-
2026,
|
| 3937 |
-
2025
|
| 3938 |
-
]
|
| 3939 |
-
},
|
| 3940 |
-
"llm.synthetic-data": {
|
| 3941 |
-
"query": "\"synthetic data\" in:description",
|
| 3942 |
-
"years": [
|
| 3943 |
-
2026,
|
| 3944 |
-
2025
|
| 3945 |
-
]
|
| 3946 |
-
},
|
| 3947 |
-
"llm.synthetic-data-generation": {
|
| 3948 |
-
"query": "\"synthetic data generation\" in:description",
|
| 3949 |
-
"years": [
|
| 3950 |
-
2026,
|
| 3951 |
-
2025
|
| 3952 |
-
]
|
| 3953 |
-
},
|
| 3954 |
-
"llm.test-time-compute": {
|
| 3955 |
-
"query": "\"test-time compute\" \"language model\" in:readme",
|
| 3956 |
-
"years": [
|
| 3957 |
-
2026,
|
| 3958 |
-
2025
|
| 3959 |
-
]
|
| 3960 |
-
},
|
| 3961 |
-
"llm.transformers": {
|
| 3962 |
-
"query": "\"transformer\" in:description",
|
| 3963 |
-
"years": [
|
| 3964 |
-
2026,
|
| 3965 |
-
2025
|
| 3966 |
-
]
|
| 3967 |
-
},
|
| 3968 |
-
"materials-battery": {
|
| 3969 |
-
"query": "\"battery materials\" \"machine learning\" in:description,readme",
|
| 3970 |
-
"years": [
|
| 3971 |
-
2026,
|
| 3972 |
-
2025
|
| 3973 |
-
]
|
| 3974 |
-
},
|
| 3975 |
-
"materials-crystal-generation": {
|
| 3976 |
-
"query": "\"crystal structure generation\" in:description,readme",
|
| 3977 |
-
"years": [
|
| 3978 |
-
2026,
|
| 3979 |
-
2025
|
| 3980 |
-
]
|
| 3981 |
-
},
|
| 3982 |
-
"materials-crystal-graph": {
|
| 3983 |
-
"query": "\"crystal graph\" in:description,readme",
|
| 3984 |
-
"years": [
|
| 3985 |
-
2026,
|
| 3986 |
-
2025
|
| 3987 |
-
]
|
| 3988 |
-
},
|
| 3989 |
-
"materials-dft-prediction": {
|
| 3990 |
-
"query": "\"density functional theory\" \"machine learning\" in:description,readme",
|
| 3991 |
-
"years": [
|
| 3992 |
-
2026,
|
| 3993 |
-
2025
|
| 3994 |
-
]
|
| 3995 |
-
},
|
| 3996 |
-
"materials-informatics": {
|
| 3997 |
-
"query": "\"materials informatics\" in:description,readme",
|
| 3998 |
-
"years": [
|
| 3999 |
-
2026,
|
| 4000 |
-
2025
|
| 4001 |
-
]
|
| 4002 |
-
},
|
| 4003 |
-
"materials-perovskite": {
|
| 4004 |
-
"query": "perovskite in:description \"machine learning\"",
|
| 4005 |
-
"years": [
|
| 4006 |
-
2026,
|
| 4007 |
-
2025
|
| 4008 |
-
]
|
| 4009 |
-
},
|
| 4010 |
-
"materials-polymer-informatics": {
|
| 4011 |
-
"query": "polymer informatics in:description,readme",
|
| 4012 |
-
"years": [
|
| 4013 |
-
2026,
|
| 4014 |
-
2025
|
| 4015 |
-
]
|
| 4016 |
-
},
|
| 4017 |
-
"medical.clinical-decision-support": {
|
| 4018 |
-
"query": "\"clinical decision support\" in:readme",
|
| 4019 |
-
"years": [
|
| 4020 |
-
2026,
|
| 4021 |
-
2025
|
| 4022 |
-
]
|
| 4023 |
-
},
|
| 4024 |
-
"medical.clinical-nlp": {
|
| 4025 |
-
"query": "\"clinical natural language processing\" in:readme",
|
| 4026 |
-
"years": [
|
| 4027 |
-
2026,
|
| 4028 |
-
2025
|
| 4029 |
-
]
|
| 4030 |
-
},
|
| 4031 |
-
"medical.clinical-time-series": {
|
| 4032 |
-
"query": "\"clinical time series\" in:description",
|
| 4033 |
-
"years": [
|
| 4034 |
-
2026,
|
| 4035 |
-
2025
|
| 4036 |
-
]
|
| 4037 |
-
},
|
| 4038 |
-
"medical.clinical-trials": {
|
| 4039 |
-
"query": "\"clinical trial prediction\" in:description",
|
| 4040 |
-
"years": [
|
| 4041 |
-
2026,
|
| 4042 |
-
2025
|
| 4043 |
-
]
|
| 4044 |
-
},
|
| 4045 |
-
"medical.dermatology": {
|
| 4046 |
-
"query": "dermatology AI dermoscopy in:description,readme",
|
| 4047 |
-
"years": [
|
| 4048 |
-
2026,
|
| 4049 |
-
2025
|
| 4050 |
-
]
|
| 4051 |
-
},
|
| 4052 |
-
"medical.diagnosis-prediction": {
|
| 4053 |
-
"query": "\"diagnosis prediction\" in:description",
|
| 4054 |
-
"years": [
|
| 4055 |
-
2026,
|
| 4056 |
-
2025
|
| 4057 |
-
]
|
| 4058 |
-
},
|
| 4059 |
-
"medical.drug-response": {
|
| 4060 |
-
"query": "\"drug response prediction\" in:description,readme",
|
| 4061 |
-
"years": [
|
| 4062 |
-
2026,
|
| 4063 |
-
2025
|
| 4064 |
-
]
|
| 4065 |
-
},
|
| 4066 |
-
"medical.ecg": {
|
| 4067 |
-
"query": "ECG \"deep learning\" in:description,readme",
|
| 4068 |
-
"years": [
|
| 4069 |
-
2026,
|
| 4070 |
-
2025
|
| 4071 |
-
]
|
| 4072 |
-
},
|
| 4073 |
-
"medical.eeg": {
|
| 4074 |
-
"query": "EEG \"deep learning\" in:description,readme",
|
| 4075 |
-
"years": [
|
| 4076 |
-
2026,
|
| 4077 |
-
2025
|
| 4078 |
-
]
|
| 4079 |
-
},
|
| 4080 |
-
"medical.ehr": {
|
| 4081 |
-
"query": "\"electronic health record\" in:readme",
|
| 4082 |
-
"years": [
|
| 4083 |
-
2026,
|
| 4084 |
-
2025
|
| 4085 |
-
]
|
| 4086 |
-
},
|
| 4087 |
-
"medical.genomics": {
|
| 4088 |
-
"query": "\"clinical genomics\" in:description,readme",
|
| 4089 |
-
"years": [
|
| 4090 |
-
2026,
|
| 4091 |
-
2025
|
| 4092 |
-
]
|
| 4093 |
-
},
|
| 4094 |
-
"medical.histopathology": {
|
| 4095 |
-
"query": "\"histopathology image analysis\" in:readme",
|
| 4096 |
-
"years": [
|
| 4097 |
-
2026,
|
| 4098 |
-
2025
|
| 4099 |
-
]
|
| 4100 |
-
},
|
| 4101 |
-
"medical.medical-image-reconstruction": {
|
| 4102 |
-
"query": "\"MRI reconstruction\" in:description,readme",
|
| 4103 |
-
"years": [
|
| 4104 |
-
2026,
|
| 4105 |
-
2025
|
| 4106 |
-
]
|
| 4107 |
-
},
|
| 4108 |
-
"medical.medical-image-registration": {
|
| 4109 |
-
"query": "\"medical image registration\" in:description,readme",
|
| 4110 |
-
"years": [
|
| 4111 |
-
2026,
|
| 4112 |
-
2025
|
| 4113 |
-
]
|
| 4114 |
-
},
|
| 4115 |
-
"medical.medical-image-segmentation": {
|
| 4116 |
-
"query": "\"medical image segmentation\" in:description",
|
| 4117 |
-
"years": [
|
| 4118 |
-
2026,
|
| 4119 |
-
2025
|
| 4120 |
-
]
|
| 4121 |
-
},
|
| 4122 |
-
"medical.medical-imaging": {
|
| 4123 |
-
"query": "\"medical imaging\" in:readme",
|
| 4124 |
-
"years": [
|
| 4125 |
-
2026,
|
| 4126 |
-
2025
|
| 4127 |
-
]
|
| 4128 |
-
},
|
| 4129 |
-
"medical.medical-language-model": {
|
| 4130 |
-
"query": "\"medical language model\" in:description",
|
| 4131 |
-
"years": [
|
| 4132 |
-
2026,
|
| 4133 |
-
2025
|
| 4134 |
-
]
|
| 4135 |
-
},
|
| 4136 |
-
"medical.medical-robotics": {
|
| 4137 |
-
"query": "\"surgical robotics\" \"machine learning\" in:description,readme",
|
| 4138 |
-
"years": [
|
| 4139 |
-
2026,
|
| 4140 |
-
2025
|
| 4141 |
-
]
|
| 4142 |
-
},
|
| 4143 |
-
"medical.neural-decoding": {
|
| 4144 |
-
"query": "\"neural data decoding\" in:description",
|
| 4145 |
-
"years": [
|
| 4146 |
-
2026,
|
| 4147 |
-
2025
|
| 4148 |
-
]
|
| 4149 |
-
},
|
| 4150 |
-
"medical.neuroimaging": {
|
| 4151 |
-
"query": "\"neuroimaging machine learning\" in:readme",
|
| 4152 |
-
"years": [
|
| 4153 |
-
2026,
|
| 4154 |
-
2025
|
| 4155 |
-
]
|
| 4156 |
-
},
|
| 4157 |
-
"medical.ophthalmology": {
|
| 4158 |
-
"query": "ophthalmology AI retinal in:description,readme",
|
| 4159 |
-
"years": [
|
| 4160 |
-
2026,
|
| 4161 |
-
2025
|
| 4162 |
-
]
|
| 4163 |
-
},
|
| 4164 |
-
"medical.pathology": {
|
| 4165 |
-
"query": "\"computational pathology\" in:description",
|
| 4166 |
-
"years": [
|
| 4167 |
-
2026,
|
| 4168 |
-
2025
|
| 4169 |
-
]
|
| 4170 |
-
},
|
| 4171 |
-
"medical.pathology-foundation-model": {
|
| 4172 |
-
"query": "\"pathology foundation model\" in:description,readme",
|
| 4173 |
-
"years": [
|
| 4174 |
-
2026,
|
| 4175 |
-
2025
|
| 4176 |
-
]
|
| 4177 |
-
},
|
| 4178 |
-
"medical.patient-risk": {
|
| 4179 |
-
"query": "\"patient deterioration prediction\" in:description,readme",
|
| 4180 |
-
"years": [
|
| 4181 |
-
2026,
|
| 4182 |
-
2025
|
| 4183 |
-
]
|
| 4184 |
-
},
|
| 4185 |
-
"medical.radiology": {
|
| 4186 |
-
"query": "\"radiology machine learning\" in:readme",
|
| 4187 |
-
"years": [
|
| 4188 |
-
2026,
|
| 4189 |
-
2025
|
| 4190 |
-
]
|
| 4191 |
-
},
|
| 4192 |
-
"medical.survival-analysis": {
|
| 4193 |
-
"query": "clinical survival prediction in:description,readme",
|
| 4194 |
-
"years": [
|
| 4195 |
-
2026,
|
| 4196 |
-
2025
|
| 4197 |
-
]
|
| 4198 |
-
},
|
| 4199 |
-
"medical.wearable-health": {
|
| 4200 |
-
"query": "\"wearable health monitoring\" in:readme",
|
| 4201 |
-
"years": [
|
| 4202 |
-
2026,
|
| 4203 |
-
2025
|
| 4204 |
-
]
|
| 4205 |
-
},
|
| 4206 |
-
"multimodal.3d-generation": {
|
| 4207 |
-
"query": "\"3D generation\" in:readme",
|
| 4208 |
-
"years": [
|
| 4209 |
-
2026,
|
| 4210 |
-
2025
|
| 4211 |
-
]
|
| 4212 |
-
},
|
| 4213 |
-
"multimodal.3d-human-pose": {
|
| 4214 |
-
"query": "\"3D human pose estimation\" in:readme",
|
| 4215 |
-
"years": [
|
| 4216 |
-
2026,
|
| 4217 |
-
2025
|
| 4218 |
-
]
|
| 4219 |
-
},
|
| 4220 |
-
"multimodal.3d-object-detection": {
|
| 4221 |
-
"query": "\"3D object detection\" in:readme",
|
| 4222 |
-
"years": [
|
| 4223 |
-
2026,
|
| 4224 |
-
2025
|
| 4225 |
-
]
|
| 4226 |
-
},
|
| 4227 |
-
"multimodal.3d-reconstruction": {
|
| 4228 |
-
"query": "\"3D reconstruction\" in:description",
|
| 4229 |
-
"years": [
|
| 4230 |
-
2026,
|
| 4231 |
-
2025
|
| 4232 |
-
]
|
| 4233 |
-
},
|
| 4234 |
-
"multimodal.audio-visual-learning": {
|
| 4235 |
-
"query": "\"audio visual learning\" in:readme",
|
| 4236 |
-
"years": [
|
| 4237 |
-
2026,
|
| 4238 |
-
2025
|
| 4239 |
-
]
|
| 4240 |
-
},
|
| 4241 |
-
"multimodal.embodied-world-models": {
|
| 4242 |
-
"query": "\"embodied world model\" in:description",
|
| 4243 |
-
"years": [
|
| 4244 |
-
2026,
|
| 4245 |
-
2025
|
| 4246 |
-
]
|
| 4247 |
-
},
|
| 4248 |
-
"multimodal.gaussian-splatting": {
|
| 4249 |
-
"query": "\"Gaussian splatting\" in:description",
|
| 4250 |
-
"years": [
|
| 4251 |
-
2026,
|
| 4252 |
-
2025
|
| 4253 |
-
]
|
| 4254 |
-
},
|
| 4255 |
-
"multimodal.image-text": {
|
| 4256 |
-
"query": "\"image-text\" in:description",
|
| 4257 |
-
"years": [
|
| 4258 |
-
2026,
|
| 4259 |
-
2025
|
| 4260 |
-
]
|
| 4261 |
-
},
|
| 4262 |
-
"multimodal.image-to-3d": {
|
| 4263 |
-
"query": "\"image to 3D\" in:readme",
|
| 4264 |
-
"years": [
|
| 4265 |
-
2026,
|
| 4266 |
-
2025
|
| 4267 |
-
]
|
| 4268 |
-
},
|
| 4269 |
-
"multimodal.image-to-video": {
|
| 4270 |
-
"query": "\"image to video\" in:readme",
|
| 4271 |
-
"years": [
|
| 4272 |
-
2026,
|
| 4273 |
-
2025
|
| 4274 |
-
]
|
| 4275 |
-
},
|
| 4276 |
-
"multimodal.motion-generation": {
|
| 4277 |
-
"query": "\"human motion generation\" in:readme",
|
| 4278 |
-
"years": [
|
| 4279 |
-
2026,
|
| 4280 |
-
2025
|
| 4281 |
-
]
|
| 4282 |
-
},
|
| 4283 |
-
"multimodal.nerf": {
|
| 4284 |
-
"query": "topic:nerf",
|
| 4285 |
-
"years": [
|
| 4286 |
-
2026,
|
| 4287 |
-
2025
|
| 4288 |
-
]
|
| 4289 |
-
},
|
| 4290 |
-
"multimodal.neural-rendering": {
|
| 4291 |
-
"query": "\"neural rendering\" \"radiance fields\" in:description",
|
| 4292 |
-
"years": [
|
| 4293 |
-
2026,
|
| 4294 |
-
2025
|
| 4295 |
-
]
|
| 4296 |
-
},
|
| 4297 |
-
"multimodal.point-cloud": {
|
| 4298 |
-
"query": "\"point cloud\" in:description",
|
| 4299 |
-
"years": [
|
| 4300 |
-
2026,
|
| 4301 |
-
2025
|
| 4302 |
-
]
|
| 4303 |
-
},
|
| 4304 |
-
"multimodal.scene-understanding": {
|
| 4305 |
-
"query": "\"scene understanding\" in:description",
|
| 4306 |
-
"years": [
|
| 4307 |
-
2026,
|
| 4308 |
-
2025
|
| 4309 |
-
]
|
| 4310 |
-
},
|
| 4311 |
-
"multimodal.text-to-3d": {
|
| 4312 |
-
"query": "\"text to 3D\" in:readme",
|
| 4313 |
-
"years": [
|
| 4314 |
-
2026,
|
| 4315 |
-
2025
|
| 4316 |
-
]
|
| 4317 |
-
},
|
| 4318 |
-
"multimodal.text-to-image": {
|
| 4319 |
-
"query": "\"text-to-image\" in:description",
|
| 4320 |
-
"years": [
|
| 4321 |
-
2026,
|
| 4322 |
-
2025
|
| 4323 |
-
]
|
| 4324 |
-
},
|
| 4325 |
-
"multimodal.text-to-video": {
|
| 4326 |
-
"query": "\"text-to-video\" in:description",
|
| 4327 |
-
"years": [
|
| 4328 |
-
2026,
|
| 4329 |
-
2025
|
| 4330 |
-
]
|
| 4331 |
-
},
|
| 4332 |
-
"multimodal.video-language-model": {
|
| 4333 |
-
"query": "\"video language model\" in:readme",
|
| 4334 |
-
"years": [
|
| 4335 |
-
2026,
|
| 4336 |
-
2025
|
| 4337 |
-
]
|
| 4338 |
-
},
|
| 4339 |
-
"multimodal.video-question-answering": {
|
| 4340 |
-
"query": "\"video question answering\" in:readme",
|
| 4341 |
-
"years": [
|
| 4342 |
-
2026,
|
| 4343 |
-
2025
|
| 4344 |
-
]
|
| 4345 |
-
},
|
| 4346 |
-
"multimodal.video-understanding": {
|
| 4347 |
-
"query": "\"video-language\" in:description",
|
| 4348 |
-
"years": [
|
| 4349 |
-
2026,
|
| 4350 |
-
2025
|
| 4351 |
-
]
|
| 4352 |
-
},
|
| 4353 |
-
"multimodal.vision-language": {
|
| 4354 |
-
"query": "\"vision-language\" in:description",
|
| 4355 |
-
"years": [
|
| 4356 |
-
2026,
|
| 4357 |
-
2025
|
| 4358 |
-
]
|
| 4359 |
-
},
|
| 4360 |
-
"multimodal.visual-question-answering": {
|
| 4361 |
-
"query": "\"visual question answering\" in:description",
|
| 4362 |
-
"years": [
|
| 4363 |
-
2026,
|
| 4364 |
-
2025
|
| 4365 |
-
]
|
| 4366 |
-
},
|
| 4367 |
-
"multimodal.vlm": {
|
| 4368 |
-
"query": "\"vision-language model\" in:description",
|
| 4369 |
-
"years": [
|
| 4370 |
-
2026,
|
| 4371 |
-
2025
|
| 4372 |
-
]
|
| 4373 |
-
},
|
| 4374 |
-
"multimodal.world-models": {
|
| 4375 |
-
"query": "\"world model\" in:description",
|
| 4376 |
-
"years": [
|
| 4377 |
-
2026,
|
| 4378 |
-
2025
|
| 4379 |
-
]
|
| 4380 |
-
},
|
| 4381 |
-
"nlp-bm25-retrieval": {
|
| 4382 |
-
"query": "BM25 retrieval in:readme",
|
| 4383 |
-
"years": [
|
| 4384 |
-
2026,
|
| 4385 |
-
2025
|
| 4386 |
-
]
|
| 4387 |
-
},
|
| 4388 |
-
"nlp-document-retrieval": {
|
| 4389 |
-
"query": "\"document retrieval\" in:readme",
|
| 4390 |
-
"years": [
|
| 4391 |
-
2026,
|
| 4392 |
-
2025
|
| 4393 |
-
]
|
| 4394 |
-
},
|
| 4395 |
-
"nlp-entity-linking": {
|
| 4396 |
-
"query": "\"entity linking\" in:readme",
|
| 4397 |
-
"years": [
|
| 4398 |
-
2026,
|
| 4399 |
-
2025
|
| 4400 |
-
]
|
| 4401 |
-
},
|
| 4402 |
-
"nlp-natural-language-inference": {
|
| 4403 |
-
"query": "\"natural language inference\" in:readme",
|
| 4404 |
-
"years": [
|
| 4405 |
-
2026,
|
| 4406 |
-
2025
|
| 4407 |
-
]
|
| 4408 |
-
},
|
| 4409 |
-
"nlp-query-understanding": {
|
| 4410 |
-
"query": "\"query understanding\" in:readme",
|
| 4411 |
-
"years": [
|
| 4412 |
-
2026,
|
| 4413 |
-
2025
|
| 4414 |
-
]
|
| 4415 |
-
},
|
| 4416 |
-
"nlp-sparse-retrieval": {
|
| 4417 |
-
"query": "\"sparse retrieval\" in:readme",
|
| 4418 |
-
"years": [
|
| 4419 |
-
2026,
|
| 4420 |
-
2025
|
| 4421 |
-
]
|
| 4422 |
-
},
|
| 4423 |
-
"nlp-speech-language-modeling": {
|
| 4424 |
-
"query": "\"speech language model\" in:readme",
|
| 4425 |
-
"years": [
|
| 4426 |
-
2026,
|
| 4427 |
-
2025
|
| 4428 |
-
]
|
| 4429 |
-
},
|
| 4430 |
-
"nlp.coreference-resolution": {
|
| 4431 |
-
"query": "\"coreference resolution\" in:readme",
|
| 4432 |
-
"years": [
|
| 4433 |
-
2026,
|
| 4434 |
-
2025
|
| 4435 |
-
]
|
| 4436 |
-
},
|
| 4437 |
-
"nlp.cross-encoder-reranking": {
|
| 4438 |
-
"query": "\"cross-encoder\" \"reranking\" in:readme",
|
| 4439 |
-
"years": [
|
| 4440 |
-
2026,
|
| 4441 |
-
2025
|
| 4442 |
-
]
|
| 4443 |
-
},
|
| 4444 |
-
"nlp.dense-retrieval": {
|
| 4445 |
-
"query": "\"dense retrieval\" in:readme",
|
| 4446 |
-
"years": [
|
| 4447 |
-
2026,
|
| 4448 |
-
2025
|
| 4449 |
-
]
|
| 4450 |
-
},
|
| 4451 |
-
"nlp.dependency-parsing": {
|
| 4452 |
-
"query": "\"dependency parsing\" in:readme",
|
| 4453 |
-
"years": [
|
| 4454 |
-
2026,
|
| 4455 |
-
2025
|
| 4456 |
-
]
|
| 4457 |
-
},
|
| 4458 |
-
"nlp.dialogue-systems": {
|
| 4459 |
-
"query": "\"dialogue system\" in:readme",
|
| 4460 |
-
"years": [
|
| 4461 |
-
2026,
|
| 4462 |
-
2025
|
| 4463 |
-
]
|
| 4464 |
-
},
|
| 4465 |
-
"nlp.information-extraction": {
|
| 4466 |
-
"query": "\"information extraction\" in:description",
|
| 4467 |
-
"years": [
|
| 4468 |
-
2026,
|
| 4469 |
-
2025
|
| 4470 |
-
]
|
| 4471 |
-
},
|
| 4472 |
-
"nlp.language-modeling": {
|
| 4473 |
-
"query": "\"language modeling\" in:description",
|
| 4474 |
-
"years": [
|
| 4475 |
-
2026,
|
| 4476 |
-
2025
|
| 4477 |
-
]
|
| 4478 |
-
},
|
| 4479 |
-
"nlp.machine-translation": {
|
| 4480 |
-
"query": "\"machine translation\" in:description",
|
| 4481 |
-
"years": [
|
| 4482 |
-
2026,
|
| 4483 |
-
2025
|
| 4484 |
-
]
|
| 4485 |
-
},
|
| 4486 |
-
"nlp.named-entity-recognition": {
|
| 4487 |
-
"query": "\"named entity recognition\" in:readme",
|
| 4488 |
-
"years": [
|
| 4489 |
-
2026,
|
| 4490 |
-
2025
|
| 4491 |
-
]
|
| 4492 |
-
},
|
| 4493 |
-
"nlp.neural-reranking": {
|
| 4494 |
-
"query": "\"neural reranking\" in:description",
|
| 4495 |
-
"years": [
|
| 4496 |
-
2026,
|
| 4497 |
-
2025
|
| 4498 |
-
]
|
| 4499 |
-
},
|
| 4500 |
-
"nlp.question-answering": {
|
| 4501 |
-
"query": "\"question answering\" in:description",
|
| 4502 |
-
"years": [
|
| 4503 |
-
2026,
|
| 4504 |
-
2025
|
| 4505 |
-
]
|
| 4506 |
-
},
|
| 4507 |
-
"nlp.rag": {
|
| 4508 |
-
"query": "\"retrieval augmented generation\" in:description",
|
| 4509 |
-
"years": [
|
| 4510 |
-
2026,
|
| 4511 |
-
2025
|
| 4512 |
-
]
|
| 4513 |
-
},
|
| 4514 |
-
"nlp.semantic-search": {
|
| 4515 |
-
"query": "\"semantic search\" in:description",
|
| 4516 |
-
"years": [
|
| 4517 |
-
2026,
|
| 4518 |
-
2025
|
| 4519 |
-
]
|
| 4520 |
-
},
|
| 4521 |
-
"nlp.sentence-embeddings": {
|
| 4522 |
-
"query": "\"sentence embeddings\" in:description",
|
| 4523 |
-
"years": [
|
| 4524 |
-
2026,
|
| 4525 |
-
2025
|
| 4526 |
-
]
|
| 4527 |
-
},
|
| 4528 |
-
"nlp.subword-tokenization": {
|
| 4529 |
-
"query": "\"subword tokenization\" in:readme",
|
| 4530 |
-
"years": [
|
| 4531 |
-
2026,
|
| 4532 |
-
2025
|
| 4533 |
-
]
|
| 4534 |
-
},
|
| 4535 |
-
"nlp.text-classification": {
|
| 4536 |
-
"query": "\"text classification\" in:description",
|
| 4537 |
-
"years": [
|
| 4538 |
-
2026,
|
| 4539 |
-
2025
|
| 4540 |
-
]
|
| 4541 |
-
},
|
| 4542 |
-
"nlp.text-generation": {
|
| 4543 |
-
"query": "\"text generation\" in:readme",
|
| 4544 |
-
"years": [
|
| 4545 |
-
2026,
|
| 4546 |
-
2025
|
| 4547 |
-
]
|
| 4548 |
-
},
|
| 4549 |
-
"nlp.text-summarization": {
|
| 4550 |
-
"query": "\"text summarization\" in:description",
|
| 4551 |
-
"years": [
|
| 4552 |
-
2026,
|
| 4553 |
-
2025
|
| 4554 |
-
]
|
| 4555 |
-
},
|
| 4556 |
-
"nlp.topic-modeling": {
|
| 4557 |
-
"query": "\"topic modeling\" in:readme",
|
| 4558 |
-
"years": [
|
| 4559 |
-
2026,
|
| 4560 |
-
2025
|
| 4561 |
-
]
|
| 4562 |
-
},
|
| 4563 |
-
"recall.archaeological-remote-sensing": {
|
| 4564 |
-
"query": "archaeology \"remote sensing\" in:name,description,readme",
|
| 4565 |
-
"years": [
|
| 4566 |
-
2026
|
| 4567 |
-
]
|
| 4568 |
-
},
|
| 4569 |
-
"rl.bandits": {
|
| 4570 |
-
"query": "\"multi-armed bandit\" in:description",
|
| 4571 |
-
"years": [
|
| 4572 |
-
2026,
|
| 4573 |
-
2025
|
| 4574 |
-
]
|
| 4575 |
-
},
|
| 4576 |
-
"rl.continual": {
|
| 4577 |
-
"query": "\"continual reinforcement learning\" in:description,readme",
|
| 4578 |
-
"years": [
|
| 4579 |
-
2026,
|
| 4580 |
-
2025
|
| 4581 |
-
]
|
| 4582 |
-
},
|
| 4583 |
-
"rl.core": {
|
| 4584 |
-
"query": "topic:reinforcement-learning",
|
| 4585 |
-
"years": [
|
| 4586 |
-
2026,
|
| 4587 |
-
2025
|
| 4588 |
-
]
|
| 4589 |
-
},
|
| 4590 |
-
"rl.deep": {
|
| 4591 |
-
"query": "\"deep reinforcement learning\" in:description",
|
| 4592 |
-
"years": [
|
| 4593 |
-
2026,
|
| 4594 |
-
2025
|
| 4595 |
-
]
|
| 4596 |
-
},
|
| 4597 |
-
"rl.deep-q-learning": {
|
| 4598 |
-
"query": "\"deep q-learning\" in:description",
|
| 4599 |
-
"years": [
|
| 4600 |
-
2026,
|
| 4601 |
-
2025
|
| 4602 |
-
]
|
| 4603 |
-
},
|
| 4604 |
-
"rl.distributional": {
|
| 4605 |
-
"query": "\"distributional reinforcement learning\" in:description,readme",
|
| 4606 |
-
"years": [
|
| 4607 |
-
2026,
|
| 4608 |
-
2025
|
| 4609 |
-
]
|
| 4610 |
-
},
|
| 4611 |
-
"rl.exploration": {
|
| 4612 |
-
"query": "\"reinforcement learning exploration\" in:description",
|
| 4613 |
-
"years": [
|
| 4614 |
-
2026,
|
| 4615 |
-
2025
|
| 4616 |
-
]
|
| 4617 |
-
},
|
| 4618 |
-
"rl.games": {
|
| 4619 |
-
"query": "topic:game-ai",
|
| 4620 |
-
"years": [
|
| 4621 |
-
2026,
|
| 4622 |
-
2025
|
| 4623 |
-
]
|
| 4624 |
-
},
|
| 4625 |
-
"rl.hierarchical": {
|
| 4626 |
-
"query": "\"hierarchical reinforcement learning\" in:description",
|
| 4627 |
-
"years": [
|
| 4628 |
-
2026,
|
| 4629 |
-
2025
|
| 4630 |
-
]
|
| 4631 |
-
},
|
| 4632 |
-
"rl.imitation-learning": {
|
| 4633 |
-
"query": "\"imitation learning\" in:description",
|
| 4634 |
-
"years": [
|
| 4635 |
-
2026,
|
| 4636 |
-
2025
|
| 4637 |
-
]
|
| 4638 |
-
},
|
| 4639 |
-
"rl.inverse": {
|
| 4640 |
-
"query": "\"inverse reinforcement learning\" in:description",
|
| 4641 |
-
"years": [
|
| 4642 |
-
2026,
|
| 4643 |
-
2025
|
| 4644 |
-
]
|
| 4645 |
-
},
|
| 4646 |
-
"rl.model-based": {
|
| 4647 |
-
"query": "\"model-based reinforcement learning\" in:description",
|
| 4648 |
-
"years": [
|
| 4649 |
-
2026,
|
| 4650 |
-
2025
|
| 4651 |
-
]
|
| 4652 |
-
},
|
| 4653 |
-
"rl.model-free": {
|
| 4654 |
-
"query": "\"model-free reinforcement learning\" in:description",
|
| 4655 |
-
"years": [
|
| 4656 |
-
2026,
|
| 4657 |
-
2025
|
| 4658 |
-
]
|
| 4659 |
-
},
|
| 4660 |
-
"rl.multiagent": {
|
| 4661 |
-
"query": "\"multi-agent reinforcement learning\" in:description",
|
| 4662 |
-
"years": [
|
| 4663 |
-
2026,
|
| 4664 |
-
2025
|
| 4665 |
-
]
|
| 4666 |
-
},
|
| 4667 |
-
"rl.offline": {
|
| 4668 |
-
"query": "\"offline reinforcement learning\" in:description",
|
| 4669 |
-
"years": [
|
| 4670 |
-
2026,
|
| 4671 |
-
2025
|
| 4672 |
-
]
|
| 4673 |
-
},
|
| 4674 |
-
"rl.planning": {
|
| 4675 |
-
"query": "\"reinforcement learning planning\" in:description",
|
| 4676 |
-
"years": [
|
| 4677 |
-
2026,
|
| 4678 |
-
2025
|
| 4679 |
-
]
|
| 4680 |
-
},
|
| 4681 |
-
"rl.policy-gradient": {
|
| 4682 |
-
"query": "\"policy gradient\" in:description",
|
| 4683 |
-
"years": [
|
| 4684 |
-
2026,
|
| 4685 |
-
2025
|
| 4686 |
-
]
|
| 4687 |
-
},
|
| 4688 |
-
"rl.ppo": {
|
| 4689 |
-
"query": "\"proximal policy optimization\" in:description",
|
| 4690 |
-
"years": [
|
| 4691 |
-
2026,
|
| 4692 |
-
2025
|
| 4693 |
-
]
|
| 4694 |
-
},
|
| 4695 |
-
"rl.preference": {
|
| 4696 |
-
"query": "\"preference-based reinforcement learning\" in:description",
|
| 4697 |
-
"years": [
|
| 4698 |
-
2026,
|
| 4699 |
-
2025
|
| 4700 |
-
]
|
| 4701 |
-
},
|
| 4702 |
-
"rl.reinforcement-learning-from-human-feedback": {
|
| 4703 |
-
"query": "\"reinforcement learning from human feedback\" in:description",
|
| 4704 |
-
"years": [
|
| 4705 |
-
2026,
|
| 4706 |
-
2025
|
| 4707 |
-
]
|
| 4708 |
-
},
|
| 4709 |
-
"rl.safe": {
|
| 4710 |
-
"query": "\"safe reinforcement learning\" in:description",
|
| 4711 |
-
"years": [
|
| 4712 |
-
2026,
|
| 4713 |
-
2025
|
| 4714 |
-
]
|
| 4715 |
-
},
|
| 4716 |
-
"rl.soft-actor-critic": {
|
| 4717 |
-
"query": "\"soft actor-critic\" in:description",
|
| 4718 |
-
"years": [
|
| 4719 |
-
2026,
|
| 4720 |
-
2025
|
| 4721 |
-
]
|
| 4722 |
-
},
|
| 4723 |
-
"robotics.autonomous-driving": {
|
| 4724 |
-
"query": "\"autonomous driving\" in:description",
|
| 4725 |
-
"years": [
|
| 4726 |
-
2026,
|
| 4727 |
-
2025
|
| 4728 |
-
]
|
| 4729 |
-
},
|
| 4730 |
-
"robotics.behavior-cloning": {
|
| 4731 |
-
"query": "\"behavior cloning\" in:description",
|
| 4732 |
-
"years": [
|
| 4733 |
-
2026,
|
| 4734 |
-
2025
|
| 4735 |
-
]
|
| 4736 |
-
},
|
| 4737 |
-
"robotics.diffusion-policy": {
|
| 4738 |
-
"query": "\"diffusion policy\" in:description",
|
| 4739 |
-
"years": [
|
| 4740 |
-
2026,
|
| 4741 |
-
2025
|
| 4742 |
-
]
|
| 4743 |
-
},
|
| 4744 |
-
"robotics.domain-randomization": {
|
| 4745 |
-
"query": "\"domain randomization\" in:description",
|
| 4746 |
-
"years": [
|
| 4747 |
-
2026,
|
| 4748 |
-
2025
|
| 4749 |
-
]
|
| 4750 |
-
},
|
| 4751 |
-
"robotics.embodied-ai": {
|
| 4752 |
-
"query": "\"embodied AI\" in:description",
|
| 4753 |
-
"years": [
|
| 4754 |
-
2026,
|
| 4755 |
-
2025
|
| 4756 |
-
]
|
| 4757 |
-
},
|
| 4758 |
-
"robotics.grasping": {
|
| 4759 |
-
"query": "\"robot grasping\" in:description",
|
| 4760 |
-
"years": [
|
| 4761 |
-
2026,
|
| 4762 |
-
2025
|
| 4763 |
-
]
|
| 4764 |
-
},
|
| 4765 |
-
"robotics.imitation-learning": {
|
| 4766 |
-
"query": "\"imitation learning\" robotics in:description",
|
| 4767 |
-
"years": [
|
| 4768 |
-
2026,
|
| 4769 |
-
2025
|
| 4770 |
-
]
|
| 4771 |
-
},
|
| 4772 |
-
"robotics.locomotion": {
|
| 4773 |
-
"query": "\"robot locomotion\" in:description",
|
| 4774 |
-
"years": [
|
| 4775 |
-
2026,
|
| 4776 |
-
2025
|
| 4777 |
-
]
|
| 4778 |
-
},
|
| 4779 |
-
"robotics.manipulation": {
|
| 4780 |
-
"query": "\"robot manipulation\" in:description",
|
| 4781 |
-
"years": [
|
| 4782 |
-
2026,
|
| 4783 |
-
2025
|
| 4784 |
-
]
|
| 4785 |
-
},
|
| 4786 |
-
"robotics.navigation": {
|
| 4787 |
-
"query": "\"robot navigation\" in:description",
|
| 4788 |
-
"years": [
|
| 4789 |
-
2026,
|
| 4790 |
-
2025
|
| 4791 |
-
]
|
| 4792 |
-
},
|
| 4793 |
-
"robotics.robot-control": {
|
| 4794 |
-
"query": "\"robot control\" in:description",
|
| 4795 |
-
"years": [
|
| 4796 |
-
2026,
|
| 4797 |
-
2025
|
| 4798 |
-
]
|
| 4799 |
-
},
|
| 4800 |
-
"robotics.robot-foundation-model": {
|
| 4801 |
-
"query": "robot foundation model in:description,readme",
|
| 4802 |
-
"years": [
|
| 4803 |
-
2026,
|
| 4804 |
-
2025
|
| 4805 |
-
]
|
| 4806 |
-
},
|
| 4807 |
-
"robotics.robot-learning": {
|
| 4808 |
-
"query": "\"robot learning\" in:description",
|
| 4809 |
-
"years": [
|
| 4810 |
-
2026,
|
| 4811 |
-
2025
|
| 4812 |
-
]
|
| 4813 |
-
},
|
| 4814 |
-
"robotics.robot-manipulation-learning": {
|
| 4815 |
-
"query": "\"robot manipulation learning\" in:readme",
|
| 4816 |
-
"years": [
|
| 4817 |
-
2026,
|
| 4818 |
-
2025
|
| 4819 |
-
]
|
| 4820 |
-
},
|
| 4821 |
-
"robotics.robot-motion-planning": {
|
| 4822 |
-
"query": "\"robot motion planning\" in:description",
|
| 4823 |
-
"years": [
|
| 4824 |
-
2026,
|
| 4825 |
-
2025
|
| 4826 |
-
]
|
| 4827 |
-
},
|
| 4828 |
-
"robotics.robotics-reinforcement-learning": {
|
| 4829 |
-
"query": "\"robotics reinforcement learning\" in:description",
|
| 4830 |
-
"years": [
|
| 4831 |
-
2026,
|
| 4832 |
-
2025
|
| 4833 |
-
]
|
| 4834 |
-
},
|
| 4835 |
-
"robotics.robotics-topic": {
|
| 4836 |
-
"query": "topic:robotics",
|
| 4837 |
-
"years": [
|
| 4838 |
-
2026,
|
| 4839 |
-
2025
|
| 4840 |
-
]
|
| 4841 |
-
},
|
| 4842 |
-
"robotics.sim2real": {
|
| 4843 |
-
"query": "\"sim-to-real\" in:description",
|
| 4844 |
-
"years": [
|
| 4845 |
-
2026,
|
| 4846 |
-
2025
|
| 4847 |
-
]
|
| 4848 |
-
},
|
| 4849 |
-
"robotics.slam": {
|
| 4850 |
-
"query": "SLAM robotics in:description",
|
| 4851 |
-
"years": [
|
| 4852 |
-
2026,
|
| 4853 |
-
2025
|
| 4854 |
-
]
|
| 4855 |
-
},
|
| 4856 |
-
"robotics.tactile-learning": {
|
| 4857 |
-
"query": "tactile robot learning in:description,readme",
|
| 4858 |
-
"years": [
|
| 4859 |
-
2026,
|
| 4860 |
-
2025
|
| 4861 |
-
]
|
| 4862 |
-
},
|
| 4863 |
-
"robotics.teleoperation": {
|
| 4864 |
-
"query": "robot teleoperation learning in:description,readme",
|
| 4865 |
-
"years": [
|
| 4866 |
-
2026,
|
| 4867 |
-
2025
|
| 4868 |
-
]
|
| 4869 |
-
},
|
| 4870 |
-
"robotics.visual-language-action": {
|
| 4871 |
-
"query": "\"vision-language-action\" in:description",
|
| 4872 |
-
"years": [
|
| 4873 |
-
2026,
|
| 4874 |
-
2025
|
| 4875 |
-
]
|
| 4876 |
-
},
|
| 4877 |
-
"robotics.visuomotor-policy": {
|
| 4878 |
-
"query": "\"visuomotor policy\" in:description",
|
| 4879 |
-
"years": [
|
| 4880 |
-
2026,
|
| 4881 |
-
2025
|
| 4882 |
-
]
|
| 4883 |
-
},
|
| 4884 |
-
"science.astronomy": {
|
| 4885 |
-
"query": "astronomy in:readme",
|
| 4886 |
-
"years": [
|
| 4887 |
-
2026,
|
| 4888 |
-
2025
|
| 4889 |
-
]
|
| 4890 |
-
},
|
| 4891 |
-
"science.astrophysics": {
|
| 4892 |
-
"query": "astrophysics in:description",
|
| 4893 |
-
"years": [
|
| 4894 |
-
2026,
|
| 4895 |
-
2025
|
| 4896 |
-
]
|
| 4897 |
-
},
|
| 4898 |
-
"science.climate-modeling": {
|
| 4899 |
-
"query": "\"climate modeling\" in:readme",
|
| 4900 |
-
"years": [
|
| 4901 |
-
2026,
|
| 4902 |
-
2025
|
| 4903 |
-
]
|
| 4904 |
-
},
|
| 4905 |
-
"science.computational-mechanics": {
|
| 4906 |
-
"query": "\"computational mechanics\" \"machine learning\" in:description,readme",
|
| 4907 |
-
"years": [
|
| 4908 |
-
2026,
|
| 4909 |
-
2025
|
| 4910 |
-
]
|
| 4911 |
-
},
|
| 4912 |
-
"science.cosmology": {
|
| 4913 |
-
"query": "cosmology in:description",
|
| 4914 |
-
"years": [
|
| 4915 |
-
2026,
|
| 4916 |
-
2025
|
| 4917 |
-
]
|
| 4918 |
-
},
|
| 4919 |
-
"science.differentiable-simulation": {
|
| 4920 |
-
"query": "\"differentiable simulation\" in:readme",
|
| 4921 |
-
"years": [
|
| 4922 |
-
2026,
|
| 4923 |
-
2025
|
| 4924 |
-
]
|
| 4925 |
-
},
|
| 4926 |
-
"science.exoplanets": {
|
| 4927 |
-
"query": "exoplanet in:description \"machine learning\"",
|
| 4928 |
-
"years": [
|
| 4929 |
-
2026,
|
| 4930 |
-
2025
|
| 4931 |
-
]
|
| 4932 |
-
},
|
| 4933 |
-
"science.fluid-dynamics": {
|
| 4934 |
-
"query": "\"fluid dynamics\" in:description",
|
| 4935 |
-
"years": [
|
| 4936 |
-
2026,
|
| 4937 |
-
2025
|
| 4938 |
-
]
|
| 4939 |
-
},
|
| 4940 |
-
"science.fusion-plasma": {
|
| 4941 |
-
"query": "fusion plasma in:description \"machine learning\"",
|
| 4942 |
-
"years": [
|
| 4943 |
-
2026,
|
| 4944 |
-
2025
|
| 4945 |
-
]
|
| 4946 |
-
},
|
| 4947 |
-
"science.gravitational-waves": {
|
| 4948 |
-
"query": "\"gravitational wave\" \"machine learning\" in:description,readme",
|
| 4949 |
-
"years": [
|
| 4950 |
-
2026,
|
| 4951 |
-
2025
|
| 4952 |
-
]
|
| 4953 |
-
},
|
| 4954 |
-
"science.inverse-problems": {
|
| 4955 |
-
"query": "\"inverse problems\" in:description",
|
| 4956 |
-
"years": [
|
| 4957 |
-
2026,
|
| 4958 |
-
2025
|
| 4959 |
-
]
|
| 4960 |
-
},
|
| 4961 |
-
"science.neural-operators": {
|
| 4962 |
-
"query": "\"neural operator\" in:description",
|
| 4963 |
-
"years": [
|
| 4964 |
-
2026,
|
| 4965 |
-
2025
|
| 4966 |
-
]
|
| 4967 |
-
},
|
| 4968 |
-
"science.ocean-modeling": {
|
| 4969 |
-
"query": "\"ocean modeling\" \"machine learning\" in:description,readme",
|
| 4970 |
-
"years": [
|
| 4971 |
-
2026,
|
| 4972 |
-
2025
|
| 4973 |
-
]
|
| 4974 |
-
},
|
| 4975 |
-
"science.particle-physics": {
|
| 4976 |
-
"query": "\"particle physics\" in:description",
|
| 4977 |
-
"years": [
|
| 4978 |
-
2026,
|
| 4979 |
-
2025
|
| 4980 |
-
]
|
| 4981 |
-
},
|
| 4982 |
-
"science.photonics": {
|
| 4983 |
-
"query": "photonics \"machine learning\" in:description,readme",
|
| 4984 |
-
"years": [
|
| 4985 |
-
2026,
|
| 4986 |
-
2025
|
| 4987 |
-
]
|
| 4988 |
-
},
|
| 4989 |
-
"science.physics": {
|
| 4990 |
-
"query": "physics in:description",
|
| 4991 |
-
"years": [
|
| 4992 |
-
2026,
|
| 4993 |
-
2025
|
| 4994 |
-
]
|
| 4995 |
-
},
|
| 4996 |
-
"science.physics-informed-neural-networks": {
|
| 4997 |
-
"query": "\"physics-informed neural network\" in:description",
|
| 4998 |
-
"years": [
|
| 4999 |
-
2026,
|
| 5000 |
-
2025
|
| 5001 |
-
]
|
| 5002 |
-
},
|
| 5003 |
-
"science.plasma-physics": {
|
| 5004 |
-
"query": "\"plasma physics\" in:description",
|
| 5005 |
-
"years": [
|
| 5006 |
-
2026,
|
| 5007 |
-
2025
|
| 5008 |
-
]
|
| 5009 |
-
},
|
| 5010 |
-
"science.quantum-materials": {
|
| 5011 |
-
"query": "\"quantum materials\" \"machine learning\" in:description,readme",
|
| 5012 |
-
"years": [
|
| 5013 |
-
2026,
|
| 5014 |
-
2025
|
| 5015 |
-
]
|
| 5016 |
-
},
|
| 5017 |
-
"science.quantum-physics": {
|
| 5018 |
-
"query": "\"quantum many-body\" \"machine learning\" in:description,readme",
|
| 5019 |
-
"years": [
|
| 5020 |
-
2026,
|
| 5021 |
-
2025
|
| 5022 |
-
]
|
| 5023 |
-
},
|
| 5024 |
-
"science.remote-sensing": {
|
| 5025 |
-
"query": "\"remote sensing\" in:description",
|
| 5026 |
-
"years": [
|
| 5027 |
-
2026,
|
| 5028 |
-
2025
|
| 5029 |
-
]
|
| 5030 |
-
},
|
| 5031 |
-
"science.scientific-machine-learning": {
|
| 5032 |
-
"query": "\"scientific machine learning\" in:readme",
|
| 5033 |
-
"years": [
|
| 5034 |
-
2026,
|
| 5035 |
-
2025
|
| 5036 |
-
]
|
| 5037 |
-
},
|
| 5038 |
-
"science.seismic": {
|
| 5039 |
-
"query": "\"seismic data\" \"machine learning\" in:description,readme",
|
| 5040 |
-
"years": [
|
| 5041 |
-
2026,
|
| 5042 |
-
2025
|
| 5043 |
-
]
|
| 5044 |
-
},
|
| 5045 |
-
"science.turbulence": {
|
| 5046 |
-
"query": "turbulence in:description \"machine learning\"",
|
| 5047 |
-
"years": [
|
| 5048 |
-
2026,
|
| 5049 |
-
2025
|
| 5050 |
-
]
|
| 5051 |
-
},
|
| 5052 |
-
"science.weather-forecasting": {
|
| 5053 |
-
"query": "\"weather forecasting\" climate in:description",
|
| 5054 |
-
"years": [
|
| 5055 |
-
2026,
|
| 5056 |
-
2025
|
| 5057 |
-
]
|
| 5058 |
-
},
|
| 5059 |
-
"science.weather-nowcasting": {
|
| 5060 |
-
"query": "nowcasting in:description \"machine learning\"",
|
| 5061 |
-
"years": [
|
| 5062 |
-
2026,
|
| 5063 |
-
2025
|
| 5064 |
-
]
|
| 5065 |
-
},
|
| 5066 |
-
"timeseries.anomaly-detection": {
|
| 5067 |
-
"query": "\"time series anomaly detection\" in:description",
|
| 5068 |
-
"years": [
|
| 5069 |
-
2026,
|
| 5070 |
-
2025
|
| 5071 |
-
]
|
| 5072 |
-
},
|
| 5073 |
-
"timeseries.classification": {
|
| 5074 |
-
"query": "\"time series classification\" in:description",
|
| 5075 |
-
"years": [
|
| 5076 |
-
2026,
|
| 5077 |
-
2025
|
| 5078 |
-
]
|
| 5079 |
-
},
|
| 5080 |
-
"timeseries.clustering": {
|
| 5081 |
-
"query": "\"time series clustering\" in:description,readme",
|
| 5082 |
-
"years": [
|
| 5083 |
-
2026,
|
| 5084 |
-
2025
|
| 5085 |
-
]
|
| 5086 |
-
},
|
| 5087 |
-
"timeseries.energy-forecasting": {
|
| 5088 |
-
"query": "\"energy forecasting\" in:description",
|
| 5089 |
-
"years": [
|
| 5090 |
-
2026,
|
| 5091 |
-
2025
|
| 5092 |
-
]
|
| 5093 |
-
},
|
| 5094 |
-
"timeseries.event-forecasting": {
|
| 5095 |
-
"query": "\"event forecasting\" in:description",
|
| 5096 |
-
"years": [
|
| 5097 |
-
2026,
|
| 5098 |
-
2025
|
| 5099 |
-
]
|
| 5100 |
-
},
|
| 5101 |
-
"timeseries.forecasting": {
|
| 5102 |
-
"query": "\"time series forecasting\" in:description",
|
| 5103 |
-
"years": [
|
| 5104 |
-
2026,
|
| 5105 |
-
2025
|
| 5106 |
-
]
|
| 5107 |
-
},
|
| 5108 |
-
"timeseries.foundation-models": {
|
| 5109 |
-
"query": "\"time series foundation model\" in:description",
|
| 5110 |
-
"years": [
|
| 5111 |
-
2026,
|
| 5112 |
-
2025
|
| 5113 |
-
]
|
| 5114 |
-
},
|
| 5115 |
-
"timeseries.imputation": {
|
| 5116 |
-
"query": "\"time series imputation\" in:description,readme",
|
| 5117 |
-
"years": [
|
| 5118 |
-
2026,
|
| 5119 |
-
2025
|
| 5120 |
-
]
|
| 5121 |
-
},
|
| 5122 |
-
"timeseries.long-horizon": {
|
| 5123 |
-
"query": "\"long horizon forecasting\" in:readme",
|
| 5124 |
-
"years": [
|
| 5125 |
-
2026,
|
| 5126 |
-
2025
|
| 5127 |
-
]
|
| 5128 |
-
},
|
| 5129 |
-
"timeseries.multivariate": {
|
| 5130 |
-
"query": "\"multivariate time series\" in:description",
|
| 5131 |
-
"years": [
|
| 5132 |
-
2026,
|
| 5133 |
-
2025
|
| 5134 |
-
]
|
| 5135 |
-
},
|
| 5136 |
-
"timeseries.nbeats": {
|
| 5137 |
-
"query": "N-BEATS in:description",
|
| 5138 |
-
"years": [
|
| 5139 |
-
2026,
|
| 5140 |
-
2025
|
| 5141 |
-
]
|
| 5142 |
-
},
|
| 5143 |
-
"timeseries.patchtst": {
|
| 5144 |
-
"query": "PatchTST in:description",
|
| 5145 |
-
"years": [
|
| 5146 |
-
2026,
|
| 5147 |
-
2025
|
| 5148 |
-
]
|
| 5149 |
-
},
|
| 5150 |
-
"timeseries.predictive-maintenance": {
|
| 5151 |
-
"query": "\"predictive maintenance\" in:description",
|
| 5152 |
-
"years": [
|
| 5153 |
-
2026,
|
| 5154 |
-
2025
|
| 5155 |
-
]
|
| 5156 |
-
},
|
| 5157 |
-
"timeseries.probabilistic-forecasting": {
|
| 5158 |
-
"query": "\"probabilistic forecasting\" in:readme",
|
| 5159 |
-
"years": [
|
| 5160 |
-
2026,
|
| 5161 |
-
2025
|
| 5162 |
-
]
|
| 5163 |
-
},
|
| 5164 |
-
"timeseries.remaining-useful-life": {
|
| 5165 |
-
"query": "\"remaining useful life\" in:readme",
|
| 5166 |
-
"years": [
|
| 5167 |
-
2026,
|
| 5168 |
-
2025
|
| 5169 |
-
]
|
| 5170 |
-
},
|
| 5171 |
-
"timeseries.sensor-data": {
|
| 5172 |
-
"query": "\"sensor time series\" in:description",
|
| 5173 |
-
"years": [
|
| 5174 |
-
2026,
|
| 5175 |
-
2025
|
| 5176 |
-
]
|
| 5177 |
-
},
|
| 5178 |
-
"timeseries.state-space-models": {
|
| 5179 |
-
"query": "\"state space model\" in:description",
|
| 5180 |
-
"years": [
|
| 5181 |
-
2026,
|
| 5182 |
-
2025
|
| 5183 |
-
]
|
| 5184 |
-
},
|
| 5185 |
-
"timeseries.streaming": {
|
| 5186 |
-
"query": "\"streaming time series\" in:readme",
|
| 5187 |
-
"years": [
|
| 5188 |
-
2026,
|
| 5189 |
-
2025
|
| 5190 |
-
]
|
| 5191 |
-
},
|
| 5192 |
-
"timeseries.synthetic-generation": {
|
| 5193 |
-
"query": "\"time series generation\" in:description,readme",
|
| 5194 |
-
"years": [
|
| 5195 |
-
2026,
|
| 5196 |
-
2025
|
| 5197 |
-
]
|
| 5198 |
-
},
|
| 5199 |
-
"timeseries.tabular-foundation-models": {
|
| 5200 |
-
"query": "\"tabular foundation model\" in:description,readme",
|
| 5201 |
-
"years": [
|
| 5202 |
-
2026,
|
| 5203 |
-
2025
|
| 5204 |
-
]
|
| 5205 |
-
},
|
| 5206 |
-
"timeseries.temporal-fusion-transformer": {
|
| 5207 |
-
"query": "\"temporal fusion transformer\" in:description",
|
| 5208 |
-
"years": [
|
| 5209 |
-
2026,
|
| 5210 |
-
2025
|
| 5211 |
-
]
|
| 5212 |
-
},
|
| 5213 |
-
"timeseries.temporal-representation": {
|
| 5214 |
-
"query": "\"temporal representation learning\" in:readme",
|
| 5215 |
-
"years": [
|
| 5216 |
-
2026,
|
| 5217 |
-
2025
|
| 5218 |
-
]
|
| 5219 |
-
},
|
| 5220 |
-
"timeseries.topic": {
|
| 5221 |
-
"query": "topic:time-series",
|
| 5222 |
-
"years": [
|
| 5223 |
-
2026,
|
| 5224 |
-
2025
|
| 5225 |
-
]
|
| 5226 |
-
},
|
| 5227 |
-
"timeseries.traffic-forecasting": {
|
| 5228 |
-
"query": "\"traffic forecasting\" in:description",
|
| 5229 |
-
"years": [
|
| 5230 |
-
2026,
|
| 5231 |
-
2025
|
| 5232 |
-
]
|
| 5233 |
-
},
|
| 5234 |
-
"timeseries.unevenly-spaced": {
|
| 5235 |
-
"query": "\"irregular time series\" in:description",
|
| 5236 |
-
"years": [
|
| 5237 |
-
2026,
|
| 5238 |
-
2025
|
| 5239 |
-
]
|
| 5240 |
-
},
|
| 5241 |
-
"timeseries.weather-forecasting": {
|
| 5242 |
-
"query": "\"weather forecasting\" in:description",
|
| 5243 |
-
"years": [
|
| 5244 |
-
2026,
|
| 5245 |
-
2025
|
| 5246 |
-
]
|
| 5247 |
-
},
|
| 5248 |
-
"trust.adversarial-robustness": {
|
| 5249 |
-
"query": "\"adversarial robustness\" in:description",
|
| 5250 |
-
"years": [
|
| 5251 |
-
2026,
|
| 5252 |
-
2025
|
| 5253 |
-
]
|
| 5254 |
-
},
|
| 5255 |
-
"trust.ai-alignment": {
|
| 5256 |
-
"query": "\"AI alignment\" in:description",
|
| 5257 |
-
"years": [
|
| 5258 |
-
2026,
|
| 5259 |
-
2025
|
| 5260 |
-
]
|
| 5261 |
-
},
|
| 5262 |
-
"trust.ai-safety": {
|
| 5263 |
-
"query": "\"AI safety\" in:description",
|
| 5264 |
-
"years": [
|
| 5265 |
-
2026,
|
| 5266 |
-
2025
|
| 5267 |
-
]
|
| 5268 |
-
},
|
| 5269 |
-
"trust.algorithmic-fairness": {
|
| 5270 |
-
"query": "\"algorithmic fairness\" in:description",
|
| 5271 |
-
"years": [
|
| 5272 |
-
2026,
|
| 5273 |
-
2025
|
| 5274 |
-
]
|
| 5275 |
-
},
|
| 5276 |
-
"trust.algorithmic-recourse": {
|
| 5277 |
-
"query": "\"algorithmic recourse\" in:description",
|
| 5278 |
-
"years": [
|
| 5279 |
-
2026,
|
| 5280 |
-
2025
|
| 5281 |
-
]
|
| 5282 |
-
},
|
| 5283 |
-
"trust.causal-discovery": {
|
| 5284 |
-
"query": "\"causal discovery\" in:description",
|
| 5285 |
-
"years": [
|
| 5286 |
-
2026,
|
| 5287 |
-
2025
|
| 5288 |
-
]
|
| 5289 |
-
},
|
| 5290 |
-
"trust.causal-effect-estimation": {
|
| 5291 |
-
"query": "\"causal effect estimation\" in:description",
|
| 5292 |
-
"years": [
|
| 5293 |
-
2026,
|
| 5294 |
-
2025
|
| 5295 |
-
]
|
| 5296 |
-
},
|
| 5297 |
-
"trust.causal-mediation": {
|
| 5298 |
-
"query": "\"causal mediation analysis\" in:description",
|
| 5299 |
-
"years": [
|
| 5300 |
-
2026,
|
| 5301 |
-
2025
|
| 5302 |
-
]
|
| 5303 |
-
},
|
| 5304 |
-
"trust.causal-ml": {
|
| 5305 |
-
"query": "\"causal machine learning\" in:description",
|
| 5306 |
-
"years": [
|
| 5307 |
-
2026,
|
| 5308 |
-
2025
|
| 5309 |
-
]
|
| 5310 |
-
},
|
| 5311 |
-
"trust.causal-representation-learning": {
|
| 5312 |
-
"query": "\"causal representation learning\" in:description",
|
| 5313 |
-
"years": [
|
| 5314 |
-
2026,
|
| 5315 |
-
2025
|
| 5316 |
-
]
|
| 5317 |
-
},
|
| 5318 |
-
"trust.conformal-prediction": {
|
| 5319 |
-
"query": "\"conformal prediction\" in:description",
|
| 5320 |
-
"years": [
|
| 5321 |
-
2026,
|
| 5322 |
-
2025
|
| 5323 |
-
]
|
| 5324 |
-
},
|
| 5325 |
-
"trust.counterfactual-explanations": {
|
| 5326 |
-
"query": "\"counterfactual explanation\" in:description",
|
| 5327 |
-
"years": [
|
| 5328 |
-
2026,
|
| 5329 |
-
2025
|
| 5330 |
-
]
|
| 5331 |
-
},
|
| 5332 |
-
"trust.differential-privacy": {
|
| 5333 |
-
"query": "\"differential privacy\" in:description",
|
| 5334 |
-
"years": [
|
| 5335 |
-
2026,
|
| 5336 |
-
2025
|
| 5337 |
-
]
|
| 5338 |
-
},
|
| 5339 |
-
"trust.distribution-shift": {
|
| 5340 |
-
"query": "\"distribution shift\" in:description",
|
| 5341 |
-
"years": [
|
| 5342 |
-
2026,
|
| 5343 |
-
2025
|
| 5344 |
-
]
|
| 5345 |
-
},
|
| 5346 |
-
"trust.domain-generalization": {
|
| 5347 |
-
"query": "\"domain generalization\" in:description",
|
| 5348 |
-
"years": [
|
| 5349 |
-
2026,
|
| 5350 |
-
2025
|
| 5351 |
-
]
|
| 5352 |
-
},
|
| 5353 |
-
"trust.explainable-ai": {
|
| 5354 |
-
"query": "\"explainable AI\" in:description",
|
| 5355 |
-
"years": [
|
| 5356 |
-
2026,
|
| 5357 |
-
2025
|
| 5358 |
-
]
|
| 5359 |
-
},
|
| 5360 |
-
"trust.federated-learning": {
|
| 5361 |
-
"query": "\"federated learning\" in:description",
|
| 5362 |
-
"years": [
|
| 5363 |
-
2026,
|
| 5364 |
-
2025
|
| 5365 |
-
]
|
| 5366 |
-
},
|
| 5367 |
-
"trust.instrumental-variable-ml": {
|
| 5368 |
-
"query": "\"double machine learning\" in:description",
|
| 5369 |
-
"years": [
|
| 5370 |
-
2026,
|
| 5371 |
-
2025
|
| 5372 |
-
]
|
| 5373 |
-
},
|
| 5374 |
-
"trust.interpretable-ml": {
|
| 5375 |
-
"query": "\"interpretable machine learning\" in:description",
|
| 5376 |
-
"years": [
|
| 5377 |
-
2026,
|
| 5378 |
-
2025
|
| 5379 |
-
]
|
| 5380 |
-
},
|
| 5381 |
-
"trust.membership-inference": {
|
| 5382 |
-
"query": "\"membership inference\" in:description",
|
| 5383 |
-
"years": [
|
| 5384 |
-
2026,
|
| 5385 |
-
2025
|
| 5386 |
-
]
|
| 5387 |
-
},
|
| 5388 |
-
"trust.ml-fairness": {
|
| 5389 |
-
"query": "\"machine learning fairness\" in:description",
|
| 5390 |
-
"years": [
|
| 5391 |
-
2026,
|
| 5392 |
-
2025
|
| 5393 |
-
]
|
| 5394 |
-
},
|
| 5395 |
-
"trust.model-calibration": {
|
| 5396 |
-
"query": "\"model calibration\" in:description",
|
| 5397 |
-
"years": [
|
| 5398 |
-
2026,
|
| 5399 |
-
2025
|
| 5400 |
-
]
|
| 5401 |
-
},
|
| 5402 |
-
"trust.neuro-symbolic": {
|
| 5403 |
-
"query": "\"neuro-symbolic\" in:description",
|
| 5404 |
-
"years": [
|
| 5405 |
-
2026,
|
| 5406 |
-
2025
|
| 5407 |
-
]
|
| 5408 |
-
},
|
| 5409 |
-
"trust.out-of-distribution-detection": {
|
| 5410 |
-
"query": "\"out-of-distribution detection\" in:description",
|
| 5411 |
-
"years": [
|
| 5412 |
-
2026,
|
| 5413 |
-
2025
|
| 5414 |
-
]
|
| 5415 |
-
},
|
| 5416 |
-
"trust.privacy-preserving-ml": {
|
| 5417 |
-
"query": "\"privacy-preserving machine learning\" in:description",
|
| 5418 |
-
"years": [
|
| 5419 |
-
2026,
|
| 5420 |
-
2025
|
| 5421 |
-
]
|
| 5422 |
-
},
|
| 5423 |
-
"trust.robustness-certification": {
|
| 5424 |
-
"query": "\"certified robustness\" in:description",
|
| 5425 |
-
"years": [
|
| 5426 |
-
2026,
|
| 5427 |
-
2025
|
| 5428 |
-
]
|
| 5429 |
-
},
|
| 5430 |
-
"trust.safe-reinforcement-learning": {
|
| 5431 |
-
"query": "\"safe reinforcement learning\" constrained in:description",
|
| 5432 |
-
"years": [
|
| 5433 |
-
2026,
|
| 5434 |
-
2025
|
| 5435 |
-
]
|
| 5436 |
-
},
|
| 5437 |
-
"trust.shap": {
|
| 5438 |
-
"query": "SHAP in:description",
|
| 5439 |
-
"years": [
|
| 5440 |
-
2026,
|
| 5441 |
-
2025
|
| 5442 |
-
]
|
| 5443 |
-
},
|
| 5444 |
-
"trust.uncertainty-quantification": {
|
| 5445 |
-
"query": "\"uncertainty quantification\" in:description",
|
| 5446 |
-
"years": [
|
| 5447 |
-
2026,
|
| 5448 |
-
2025
|
| 5449 |
-
]
|
| 5450 |
-
},
|
| 5451 |
-
"vision.action-recognition": {
|
| 5452 |
-
"query": "\"action recognition\" in:description",
|
| 5453 |
-
"years": [
|
| 5454 |
-
2026,
|
| 5455 |
-
2025
|
| 5456 |
-
]
|
| 5457 |
-
},
|
| 5458 |
-
"vision.change-detection": {
|
| 5459 |
-
"query": "\"change detection\" in:readme",
|
| 5460 |
-
"years": [
|
| 5461 |
-
2026,
|
| 5462 |
-
2025
|
| 5463 |
-
]
|
| 5464 |
-
},
|
| 5465 |
-
"vision.depth-estimation": {
|
| 5466 |
-
"query": "\"depth estimation\" in:readme",
|
| 5467 |
-
"years": [
|
| 5468 |
-
2026,
|
| 5469 |
-
2025
|
| 5470 |
-
]
|
| 5471 |
-
},
|
| 5472 |
-
"vision.face-recognition": {
|
| 5473 |
-
"query": "\"face recognition\" in:readme",
|
| 5474 |
-
"years": [
|
| 5475 |
-
2026,
|
| 5476 |
-
2025
|
| 5477 |
-
]
|
| 5478 |
-
},
|
| 5479 |
-
"vision.image-captioning": {
|
| 5480 |
-
"query": "\"image captioning\" in:readme",
|
| 5481 |
-
"years": [
|
| 5482 |
-
2026,
|
| 5483 |
-
2025
|
| 5484 |
-
]
|
| 5485 |
-
},
|
| 5486 |
-
"vision.image-classification": {
|
| 5487 |
-
"query": "\"image classification\" in:description",
|
| 5488 |
-
"years": [
|
| 5489 |
-
2026,
|
| 5490 |
-
2025
|
| 5491 |
-
]
|
| 5492 |
-
},
|
| 5493 |
-
"vision.image-diffusion": {
|
| 5494 |
-
"query": "\"image diffusion model\" in:readme",
|
| 5495 |
-
"years": [
|
| 5496 |
-
2026,
|
| 5497 |
-
2025
|
| 5498 |
-
]
|
| 5499 |
-
},
|
| 5500 |
-
"vision.image-restoration": {
|
| 5501 |
-
"query": "\"image restoration\" in:readme",
|
| 5502 |
-
"years": [
|
| 5503 |
-
2026,
|
| 5504 |
-
2025
|
| 5505 |
-
]
|
| 5506 |
-
},
|
| 5507 |
-
"vision.image-retrieval": {
|
| 5508 |
-
"query": "\"image retrieval\" in:readme",
|
| 5509 |
-
"years": [
|
| 5510 |
-
2026,
|
| 5511 |
-
2025
|
| 5512 |
-
]
|
| 5513 |
-
},
|
| 5514 |
-
"vision.image-super-resolution": {
|
| 5515 |
-
"query": "\"image super-resolution\" in:readme",
|
| 5516 |
-
"years": [
|
| 5517 |
-
2026,
|
| 5518 |
-
2025
|
| 5519 |
-
]
|
| 5520 |
-
},
|
| 5521 |
-
"vision.image-to-image-translation": {
|
| 5522 |
-
"query": "\"image-to-image translation\" in:readme",
|
| 5523 |
-
"years": [
|
| 5524 |
-
2026,
|
| 5525 |
-
2025
|
| 5526 |
-
]
|
| 5527 |
-
},
|
| 5528 |
-
"vision.neural-rendering": {
|
| 5529 |
-
"query": "\"neural rendering\" in:description",
|
| 5530 |
-
"years": [
|
| 5531 |
-
2026,
|
| 5532 |
-
2025
|
| 5533 |
-
]
|
| 5534 |
-
},
|
| 5535 |
-
"vision.object-detection": {
|
| 5536 |
-
"query": "\"object detection\" in:description",
|
| 5537 |
-
"years": [
|
| 5538 |
-
2026,
|
| 5539 |
-
2025
|
| 5540 |
-
]
|
| 5541 |
-
},
|
| 5542 |
-
"vision.object-tracking": {
|
| 5543 |
-
"query": "\"object tracking\" in:readme",
|
| 5544 |
-
"years": [
|
| 5545 |
-
2026,
|
| 5546 |
-
2025
|
| 5547 |
-
]
|
| 5548 |
-
},
|
| 5549 |
-
"vision.ocr": {
|
| 5550 |
-
"query": "topic:ocr",
|
| 5551 |
-
"years": [
|
| 5552 |
-
2026,
|
| 5553 |
-
2025
|
| 5554 |
-
]
|
| 5555 |
-
},
|
| 5556 |
-
"vision.open-vocabulary-detection": {
|
| 5557 |
-
"query": "\"open-vocabulary detection\" in:readme",
|
| 5558 |
-
"years": [
|
| 5559 |
-
2026,
|
| 5560 |
-
2025
|
| 5561 |
-
]
|
| 5562 |
-
},
|
| 5563 |
-
"vision.optical-flow": {
|
| 5564 |
-
"query": "topic:optical-flow",
|
| 5565 |
-
"years": [
|
| 5566 |
-
2026,
|
| 5567 |
-
2025
|
| 5568 |
-
]
|
| 5569 |
-
},
|
| 5570 |
-
"vision.pose-estimation": {
|
| 5571 |
-
"query": "\"human pose estimation\" in:readme",
|
| 5572 |
-
"years": [
|
| 5573 |
-
2026,
|
| 5574 |
-
2025
|
| 5575 |
-
]
|
| 5576 |
-
},
|
| 5577 |
-
"vision.promptable-segmentation": {
|
| 5578 |
-
"query": "\"promptable segmentation\" in:readme",
|
| 5579 |
-
"years": [
|
| 5580 |
-
2026,
|
| 5581 |
-
2025
|
| 5582 |
-
]
|
| 5583 |
-
},
|
| 5584 |
-
"vision.segmentation": {
|
| 5585 |
-
"query": "\"image segmentation\" in:description",
|
| 5586 |
-
"years": [
|
| 5587 |
-
2026,
|
| 5588 |
-
2025
|
| 5589 |
-
]
|
| 5590 |
-
},
|
| 5591 |
-
"vision.self-supervised-vision": {
|
| 5592 |
-
"query": "\"self-supervised vision\" in:description",
|
| 5593 |
-
"years": [
|
| 5594 |
-
2026,
|
| 5595 |
-
2025
|
| 5596 |
-
]
|
| 5597 |
-
},
|
| 5598 |
-
"vision.topic-computer-vision": {
|
| 5599 |
-
"query": "topic:computer-vision",
|
| 5600 |
-
"years": [
|
| 5601 |
-
2026,
|
| 5602 |
-
2025
|
| 5603 |
-
]
|
| 5604 |
-
},
|
| 5605 |
-
"vision.video-generation": {
|
| 5606 |
-
"query": "\"video generation\" in:readme",
|
| 5607 |
-
"years": [
|
| 5608 |
-
2026,
|
| 5609 |
-
2025
|
| 5610 |
-
]
|
| 5611 |
-
},
|
| 5612 |
-
"vision.video-understanding": {
|
| 5613 |
-
"query": "\"video understanding\" in:readme",
|
| 5614 |
-
"years": [
|
| 5615 |
-
2026,
|
| 5616 |
-
2025
|
| 5617 |
-
]
|
| 5618 |
-
},
|
| 5619 |
-
"vision.vision-language": {
|
| 5620 |
-
"query": "\"vision-language model\" in:readme",
|
| 5621 |
-
"years": [
|
| 5622 |
-
2026,
|
| 5623 |
-
2025
|
| 5624 |
-
]
|
| 5625 |
-
},
|
| 5626 |
-
"vision.visual-anomaly-detection": {
|
| 5627 |
-
"query": "\"visual anomaly detection\" in:readme",
|
| 5628 |
-
"years": [
|
| 5629 |
-
2026,
|
| 5630 |
-
2025
|
| 5631 |
-
]
|
| 5632 |
-
},
|
| 5633 |
-
"vision.visual-localization": {
|
| 5634 |
-
"query": "\"visual localization\" in:readme",
|
| 5635 |
-
"years": [
|
| 5636 |
-
2026,
|
| 5637 |
-
2025
|
| 5638 |
-
]
|
| 5639 |
}
|
| 5640 |
},
|
| 5641 |
"end": "2026-09-24",
|
|
@@ -5645,6 +2264,7 @@
|
|
| 5645 |
"version": 2
|
| 5646 |
},
|
| 5647 |
"end": "2026-09-24",
|
|
|
|
| 5648 |
"start_year": 2008,
|
| 5649 |
-
"updated_at": "2026-09-
|
| 5650 |
}
|
|
|
|
| 10 |
},
|
| 11 |
{
|
| 12 |
"id": "applied.algorithmic-trading",
|
| 13 |
+
"query": "\"algorithmic trading\" \"machine learning\" in:readme"
|
| 14 |
},
|
| 15 |
{
|
| 16 |
"id": "applied.causal-inference",
|
|
|
|
| 38 |
},
|
| 39 |
{
|
| 40 |
"id": "applied.customer-segmentation",
|
| 41 |
+
"query": "\"customer segmentation\" \"machine learning\" in:description,readme"
|
| 42 |
},
|
| 43 |
{
|
| 44 |
"id": "applied.cybersecurity-ml",
|
|
|
|
| 50 |
},
|
| 51 |
{
|
| 52 |
"id": "applied.dynamic-pricing",
|
| 53 |
+
"query": "\"dynamic pricing\" \"machine learning\" in:description,readme"
|
| 54 |
},
|
| 55 |
{
|
| 56 |
"id": "applied.ecological-forecasting",
|
|
|
|
| 66 |
},
|
| 67 |
{
|
| 68 |
"id": "applied.fraud-detection",
|
| 69 |
+
"query": "\"fraud detection\" \"machine learning\" in:description,readme"
|
| 70 |
},
|
| 71 |
{
|
| 72 |
"id": "applied.industrial-defect-detection",
|
|
|
|
| 482 |
},
|
| 483 |
{
|
| 484 |
"id": "efficiency.inference-systems",
|
| 485 |
+
"query": "\"inference engine\" \"machine learning\" in:description,readme"
|
| 486 |
},
|
| 487 |
{
|
| 488 |
"id": "efficiency.knowledge-distillation",
|
|
|
|
| 548 |
"id": "efficiency.speculative-decoding",
|
| 549 |
"query": "\"speculative decoding\" in:description"
|
| 550 |
},
|
| 551 |
+
{
|
| 552 |
+
"id": "gap2026.archaeology-ml",
|
| 553 |
+
"query": "\"machine learning archaeology\" in:description,readme"
|
| 554 |
+
},
|
| 555 |
+
{
|
| 556 |
+
"id": "gap2026.climate-downscaling",
|
| 557 |
+
"query": "\"machine learning climate downscaling\" in:description,readme"
|
| 558 |
+
},
|
| 559 |
+
{
|
| 560 |
+
"id": "gap2026.clinical-ehr-representation",
|
| 561 |
+
"query": "\"electronic health record\" representation learning in:description,readme"
|
| 562 |
+
},
|
| 563 |
+
{
|
| 564 |
+
"id": "gap2026.clinical-risk-prediction",
|
| 565 |
+
"query": "\"clinical risk prediction\" machine learning in:description,readme"
|
| 566 |
+
},
|
| 567 |
+
{
|
| 568 |
+
"id": "gap2026.clinical-survival-ml",
|
| 569 |
+
"query": "\"survival analysis\" deep learning clinical in:description,readme"
|
| 570 |
+
},
|
| 571 |
+
{
|
| 572 |
+
"id": "gap2026.clinical-treatment-effect",
|
| 573 |
+
"query": "\"treatment effect estimation\" clinical in:description,readme"
|
| 574 |
+
},
|
| 575 |
+
{
|
| 576 |
+
"id": "gap2026.clinical-trial-matching",
|
| 577 |
+
"query": "\"clinical trial matching\" machine learning in:description,readme"
|
| 578 |
+
},
|
| 579 |
+
{
|
| 580 |
+
"id": "gap2026.econometrics-causal-forest",
|
| 581 |
+
"query": "\"causal forest\" econometrics in:description,readme"
|
| 582 |
+
},
|
| 583 |
+
{
|
| 584 |
+
"id": "gap2026.econometrics-double-ml",
|
| 585 |
+
"query": "\"double machine learning\" econometrics in:description,readme"
|
| 586 |
+
},
|
| 587 |
+
{
|
| 588 |
+
"id": "gap2026.econometrics-heterogeneous-effects",
|
| 589 |
+
"query": "\"heterogeneous treatment effects\" machine learning economics in:description,readme"
|
| 590 |
+
},
|
| 591 |
+
{
|
| 592 |
+
"id": "gap2026.econometrics-policy-learning",
|
| 593 |
+
"query": "\"policy learning\" causal machine learning economics in:description,readme"
|
| 594 |
+
},
|
| 595 |
+
{
|
| 596 |
+
"id": "gap2026.forecasting-count-data",
|
| 597 |
+
"query": "\"count time series forecasting\" neural in:description,readme"
|
| 598 |
+
},
|
| 599 |
+
{
|
| 600 |
+
"id": "gap2026.forecasting-hierarchical",
|
| 601 |
+
"query": "\"hierarchical time series forecasting\" machine learning in:description,readme"
|
| 602 |
+
},
|
| 603 |
+
{
|
| 604 |
+
"id": "gap2026.forecasting-intermittent-demand",
|
| 605 |
+
"query": "\"intermittent demand forecasting\" machine learning in:description,readme"
|
| 606 |
+
},
|
| 607 |
+
{
|
| 608 |
+
"id": "gap2026.forecasting-quantile",
|
| 609 |
+
"query": "\"neural quantile forecasting\" in:description,readme"
|
| 610 |
+
},
|
| 611 |
+
{
|
| 612 |
+
"id": "gap2026.green-ai-carbon",
|
| 613 |
+
"query": "\"carbon emissions\" machine learning training in:description,readme"
|
| 614 |
+
},
|
| 615 |
+
{
|
| 616 |
+
"id": "gap2026.green-ai-carbon-aware-scheduling",
|
| 617 |
+
"query": "\"carbon-aware\" machine learning scheduling in:description,readme"
|
| 618 |
+
},
|
| 619 |
+
{
|
| 620 |
+
"id": "gap2026.green-ai-efficient-inference",
|
| 621 |
+
"query": "\"energy efficient inference\" neural network in:description,readme"
|
| 622 |
+
},
|
| 623 |
+
{
|
| 624 |
+
"id": "gap2026.green-ai-energy",
|
| 625 |
+
"query": "\"energy efficient machine learning\" in:description,readme"
|
| 626 |
+
},
|
| 627 |
+
{
|
| 628 |
+
"id": "gap2026.math-learned-optimization",
|
| 629 |
+
"query": "\"learned optimizer\" optimization algorithm in:description,readme"
|
| 630 |
+
},
|
| 631 |
+
{
|
| 632 |
+
"id": "gap2026.math-neural-combinatorial-optimization",
|
| 633 |
+
"query": "\"neural combinatorial optimization\" in:description,readme"
|
| 634 |
+
},
|
| 635 |
+
{
|
| 636 |
+
"id": "gap2026.neuroimaging-connectome",
|
| 637 |
+
"query": "\"connectome prediction\" machine learning in:description,readme"
|
| 638 |
+
},
|
| 639 |
+
{
|
| 640 |
+
"id": "gap2026.neuroimaging-eeg",
|
| 641 |
+
"query": "\"EEG deep learning\" in:description,readme"
|
| 642 |
+
},
|
| 643 |
+
{
|
| 644 |
+
"id": "gap2026.neuroimaging-fmri",
|
| 645 |
+
"query": "\"fMRI machine learning\" in:description,readme"
|
| 646 |
+
},
|
| 647 |
+
{
|
| 648 |
+
"id": "gap2026.neuroimaging-neural-decoding",
|
| 649 |
+
"query": "\"neural decoding\" machine learning in:description,readme"
|
| 650 |
+
},
|
| 651 |
+
{
|
| 652 |
+
"id": "gap2026.physics-differentiable-simulation",
|
| 653 |
+
"query": "\"differentiable physics simulation\" in:description,readme"
|
| 654 |
+
},
|
| 655 |
+
{
|
| 656 |
+
"id": "gap2026.physics-inverse-problem",
|
| 657 |
+
"query": "\"inverse problems\" neural network physics in:description,readme"
|
| 658 |
+
},
|
| 659 |
+
{
|
| 660 |
+
"id": "gap2026.physics-neural-pde-solver",
|
| 661 |
+
"query": "\"neural PDE solver\" in:description,readme"
|
| 662 |
+
},
|
| 663 |
+
{
|
| 664 |
+
"id": "gap2026.physics-quantum-ml",
|
| 665 |
+
"query": "\"quantum machine learning\" physics in:description,readme"
|
| 666 |
+
},
|
| 667 |
{
|
| 668 |
"id": "general.active-learning",
|
| 669 |
"query": "\"active learning\" in:description"
|
|
|
|
| 674 |
},
|
| 675 |
{
|
| 676 |
"id": "general.arxiv",
|
| 677 |
+
"query": "arxiv \"machine learning\" in:description,readme"
|
| 678 |
},
|
| 679 |
{
|
| 680 |
"id": "general.automated-machine-learning",
|
|
|
|
| 690 |
},
|
| 691 |
{
|
| 692 |
"id": "general.clustering",
|
| 693 |
+
"query": "\"clustering algorithm\" \"machine learning\" in:description,readme"
|
| 694 |
},
|
| 695 |
{
|
| 696 |
"id": "general.continual-learning",
|
|
|
|
| 806 |
},
|
| 807 |
{
|
| 808 |
"id": "general.research-code",
|
| 809 |
+
"query": "\"research code\" \"machine learning\" in:description,readme"
|
| 810 |
},
|
| 811 |
{
|
| 812 |
"id": "general.self-supervised-learning",
|
|
|
|
| 2252 |
"completed": {
|
| 2253 |
"applied.ad-ranking": {
|
| 2254 |
"query": "\"ad ranking\" in:description",
|
|
|
|
|
|
|
|
|
|
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| 2255 |
"years": [
|
| 2256 |
2026
|
| 2257 |
]
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| 2258 |
}
|
| 2259 |
},
|
| 2260 |
"end": "2026-09-24",
|
|
|
|
| 2264 |
"version": 2
|
| 2265 |
},
|
| 2266 |
"end": "2026-09-24",
|
| 2267 |
+
"search_policy_version": 2,
|
| 2268 |
"start_year": 2008,
|
| 2269 |
+
"updated_at": "2026-09-24T20:08:37Z"
|
| 2270 |
}
|