Add GitHub ML run 20260924T183058000000Z-a292eb13

README.md

@@ -8,6 +8,12 @@ tags:
 - modelome
 license: other
 pretty_name: GitHub ML
+configs:
+- config_name: observations
+  data_files:
+  - split: train
+    path: data/observations/**/*.jsonl
+  default: true
 ---
 
 # GitHub ML
@@ -24,13 +30,19 @@ The collector appends machine-readable JSON Lines observations by UTC publicatio
 
 ```text
 README.md
+data/current/repositories.parquet  # planned derived snapshot
 data/observations/YYYY/MM/DD/<run-id>.jsonl
 coverage/<run-id>.json
 state/checkpoint.json
+state/sample.json
+state/historical-sample.json
 state/backfill.json
+state/backfill-fair.json
 ```
 
-Each JSONL row is one observation, not a unique repository across the full history. A run deduplicates matches by `github_id`, but later runs append new snapshots. To create a current view, combine all observation files, group by numeric `github_id`, and retain the row with the greatest `observed_at`; `github_id` remains stable if the repository is renamed. Keep the full history when change over time matters. Coverage JSON records the query or query/date partitions attempted, result counts, pages scanned, outcome status, and any coverage gap. `state/checkpoint.json` stores daily collection progress and `state/backfill.json` stores independent historical backfill progress. Successful empty sweeps still add coverage and update the checkpoint, but have no JSONL file. Run records and coverage are append-only; checkpoint files and the dataset card are updated as collection continues. See [Hugging Face repository structure](https://huggingface.co/docs/datasets/main/repository_structure) and [dataset cards](https://huggingface.co/docs/hub/en/datasets-cards).
+Each JSONL row is one observation, not a unique repository across the full history. A run deduplicates matches by `github_id`, but later runs append new snapshots. To create a current view, combine all observation files, group by numeric `github_id`, and retain the row with the greatest `observed_at`; `github_id` remains stable if the repository is renamed. Keep the full history when change over time matters. Coverage JSON records the query or query/date partitions attempted, result counts, pages scanned, outcome status, and any coverage gap. `state/sample.json` stores breadth-sample progress, `state/historical-sample.json` stores annual historical-sample progress, `state/checkpoint.json` stores daily recent collection progress, `state/backfill.json` stores legacy historical backfill progress, and `state/backfill-fair.json` stores fair historical backfill progress. Successful empty passes still add coverage and update their checkpoint, but have no JSONL file. Run records and coverage are append-only; checkpoint files and the dataset card are updated as collection continues. See [Hugging Face repository structure](https://huggingface.co/docs/datasets/main/repository_structure) and [dataset cards](https://huggingface.co/docs/hub/en/datasets-cards).
+
+The planned `data/current/repositories.parquet` is a derived snapshot, separate from the append-only observations. It is not available as a dataset configuration until the Parquet artifact is published. When released, it will become the default `current` configuration and will be refreshed from the complete observation history when the snapshot is rebuilt; it is not an independently collected or authoritative source. The default `observations` configuration exposes the underlying JSONL history. The local current-view manifest records the input files, row counts, and deterministic latest-row selection; retain it with the release provenance when rebuilding the Parquet snapshot.
 
 ## Fields
 
@@ -53,50 +65,36 @@ Labels are open vocabulary, multi-label, and subject to change. They can describ
 
 ## Updates and deduplication
 
-Daily collection searches for recently pushed candidates, records the queries that found each repository, and appends observations. Repository searches include `fork:true` unless a configured query already specifies a `fork:` qualifier, so forks and reproductions can be discovered; `fork` marks forked projects. Within a run, matches merge by numeric `github_id`, never by mutable `name`. Across runs, the observation history is append-only; to materialize a current view, group by `github_id` and select the greatest `observed_at`. Historical backfill searches creation dates beginning at 2008-01-01 by default. It partitions by date and recursively splits dense intervals when GitHub approaches its per-search result ceiling; coverage records each query and date partition. It resumes from the separate `state/backfill.json` checkpoint, while daily progress uses `state/checkpoint.json`. An older backfill observation does not displace a newer snapshot in a current view.
+The scheduled workflow runs four passes for a total default Search request budget of 2,000: breadth sample (500), recent collection (600), annual historical sample (200), and fair historical backfill (700), subject to Actions timeout and GitHub API limits. The breadth catalog has more queries than its 500-request daily budget, so each run samples only a budget-selected subset, not every configured query; its rotating checkpoint spreads requests across queries over time. It runs one `created:` first-page search per selected query. This improves breadth, but it only sees first-page results and cannot guarantee complete coverage or identify verified novelty. The recent pass searches recently pushed candidates and records the queries that found each repository. Repository searches include `fork:true` unless a configured query already specifies a `fork:` qualifier, so forks and reproductions can be discovered; `fork` marks forked projects. The experimental repository census described below is local-only and is not part of this scheduled four-pass workflow.
+
+The `historical-sample` pass takes one ranked first-page search for every query/year lane from 2008 through a campaign end date fixed when the campaign starts. That end date stays fixed across partial runs and later runs with an expanded catalog. Its v2 completion ledger preserves completed `(query ID, query text, year)` lanes across catalog additions and edits. New or changed queries are sampled for every year in the fixed campaign, removed queries are dropped, and a legacy v1 cursor is safely migrated by matching query signatures. The checkpoint is `state/historical-sample.json` on the Hub and `historical-sample-state.json` in local output. Completed ledger entries persist, so future catalog additions resume only their missing lanes. The bounded request budget remains 200 per scheduled day, processed round-robin across query groups. These annual samples can improve breadth across creation years, but ranked first-page sampling can omit matching repositories; they do not replace historical backfill or establish completeness. The scheduled `backfill-fair` pass rotates through query/date-partition work, issuing one Search request per query in each rotation to spread its bounded budget across queries. Its checkpoints are `state/backfill-fair.json` on the Hub and `backfill-fair-state.json` in local output. The legacy `backfill` command still uses `state/backfill.json` / `backfill-state.json`; fair backfill uses a separate checkpoint and does not migrate the old cursor. Do not claim exhaustive coverage until all date partitions have been scanned and coverage records show a completed sweep. Recent and breadth checkpoints remain at `state/checkpoint.json` and `state/sample.json`, respectively. Within a run, matches merge by numeric `github_id`, never by mutable `name`. Across runs, the observation history is append-only; `uv run gh-ml current-view /path/to/downloaded-dataset --output ~/.local/share/modelomics-gh-ml/current-view.jsonl` builds one row per ID from the local `data/observations/**/*.jsonl` files. It selects the greatest `observed_at`; any `all_*` accumulated-label fields retain values across the history while the other fields come from that latest row. The command also writes a manifest and does not modify the Hub. For Parquet output, install `uv sync --extra parquet` and provide `--parquet-output <path>`.
+
+The experimental `uv run gh-ml census --max-pages 2` command collects bounded pages from GitHub's queryless public repository Core API into local candidate-only files. It requires `GITHUB_TOKEN`, resumes from the local checkpoint in `~/.local/share/modelomics-gh-ml/census`, and supports `--since` and `--output-dir`. This is experimental local output, not a complete census or verified ML catalog. It is not scheduled: remote retry/checkpoint publication has not been integrated, so the four GitHub Search passes above remain the only scheduled collectors.
 
-The source is GitHub's public repository metadata and Search API. GitHub Search caps each query at 1,000 returned results and at 4,000 repositories searched, and is subject to request limits, timeouts, incomplete responses, and indexing gaps. A coverage `status` of `capped` or `incomplete`, or `coverage_gap: true`, flags known gaps; check `coverage_gap_reason` and the other per-query fields. Broad query coverage and backfills improve recall but cannot guarantee exhaustiveness. Results can include false positives, and not all novel ML work is hosted on GitHub or discoverable by the configured queries. See the [official Search API documentation](https://docs.github.com/en/rest/search/search).
+The local current view preserves the selected latest observation's ordinary fields and adds `first_observed_at` and `observation_count`. It also adds sorted cross-observation unions: `all_query_ids`, `all_domains`, `all_methods`, and `all_novelty_signals`. These accumulated labels describe signals seen anywhere in the observation history; they do not change the latest row's repository metadata or establish novelty. To rebuild the snapshot locally from a downloaded dataset, run `uv run gh-ml current-view /path/to/downloaded-dataset --output /tmp/gh-ml-current-view.jsonl --parquet-output /path/to/staging/data/current/repositories.parquet`. Keep the generated manifest beside the staged release materials; its `input_files` identify the exact observation JSONLs used.
+
+The source is GitHub's public repository metadata and Search API. GitHub Search caps each query at 1,000 returned results and at 4,000 repositories searched, and is subject to request limits, timeouts, incomplete responses, and indexing gaps. Annual historical sampling covers only the ranked first page for each query/year, so its query/year attempts do not mean it collected every matching repository. A coverage `status` of `capped` or `incomplete`, or `coverage_gap: true`, flags known gaps; check `coverage_gap_reason` and the other per-query fields. Broad query coverage and exhaustive backfills improve recall but cannot guarantee exhaustiveness. Results can include false positives, and not all novel ML work is hosted on GitHub or discoverable by the configured queries. See the [official Search API documentation](https://docs.github.com/en/rest/search/search).
 
 ## Access
 
-This dataset is maintained at [`modelomics/gh-ml`](https://huggingface.co/datasets/modelomics/gh-ml). The collection workflow appends new observations and coverage records to this repository over time. Publishing requires an `HF_TOKEN` with write permission to the dataset repository; the GitHub Actions workflow checks for the secret and exits before collection if it is missing. Actions supplies `GITHUB_TOKEN` for GitHub Search. To recover a scheduled run, use **Run workflow** in Actions: each successful run commits its cursor with the observations and coverage, while a failed search leaves the prior Hub checkpoint available for retry.
+This dataset is maintained at [`modelomics/gh-ml`](https://huggingface.co/datasets/modelomics/gh-ml). The accumulated seed and backfill observations in open Hugging Face dataset proposal #1 will appear on `main` when a Modelomics dataset writer merges it. Future collection runs can publish independently once authentication is configured. The collection workflow appends new observations and coverage records to this repository over time. Publishing can use an `HF_TOKEN` with write permission to the dataset repository, or keyless Hugging Face Trusted Publishers authentication. To enable keyless publishing, a dataset organization writer must add a Trusted Publisher at [the dataset settings](https://huggingface.co/datasets/modelomics/gh-ml/settings) with GitHub Actions claims repository `modelomics/gh-ml`, branch `main`, and workflow `daily.yml`. The workflow requests `id-token: write` and exchanges its GitHub identity using `HF_OIDC_RESOURCE=datasets/modelomics/gh-ml`; see [Hugging Face Trusted Publishers](https://huggingface.co/docs/hub/trusted-publishers). When `HF_TOKEN` is set, it takes precedence over OIDC. Actions supplies `GITHUB_TOKEN` for GitHub Search. To recover a scheduled run, use **Run workflow** in Actions: each successful pass commits its cursor with the observations and coverage, while a failed search leaves the prior Hub checkpoint available for retry.
 
 For local publishing, set `HF_TOKEN` in the environment or sign in with `uv run hf auth login`; the collector reads the saved Hugging Face CLI token. GitHub authentication can be provided through `GITHUB_TOKEN` or `gh auth login` (the collector reads `gh auth token`). The HF token must have write permission on `modelomics/gh-ml`. For local recovery, rerun using the same `--output-dir` so the local cursor is reused; the default is `~/.local/share/modelomics-gh-ml/runs`. Use `--no-publish` for local-only collection, which writes run files without requiring Hugging Face credentials.
 
-Observation files are JSON Lines stored under date-partitioned Hub paths. To load them with 🤗 Datasets, first download the repository files locally, then pass all observation paths. `datasets` is an additional package for this example (`uv add datasets` in a local environment, or install it separately); it is not needed by the collector.
+The `observations` configuration is currently the default and loads the append-only source history. The `current` configuration will be available only after its Parquet artifact is published; at that point it will become the default. These configs follow the Hub's [dataset repository structure](https://huggingface.co/docs/datasets/repository_structure); `datasets` is needed only by consumers, not by the collector.
 
 ```python
-from pathlib import Path
-import json
-from huggingface_hub import snapshot_download
 from datasets import load_dataset
 
-root = Path(snapshot_download(
-    repo_id="modelomics/gh-ml",
-    repo_type="dataset",
-    allow_patterns=["data/observations/**/*.jsonl", "coverage/*.json"],
-))
-files = sorted(str(path) for path in root.glob("data/observations/**/*.jsonl"))
-if not files:
-    raise RuntimeError("The dataset has no observation files yet")
-observations = load_dataset("json", data_files=files, split="train")
-
-# Current snapshot: one latest row for each stable GitHub repository ID.
-latest = {}
-for row in observations:
-    previous = latest.get(row["github_id"])
-    if previous is None or row["observed_at"] > previous["observed_at"]:
-        latest[row["github_id"]] = row
-current = list(latest.values())
-
-# Coverage is per run; keep it separate from repository rows.
-coverage = [
-    json.loads(path.read_text())
-    for path in sorted((root / "coverage").glob("*.json"))
-]
+# Current default: append-only observation history, with multiple rows per github_id.
+observations = load_dataset("modelomics/gh-ml")["train"]
+
+# After the Parquet artifact is published, this will load the current snapshot.
+# Until then, build a local current view from the observations configuration.
+# current = load_dataset("modelomics/gh-ml", "current")["train"]
 ```
 
-Coverage is per run; it is not a list of repositories. Daily coverage rows describe pushed-date search windows; backfill rows describe created-date partitions. A `complete_sweep` of `false` usually means the request budget left a cursor to resume. Within each run's `queries`, review `status`, `coverage_gap`, `coverage_gap_reason`, `incomplete_results`, and `search_limit_reached`. Coverage and checkpoint JSON are operational metadata, not rows in the repository table. Hub checkpoints let scheduled or manually dispatched Actions runs resume; locally, reuse the same `--output-dir` to resume local state.
+Coverage is per run; it is not a list of repositories. Breadth sample coverage describes one first page per selected catalog query; annual historical-sample coverage describes one first page per query/year; daily recent coverage describes pushed-date search windows; fair and legacy backfill coverage describe created-date partitions. A `complete_sweep` of `false` usually means the request budget left a cursor to resume. Within each run's `queries`, review `status`, `coverage_gap`, `coverage_gap_reason`, `incomplete_results`, and `search_limit_reached`. Fair backfill is not evidence of exhaustive coverage until all date partitions have been scanned and coverage records show the completed sweep. Coverage and checkpoint JSON are operational metadata, not rows in the repository table. Hub checkpoints let scheduled or manually dispatched Actions runs resume; locally, reuse the same `--output-dir` to resume local state. Run the breadth sample locally with `uv run gh-ml sample --max-requests 500 --since-days 1`; run the annual sample with `uv run gh-ml historical-sample --max-requests 200`; run fair backfill locally without publishing with `uv run gh-ml backfill-fair --max-requests 700 --no-publish`. The local fair checkpoint is `backfill-fair-state.json` and is independent from the legacy backfill checkpoint. Add `--no-publish` to keep output local.
 
 ## License and attribution
 

coverage/20260924T183058000000Z-a292eb13.json

@@ -0,0 +1,25 @@
+{
+  "complete_sweep": false,
+  "date_field": "created",
+  "mode": "sample",
+  "queries": [
+    {
+      "coverage_gap": false,
+      "coverage_gap_reason": null,
+      "incomplete_results": false,
+      "pages_scanned": 1,
+      "query": "\"ad ranking\" in:description fork:true created:2026-09-24..2026-09-24",
+      "query_id": "applied.ad-ranking",
+      "repositories_observed_on_last_page": 1,
+      "sampled_first_page": true,
+      "search_limit_reached": false,
+      "status": "complete",
+      "total_count": 1
+    }
+  ],
+  "requests_used": 1,
+  "run_id": "20260924T183058000000Z-a292eb13",
+  "since": "2026-09-24",
+  "started_at": "2026-09-24T18:30:58Z",
+  "until": "2026-09-24"
+}

data/observations/2026/09/24/20260924T183058000000Z-a292eb13.jsonl

@@ -0,0 +1 @@
+{"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":"2026-09-24T18:30:58Z","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"}

state/sample.json

@@ -1,5 +1,2143 @@
 {
-  "cursor": null,
+  "cursor": {
+    "end": "2026-09-24",
+    "field": "created",
+    "per_page": 100,
+    "query_id": "applied.agricultural-ml",
+    "query_index": 1,
+    "specs": [
+      {
+        "id": "applied.ad-ranking",
+        "query": "\"ad ranking\" in:description"
+      },
+      {
+        "id": "applied.agricultural-ml",
+        "query": "\"agricultural machine learning\" in:description,readme"
+      },
+      {
+        "id": "applied.algorithmic-trading",
+        "query": "\"algorithmic trading\" in:description"
+      },
+      {
+        "id": "applied.causal-inference",
+        "query": "\"causal inference\" in:description"
+      },
+      {
+        "id": "applied.churn-prediction",
+        "query": "\"customer churn prediction\" in:description"
+      },
+      {
+        "id": "applied.click-through-rate",
+        "query": "\"click-through rate prediction\" in:readme"
+      },
+      {
+        "id": "applied.collaborative-filtering",
+        "query": "\"collaborative filtering\" in:description"
+      },
+      {
+        "id": "applied.credit-scoring",
+        "query": "\"credit scoring\" in:description"
+      },
+      {
+        "id": "applied.customer-lifetime-value",
+        "query": "\"customer lifetime value\" in:description"
+      },
+      {
+        "id": "applied.customer-segmentation",
+        "query": "\"customer segmentation\" in:readme"
+      },
+      {
+        "id": "applied.cybersecurity-ml",
+        "query": "\"machine learning\" cybersecurity in:description,readme"
+      },
+      {
+        "id": "applied.demand-forecasting",
+        "query": "\"demand forecasting\" in:description"
+      },
+      {
+        "id": "applied.dynamic-pricing",
+        "query": "\"dynamic pricing\" in:readme"
+      },
+      {
+        "id": "applied.ecological-forecasting",
+        "query": "\"ecological forecasting\" \"machine learning\" in:description,readme"
+      },
+      {
+        "id": "applied.educational-data-mining",
+        "query": "\"educational data mining\" in:description,readme"
+      },
+      {
+        "id": "applied.energy-load-forecasting",
+        "query": "\"load forecasting\" energy in:description,readme"
+      },
+      {
+        "id": "applied.fraud-detection",
+        "query": "\"fraud detection\" in:readme"
+      },
+      {
+        "id": "applied.industrial-defect-detection",
+        "query": "\"industrial defect detection\" \"deep learning\" in:description,readme"
+      },
+      {
+        "id": "applied.intrusion-detection",
+        "query": "\"intrusion detection\" \"machine learning\" in:description,readme"
+      },
+      {
+        "id": "applied.inventory-optimization",
+        "query": "\"inventory optimization\" in:readme"
+      },
+      {
+        "id": "applied.learning-to-rank",
+        "query": "\"learning to rank\" in:description"
+      },
+      {
+        "id": "applied.malware-detection",
+        "query": "\"malware detection\" \"machine learning\" in:description,readme"
+      },
+      {
+        "id": "applied.ml-econometrics",
+        "query": "\"machine learning econometrics\" in:readme"
+      },
+      {
+        "id": "applied.plant-disease-detection",
+        "query": "\"plant disease detection\" \"deep learning\" in:description,readme"
+      },
+      {
+        "id": "applied.portfolio-optimization",
+        "query": "\"portfolio optimization\" in:readme"
+      },
+      {
+        "id": "applied.predictive-maintenance",
+        "query": "\"predictive maintenance\" industrial in:description"
+      },
+      {
+        "id": "applied.process-mining-prediction",
+        "query": "\"predictive process monitoring\" in:description,readme"
+      },
+      {
+        "id": "applied.quantitative-finance",
+        "query": "\"quantitative finance\" in:readme"
+      },
+      {
+        "id": "applied.recommender-systems",
+        "query": "\"recommender system\" in:readme"
+      },
+      {
+        "id": "applied.search-ranking",
+        "query": "\"search ranking\" in:readme"
+      },
+      {
+        "id": "applied.species-distribution-modeling",
+        "query": "\"species distribution modeling\" in:description,readme"
+      },
+      {
+        "id": "applied.supply-chain-optimization",
+        "query": "\"supply chain optimization\" in:readme"
+      },
+      {
+        "id": "applied.tabular-ml",
+        "query": "\"tabular data\" in:readme"
+      },
+      {
+        "id": "applied.time-series-forecasting",
+        "query": "\"time series forecasting\" manufacturing in:description"
+      },
+      {
+        "id": "applied.traffic-prediction",
+        "query": "\"traffic prediction\" \"machine learning\" in:description,readme"
+      },
+      {
+        "id": "applied.uplift-modeling",
+        "query": "\"uplift modeling\" in:description"
+      },
+      {
+        "id": "audio-acoustic-event-classification",
+        "query": "\"acoustic scene classification\" in:readme"
+      },
+      {
+        "id": "audio-anomaly-detection",
+        "query": "\"audio anomaly detection\" in:readme"
+      },
+      {
+        "id": "audio-asr",
+        "query": "\"automatic speech recognition\" in:readme"
+      },
+      {
+        "id": "audio-audio-captioning",
+        "query": "\"audio captioning\" in:readme"
+      },
+      {
+        "id": "audio-audio-classification",
+        "query": "\"audio classification\" in:readme"
+      },
+      {
+        "id": "audio-audio-question-answering",
+        "query": "\"audio question answering\" in:readme"
+      },
+      {
+        "id": "audio-audio-super-resolution",
+        "query": "\"audio super-resolution\" in:readme"
+      },
+      {
+        "id": "audio-generation",
+        "query": "\"audio generation\" in:readme"
+      },
+      {
+        "id": "audio-keyword-spotting",
+        "query": "\"keyword spotting\" audio in:readme"
+      },
+      {
+        "id": "audio-music-generation",
+        "query": "\"music generation\" in:readme"
+      },
+      {
+        "id": "audio-music-information-retrieval",
+        "query": "\"music information retrieval\" in:readme"
+      },
+      {
+        "id": "audio-music-recommendation",
+        "query": "\"music recommendation\" in:readme"
+      },
+      {
+        "id": "audio-music-source-separation",
+        "query": "\"music source separation\" in:readme"
+      },
+      {
+        "id": "audio-music-transcription",
+        "query": "\"automatic music transcription\" in:readme"
+      },
+      {
+        "id": "audio-neural-codec",
+        "query": "\"neural audio codec\" in:readme"
+      },
+      {
+        "id": "audio-sound-event-detection",
+        "query": "\"sound event detection\" in:readme"
+      },
+      {
+        "id": "audio-source-separation",
+        "query": "\"audio source separation\" in:readme"
+      },
+      {
+        "id": "audio-speaker-diarization",
+        "query": "\"speaker diarization\" in:readme"
+      },
+      {
+        "id": "audio-speaker-verification",
+        "query": "\"speaker verification\" in:readme"
+      },
+      {
+        "id": "audio-speech-emotion-recognition",
+        "query": "\"speech emotion recognition\" in:readme"
+      },
+      {
+        "id": "audio-speech-enhancement",
+        "query": "\"speech enhancement\" in:readme"
+      },
+      {
+        "id": "audio-speech-synthesis",
+        "query": "\"speech synthesis\" in:readme"
+      },
+      {
+        "id": "audio-speech-to-text",
+        "query": "\"speech to text\" in:readme"
+      },
+      {
+        "id": "audio-speech-translation",
+        "query": "\"speech translation\" in:readme"
+      },
+      {
+        "id": "audio-text-to-audio",
+        "query": "\"text to audio\" in:readme"
+      },
+      {
+        "id": "audio-text-to-speech",
+        "query": "\"text to speech\" in:readme"
+      },
+      {
+        "id": "audio-voice-conversion",
+        "query": "\"voice conversion\" in:readme"
+      },
+      {
+        "id": "bio.cell-atlas",
+        "query": "\"cell atlas\" in:description,readme"
+      },
+      {
+        "id": "bio.chemical-biology",
+        "query": "\"protein function prediction\" in:description,readme"
+      },
+      {
+        "id": "bio.crispr",
+        "query": "\"CRISPR screen\" in:description,readme"
+      },
+      {
+        "id": "bio.dna-language-models",
+        "query": "\"DNA language model\" in:description"
+      },
+      {
+        "id": "bio.gene-regulatory-networks",
+        "query": "\"gene regulatory network\" in:description,readme"
+      },
+      {
+        "id": "bio.genome-assembly",
+        "query": "\"genome assembly\" \"neural network\" in:description,readme"
+      },
+      {
+        "id": "bio.genomics",
+        "query": "\"genomics\" in:description"
+      },
+      {
+        "id": "bio.inverse-folding",
+        "query": "\"inverse folding\" in:readme"
+      },
+      {
+        "id": "bio.metagenomics",
+        "query": "\"metagenomic classification\" in:description,readme"
+      },
+      {
+        "id": "bio.microbiome",
+        "query": "\"microbiome\" \"machine learning\" in:description,readme"
+      },
+      {
+        "id": "bio.microscopy-cell-analysis",
+        "query": "\"cell segmentation\" microscopy in:description,readme"
+      },
+      {
+        "id": "bio.molecular-dynamics",
+        "query": "\"molecular dynamics\" in:description"
+      },
+      {
+        "id": "bio.molecular-force-fields",
+        "query": "\"machine learning force field\" in:readme"
+      },
+      {
+        "id": "bio.phylogenetics",
+        "query": "\"phylogenetics\" in:description"
+      },
+      {
+        "id": "bio.protein-design",
+        "query": "\"protein design\" in:description"
+      },
+      {
+        "id": "bio.protein-folding",
+        "query": "\"protein folding\" in:readme"
+      },
+      {
+        "id": "bio.protein-interactions",
+        "query": "\"protein-protein interaction prediction\" in:readme"
+      },
+      {
+        "id": "bio.protein-structure",
+        "query": "\"protein structure prediction\" in:description"
+      },
+      {
+        "id": "bio.regulatory-sequence-modeling",
+        "query": "enhancer prediction genomics in:description,readme"
+      },
+      {
+        "id": "bio.rna-language-models",
+        "query": "\"RNA language model\" in:description"
+      },
+      {
+        "id": "bio.rna-structure",
+        "query": "\"RNA structure prediction\" in:description,readme"
+      },
+      {
+        "id": "bio.scrna-seq",
+        "query": "\"scRNA-seq\" in:readme"
+      },
+      {
+        "id": "bio.single-cell",
+        "query": "\"single-cell\" in:description"
+      },
+      {
+        "id": "bio.spatial-transcriptomics",
+        "query": "\"spatial transcriptomics\" in:description,readme"
+      },
+      {
+        "id": "bio.transcriptomics",
+        "query": "\"transcriptomics\" in:description"
+      },
+      {
+        "id": "bio.variant-effects",
+        "query": "\"variant effect prediction\" in:readme"
+      },
+      {
+        "id": "chem-admet",
+        "query": "\"ADMET prediction\" in:description,readme"
+      },
+      {
+        "id": "chem-binding-affinity",
+        "query": "\"binding affinity prediction\" in:description,readme"
+      },
+      {
+        "id": "chem-catalyst-discovery",
+        "query": "\"catalyst discovery\" in:description,readme"
+      },
+      {
+        "id": "chem-cheminformatics",
+        "query": "topic:cheminformatics"
+      },
+      {
+        "id": "chem-drug-discovery",
+        "query": "\"drug discovery\" in:description,readme"
+      },
+      {
+        "id": "chem-interatomic-potentials",
+        "query": "\"machine learning interatomic potential\" in:description,readme"
+      },
+      {
+        "id": "chem-molecular-docking",
+        "query": "\"molecular docking\" in:description,readme"
+      },
+      {
+        "id": "chem-molecular-dynamics",
+        "query": "\"molecular dynamics\" \"machine learning\" in:description,readme"
+      },
+      {
+        "id": "chem-molecular-generation",
+        "query": "\"molecular generation\" in:description,readme"
+      },
+      {
+        "id": "chem-molecular-property-benchmark",
+        "query": "MoleculeNet in:description,readme"
+      },
+      {
+        "id": "chem-molecular-spectroscopy",
+        "query": "molecular spectroscopy \"machine learning\" in:description,readme"
+      },
+      {
+        "id": "chem-property-prediction",
+        "query": "\"molecular property prediction\" in:description,readme"
+      },
+      {
+        "id": "chem-protein-ligand-docking",
+        "query": "\"protein-ligand docking\" in:description,readme"
+      },
+      {
+        "id": "chem-quantum-chemistry",
+        "query": "\"quantum chemistry\" \"machine learning\" in:description,readme"
+      },
+      {
+        "id": "chem-reaction-conditions",
+        "query": "\"reaction condition prediction\" in:description,readme"
+      },
+      {
+        "id": "chem-reaction-prediction",
+        "query": "\"reaction prediction\" in:description,readme"
+      },
+      {
+        "id": "chem-reaction-yield",
+        "query": "\"reaction yield prediction\" in:description,readme"
+      },
+      {
+        "id": "chem-retrosynthesis",
+        "query": "\"computer aided synthesis planning\" in:description,readme"
+      },
+      {
+        "id": "chem-retrosynthesis-planning",
+        "query": "retrosynthesis in:description,readme"
+      },
+      {
+        "id": "chem-virtual-screening",
+        "query": "\"virtual screening\" in:description,readme"
+      },
+      {
+        "id": "efficiency.activation-checkpointing",
+        "query": "\"activation checkpointing\" in:description"
+      },
+      {
+        "id": "efficiency.automl",
+        "query": "AutoML in:description"
+      },
+      {
+        "id": "efficiency.communication-efficient-training",
+        "query": "\"communication efficient training\" in:description"
+      },
+      {
+        "id": "efficiency.compiler-optimization",
+        "query": "\"machine learning compiler\" in:description"
+      },
+      {
+        "id": "efficiency.distributed-training",
+        "query": "\"distributed training\" in:description"
+      },
+      {
+        "id": "efficiency.edge-ai",
+        "query": "\"edge AI\" in:description"
+      },
+      {
+        "id": "efficiency.energy-efficient-ml",
+        "query": "\"energy efficient machine learning\" in:description"
+      },
+      {
+        "id": "efficiency.flash-attention",
+        "query": "\"FlashAttention\" in:description"
+      },
+      {
+        "id": "efficiency.gpu-kernels",
+        "query": "\"GPU kernel\" in:description"
+      },
+      {
+        "id": "efficiency.gradient-optimization",
+        "query": "\"gradient-based optimization\" in:description"
+      },
+      {
+        "id": "efficiency.hyperparameter-optimization",
+        "query": "\"hyperparameter optimization\" in:description"
+      },
+      {
+        "id": "efficiency.inference-systems",
+        "query": "\"inference engine\" in:description"
+      },
+      {
+        "id": "efficiency.knowledge-distillation",
+        "query": "\"knowledge distillation\" in:description"
+      },
+      {
+        "id": "efficiency.kv-cache",
+        "query": "\"KV cache\" in:description"
+      },
+      {
+        "id": "efficiency.lora",
+        "query": "LoRA in:description"
+      },
+      {
+        "id": "efficiency.low-rank-factorization",
+        "query": "\"low-rank factorization\" in:description"
+      },
+      {
+        "id": "efficiency.mixed-precision-training",
+        "query": "\"mixed precision training\" in:description"
+      },
+      {
+        "id": "efficiency.mixture-of-experts",
+        "query": "\"mixture of experts\" in:description"
+      },
+      {
+        "id": "efficiency.model-compression",
+        "query": "\"model compression\" in:description"
+      },
+      {
+        "id": "efficiency.neural-architecture-search",
+        "query": "\"neural architecture search\" in:description"
+      },
+      {
+        "id": "efficiency.optimizer-state-sharding",
+        "query": "\"optimizer state sharding\" in:description"
+      },
+      {
+        "id": "efficiency.parameter-efficient-finetuning",
+        "query": "\"parameter-efficient fine-tuning\" in:description"
+      },
+      {
+        "id": "efficiency.post-training-quantization",
+        "query": "\"post-training quantization\" in:description"
+      },
+      {
+        "id": "efficiency.pruning",
+        "query": "pruning in:description"
+      },
+      {
+        "id": "efficiency.quantization",
+        "query": "quantization in:description"
+      },
+      {
+        "id": "efficiency.quantization-aware-training",
+        "query": "\"quantization-aware training\" in:description"
+      },
+      {
+        "id": "efficiency.sparse-neural-networks",
+        "query": "\"sparse neural network\" in:description"
+      },
+      {
+        "id": "efficiency.speculative-decoding",
+        "query": "\"speculative decoding\" in:description"
+      },
+      {
+        "id": "general.active-learning",
+        "query": "\"active learning\" in:description"
+      },
+      {
+        "id": "general.artificial-intelligence-description",
+        "query": "\"artificial intelligence\" in:description"
+      },
+      {
+        "id": "general.arxiv",
+        "query": "arxiv in:description"
+      },
+      {
+        "id": "general.automated-machine-learning",
+        "query": "\"automated machine learning\" in:readme"
+      },
+      {
+        "id": "general.bayesian-deep-learning",
+        "query": "\"Bayesian deep learning\" in:description,readme"
+      },
+      {
+        "id": "general.bayesian-optimization",
+        "query": "\"Bayesian optimization\" in:description"
+      },
+      {
+        "id": "general.clustering",
+        "query": "clustering in:description"
+      },
+      {
+        "id": "general.continual-learning",
+        "query": "\"continual learning\" in:description,readme"
+      },
+      {
+        "id": "general.contrastive-learning",
+        "query": "\"contrastive learning\" in:description,readme"
+      },
+      {
+        "id": "general.decision-trees",
+        "query": "\"decision tree\" \"machine learning\" in:description,readme"
+      },
+      {
+        "id": "general.deep-learning-description",
+        "query": "\"deep learning\" in:description"
+      },
+      {
+        "id": "general.deep-learning-readme",
+        "query": "\"deep learning\" in:readme"
+      },
+      {
+        "id": "general.dimensionality-reduction",
+        "query": "\"dimensionality reduction\" in:description,readme"
+      },
+      {
+        "id": "general.domain-adaptation",
+        "query": "\"domain adaptation\" \"machine learning\" in:description,readme"
+      },
+      {
+        "id": "general.gaussian-mixture-models",
+        "query": "\"Gaussian mixture model\" in:description,readme"
+      },
+      {
+        "id": "general.gaussian-process",
+        "query": "\"Gaussian process\" in:description"
+      },
+      {
+        "id": "general.gradient-boosting",
+        "query": "\"gradient boosting\" in:description"
+      },
+      {
+        "id": "general.hidden-markov-models",
+        "query": "\"hidden Markov model\" \"machine learning\" in:description,readme"
+      },
+      {
+        "id": "general.logistic-regression",
+        "query": "\"logistic regression\" in:description,readme"
+      },
+      {
+        "id": "general.machine-learning-description",
+        "query": "\"machine learning\" in:description"
+      },
+      {
+        "id": "general.machine-learning-readme",
+        "query": "\"machine learning\" in:readme"
+      },
+      {
+        "id": "general.matrix-factorization",
+        "query": "\"matrix factorization\" \"machine learning\" in:description,readme"
+      },
+      {
+        "id": "general.meta-learning",
+        "query": "\"meta-learning\" in:description,readme"
+      },
+      {
+        "id": "general.metric-learning",
+        "query": "\"metric learning\" in:description,readme"
+      },
+      {
+        "id": "general.model-architecture",
+        "query": "\"model architecture\" in:description"
+      },
+      {
+        "id": "general.multi-task-learning",
+        "query": "\"multi-task learning\" in:description,readme"
+      },
+      {
+        "id": "general.naive-bayes",
+        "query": "\"naive Bayes\" in:description,readme"
+      },
+      {
+        "id": "general.nearest-neighbors",
+        "query": "\"nearest neighbors\" \"machine learning\" in:description,readme"
+      },
+      {
+        "id": "general.neural-network",
+        "query": "\"neural network\" in:description"
+      },
+      {
+        "id": "general.neural-processes",
+        "query": "\"neural processes\" in:description,readme"
+      },
+      {
+        "id": "general.normalizing-flows",
+        "query": "\"normalizing flows\" \"machine learning\" in:description,readme"
+      },
+      {
+        "id": "general.novel-method",
+        "query": "\"novel method\" in:description"
+      },
+      {
+        "id": "general.paper-implementation",
+        "query": "\"paper implementation\" in:description"
+      },
+      {
+        "id": "general.probabilistic-programming",
+        "query": "\"probabilistic programming\" in:description,readme"
+      },
+      {
+        "id": "general.random-forest",
+        "query": "\"random forest\" in:description"
+      },
+      {
+        "id": "general.research-code",
+        "query": "\"research code\" in:description"
+      },
+      {
+        "id": "general.self-supervised-learning",
+        "query": "\"self-supervised learning\" in:description"
+      },
+      {
+        "id": "general.semi-supervised-learning",
+        "query": "\"semi-supervised learning\" in:description,readme"
+      },
+      {
+        "id": "general.support-vector-machine",
+        "query": "\"support vector machine\" in:description"
+      },
+      {
+        "id": "general.topic-artificial-intelligence",
+        "query": "topic:artificial-intelligence"
+      },
+      {
+        "id": "general.topic-deep-learning",
+        "query": "topic:deep-learning"
+      },
+      {
+        "id": "general.topic-machine-learning",
+        "query": "topic:machine-learning"
+      },
+      {
+        "id": "general.transfer-learning",
+        "query": "\"transfer learning\" in:description,readme"
+      },
+      {
+        "id": "general.weak-supervision",
+        "query": "\"weak supervision\" \"machine learning\" in:description,readme"
+      },
+      {
+        "id": "geo.building-footprints",
+        "query": "\"building footprint extraction\" in:description,readme"
+      },
+      {
+        "id": "geo.change-detection",
+        "query": "\"remote sensing change detection\" in:description,readme"
+      },
+      {
+        "id": "geo.climate-mapping",
+        "query": "\"climate downscaling\" in:readme"
+      },
+      {
+        "id": "geo.crop-monitoring",
+        "query": "\"crop yield prediction\" in:description,readme"
+      },
+      {
+        "id": "geo.earth-observation-models",
+        "query": "\"Earth observation model\" in:description"
+      },
+      {
+        "id": "geo.environmental-monitoring",
+        "query": "\"environmental monitoring\" in:readme"
+      },
+      {
+        "id": "geo.flood-mapping",
+        "query": "\"flood mapping\" in:readme"
+      },
+      {
+        "id": "geo.foundation-models-earth",
+        "query": "\"Earth observation foundation model\" in:description,readme"
+      },
+      {
+        "id": "geo.geospatial-foundation-models",
+        "query": "\"geospatial foundation model\" in:readme"
+      },
+      {
+        "id": "geo.geospatial-knowledge-graph",
+        "query": "\"geospatial knowledge graph\" in:description,readme"
+      },
+      {
+        "id": "geo.hydrology",
+        "query": "\"hydrology machine learning\" in:readme"
+      },
+      {
+        "id": "geo.land-cover",
+        "query": "\"land cover\" in:readme"
+      },
+      {
+        "id": "geo.land-use",
+        "query": "\"land use\" in:readme"
+      },
+      {
+        "id": "geo.lidar",
+        "query": "LiDAR in:description \"deep learning\""
+      },
+      {
+        "id": "geo.mobility-modeling",
+        "query": "\"human mobility\" \"machine learning\" in:description,readme"
+      },
+      {
+        "id": "geo.object-detection",
+        "query": "\"object detection\" \"remote sensing\" in:description,readme"
+      },
+      {
+        "id": "geo.ocean-remote-sensing",
+        "query": "\"ocean remote sensing\" in:description,readme"
+      },
+      {
+        "id": "geo.precipitation-forecasting",
+        "query": "\"precipitation forecasting\" in:description,readme"
+      },
+      {
+        "id": "geo.sar-imagery",
+        "query": "\"synthetic aperture radar\" in:readme"
+      },
+      {
+        "id": "geo.satellite-imagery",
+        "query": "\"satellite imagery\" in:readme"
+      },
+      {
+        "id": "geo.topic-earth-observation",
+        "query": "topic:earth-observation"
+      },
+      {
+        "id": "geo.topic-geospatial",
+        "query": "topic:geospatial"
+      },
+      {
+        "id": "geo.topic-gis",
+        "query": "topic:gis"
+      },
+      {
+        "id": "geo.topic-remote-sensing",
+        "query": "topic:remote-sensing"
+      },
+      {
+        "id": "geo.trajectory-prediction",
+        "query": "\"trajectory prediction\" geospatial in:description,readme"
+      },
+      {
+        "id": "geo.urban-sensing",
+        "query": "\"urban remote sensing\" in:readme"
+      },
+      {
+        "id": "geo.wildfire-mapping",
+        "query": "\"wildfire detection\" in:description,readme"
+      },
+      {
+        "id": "graph.e3-equivariance",
+        "query": "\"E(3)-equivariant\" in:readme"
+      },
+      {
+        "id": "graph.equivariant-networks",
+        "query": "\"equivariant neural network\" in:description"
+      },
+      {
+        "id": "graph.geometric-learning",
+        "query": "\"geometric deep learning\" in:readme"
+      },
+      {
+        "id": "graph.gnn-description",
+        "query": "\"graph neural network\" in:description"
+      },
+      {
+        "id": "graph.graph-anomaly-detection",
+        "query": "\"graph anomaly detection\" in:description,readme"
+      },
+      {
+        "id": "graph.graph-classification",
+        "query": "\"graph classification\" in:description"
+      },
+      {
+        "id": "graph.graph-contrastive-learning",
+        "query": "\"graph contrastive learning\" in:description"
+      },
+      {
+        "id": "graph.graph-diffusion",
+        "query": "\"graph diffusion model\" in:description"
+      },
+      {
+        "id": "graph.graph-foundation-model",
+        "query": "\"graph foundation model\" in:description"
+      },
+      {
+        "id": "graph.graph-generation",
+        "query": "\"graph generation\" in:readme"
+      },
+      {
+        "id": "graph.graph-transformer",
+        "query": "\"graph transformer\" in:description"
+      },
+      {
+        "id": "graph.hypergraph-neural-network",
+        "query": "\"hypergraph neural network\" in:description"
+      },
+      {
+        "id": "graph.knowledge-graph-embedding",
+        "query": "\"knowledge graph embedding\" in:description"
+      },
+      {
+        "id": "graph.knowledge-graphs",
+        "query": "topic:knowledge-graph"
+      },
+      {
+        "id": "graph.link-prediction",
+        "query": "\"link prediction\" in:description"
+      },
+      {
+        "id": "graph.manifold-learning",
+        "query": "\"manifold learning\" in:description"
+      },
+      {
+        "id": "graph.mesh-graph-learning",
+        "query": "\"mesh graph neural network\" in:description"
+      },
+      {
+        "id": "graph.molecular-graphs",
+        "query": "\"molecular graph neural network\" in:readme"
+      },
+      {
+        "id": "graph.neural-ode-graph",
+        "query": "\"graph neural ODE\" in:description"
+      },
+      {
+        "id": "graph.persistent-homology",
+        "query": "\"persistent homology\" in:description"
+      },
+      {
+        "id": "graph.point-cloud-learning",
+        "query": "\"point cloud deep learning\" in:description"
+      },
+      {
+        "id": "graph.point-clouds",
+        "query": "topic:point-cloud"
+      },
+      {
+        "id": "graph.representation-learning",
+        "query": "\"graph representation learning\" in:readme"
+      },
+      {
+        "id": "graph.signal-processing",
+        "query": "\"graph signal processing\" in:description,readme"
+      },
+      {
+        "id": "graph.temporal-learning",
+        "query": "\"temporal graph\" in:description,readme"
+      },
+      {
+        "id": "graph.topic-gnn",
+        "query": "topic:graph-neural-network"
+      },
+      {
+        "id": "graph.topological-ml",
+        "query": "\"topological machine learning\" in:readme"
+      },
+      {
+        "id": "llm-code-generation",
+        "query": "\"code generation\" \"language model\" in:readme"
+      },
+      {
+        "id": "llm-reasoning",
+        "query": "\"reasoning model\" in:readme"
+      },
+      {
+        "id": "llm-retrieval-augmented-generation",
+        "query": "\"retrieval-augmented generation\" in:readme"
+      },
+      {
+        "id": "llm-text-to-speech",
+        "query": "\"text-to-speech\" \"language model\" in:readme"
+      },
+      {
+        "id": "llm.agents",
+        "query": "topic:ai-agents"
+      },
+      {
+        "id": "llm.ai-agent",
+        "query": "\"AI agent\" in:description"
+      },
+      {
+        "id": "llm.alignment",
+        "query": "\"alignment\" in:description"
+      },
+      {
+        "id": "llm.diffusion",
+        "query": "topic:diffusion-model"
+      },
+      {
+        "id": "llm.diffusion-phrase",
+        "query": "\"diffusion model\" in:description"
+      },
+      {
+        "id": "llm.evaluation",
+        "query": "\"LLM evaluation\" in:readme"
+      },
+      {
+        "id": "llm.finetuning",
+        "query": "\"fine-tuning\" in:description"
+      },
+      {
+        "id": "llm.foundation-models",
+        "query": "\"foundation model\" in:description"
+      },
+      {
+        "id": "llm.generative-ai",
+        "query": "\"generative AI\" in:description"
+      },
+      {
+        "id": "llm.instruction-tuning",
+        "query": "\"instruction tuning\" in:readme"
+      },
+      {
+        "id": "llm.language-model",
+        "query": "\"language model\" in:description"
+      },
+      {
+        "id": "llm.language-models",
+        "query": "topic:large-language-models"
+      },
+      {
+        "id": "llm.large-language-model",
+        "query": "\"large language model\" in:description"
+      },
+      {
+        "id": "llm.long-context",
+        "query": "\"long context\" \"language model\" in:readme"
+      },
+      {
+        "id": "llm.multimodal",
+        "query": "\"multimodal language model\" in:description"
+      },
+      {
+        "id": "llm.parameter-efficient-finetuning",
+        "query": "\"parameter-efficient fine-tuning\" in:readme"
+      },
+      {
+        "id": "llm.preference-optimization",
+        "query": "\"direct preference optimization\" in:readme"
+      },
+      {
+        "id": "llm.quantization",
+        "query": "\"LLM quantization\" in:readme"
+      },
+      {
+        "id": "llm.speculative-decoding",
+        "query": "\"speculative decoding\" in:readme"
+      },
+      {
+        "id": "llm.synthetic-data",
+        "query": "\"synthetic data\" in:description"
+      },
+      {
+        "id": "llm.synthetic-data-generation",
+        "query": "\"synthetic data generation\" in:description"
+      },
+      {
+        "id": "llm.test-time-compute",
+        "query": "\"test-time compute\" \"language model\" in:readme"
+      },
+      {
+        "id": "llm.transformers",
+        "query": "\"transformer\" in:description"
+      },
+      {
+        "id": "materials-battery",
+        "query": "\"battery materials\" \"machine learning\" in:description,readme"
+      },
+      {
+        "id": "materials-crystal-generation",
+        "query": "\"crystal structure generation\" in:description,readme"
+      },
+      {
+        "id": "materials-crystal-graph",
+        "query": "\"crystal graph\" in:description,readme"
+      },
+      {
+        "id": "materials-dft-prediction",
+        "query": "\"density functional theory\" \"machine learning\" in:description,readme"
+      },
+      {
+        "id": "materials-informatics",
+        "query": "\"materials informatics\" in:description,readme"
+      },
+      {
+        "id": "materials-perovskite",
+        "query": "perovskite in:description \"machine learning\""
+      },
+      {
+        "id": "materials-polymer-informatics",
+        "query": "polymer informatics in:description,readme"
+      },
+      {
+        "id": "medical.clinical-decision-support",
+        "query": "\"clinical decision support\" in:readme"
+      },
+      {
+        "id": "medical.clinical-nlp",
+        "query": "\"clinical natural language processing\" in:readme"
+      },
+      {
+        "id": "medical.clinical-time-series",
+        "query": "\"clinical time series\" in:description"
+      },
+      {
+        "id": "medical.clinical-trials",
+        "query": "\"clinical trial prediction\" in:description"
+      },
+      {
+        "id": "medical.dermatology",
+        "query": "dermatology AI dermoscopy in:description,readme"
+      },
+      {
+        "id": "medical.diagnosis-prediction",
+        "query": "\"diagnosis prediction\" in:description"
+      },
+      {
+        "id": "medical.drug-response",
+        "query": "\"drug response prediction\" in:description,readme"
+      },
+      {
+        "id": "medical.ecg",
+        "query": "ECG \"deep learning\" in:description,readme"
+      },
+      {
+        "id": "medical.eeg",
+        "query": "EEG \"deep learning\" in:description,readme"
+      },
+      {
+        "id": "medical.ehr",
+        "query": "\"electronic health record\" in:readme"
+      },
+      {
+        "id": "medical.genomics",
+        "query": "\"clinical genomics\" in:description,readme"
+      },
+      {
+        "id": "medical.histopathology",
+        "query": "\"histopathology image analysis\" in:readme"
+      },
+      {
+        "id": "medical.medical-image-reconstruction",
+        "query": "\"MRI reconstruction\" in:description,readme"
+      },
+      {
+        "id": "medical.medical-image-registration",
+        "query": "\"medical image registration\" in:description,readme"
+      },
+      {
+        "id": "medical.medical-image-segmentation",
+        "query": "\"medical image segmentation\" in:description"
+      },
+      {
+        "id": "medical.medical-imaging",
+        "query": "\"medical imaging\" in:readme"
+      },
+      {
+        "id": "medical.medical-language-model",
+        "query": "\"medical language model\" in:description"
+      },
+      {
+        "id": "medical.medical-robotics",
+        "query": "\"surgical robotics\" \"machine learning\" in:description,readme"
+      },
+      {
+        "id": "medical.neural-decoding",
+        "query": "\"neural data decoding\" in:description"
+      },
+      {
+        "id": "medical.neuroimaging",
+        "query": "\"neuroimaging machine learning\" in:readme"
+      },
+      {
+        "id": "medical.ophthalmology",
+        "query": "ophthalmology AI retinal in:description,readme"
+      },
+      {
+        "id": "medical.pathology",
+        "query": "\"computational pathology\" in:description"
+      },
+      {
+        "id": "medical.pathology-foundation-model",
+        "query": "\"pathology foundation model\" in:description,readme"
+      },
+      {
+        "id": "medical.patient-risk",
+        "query": "\"patient deterioration prediction\" in:description,readme"
+      },
+      {
+        "id": "medical.radiology",
+        "query": "\"radiology machine learning\" in:readme"
+      },
+      {
+        "id": "medical.survival-analysis",
+        "query": "clinical survival prediction in:description,readme"
+      },
+      {
+        "id": "medical.wearable-health",
+        "query": "\"wearable health monitoring\" in:readme"
+      },
+      {
+        "id": "multimodal.3d-generation",
+        "query": "\"3D generation\" in:readme"
+      },
+      {
+        "id": "multimodal.3d-human-pose",
+        "query": "\"3D human pose estimation\" in:readme"
+      },
+      {
+        "id": "multimodal.3d-object-detection",
+        "query": "\"3D object detection\" in:readme"
+      },
+      {
+        "id": "multimodal.3d-reconstruction",
+        "query": "\"3D reconstruction\" in:description"
+      },
+      {
+        "id": "multimodal.audio-visual-learning",
+        "query": "\"audio visual learning\" in:readme"
+      },
+      {
+        "id": "multimodal.embodied-world-models",
+        "query": "\"embodied world model\" in:description"
+      },
+      {
+        "id": "multimodal.gaussian-splatting",
+        "query": "\"Gaussian splatting\" in:description"
+      },
+      {
+        "id": "multimodal.image-text",
+        "query": "\"image-text\" in:description"
+      },
+      {
+        "id": "multimodal.image-to-3d",
+        "query": "\"image to 3D\" in:readme"
+      },
+      {
+        "id": "multimodal.image-to-video",
+        "query": "\"image to video\" in:readme"
+      },
+      {
+        "id": "multimodal.motion-generation",
+        "query": "\"human motion generation\" in:readme"
+      },
+      {
+        "id": "multimodal.nerf",
+        "query": "topic:nerf"
+      },
+      {
+        "id": "multimodal.neural-rendering",
+        "query": "\"neural rendering\" \"radiance fields\" in:description"
+      },
+      {
+        "id": "multimodal.point-cloud",
+        "query": "\"point cloud\" in:description"
+      },
+      {
+        "id": "multimodal.scene-understanding",
+        "query": "\"scene understanding\" in:description"
+      },
+      {
+        "id": "multimodal.text-to-3d",
+        "query": "\"text to 3D\" in:readme"
+      },
+      {
+        "id": "multimodal.text-to-image",
+        "query": "\"text-to-image\" in:description"
+      },
+      {
+        "id": "multimodal.text-to-video",
+        "query": "\"text-to-video\" in:description"
+      },
+      {
+        "id": "multimodal.video-language-model",
+        "query": "\"video language model\" in:readme"
+      },
+      {
+        "id": "multimodal.video-question-answering",
+        "query": "\"video question answering\" in:readme"
+      },
+      {
+        "id": "multimodal.video-understanding",
+        "query": "\"video-language\" in:description"
+      },
+      {
+        "id": "multimodal.vision-language",
+        "query": "\"vision-language\" in:description"
+      },
+      {
+        "id": "multimodal.visual-question-answering",
+        "query": "\"visual question answering\" in:description"
+      },
+      {
+        "id": "multimodal.vlm",
+        "query": "\"vision-language model\" in:description"
+      },
+      {
+        "id": "multimodal.world-models",
+        "query": "\"world model\" in:description"
+      },
+      {
+        "id": "nlp-bm25-retrieval",
+        "query": "BM25 retrieval in:readme"
+      },
+      {
+        "id": "nlp-document-retrieval",
+        "query": "\"document retrieval\" in:readme"
+      },
+      {
+        "id": "nlp-entity-linking",
+        "query": "\"entity linking\" in:readme"
+      },
+      {
+        "id": "nlp-natural-language-inference",
+        "query": "\"natural language inference\" in:readme"
+      },
+      {
+        "id": "nlp-query-understanding",
+        "query": "\"query understanding\" in:readme"
+      },
+      {
+        "id": "nlp-sparse-retrieval",
+        "query": "\"sparse retrieval\" in:readme"
+      },
+      {
+        "id": "nlp-speech-language-modeling",
+        "query": "\"speech language model\" in:readme"
+      },
+      {
+        "id": "nlp.coreference-resolution",
+        "query": "\"coreference resolution\" in:readme"
+      },
+      {
+        "id": "nlp.cross-encoder-reranking",
+        "query": "\"cross-encoder\" \"reranking\" in:readme"
+      },
+      {
+        "id": "nlp.dense-retrieval",
+        "query": "\"dense retrieval\" in:readme"
+      },
+      {
+        "id": "nlp.dependency-parsing",
+        "query": "\"dependency parsing\" in:readme"
+      },
+      {
+        "id": "nlp.dialogue-systems",
+        "query": "\"dialogue system\" in:readme"
+      },
+      {
+        "id": "nlp.information-extraction",
+        "query": "\"information extraction\" in:description"
+      },
+      {
+        "id": "nlp.language-modeling",
+        "query": "\"language modeling\" in:description"
+      },
+      {
+        "id": "nlp.machine-translation",
+        "query": "\"machine translation\" in:description"
+      },
+      {
+        "id": "nlp.named-entity-recognition",
+        "query": "\"named entity recognition\" in:readme"
+      },
+      {
+        "id": "nlp.neural-reranking",
+        "query": "\"neural reranking\" in:description"
+      },
+      {
+        "id": "nlp.question-answering",
+        "query": "\"question answering\" in:description"
+      },
+      {
+        "id": "nlp.rag",
+        "query": "\"retrieval augmented generation\" in:description"
+      },
+      {
+        "id": "nlp.semantic-search",
+        "query": "\"semantic search\" in:description"
+      },
+      {
+        "id": "nlp.sentence-embeddings",
+        "query": "\"sentence embeddings\" in:description"
+      },
+      {
+        "id": "nlp.subword-tokenization",
+        "query": "\"subword tokenization\" in:readme"
+      },
+      {
+        "id": "nlp.text-classification",
+        "query": "\"text classification\" in:description"
+      },
+      {
+        "id": "nlp.text-generation",
+        "query": "\"text generation\" in:readme"
+      },
+      {
+        "id": "nlp.text-summarization",
+        "query": "\"text summarization\" in:description"
+      },
+      {
+        "id": "nlp.topic-modeling",
+        "query": "\"topic modeling\" in:readme"
+      },
+      {
+        "id": "recall.archaeological-remote-sensing",
+        "query": "archaeology \"remote sensing\" in:name,description,readme"
+      },
+      {
+        "id": "recall.case-outcome-prediction",
+        "query": "\"case outcome prediction\" in:name,description,readme"
+      },
+      {
+        "id": "recall.computational-linguistics",
+        "query": "\"computational linguistics\" model in:name,description,readme"
+      },
+      {
+        "id": "recall.computational-social-science",
+        "query": "\"computational social science\" in:name,description,readme"
+      },
+      {
+        "id": "recall.digital-humanities-nlp",
+        "query": "\"digital humanities\" NLP in:name,description,readme"
+      },
+      {
+        "id": "recall.disease-surveillance",
+        "query": "\"disease surveillance\" machine learning in:name,description,readme"
+      },
+      {
+        "id": "recall.educational-recommendation",
+        "query": "\"educational recommendation\" in:name,description,readme"
+      },
+      {
+        "id": "recall.epidemic-forecasting",
+        "query": "\"epidemic forecasting\" in:name,description,readme"
+      },
+      {
+        "id": "recall.epidemiological-modeling",
+        "query": "\"epidemiological modeling\" machine learning in:name,description,readme"
+      },
+      {
+        "id": "recall.grid-stability-prediction",
+        "query": "\"grid stability prediction\" in:name,description,readme"
+      },
+      {
+        "id": "recall.historical-document-recognition",
+        "query": "\"historical document recognition\" in:name,description,readme"
+      },
+      {
+        "id": "recall.infrastructure-damage-detection",
+        "query": "\"infrastructure damage detection\" deep learning in:name,description,readme"
+      },
+      {
+        "id": "recall.insurance-risk-modeling",
+        "query": "\"insurance risk\" machine learning in:name,description,readme"
+      },
+      {
+        "id": "recall.legal-document-classification",
+        "query": "\"legal document classification\" in:name,description,readme"
+      },
+      {
+        "id": "recall.legal-nlp",
+        "query": "legal NLP in:name,description,readme"
+      },
+      {
+        "id": "recall.name.artificial-intelligence",
+        "query": "\"artificial intelligence\" in:name,description,readme"
+      },
+      {
+        "id": "recall.name.deep-learning",
+        "query": "\"deep learning\" in:name,description,readme"
+      },
+      {
+        "id": "recall.name.diffusion-model",
+        "query": "diffusion model in:name,description,readme"
+      },
+      {
+        "id": "recall.name.foundation-model",
+        "query": "foundation model in:name,description,readme"
+      },
+      {
+        "id": "recall.name.language-model",
+        "query": "\"language model\" in:name,description,readme"
+      },
+      {
+        "id": "recall.name.large-language-model",
+        "query": "\"large language model\" in:name,description,readme"
+      },
+      {
+        "id": "recall.name.machine-learning",
+        "query": "\"machine learning\" in:name,description,readme"
+      },
+      {
+        "id": "recall.name.multimodal-model",
+        "query": "multimodal model in:name,description,readme"
+      },
+      {
+        "id": "recall.name.neural-network",
+        "query": "\"neural network\" in:name,description,readme"
+      },
+      {
+        "id": "recall.name.transformer-model",
+        "query": "transformer model in:name,description,readme"
+      },
+      {
+        "id": "recall.player-tracking",
+        "query": "\"player tracking\" computer vision in:name,description,readme"
+      },
+      {
+        "id": "recall.population-health-prediction",
+        "query": "\"population health prediction\" in:name,description,readme"
+      },
+      {
+        "id": "recall.power-system-forecasting",
+        "query": "\"power system forecasting\" in:name,description,readme"
+      },
+      {
+        "id": "recall.social-media-analysis",
+        "query": "\"social media analysis\" NLP in:name,description,readme"
+      },
+      {
+        "id": "recall.sports-analytics",
+        "query": "\"sports analytics\" model in:name,description,readme"
+      },
+      {
+        "id": "recall.structural-health-monitoring",
+        "query": "\"structural health monitoring\" machine learning in:name,description,readme"
+      },
+      {
+        "id": "recall.student-performance-prediction",
+        "query": "\"student performance prediction\" in:name,description,readme"
+      },
+      {
+        "id": "rl.bandits",
+        "query": "\"multi-armed bandit\" in:description"
+      },
+      {
+        "id": "rl.continual",
+        "query": "\"continual reinforcement learning\" in:description,readme"
+      },
+      {
+        "id": "rl.core",
+        "query": "topic:reinforcement-learning"
+      },
+      {
+        "id": "rl.deep",
+        "query": "\"deep reinforcement learning\" in:description"
+      },
+      {
+        "id": "rl.deep-q-learning",
+        "query": "\"deep q-learning\" in:description"
+      },
+      {
+        "id": "rl.distributional",
+        "query": "\"distributional reinforcement learning\" in:description,readme"
+      },
+      {
+        "id": "rl.exploration",
+        "query": "\"reinforcement learning exploration\" in:description"
+      },
+      {
+        "id": "rl.games",
+        "query": "topic:game-ai"
+      },
+      {
+        "id": "rl.hierarchical",
+        "query": "\"hierarchical reinforcement learning\" in:description"
+      },
+      {
+        "id": "rl.imitation-learning",
+        "query": "\"imitation learning\" in:description"
+      },
+      {
+        "id": "rl.inverse",
+        "query": "\"inverse reinforcement learning\" in:description"
+      },
+      {
+        "id": "rl.model-based",
+        "query": "\"model-based reinforcement learning\" in:description"
+      },
+      {
+        "id": "rl.model-free",
+        "query": "\"model-free reinforcement learning\" in:description"
+      },
+      {
+        "id": "rl.multiagent",
+        "query": "\"multi-agent reinforcement learning\" in:description"
+      },
+      {
+        "id": "rl.offline",
+        "query": "\"offline reinforcement learning\" in:description"
+      },
+      {
+        "id": "rl.planning",
+        "query": "\"reinforcement learning planning\" in:description"
+      },
+      {
+        "id": "rl.policy-gradient",
+        "query": "\"policy gradient\" in:description"
+      },
+      {
+        "id": "rl.ppo",
+        "query": "\"proximal policy optimization\" in:description"
+      },
+      {
+        "id": "rl.preference",
+        "query": "\"preference-based reinforcement learning\" in:description"
+      },
+      {
+        "id": "rl.reinforcement-learning-from-human-feedback",
+        "query": "\"reinforcement learning from human feedback\" in:description"
+      },
+      {
+        "id": "rl.safe",
+        "query": "\"safe reinforcement learning\" in:description"
+      },
+      {
+        "id": "rl.soft-actor-critic",
+        "query": "\"soft actor-critic\" in:description"
+      },
+      {
+        "id": "robotics.autonomous-driving",
+        "query": "\"autonomous driving\" in:description"
+      },
+      {
+        "id": "robotics.behavior-cloning",
+        "query": "\"behavior cloning\" in:description"
+      },
+      {
+        "id": "robotics.diffusion-policy",
+        "query": "\"diffusion policy\" in:description"
+      },
+      {
+        "id": "robotics.domain-randomization",
+        "query": "\"domain randomization\" in:description"
+      },
+      {
+        "id": "robotics.embodied-ai",
+        "query": "\"embodied AI\" in:description"
+      },
+      {
+        "id": "robotics.grasping",
+        "query": "\"robot grasping\" in:description"
+      },
+      {
+        "id": "robotics.imitation-learning",
+        "query": "\"imitation learning\" robotics in:description"
+      },
+      {
+        "id": "robotics.locomotion",
+        "query": "\"robot locomotion\" in:description"
+      },
+      {
+        "id": "robotics.manipulation",
+        "query": "\"robot manipulation\" in:description"
+      },
+      {
+        "id": "robotics.navigation",
+        "query": "\"robot navigation\" in:description"
+      },
+      {
+        "id": "robotics.robot-control",
+        "query": "\"robot control\" in:description"
+      },
+      {
+        "id": "robotics.robot-foundation-model",
+        "query": "robot foundation model in:description,readme"
+      },
+      {
+        "id": "robotics.robot-learning",
+        "query": "\"robot learning\" in:description"
+      },
+      {
+        "id": "robotics.robot-manipulation-learning",
+        "query": "\"robot manipulation learning\" in:readme"
+      },
+      {
+        "id": "robotics.robot-motion-planning",
+        "query": "\"robot motion planning\" in:description"
+      },
+      {
+        "id": "robotics.robotics-reinforcement-learning",
+        "query": "\"robotics reinforcement learning\" in:description"
+      },
+      {
+        "id": "robotics.robotics-topic",
+        "query": "topic:robotics"
+      },
+      {
+        "id": "robotics.sim2real",
+        "query": "\"sim-to-real\" in:description"
+      },
+      {
+        "id": "robotics.slam",
+        "query": "SLAM robotics in:description"
+      },
+      {
+        "id": "robotics.tactile-learning",
+        "query": "tactile robot learning in:description,readme"
+      },
+      {
+        "id": "robotics.teleoperation",
+        "query": "robot teleoperation learning in:description,readme"
+      },
+      {
+        "id": "robotics.visual-language-action",
+        "query": "\"vision-language-action\" in:description"
+      },
+      {
+        "id": "robotics.visuomotor-policy",
+        "query": "\"visuomotor policy\" in:description"
+      },
+      {
+        "id": "science.astronomy",
+        "query": "astronomy in:readme"
+      },
+      {
+        "id": "science.astrophysics",
+        "query": "astrophysics in:description"
+      },
+      {
+        "id": "science.climate-modeling",
+        "query": "\"climate modeling\" in:readme"
+      },
+      {
+        "id": "science.computational-mechanics",
+        "query": "\"computational mechanics\" \"machine learning\" in:description,readme"
+      },
+      {
+        "id": "science.cosmology",
+        "query": "cosmology in:description"
+      },
+      {
+        "id": "science.differentiable-simulation",
+        "query": "\"differentiable simulation\" in:readme"
+      },
+      {
+        "id": "science.exoplanets",
+        "query": "exoplanet in:description \"machine learning\""
+      },
+      {
+        "id": "science.fluid-dynamics",
+        "query": "\"fluid dynamics\" in:description"
+      },
+      {
+        "id": "science.fusion-plasma",
+        "query": "fusion plasma in:description \"machine learning\""
+      },
+      {
+        "id": "science.gravitational-waves",
+        "query": "\"gravitational wave\" \"machine learning\" in:description,readme"
+      },
+      {
+        "id": "science.inverse-problems",
+        "query": "\"inverse problems\" in:description"
+      },
+      {
+        "id": "science.neural-operators",
+        "query": "\"neural operator\" in:description"
+      },
+      {
+        "id": "science.ocean-modeling",
+        "query": "\"ocean modeling\" \"machine learning\" in:description,readme"
+      },
+      {
+        "id": "science.particle-physics",
+        "query": "\"particle physics\" in:description"
+      },
+      {
+        "id": "science.photonics",
+        "query": "photonics \"machine learning\" in:description,readme"
+      },
+      {
+        "id": "science.physics",
+        "query": "physics in:description"
+      },
+      {
+        "id": "science.physics-informed-neural-networks",
+        "query": "\"physics-informed neural network\" in:description"
+      },
+      {
+        "id": "science.plasma-physics",
+        "query": "\"plasma physics\" in:description"
+      },
+      {
+        "id": "science.quantum-materials",
+        "query": "\"quantum materials\" \"machine learning\" in:description,readme"
+      },
+      {
+        "id": "science.quantum-physics",
+        "query": "\"quantum many-body\" \"machine learning\" in:description,readme"
+      },
+      {
+        "id": "science.remote-sensing",
+        "query": "\"remote sensing\" in:description"
+      },
+      {
+        "id": "science.scientific-machine-learning",
+        "query": "\"scientific machine learning\" in:readme"
+      },
+      {
+        "id": "science.seismic",
+        "query": "\"seismic data\" \"machine learning\" in:description,readme"
+      },
+      {
+        "id": "science.turbulence",
+        "query": "turbulence in:description \"machine learning\""
+      },
+      {
+        "id": "science.weather-forecasting",
+        "query": "\"weather forecasting\" climate in:description"
+      },
+      {
+        "id": "science.weather-nowcasting",
+        "query": "nowcasting in:description \"machine learning\""
+      },
+      {
+        "id": "timeseries.anomaly-detection",
+        "query": "\"time series anomaly detection\" in:description"
+      },
+      {
+        "id": "timeseries.classification",
+        "query": "\"time series classification\" in:description"
+      },
+      {
+        "id": "timeseries.clustering",
+        "query": "\"time series clustering\" in:description,readme"
+      },
+      {
+        "id": "timeseries.energy-forecasting",
+        "query": "\"energy forecasting\" in:description"
+      },
+      {
+        "id": "timeseries.event-forecasting",
+        "query": "\"event forecasting\" in:description"
+      },
+      {
+        "id": "timeseries.forecasting",
+        "query": "\"time series forecasting\" in:description"
+      },
+      {
+        "id": "timeseries.foundation-models",
+        "query": "\"time series foundation model\" in:description"
+      },
+      {
+        "id": "timeseries.imputation",
+        "query": "\"time series imputation\" in:description,readme"
+      },
+      {
+        "id": "timeseries.long-horizon",
+        "query": "\"long horizon forecasting\" in:readme"
+      },
+      {
+        "id": "timeseries.multivariate",
+        "query": "\"multivariate time series\" in:description"
+      },
+      {
+        "id": "timeseries.nbeats",
+        "query": "N-BEATS in:description"
+      },
+      {
+        "id": "timeseries.patchtst",
+        "query": "PatchTST in:description"
+      },
+      {
+        "id": "timeseries.predictive-maintenance",
+        "query": "\"predictive maintenance\" in:description"
+      },
+      {
+        "id": "timeseries.probabilistic-forecasting",
+        "query": "\"probabilistic forecasting\" in:readme"
+      },
+      {
+        "id": "timeseries.remaining-useful-life",
+        "query": "\"remaining useful life\" in:readme"
+      },
+      {
+        "id": "timeseries.sensor-data",
+        "query": "\"sensor time series\" in:description"
+      },
+      {
+        "id": "timeseries.state-space-models",
+        "query": "\"state space model\" in:description"
+      },
+      {
+        "id": "timeseries.streaming",
+        "query": "\"streaming time series\" in:readme"
+      },
+      {
+        "id": "timeseries.synthetic-generation",
+        "query": "\"time series generation\" in:description,readme"
+      },
+      {
+        "id": "timeseries.tabular-foundation-models",
+        "query": "\"tabular foundation model\" in:description,readme"
+      },
+      {
+        "id": "timeseries.temporal-fusion-transformer",
+        "query": "\"temporal fusion transformer\" in:description"
+      },
+      {
+        "id": "timeseries.temporal-representation",
+        "query": "\"temporal representation learning\" in:readme"
+      },
+      {
+        "id": "timeseries.topic",
+        "query": "topic:time-series"
+      },
+      {
+        "id": "timeseries.traffic-forecasting",
+        "query": "\"traffic forecasting\" in:description"
+      },
+      {
+        "id": "timeseries.unevenly-spaced",
+        "query": "\"irregular time series\" in:description"
+      },
+      {
+        "id": "timeseries.weather-forecasting",
+        "query": "\"weather forecasting\" in:description"
+      },
+      {
+        "id": "trust.adversarial-robustness",
+        "query": "\"adversarial robustness\" in:description"
+      },
+      {
+        "id": "trust.ai-alignment",
+        "query": "\"AI alignment\" in:description"
+      },
+      {
+        "id": "trust.ai-safety",
+        "query": "\"AI safety\" in:description"
+      },
+      {
+        "id": "trust.algorithmic-fairness",
+        "query": "\"algorithmic fairness\" in:description"
+      },
+      {
+        "id": "trust.algorithmic-recourse",
+        "query": "\"algorithmic recourse\" in:description"
+      },
+      {
+        "id": "trust.causal-discovery",
+        "query": "\"causal discovery\" in:description"
+      },
+      {
+        "id": "trust.causal-effect-estimation",
+        "query": "\"causal effect estimation\" in:description"
+      },
+      {
+        "id": "trust.causal-mediation",
+        "query": "\"causal mediation analysis\" in:description"
+      },
+      {
+        "id": "trust.causal-ml",
+        "query": "\"causal machine learning\" in:description"
+      },
+      {
+        "id": "trust.causal-representation-learning",
+        "query": "\"causal representation learning\" in:description"
+      },
+      {
+        "id": "trust.conformal-prediction",
+        "query": "\"conformal prediction\" in:description"
+      },
+      {
+        "id": "trust.counterfactual-explanations",
+        "query": "\"counterfactual explanation\" in:description"
+      },
+      {
+        "id": "trust.differential-privacy",
+        "query": "\"differential privacy\" in:description"
+      },
+      {
+        "id": "trust.distribution-shift",
+        "query": "\"distribution shift\" in:description"
+      },
+      {
+        "id": "trust.domain-generalization",
+        "query": "\"domain generalization\" in:description"
+      },
+      {
+        "id": "trust.explainable-ai",
+        "query": "\"explainable AI\" in:description"
+      },
+      {
+        "id": "trust.federated-learning",
+        "query": "\"federated learning\" in:description"
+      },
+      {
+        "id": "trust.instrumental-variable-ml",
+        "query": "\"double machine learning\" in:description"
+      },
+      {
+        "id": "trust.interpretable-ml",
+        "query": "\"interpretable machine learning\" in:description"
+      },
+      {
+        "id": "trust.membership-inference",
+        "query": "\"membership inference\" in:description"
+      },
+      {
+        "id": "trust.ml-fairness",
+        "query": "\"machine learning fairness\" in:description"
+      },
+      {
+        "id": "trust.model-calibration",
+        "query": "\"model calibration\" in:description"
+      },
+      {
+        "id": "trust.neuro-symbolic",
+        "query": "\"neuro-symbolic\" in:description"
+      },
+      {
+        "id": "trust.out-of-distribution-detection",
+        "query": "\"out-of-distribution detection\" in:description"
+      },
+      {
+        "id": "trust.privacy-preserving-ml",
+        "query": "\"privacy-preserving machine learning\" in:description"
+      },
+      {
+        "id": "trust.robustness-certification",
+        "query": "\"certified robustness\" in:description"
+      },
+      {
+        "id": "trust.safe-reinforcement-learning",
+        "query": "\"safe reinforcement learning\" constrained in:description"
+      },
+      {
+        "id": "trust.shap",
+        "query": "SHAP in:description"
+      },
+      {
+        "id": "trust.uncertainty-quantification",
+        "query": "\"uncertainty quantification\" in:description"
+      },
+      {
+        "id": "vision.action-recognition",
+        "query": "\"action recognition\" in:description"
+      },
+      {
+        "id": "vision.change-detection",
+        "query": "\"change detection\" in:readme"
+      },
+      {
+        "id": "vision.depth-estimation",
+        "query": "\"depth estimation\" in:readme"
+      },
+      {
+        "id": "vision.face-recognition",
+        "query": "\"face recognition\" in:readme"
+      },
+      {
+        "id": "vision.image-captioning",
+        "query": "\"image captioning\" in:readme"
+      },
+      {
+        "id": "vision.image-classification",
+        "query": "\"image classification\" in:description"
+      },
+      {
+        "id": "vision.image-diffusion",
+        "query": "\"image diffusion model\" in:readme"
+      },
+      {
+        "id": "vision.image-restoration",
+        "query": "\"image restoration\" in:readme"
+      },
+      {
+        "id": "vision.image-retrieval",
+        "query": "\"image retrieval\" in:readme"
+      },
+      {
+        "id": "vision.image-super-resolution",
+        "query": "\"image super-resolution\" in:readme"
+      },
+      {
+        "id": "vision.image-to-image-translation",
+        "query": "\"image-to-image translation\" in:readme"
+      },
+      {
+        "id": "vision.neural-rendering",
+        "query": "\"neural rendering\" in:description"
+      },
+      {
+        "id": "vision.object-detection",
+        "query": "\"object detection\" in:description"
+      },
+      {
+        "id": "vision.object-tracking",
+        "query": "\"object tracking\" in:readme"
+      },
+      {
+        "id": "vision.ocr",
+        "query": "topic:ocr"
+      },
+      {
+        "id": "vision.open-vocabulary-detection",
+        "query": "\"open-vocabulary detection\" in:readme"
+      },
+      {
+        "id": "vision.optical-flow",
+        "query": "topic:optical-flow"
+      },
+      {
+        "id": "vision.pose-estimation",
+        "query": "\"human pose estimation\" in:readme"
+      },
+      {
+        "id": "vision.promptable-segmentation",
+        "query": "\"promptable segmentation\" in:readme"
+      },
+      {
+        "id": "vision.segmentation",
+        "query": "\"image segmentation\" in:description"
+      },
+      {
+        "id": "vision.self-supervised-vision",
+        "query": "\"self-supervised vision\" in:description"
+      },
+      {
+        "id": "vision.topic-computer-vision",
+        "query": "topic:computer-vision"
+      },
+      {
+        "id": "vision.video-generation",
+        "query": "\"video generation\" in:readme"
+      },
+      {
+        "id": "vision.video-understanding",
+        "query": "\"video understanding\" in:readme"
+      },
+      {
+        "id": "vision.vision-language",
+        "query": "\"vision-language model\" in:readme"
+      },
+      {
+        "id": "vision.visual-anomaly-detection",
+        "query": "\"visual anomaly detection\" in:readme"
+      },
+      {
+        "id": "vision.visual-localization",
+        "query": "\"visual localization\" in:readme"
+      }
+    ],
+    "start": "2026-09-24"
+  },
   "since": "2026-09-24",
-  "updated_at": "2026-09-24T13:28:22.855974Z"
+  "until": "2026-09-24",
+  "updated_at": "2026-09-24T18:30:58Z"
 }