Add GitHub ML run 20260930T163043000000Z-b69db13d
Browse files
coverage/20260930T163043000000Z-b69db13d.json
ADDED
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@@ -0,0 +1,2812 @@
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"pages_scanned": 1,
|
| 2756 |
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"query": "\"named entity recognition\" in:readme created:2024-01-01..2024-12-31",
|
| 2757 |
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"query_id": "nlp.named-entity-recognition",
|
| 2758 |
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|
| 2759 |
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| 2760 |
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|
| 2761 |
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|
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|
| 2763 |
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"year": 2024
|
| 2764 |
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|
| 2765 |
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{
|
| 2766 |
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"coverage_gap": false,
|
| 2767 |
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"coverage_gap_reason": null,
|
| 2768 |
+
"incomplete_results": false,
|
| 2769 |
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"pages_scanned": 1,
|
| 2770 |
+
"query": "\"epidemiological modeling\" machine learning in:name,description,readme created:2024-01-01..2024-12-31",
|
| 2771 |
+
"query_id": "recall.epidemiological-modeling",
|
| 2772 |
+
"repositories_observed_on_last_page": 45,
|
| 2773 |
+
"sampled_first_page": true,
|
| 2774 |
+
"search_limit_reached": false,
|
| 2775 |
+
"status": "complete",
|
| 2776 |
+
"total_count": 45,
|
| 2777 |
+
"year": 2024
|
| 2778 |
+
},
|
| 2779 |
+
{
|
| 2780 |
+
"coverage_gap": false,
|
| 2781 |
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"coverage_gap_reason": null,
|
| 2782 |
+
"incomplete_results": false,
|
| 2783 |
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"pages_scanned": 1,
|
| 2784 |
+
"query": "\"hierarchical reinforcement learning\" in:description created:2024-01-01..2024-12-31",
|
| 2785 |
+
"query_id": "rl.hierarchical",
|
| 2786 |
+
"repositories_observed_on_last_page": 16,
|
| 2787 |
+
"sampled_first_page": true,
|
| 2788 |
+
"search_limit_reached": false,
|
| 2789 |
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"status": "complete",
|
| 2790 |
+
"total_count": 16,
|
| 2791 |
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"year": 2024
|
| 2792 |
+
},
|
| 2793 |
+
{
|
| 2794 |
+
"coverage_gap": true,
|
| 2795 |
+
"coverage_gap_reason": "first_page_sample_only",
|
| 2796 |
+
"incomplete_results": false,
|
| 2797 |
+
"pages_scanned": 1,
|
| 2798 |
+
"query": "\"robot manipulation\" in:description created:2024-01-01..2024-12-31",
|
| 2799 |
+
"query_id": "robotics.manipulation",
|
| 2800 |
+
"repositories_observed_on_last_page": 100,
|
| 2801 |
+
"sampled_first_page": true,
|
| 2802 |
+
"search_limit_reached": false,
|
| 2803 |
+
"status": "sampled",
|
| 2804 |
+
"total_count": 140,
|
| 2805 |
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"year": 2024
|
| 2806 |
+
}
|
| 2807 |
+
],
|
| 2808 |
+
"requests_used": 200,
|
| 2809 |
+
"run_id": "20260930T163043000000Z-b69db13d",
|
| 2810 |
+
"start_year": 2008,
|
| 2811 |
+
"started_at": "2026-09-30T16:30:43Z"
|
| 2812 |
+
}
|
data/observations/2026/09/30/20260930T163043000000Z-b69db13d.jsonl
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
state/historical-sample.json
CHANGED
|
@@ -2354,56 +2354,64 @@
|
|
| 2354 |
"query": "\"agricultural machine learning\" in:description,readme",
|
| 2355 |
"years": [
|
| 2356 |
2026,
|
| 2357 |
-
2025
|
|
|
|
| 2358 |
]
|
| 2359 |
},
|
| 2360 |
"applied.algorithmic-trading": {
|
| 2361 |
"query": "\"algorithmic trading\" \"machine learning\" in:readme",
|
| 2362 |
"years": [
|
| 2363 |
2026,
|
| 2364 |
-
2025
|
|
|
|
| 2365 |
]
|
| 2366 |
},
|
| 2367 |
"applied.causal-inference": {
|
| 2368 |
"query": "\"causal inference\" in:description",
|
| 2369 |
"years": [
|
| 2370 |
2026,
|
| 2371 |
-
2025
|
|
|
|
| 2372 |
]
|
| 2373 |
},
|
| 2374 |
"applied.churn-prediction": {
|
| 2375 |
"query": "\"customer churn prediction\" in:description",
|
| 2376 |
"years": [
|
| 2377 |
2026,
|
| 2378 |
-
2025
|
|
|
|
| 2379 |
]
|
| 2380 |
},
|
| 2381 |
"applied.click-through-rate": {
|
| 2382 |
"query": "\"click-through rate prediction\" in:readme",
|
| 2383 |
"years": [
|
| 2384 |
2026,
|
| 2385 |
-
2025
|
|
|
|
| 2386 |
]
|
| 2387 |
},
|
| 2388 |
"applied.collaborative-filtering": {
|
| 2389 |
"query": "\"collaborative filtering\" in:description",
|
| 2390 |
"years": [
|
| 2391 |
2026,
|
| 2392 |
-
2025
|
|
|
|
| 2393 |
]
|
| 2394 |
},
|
| 2395 |
"applied.credit-scoring": {
|
| 2396 |
"query": "\"credit scoring\" in:description",
|
| 2397 |
"years": [
|
| 2398 |
2026,
|
| 2399 |
-
2025
|
|
|
|
| 2400 |
]
|
| 2401 |
},
|
| 2402 |
"applied.customer-lifetime-value": {
|
| 2403 |
"query": "\"customer lifetime value\" in:description",
|
| 2404 |
"years": [
|
| 2405 |
2026,
|
| 2406 |
-
2025
|
|
|
|
| 2407 |
]
|
| 2408 |
},
|
| 2409 |
"applied.customer-segmentation": {
|
|
@@ -2823,56 +2831,64 @@
|
|
| 2823 |
"query": "\"protein function prediction\" in:description,readme",
|
| 2824 |
"years": [
|
| 2825 |
2026,
|
| 2826 |
-
2025
|
|
|
|
| 2827 |
]
|
| 2828 |
},
|
| 2829 |
"bio.crispr": {
|
| 2830 |
"query": "\"CRISPR screen\" in:description,readme",
|
| 2831 |
"years": [
|
| 2832 |
2026,
|
| 2833 |
-
2025
|
|
|
|
| 2834 |
]
|
| 2835 |
},
|
| 2836 |
"bio.dna-language-models": {
|
| 2837 |
"query": "\"DNA language model\" in:description",
|
| 2838 |
"years": [
|
| 2839 |
2026,
|
| 2840 |
-
2025
|
|
|
|
| 2841 |
]
|
| 2842 |
},
|
| 2843 |
"bio.gene-regulatory-networks": {
|
| 2844 |
"query": "\"gene regulatory network\" in:description,readme",
|
| 2845 |
"years": [
|
| 2846 |
2026,
|
| 2847 |
-
2025
|
|
|
|
| 2848 |
]
|
| 2849 |
},
|
| 2850 |
"bio.genome-assembly": {
|
| 2851 |
"query": "\"genome assembly\" \"neural network\" in:description,readme",
|
| 2852 |
"years": [
|
| 2853 |
2026,
|
| 2854 |
-
2025
|
|
|
|
| 2855 |
]
|
| 2856 |
},
|
| 2857 |
"bio.genomics": {
|
| 2858 |
"query": "\"genomics\" in:description",
|
| 2859 |
"years": [
|
| 2860 |
2026,
|
| 2861 |
-
2025
|
|
|
|
| 2862 |
]
|
| 2863 |
},
|
| 2864 |
"bio.inverse-folding": {
|
| 2865 |
"query": "\"inverse folding\" in:readme",
|
| 2866 |
"years": [
|
| 2867 |
2026,
|
| 2868 |
-
2025
|
|
|
|
| 2869 |
]
|
| 2870 |
},
|
| 2871 |
"bio.metagenomics": {
|
| 2872 |
"query": "\"metagenomic classification\" in:description,readme",
|
| 2873 |
"years": [
|
| 2874 |
2026,
|
| 2875 |
-
2025
|
|
|
|
| 2876 |
]
|
| 2877 |
},
|
| 2878 |
"bio.microbiome": {
|
|
@@ -3078,133 +3094,152 @@
|
|
| 3078 |
"query": "molecular spectroscopy \"machine learning\" in:description,readme",
|
| 3079 |
"years": [
|
| 3080 |
2026,
|
| 3081 |
-
2025
|
|
|
|
| 3082 |
]
|
| 3083 |
},
|
| 3084 |
"chem-property-prediction": {
|
| 3085 |
"query": "\"molecular property prediction\" in:description,readme",
|
| 3086 |
"years": [
|
| 3087 |
2026,
|
| 3088 |
-
2025
|
|
|
|
| 3089 |
]
|
| 3090 |
},
|
| 3091 |
"chem-protein-ligand-docking": {
|
| 3092 |
"query": "\"protein-ligand docking\" in:description,readme",
|
| 3093 |
"years": [
|
| 3094 |
2026,
|
| 3095 |
-
2025
|
|
|
|
| 3096 |
]
|
| 3097 |
},
|
| 3098 |
"chem-quantum-chemistry": {
|
| 3099 |
"query": "\"quantum chemistry\" \"machine learning\" in:description,readme",
|
| 3100 |
"years": [
|
| 3101 |
2026,
|
| 3102 |
-
2025
|
|
|
|
| 3103 |
]
|
| 3104 |
},
|
| 3105 |
"chem-reaction-conditions": {
|
| 3106 |
"query": "\"reaction condition prediction\" in:description,readme",
|
| 3107 |
"years": [
|
| 3108 |
2026,
|
| 3109 |
-
2025
|
|
|
|
| 3110 |
]
|
| 3111 |
},
|
| 3112 |
"chem-reaction-prediction": {
|
| 3113 |
"query": "\"reaction prediction\" in:description,readme",
|
| 3114 |
"years": [
|
| 3115 |
2026,
|
| 3116 |
-
2025
|
|
|
|
| 3117 |
]
|
| 3118 |
},
|
| 3119 |
"chem-reaction-yield": {
|
| 3120 |
"query": "\"reaction yield prediction\" in:description,readme",
|
| 3121 |
"years": [
|
| 3122 |
2026,
|
| 3123 |
-
2025
|
|
|
|
| 3124 |
]
|
| 3125 |
},
|
| 3126 |
"chem-retrosynthesis": {
|
| 3127 |
"query": "\"computer aided synthesis planning\" in:description,readme",
|
| 3128 |
"years": [
|
| 3129 |
2026,
|
| 3130 |
-
2025
|
|
|
|
| 3131 |
]
|
| 3132 |
},
|
| 3133 |
"chem-retrosynthesis-planning": {
|
| 3134 |
"query": "retrosynthesis in:description,readme",
|
| 3135 |
"years": [
|
| 3136 |
2026,
|
| 3137 |
-
2025
|
|
|
|
| 3138 |
]
|
| 3139 |
},
|
| 3140 |
"chem-virtual-screening": {
|
| 3141 |
"query": "\"virtual screening\" in:description,readme",
|
| 3142 |
"years": [
|
| 3143 |
2026,
|
| 3144 |
-
2025
|
|
|
|
| 3145 |
]
|
| 3146 |
},
|
| 3147 |
"efficiency.activation-checkpointing": {
|
| 3148 |
"query": "\"activation checkpointing\" in:description",
|
| 3149 |
"years": [
|
| 3150 |
2026,
|
| 3151 |
-
2025
|
|
|
|
| 3152 |
]
|
| 3153 |
},
|
| 3154 |
"efficiency.automl": {
|
| 3155 |
"query": "AutoML in:description",
|
| 3156 |
"years": [
|
| 3157 |
2026,
|
| 3158 |
-
2025
|
|
|
|
| 3159 |
]
|
| 3160 |
},
|
| 3161 |
"efficiency.communication-efficient-training": {
|
| 3162 |
"query": "\"communication efficient training\" in:description",
|
| 3163 |
"years": [
|
| 3164 |
2026,
|
| 3165 |
-
2025
|
|
|
|
| 3166 |
]
|
| 3167 |
},
|
| 3168 |
"efficiency.compiler-optimization": {
|
| 3169 |
"query": "\"machine learning compiler\" in:description",
|
| 3170 |
"years": [
|
| 3171 |
2026,
|
| 3172 |
-
2025
|
|
|
|
| 3173 |
]
|
| 3174 |
},
|
| 3175 |
"efficiency.distributed-training": {
|
| 3176 |
"query": "\"distributed training\" in:description",
|
| 3177 |
"years": [
|
| 3178 |
2026,
|
| 3179 |
-
2025
|
|
|
|
| 3180 |
]
|
| 3181 |
},
|
| 3182 |
"efficiency.edge-ai": {
|
| 3183 |
"query": "\"edge AI\" in:description",
|
| 3184 |
"years": [
|
| 3185 |
2026,
|
| 3186 |
-
2025
|
|
|
|
| 3187 |
]
|
| 3188 |
},
|
| 3189 |
"efficiency.energy-efficient-ml": {
|
| 3190 |
"query": "\"energy efficient machine learning\" in:description",
|
| 3191 |
"years": [
|
| 3192 |
2026,
|
| 3193 |
-
2025
|
|
|
|
| 3194 |
]
|
| 3195 |
},
|
| 3196 |
"efficiency.flash-attention": {
|
| 3197 |
"query": "\"FlashAttention\" in:description",
|
| 3198 |
"years": [
|
| 3199 |
2026,
|
| 3200 |
-
2025
|
|
|
|
| 3201 |
]
|
| 3202 |
},
|
| 3203 |
"efficiency.gpu-kernels": {
|
| 3204 |
"query": "\"GPU kernel\" in:description",
|
| 3205 |
"years": [
|
| 3206 |
2026,
|
| 3207 |
-
2025
|
|
|
|
| 3208 |
]
|
| 3209 |
},
|
| 3210 |
"efficiency.gradient-optimization": {
|
|
@@ -3344,63 +3379,72 @@
|
|
| 3344 |
"query": "\"machine learning archaeology\" in:description,readme",
|
| 3345 |
"years": [
|
| 3346 |
2026,
|
| 3347 |
-
2025
|
|
|
|
| 3348 |
]
|
| 3349 |
},
|
| 3350 |
"gap2026.climate-downscaling": {
|
| 3351 |
"query": "\"machine learning climate downscaling\" in:description,readme",
|
| 3352 |
"years": [
|
| 3353 |
2026,
|
| 3354 |
-
2025
|
|
|
|
| 3355 |
]
|
| 3356 |
},
|
| 3357 |
"gap2026.clinical-ehr-representation": {
|
| 3358 |
"query": "\"electronic health record\" representation learning in:description,readme",
|
| 3359 |
"years": [
|
| 3360 |
2026,
|
| 3361 |
-
2025
|
|
|
|
| 3362 |
]
|
| 3363 |
},
|
| 3364 |
"gap2026.clinical-risk-prediction": {
|
| 3365 |
"query": "\"clinical risk prediction\" machine learning in:description,readme",
|
| 3366 |
"years": [
|
| 3367 |
2026,
|
| 3368 |
-
2025
|
|
|
|
| 3369 |
]
|
| 3370 |
},
|
| 3371 |
"gap2026.clinical-survival-ml": {
|
| 3372 |
"query": "\"survival analysis\" deep learning clinical in:description,readme",
|
| 3373 |
"years": [
|
| 3374 |
2026,
|
| 3375 |
-
2025
|
|
|
|
| 3376 |
]
|
| 3377 |
},
|
| 3378 |
"gap2026.clinical-treatment-effect": {
|
| 3379 |
"query": "\"treatment effect estimation\" clinical in:description,readme",
|
| 3380 |
"years": [
|
| 3381 |
2026,
|
| 3382 |
-
2025
|
|
|
|
| 3383 |
]
|
| 3384 |
},
|
| 3385 |
"gap2026.clinical-trial-matching": {
|
| 3386 |
"query": "\"clinical trial matching\" machine learning in:description,readme",
|
| 3387 |
"years": [
|
| 3388 |
2026,
|
| 3389 |
-
2025
|
|
|
|
| 3390 |
]
|
| 3391 |
},
|
| 3392 |
"gap2026.econometrics-causal-forest": {
|
| 3393 |
"query": "\"causal forest\" econometrics in:description,readme",
|
| 3394 |
"years": [
|
| 3395 |
2026,
|
| 3396 |
-
2025
|
|
|
|
| 3397 |
]
|
| 3398 |
},
|
| 3399 |
"gap2026.econometrics-double-ml": {
|
| 3400 |
"query": "\"double machine learning\" econometrics in:description,readme",
|
| 3401 |
"years": [
|
| 3402 |
2026,
|
| 3403 |
-
2025
|
|
|
|
| 3404 |
]
|
| 3405 |
},
|
| 3406 |
"gap2026.econometrics-heterogeneous-effects": {
|
|
@@ -3547,63 +3591,72 @@
|
|
| 3547 |
"query": "\"active learning\" in:description",
|
| 3548 |
"years": [
|
| 3549 |
2026,
|
| 3550 |
-
2025
|
|
|
|
| 3551 |
]
|
| 3552 |
},
|
| 3553 |
"general.artificial-intelligence-description": {
|
| 3554 |
"query": "\"artificial intelligence\" in:description",
|
| 3555 |
"years": [
|
| 3556 |
2026,
|
| 3557 |
-
2025
|
|
|
|
| 3558 |
]
|
| 3559 |
},
|
| 3560 |
"general.arxiv": {
|
| 3561 |
"query": "arxiv \"machine learning\" in:description,readme",
|
| 3562 |
"years": [
|
| 3563 |
2026,
|
| 3564 |
-
2025
|
|
|
|
| 3565 |
]
|
| 3566 |
},
|
| 3567 |
"general.automated-machine-learning": {
|
| 3568 |
"query": "\"automated machine learning\" in:readme",
|
| 3569 |
"years": [
|
| 3570 |
2026,
|
| 3571 |
-
2025
|
|
|
|
| 3572 |
]
|
| 3573 |
},
|
| 3574 |
"general.bayesian-deep-learning": {
|
| 3575 |
"query": "\"Bayesian deep learning\" in:description,readme",
|
| 3576 |
"years": [
|
| 3577 |
2026,
|
| 3578 |
-
2025
|
|
|
|
| 3579 |
]
|
| 3580 |
},
|
| 3581 |
"general.bayesian-optimization": {
|
| 3582 |
"query": "\"Bayesian optimization\" in:description",
|
| 3583 |
"years": [
|
| 3584 |
2026,
|
| 3585 |
-
2025
|
|
|
|
| 3586 |
]
|
| 3587 |
},
|
| 3588 |
"general.clustering": {
|
| 3589 |
"query": "\"clustering algorithm\" \"machine learning\" in:description,readme",
|
| 3590 |
"years": [
|
| 3591 |
2026,
|
| 3592 |
-
2025
|
|
|
|
| 3593 |
]
|
| 3594 |
},
|
| 3595 |
"general.continual-learning": {
|
| 3596 |
"query": "\"continual learning\" in:description,readme",
|
| 3597 |
"years": [
|
| 3598 |
2026,
|
| 3599 |
-
2025
|
|
|
|
| 3600 |
]
|
| 3601 |
},
|
| 3602 |
"general.contrastive-learning": {
|
| 3603 |
"query": "\"contrastive learning\" in:description,readme",
|
| 3604 |
"years": [
|
| 3605 |
2026,
|
| 3606 |
-
2025
|
|
|
|
| 3607 |
]
|
| 3608 |
},
|
| 3609 |
"general.decision-trees": {
|
|
@@ -3855,63 +3908,72 @@
|
|
| 3855 |
"query": "\"building footprint extraction\" in:description,readme",
|
| 3856 |
"years": [
|
| 3857 |
2026,
|
| 3858 |
-
2025
|
|
|
|
| 3859 |
]
|
| 3860 |
},
|
| 3861 |
"geo.change-detection": {
|
| 3862 |
"query": "\"remote sensing change detection\" in:description,readme",
|
| 3863 |
"years": [
|
| 3864 |
2026,
|
| 3865 |
-
2025
|
|
|
|
| 3866 |
]
|
| 3867 |
},
|
| 3868 |
"geo.climate-mapping": {
|
| 3869 |
"query": "\"climate downscaling\" in:readme",
|
| 3870 |
"years": [
|
| 3871 |
2026,
|
| 3872 |
-
2025
|
|
|
|
| 3873 |
]
|
| 3874 |
},
|
| 3875 |
"geo.crop-monitoring": {
|
| 3876 |
"query": "\"crop yield prediction\" in:description,readme",
|
| 3877 |
"years": [
|
| 3878 |
2026,
|
| 3879 |
-
2025
|
|
|
|
| 3880 |
]
|
| 3881 |
},
|
| 3882 |
"geo.earth-observation-models": {
|
| 3883 |
"query": "\"Earth observation model\" in:description",
|
| 3884 |
"years": [
|
| 3885 |
2026,
|
| 3886 |
-
2025
|
|
|
|
| 3887 |
]
|
| 3888 |
},
|
| 3889 |
"geo.environmental-monitoring": {
|
| 3890 |
"query": "\"environmental monitoring\" in:readme",
|
| 3891 |
"years": [
|
| 3892 |
2026,
|
| 3893 |
-
2025
|
|
|
|
| 3894 |
]
|
| 3895 |
},
|
| 3896 |
"geo.flood-mapping": {
|
| 3897 |
"query": "\"flood mapping\" in:readme",
|
| 3898 |
"years": [
|
| 3899 |
2026,
|
| 3900 |
-
2025
|
|
|
|
| 3901 |
]
|
| 3902 |
},
|
| 3903 |
"geo.foundation-models-earth": {
|
| 3904 |
"query": "\"Earth observation foundation model\" in:description,readme",
|
| 3905 |
"years": [
|
| 3906 |
2026,
|
| 3907 |
-
2025
|
|
|
|
| 3908 |
]
|
| 3909 |
},
|
| 3910 |
"geo.geospatial-foundation-models": {
|
| 3911 |
"query": "\"geospatial foundation model\" in:readme",
|
| 3912 |
"years": [
|
| 3913 |
2026,
|
| 3914 |
-
2025
|
|
|
|
| 3915 |
]
|
| 3916 |
},
|
| 3917 |
"geo.geospatial-knowledge-graph": {
|
|
@@ -4044,63 +4106,72 @@
|
|
| 4044 |
"query": "\"E(3)-equivariant\" in:readme",
|
| 4045 |
"years": [
|
| 4046 |
2026,
|
| 4047 |
-
2025
|
|
|
|
| 4048 |
]
|
| 4049 |
},
|
| 4050 |
"graph.equivariant-networks": {
|
| 4051 |
"query": "\"equivariant neural network\" in:description",
|
| 4052 |
"years": [
|
| 4053 |
2026,
|
| 4054 |
-
2025
|
|
|
|
| 4055 |
]
|
| 4056 |
},
|
| 4057 |
"graph.geometric-learning": {
|
| 4058 |
"query": "\"geometric deep learning\" in:readme",
|
| 4059 |
"years": [
|
| 4060 |
2026,
|
| 4061 |
-
2025
|
|
|
|
| 4062 |
]
|
| 4063 |
},
|
| 4064 |
"graph.gnn-description": {
|
| 4065 |
"query": "\"graph neural network\" in:description",
|
| 4066 |
"years": [
|
| 4067 |
2026,
|
| 4068 |
-
2025
|
|
|
|
| 4069 |
]
|
| 4070 |
},
|
| 4071 |
"graph.graph-anomaly-detection": {
|
| 4072 |
"query": "\"graph anomaly detection\" in:description,readme",
|
| 4073 |
"years": [
|
| 4074 |
2026,
|
| 4075 |
-
2025
|
|
|
|
| 4076 |
]
|
| 4077 |
},
|
| 4078 |
"graph.graph-classification": {
|
| 4079 |
"query": "\"graph classification\" in:description",
|
| 4080 |
"years": [
|
| 4081 |
2026,
|
| 4082 |
-
2025
|
|
|
|
| 4083 |
]
|
| 4084 |
},
|
| 4085 |
"graph.graph-contrastive-learning": {
|
| 4086 |
"query": "\"graph contrastive learning\" in:description",
|
| 4087 |
"years": [
|
| 4088 |
2026,
|
| 4089 |
-
2025
|
|
|
|
| 4090 |
]
|
| 4091 |
},
|
| 4092 |
"graph.graph-diffusion": {
|
| 4093 |
"query": "\"graph diffusion model\" in:description",
|
| 4094 |
"years": [
|
| 4095 |
2026,
|
| 4096 |
-
2025
|
|
|
|
| 4097 |
]
|
| 4098 |
},
|
| 4099 |
"graph.graph-foundation-model": {
|
| 4100 |
"query": "\"graph foundation model\" in:description",
|
| 4101 |
"years": [
|
| 4102 |
2026,
|
| 4103 |
-
2025
|
|
|
|
| 4104 |
]
|
| 4105 |
},
|
| 4106 |
"graph.graph-generation": {
|
|
@@ -4233,91 +4304,104 @@
|
|
| 4233 |
"query": "\"code generation\" \"language model\" in:readme",
|
| 4234 |
"years": [
|
| 4235 |
2026,
|
| 4236 |
-
2025
|
|
|
|
| 4237 |
]
|
| 4238 |
},
|
| 4239 |
"llm-reasoning": {
|
| 4240 |
"query": "\"reasoning model\" in:readme",
|
| 4241 |
"years": [
|
| 4242 |
2026,
|
| 4243 |
-
2025
|
|
|
|
| 4244 |
]
|
| 4245 |
},
|
| 4246 |
"llm-retrieval-augmented-generation": {
|
| 4247 |
"query": "\"retrieval-augmented generation\" in:readme",
|
| 4248 |
"years": [
|
| 4249 |
2026,
|
| 4250 |
-
2025
|
|
|
|
| 4251 |
]
|
| 4252 |
},
|
| 4253 |
"llm-text-to-speech": {
|
| 4254 |
"query": "\"text-to-speech\" \"language model\" in:readme",
|
| 4255 |
"years": [
|
| 4256 |
2026,
|
| 4257 |
-
2025
|
|
|
|
| 4258 |
]
|
| 4259 |
},
|
| 4260 |
"llm.agents": {
|
| 4261 |
"query": "topic:ai-agents",
|
| 4262 |
"years": [
|
| 4263 |
2026,
|
| 4264 |
-
2025
|
|
|
|
| 4265 |
]
|
| 4266 |
},
|
| 4267 |
"llm.ai-agent": {
|
| 4268 |
"query": "\"AI agent\" in:description",
|
| 4269 |
"years": [
|
| 4270 |
2026,
|
| 4271 |
-
2025
|
|
|
|
| 4272 |
]
|
| 4273 |
},
|
| 4274 |
"llm.alignment": {
|
| 4275 |
"query": "\"alignment\" in:description",
|
| 4276 |
"years": [
|
| 4277 |
2026,
|
| 4278 |
-
2025
|
|
|
|
| 4279 |
]
|
| 4280 |
},
|
| 4281 |
"llm.diffusion": {
|
| 4282 |
"query": "topic:diffusion-model",
|
| 4283 |
"years": [
|
| 4284 |
2026,
|
| 4285 |
-
2025
|
|
|
|
| 4286 |
]
|
| 4287 |
},
|
| 4288 |
"llm.diffusion-phrase": {
|
| 4289 |
"query": "\"diffusion model\" in:description",
|
| 4290 |
"years": [
|
| 4291 |
2026,
|
| 4292 |
-
2025
|
|
|
|
| 4293 |
]
|
| 4294 |
},
|
| 4295 |
"llm.evaluation": {
|
| 4296 |
"query": "\"LLM evaluation\" in:readme",
|
| 4297 |
"years": [
|
| 4298 |
2026,
|
| 4299 |
-
2025
|
|
|
|
| 4300 |
]
|
| 4301 |
},
|
| 4302 |
"llm.finetuning": {
|
| 4303 |
"query": "\"fine-tuning\" in:description",
|
| 4304 |
"years": [
|
| 4305 |
2026,
|
| 4306 |
-
2025
|
|
|
|
| 4307 |
]
|
| 4308 |
},
|
| 4309 |
"llm.foundation-models": {
|
| 4310 |
"query": "\"foundation model\" in:description",
|
| 4311 |
"years": [
|
| 4312 |
2026,
|
| 4313 |
-
2025
|
|
|
|
| 4314 |
]
|
| 4315 |
},
|
| 4316 |
"llm.generative-ai": {
|
| 4317 |
"query": "\"generative AI\" in:description",
|
| 4318 |
"years": [
|
| 4319 |
2026,
|
| 4320 |
-
2025
|
|
|
|
| 4321 |
]
|
| 4322 |
},
|
| 4323 |
"llm.instruction-tuning": {
|
|
@@ -4422,112 +4506,128 @@
|
|
| 4422 |
"query": "\"battery materials\" \"machine learning\" in:description,readme",
|
| 4423 |
"years": [
|
| 4424 |
2026,
|
| 4425 |
-
2025
|
|
|
|
| 4426 |
]
|
| 4427 |
},
|
| 4428 |
"materials-crystal-generation": {
|
| 4429 |
"query": "\"crystal structure generation\" in:description,readme",
|
| 4430 |
"years": [
|
| 4431 |
2026,
|
| 4432 |
-
2025
|
|
|
|
| 4433 |
]
|
| 4434 |
},
|
| 4435 |
"materials-crystal-graph": {
|
| 4436 |
"query": "\"crystal graph\" in:description,readme",
|
| 4437 |
"years": [
|
| 4438 |
2026,
|
| 4439 |
-
2025
|
|
|
|
| 4440 |
]
|
| 4441 |
},
|
| 4442 |
"materials-dft-prediction": {
|
| 4443 |
"query": "\"density functional theory\" \"machine learning\" in:description,readme",
|
| 4444 |
"years": [
|
| 4445 |
2026,
|
| 4446 |
-
2025
|
|
|
|
| 4447 |
]
|
| 4448 |
},
|
| 4449 |
"materials-informatics": {
|
| 4450 |
"query": "\"materials informatics\" in:description,readme",
|
| 4451 |
"years": [
|
| 4452 |
2026,
|
| 4453 |
-
2025
|
|
|
|
| 4454 |
]
|
| 4455 |
},
|
| 4456 |
"materials-perovskite": {
|
| 4457 |
"query": "perovskite in:description \"machine learning\"",
|
| 4458 |
"years": [
|
| 4459 |
2026,
|
| 4460 |
-
2025
|
|
|
|
| 4461 |
]
|
| 4462 |
},
|
| 4463 |
"materials-polymer-informatics": {
|
| 4464 |
"query": "polymer informatics in:description,readme",
|
| 4465 |
"years": [
|
| 4466 |
2026,
|
| 4467 |
-
2025
|
|
|
|
| 4468 |
]
|
| 4469 |
},
|
| 4470 |
"medical.clinical-decision-support": {
|
| 4471 |
"query": "\"clinical decision support\" in:readme",
|
| 4472 |
"years": [
|
| 4473 |
2026,
|
| 4474 |
-
2025
|
|
|
|
| 4475 |
]
|
| 4476 |
},
|
| 4477 |
"medical.clinical-nlp": {
|
| 4478 |
"query": "\"clinical natural language processing\" in:readme",
|
| 4479 |
"years": [
|
| 4480 |
2026,
|
| 4481 |
-
2025
|
|
|
|
| 4482 |
]
|
| 4483 |
},
|
| 4484 |
"medical.clinical-time-series": {
|
| 4485 |
"query": "\"clinical time series\" in:description",
|
| 4486 |
"years": [
|
| 4487 |
2026,
|
| 4488 |
-
2025
|
|
|
|
| 4489 |
]
|
| 4490 |
},
|
| 4491 |
"medical.clinical-trials": {
|
| 4492 |
"query": "\"clinical trial prediction\" in:description",
|
| 4493 |
"years": [
|
| 4494 |
2026,
|
| 4495 |
-
2025
|
|
|
|
| 4496 |
]
|
| 4497 |
},
|
| 4498 |
"medical.dermatology": {
|
| 4499 |
"query": "dermatology AI dermoscopy in:description,readme",
|
| 4500 |
"years": [
|
| 4501 |
2026,
|
| 4502 |
-
2025
|
|
|
|
| 4503 |
]
|
| 4504 |
},
|
| 4505 |
"medical.diagnosis-prediction": {
|
| 4506 |
"query": "\"diagnosis prediction\" in:description",
|
| 4507 |
"years": [
|
| 4508 |
2026,
|
| 4509 |
-
2025
|
|
|
|
| 4510 |
]
|
| 4511 |
},
|
| 4512 |
"medical.drug-response": {
|
| 4513 |
"query": "\"drug response prediction\" in:description,readme",
|
| 4514 |
"years": [
|
| 4515 |
2026,
|
| 4516 |
-
2025
|
|
|
|
| 4517 |
]
|
| 4518 |
},
|
| 4519 |
"medical.ecg": {
|
| 4520 |
"query": "ECG \"deep learning\" in:description,readme",
|
| 4521 |
"years": [
|
| 4522 |
2026,
|
| 4523 |
-
2025
|
|
|
|
| 4524 |
]
|
| 4525 |
},
|
| 4526 |
"medical.eeg": {
|
| 4527 |
"query": "EEG \"deep learning\" in:description,readme",
|
| 4528 |
"years": [
|
| 4529 |
2026,
|
| 4530 |
-
2025
|
|
|
|
| 4531 |
]
|
| 4532 |
},
|
| 4533 |
"medical.ehr": {
|
|
@@ -4660,63 +4760,72 @@
|
|
| 4660 |
"query": "\"3D generation\" in:readme",
|
| 4661 |
"years": [
|
| 4662 |
2026,
|
| 4663 |
-
2025
|
|
|
|
| 4664 |
]
|
| 4665 |
},
|
| 4666 |
"multimodal.3d-human-pose": {
|
| 4667 |
"query": "\"3D human pose estimation\" in:readme",
|
| 4668 |
"years": [
|
| 4669 |
2026,
|
| 4670 |
-
2025
|
|
|
|
| 4671 |
]
|
| 4672 |
},
|
| 4673 |
"multimodal.3d-object-detection": {
|
| 4674 |
"query": "\"3D object detection\" in:readme",
|
| 4675 |
"years": [
|
| 4676 |
2026,
|
| 4677 |
-
2025
|
|
|
|
| 4678 |
]
|
| 4679 |
},
|
| 4680 |
"multimodal.3d-reconstruction": {
|
| 4681 |
"query": "\"3D reconstruction\" in:description",
|
| 4682 |
"years": [
|
| 4683 |
2026,
|
| 4684 |
-
2025
|
|
|
|
| 4685 |
]
|
| 4686 |
},
|
| 4687 |
"multimodal.audio-visual-learning": {
|
| 4688 |
"query": "\"audio visual learning\" in:readme",
|
| 4689 |
"years": [
|
| 4690 |
2026,
|
| 4691 |
-
2025
|
|
|
|
| 4692 |
]
|
| 4693 |
},
|
| 4694 |
"multimodal.embodied-world-models": {
|
| 4695 |
"query": "\"embodied world model\" in:description",
|
| 4696 |
"years": [
|
| 4697 |
2026,
|
| 4698 |
-
2025
|
|
|
|
| 4699 |
]
|
| 4700 |
},
|
| 4701 |
"multimodal.gaussian-splatting": {
|
| 4702 |
"query": "\"Gaussian splatting\" in:description",
|
| 4703 |
"years": [
|
| 4704 |
2026,
|
| 4705 |
-
2025
|
|
|
|
| 4706 |
]
|
| 4707 |
},
|
| 4708 |
"multimodal.image-text": {
|
| 4709 |
"query": "\"image-text\" in:description",
|
| 4710 |
"years": [
|
| 4711 |
2026,
|
| 4712 |
-
2025
|
|
|
|
| 4713 |
]
|
| 4714 |
},
|
| 4715 |
"multimodal.image-to-3d": {
|
| 4716 |
"query": "\"image to 3D\" in:readme",
|
| 4717 |
"years": [
|
| 4718 |
2026,
|
| 4719 |
-
2025
|
|
|
|
| 4720 |
]
|
| 4721 |
},
|
| 4722 |
"multimodal.image-to-video": {
|
|
@@ -4835,112 +4944,128 @@
|
|
| 4835 |
"query": "BM25 retrieval in:readme",
|
| 4836 |
"years": [
|
| 4837 |
2026,
|
| 4838 |
-
2025
|
|
|
|
| 4839 |
]
|
| 4840 |
},
|
| 4841 |
"nlp-document-retrieval": {
|
| 4842 |
"query": "\"document retrieval\" in:readme",
|
| 4843 |
"years": [
|
| 4844 |
2026,
|
| 4845 |
-
2025
|
|
|
|
| 4846 |
]
|
| 4847 |
},
|
| 4848 |
"nlp-entity-linking": {
|
| 4849 |
"query": "\"entity linking\" in:readme",
|
| 4850 |
"years": [
|
| 4851 |
2026,
|
| 4852 |
-
2025
|
|
|
|
| 4853 |
]
|
| 4854 |
},
|
| 4855 |
"nlp-natural-language-inference": {
|
| 4856 |
"query": "\"natural language inference\" in:readme",
|
| 4857 |
"years": [
|
| 4858 |
2026,
|
| 4859 |
-
2025
|
|
|
|
| 4860 |
]
|
| 4861 |
},
|
| 4862 |
"nlp-query-understanding": {
|
| 4863 |
"query": "\"query understanding\" in:readme",
|
| 4864 |
"years": [
|
| 4865 |
2026,
|
| 4866 |
-
2025
|
|
|
|
| 4867 |
]
|
| 4868 |
},
|
| 4869 |
"nlp-sparse-retrieval": {
|
| 4870 |
"query": "\"sparse retrieval\" in:readme",
|
| 4871 |
"years": [
|
| 4872 |
2026,
|
| 4873 |
-
2025
|
|
|
|
| 4874 |
]
|
| 4875 |
},
|
| 4876 |
"nlp-speech-language-modeling": {
|
| 4877 |
"query": "\"speech language model\" in:readme",
|
| 4878 |
"years": [
|
| 4879 |
2026,
|
| 4880 |
-
2025
|
|
|
|
| 4881 |
]
|
| 4882 |
},
|
| 4883 |
"nlp.coreference-resolution": {
|
| 4884 |
"query": "\"coreference resolution\" in:readme",
|
| 4885 |
"years": [
|
| 4886 |
2026,
|
| 4887 |
-
2025
|
|
|
|
| 4888 |
]
|
| 4889 |
},
|
| 4890 |
"nlp.cross-encoder-reranking": {
|
| 4891 |
"query": "\"cross-encoder\" \"reranking\" in:readme",
|
| 4892 |
"years": [
|
| 4893 |
2026,
|
| 4894 |
-
2025
|
|
|
|
| 4895 |
]
|
| 4896 |
},
|
| 4897 |
"nlp.dense-retrieval": {
|
| 4898 |
"query": "\"dense retrieval\" in:readme",
|
| 4899 |
"years": [
|
| 4900 |
2026,
|
| 4901 |
-
2025
|
|
|
|
| 4902 |
]
|
| 4903 |
},
|
| 4904 |
"nlp.dependency-parsing": {
|
| 4905 |
"query": "\"dependency parsing\" in:readme",
|
| 4906 |
"years": [
|
| 4907 |
2026,
|
| 4908 |
-
2025
|
|
|
|
| 4909 |
]
|
| 4910 |
},
|
| 4911 |
"nlp.dialogue-systems": {
|
| 4912 |
"query": "\"dialogue system\" in:readme",
|
| 4913 |
"years": [
|
| 4914 |
2026,
|
| 4915 |
-
2025
|
|
|
|
| 4916 |
]
|
| 4917 |
},
|
| 4918 |
"nlp.information-extraction": {
|
| 4919 |
"query": "\"information extraction\" in:description",
|
| 4920 |
"years": [
|
| 4921 |
2026,
|
| 4922 |
-
2025
|
|
|
|
| 4923 |
]
|
| 4924 |
},
|
| 4925 |
"nlp.language-modeling": {
|
| 4926 |
"query": "\"language modeling\" in:description",
|
| 4927 |
"years": [
|
| 4928 |
2026,
|
| 4929 |
-
2025
|
|
|
|
| 4930 |
]
|
| 4931 |
},
|
| 4932 |
"nlp.machine-translation": {
|
| 4933 |
"query": "\"machine translation\" in:description",
|
| 4934 |
"years": [
|
| 4935 |
2026,
|
| 4936 |
-
2025
|
|
|
|
| 4937 |
]
|
| 4938 |
},
|
| 4939 |
"nlp.named-entity-recognition": {
|
| 4940 |
"query": "\"named entity recognition\" in:readme",
|
| 4941 |
"years": [
|
| 4942 |
2026,
|
| 4943 |
-
2025
|
|
|
|
| 4944 |
]
|
| 4945 |
},
|
| 4946 |
"nlp.neural-reranking": {
|
|
@@ -5017,63 +5142,72 @@
|
|
| 5017 |
"query": "archaeology \"remote sensing\" in:name,description,readme",
|
| 5018 |
"years": [
|
| 5019 |
2026,
|
| 5020 |
-
2025
|
|
|
|
| 5021 |
]
|
| 5022 |
},
|
| 5023 |
"recall.case-outcome-prediction": {
|
| 5024 |
"query": "\"case outcome prediction\" in:name,description,readme",
|
| 5025 |
"years": [
|
| 5026 |
2026,
|
| 5027 |
-
2025
|
|
|
|
| 5028 |
]
|
| 5029 |
},
|
| 5030 |
"recall.computational-linguistics": {
|
| 5031 |
"query": "\"computational linguistics\" model in:name,description,readme",
|
| 5032 |
"years": [
|
| 5033 |
2026,
|
| 5034 |
-
2025
|
|
|
|
| 5035 |
]
|
| 5036 |
},
|
| 5037 |
"recall.computational-social-science": {
|
| 5038 |
"query": "\"computational social science\" in:name,description,readme",
|
| 5039 |
"years": [
|
| 5040 |
2026,
|
| 5041 |
-
2025
|
|
|
|
| 5042 |
]
|
| 5043 |
},
|
| 5044 |
"recall.digital-humanities-nlp": {
|
| 5045 |
"query": "\"digital humanities\" NLP in:name,description,readme",
|
| 5046 |
"years": [
|
| 5047 |
2026,
|
| 5048 |
-
2025
|
|
|
|
| 5049 |
]
|
| 5050 |
},
|
| 5051 |
"recall.disease-surveillance": {
|
| 5052 |
"query": "\"disease surveillance\" machine learning in:name,description,readme",
|
| 5053 |
"years": [
|
| 5054 |
2026,
|
| 5055 |
-
2025
|
|
|
|
| 5056 |
]
|
| 5057 |
},
|
| 5058 |
"recall.educational-recommendation": {
|
| 5059 |
"query": "\"educational recommendation\" in:name,description,readme",
|
| 5060 |
"years": [
|
| 5061 |
2026,
|
| 5062 |
-
2025
|
|
|
|
| 5063 |
]
|
| 5064 |
},
|
| 5065 |
"recall.epidemic-forecasting": {
|
| 5066 |
"query": "\"epidemic forecasting\" in:name,description,readme",
|
| 5067 |
"years": [
|
| 5068 |
2026,
|
| 5069 |
-
2025
|
|
|
|
| 5070 |
]
|
| 5071 |
},
|
| 5072 |
"recall.epidemiological-modeling": {
|
| 5073 |
"query": "\"epidemiological modeling\" machine learning in:name,description,readme",
|
| 5074 |
"years": [
|
| 5075 |
2026,
|
| 5076 |
-
2025
|
|
|
|
| 5077 |
]
|
| 5078 |
},
|
| 5079 |
"recall.grid-stability-prediction": {
|
|
@@ -5241,119 +5375,136 @@
|
|
| 5241 |
"query": "\"audio language model\" in:description,readme",
|
| 5242 |
"years": [
|
| 5243 |
2026,
|
| 5244 |
-
2025
|
|
|
|
| 5245 |
]
|
| 5246 |
},
|
| 5247 |
"recall26.class-imbalance": {
|
| 5248 |
"query": "\"class imbalance\" \"machine learning\" in:description,readme",
|
| 5249 |
"years": [
|
| 5250 |
2026,
|
| 5251 |
-
2025
|
|
|
|
| 5252 |
]
|
| 5253 |
},
|
| 5254 |
"recall26.image-dehazing": {
|
| 5255 |
"query": "\"image dehazing\" in:description,readme",
|
| 5256 |
"years": [
|
| 5257 |
2026,
|
| 5258 |
-
2025
|
|
|
|
| 5259 |
]
|
| 5260 |
},
|
| 5261 |
"recall26.image-denoising": {
|
| 5262 |
"query": "\"image denoising\" in:description,readme",
|
| 5263 |
"years": [
|
| 5264 |
2026,
|
| 5265 |
-
2025
|
|
|
|
| 5266 |
]
|
| 5267 |
},
|
| 5268 |
"recall26.long-tailed-recognition": {
|
| 5269 |
"query": "\"long-tailed recognition\" in:description,readme",
|
| 5270 |
"years": [
|
| 5271 |
2026,
|
| 5272 |
-
2025
|
|
|
|
| 5273 |
]
|
| 5274 |
},
|
| 5275 |
"recall26.low-light-enhancement": {
|
| 5276 |
"query": "\"low-light image enhancement\" in:description,readme",
|
| 5277 |
"years": [
|
| 5278 |
2026,
|
| 5279 |
-
2025
|
|
|
|
| 5280 |
]
|
| 5281 |
},
|
| 5282 |
"recall26.sign-language": {
|
| 5283 |
"query": "\"sign language\" in:description,readme",
|
| 5284 |
"years": [
|
| 5285 |
2026,
|
| 5286 |
-
2025
|
|
|
|
| 5287 |
]
|
| 5288 |
},
|
| 5289 |
"recall26.visual-grounding": {
|
| 5290 |
"query": "\"visual grounding\" in:description,readme",
|
| 5291 |
"years": [
|
| 5292 |
2026,
|
| 5293 |
-
2025
|
|
|
|
| 5294 |
]
|
| 5295 |
},
|
| 5296 |
"rl.bandits": {
|
| 5297 |
"query": "\"multi-armed bandit\" in:description",
|
| 5298 |
"years": [
|
| 5299 |
2026,
|
| 5300 |
-
2025
|
|
|
|
| 5301 |
]
|
| 5302 |
},
|
| 5303 |
"rl.continual": {
|
| 5304 |
"query": "\"continual reinforcement learning\" in:description,readme",
|
| 5305 |
"years": [
|
| 5306 |
2026,
|
| 5307 |
-
2025
|
|
|
|
| 5308 |
]
|
| 5309 |
},
|
| 5310 |
"rl.core": {
|
| 5311 |
"query": "topic:reinforcement-learning",
|
| 5312 |
"years": [
|
| 5313 |
2026,
|
| 5314 |
-
2025
|
|
|
|
| 5315 |
]
|
| 5316 |
},
|
| 5317 |
"rl.deep": {
|
| 5318 |
"query": "\"deep reinforcement learning\" in:description",
|
| 5319 |
"years": [
|
| 5320 |
2026,
|
| 5321 |
-
2025
|
|
|
|
| 5322 |
]
|
| 5323 |
},
|
| 5324 |
"rl.deep-q-learning": {
|
| 5325 |
"query": "\"deep q-learning\" in:description",
|
| 5326 |
"years": [
|
| 5327 |
2026,
|
| 5328 |
-
2025
|
|
|
|
| 5329 |
]
|
| 5330 |
},
|
| 5331 |
"rl.distributional": {
|
| 5332 |
"query": "\"distributional reinforcement learning\" in:description,readme",
|
| 5333 |
"years": [
|
| 5334 |
2026,
|
| 5335 |
-
2025
|
|
|
|
| 5336 |
]
|
| 5337 |
},
|
| 5338 |
"rl.exploration": {
|
| 5339 |
"query": "\"reinforcement learning exploration\" in:description",
|
| 5340 |
"years": [
|
| 5341 |
2026,
|
| 5342 |
-
2025
|
|
|
|
| 5343 |
]
|
| 5344 |
},
|
| 5345 |
"rl.games": {
|
| 5346 |
"query": "topic:game-ai",
|
| 5347 |
"years": [
|
| 5348 |
2026,
|
| 5349 |
-
2025
|
|
|
|
| 5350 |
]
|
| 5351 |
},
|
| 5352 |
"rl.hierarchical": {
|
| 5353 |
"query": "\"hierarchical reinforcement learning\" in:description",
|
| 5354 |
"years": [
|
| 5355 |
2026,
|
| 5356 |
-
2025
|
|
|
|
| 5357 |
]
|
| 5358 |
},
|
| 5359 |
"rl.imitation-learning": {
|
|
@@ -5451,63 +5602,72 @@
|
|
| 5451 |
"query": "\"autonomous driving\" in:description",
|
| 5452 |
"years": [
|
| 5453 |
2026,
|
| 5454 |
-
2025
|
|
|
|
| 5455 |
]
|
| 5456 |
},
|
| 5457 |
"robotics.behavior-cloning": {
|
| 5458 |
"query": "\"behavior cloning\" in:description",
|
| 5459 |
"years": [
|
| 5460 |
2026,
|
| 5461 |
-
2025
|
|
|
|
| 5462 |
]
|
| 5463 |
},
|
| 5464 |
"robotics.diffusion-policy": {
|
| 5465 |
"query": "\"diffusion policy\" in:description",
|
| 5466 |
"years": [
|
| 5467 |
2026,
|
| 5468 |
-
2025
|
|
|
|
| 5469 |
]
|
| 5470 |
},
|
| 5471 |
"robotics.domain-randomization": {
|
| 5472 |
"query": "\"domain randomization\" in:description",
|
| 5473 |
"years": [
|
| 5474 |
2026,
|
| 5475 |
-
2025
|
|
|
|
| 5476 |
]
|
| 5477 |
},
|
| 5478 |
"robotics.embodied-ai": {
|
| 5479 |
"query": "\"embodied AI\" in:description",
|
| 5480 |
"years": [
|
| 5481 |
2026,
|
| 5482 |
-
2025
|
|
|
|
| 5483 |
]
|
| 5484 |
},
|
| 5485 |
"robotics.grasping": {
|
| 5486 |
"query": "\"robot grasping\" in:description",
|
| 5487 |
"years": [
|
| 5488 |
2026,
|
| 5489 |
-
2025
|
|
|
|
| 5490 |
]
|
| 5491 |
},
|
| 5492 |
"robotics.imitation-learning": {
|
| 5493 |
"query": "\"imitation learning\" robotics in:description",
|
| 5494 |
"years": [
|
| 5495 |
2026,
|
| 5496 |
-
2025
|
|
|
|
| 5497 |
]
|
| 5498 |
},
|
| 5499 |
"robotics.locomotion": {
|
| 5500 |
"query": "\"robot locomotion\" in:description",
|
| 5501 |
"years": [
|
| 5502 |
2026,
|
| 5503 |
-
2025
|
|
|
|
| 5504 |
]
|
| 5505 |
},
|
| 5506 |
"robotics.manipulation": {
|
| 5507 |
"query": "\"robot manipulation\" in:description",
|
| 5508 |
"years": [
|
| 5509 |
2026,
|
| 5510 |
-
2025
|
|
|
|
| 5511 |
]
|
| 5512 |
},
|
| 5513 |
"robotics.navigation": {
|
|
@@ -5612,56 +5772,64 @@
|
|
| 5612 |
"query": "astronomy in:readme",
|
| 5613 |
"years": [
|
| 5614 |
2026,
|
| 5615 |
-
2025
|
|
|
|
| 5616 |
]
|
| 5617 |
},
|
| 5618 |
"science.astrophysics": {
|
| 5619 |
"query": "astrophysics in:description",
|
| 5620 |
"years": [
|
| 5621 |
2026,
|
| 5622 |
-
2025
|
|
|
|
| 5623 |
]
|
| 5624 |
},
|
| 5625 |
"science.climate-modeling": {
|
| 5626 |
"query": "\"climate modeling\" in:readme",
|
| 5627 |
"years": [
|
| 5628 |
2026,
|
| 5629 |
-
2025
|
|
|
|
| 5630 |
]
|
| 5631 |
},
|
| 5632 |
"science.computational-mechanics": {
|
| 5633 |
"query": "\"computational mechanics\" \"machine learning\" in:description,readme",
|
| 5634 |
"years": [
|
| 5635 |
2026,
|
| 5636 |
-
2025
|
|
|
|
| 5637 |
]
|
| 5638 |
},
|
| 5639 |
"science.cosmology": {
|
| 5640 |
"query": "cosmology in:description",
|
| 5641 |
"years": [
|
| 5642 |
2026,
|
| 5643 |
-
2025
|
|
|
|
| 5644 |
]
|
| 5645 |
},
|
| 5646 |
"science.differentiable-simulation": {
|
| 5647 |
"query": "\"differentiable simulation\" in:readme",
|
| 5648 |
"years": [
|
| 5649 |
2026,
|
| 5650 |
-
2025
|
|
|
|
| 5651 |
]
|
| 5652 |
},
|
| 5653 |
"science.exoplanets": {
|
| 5654 |
"query": "exoplanet in:description \"machine learning\"",
|
| 5655 |
"years": [
|
| 5656 |
2026,
|
| 5657 |
-
2025
|
|
|
|
| 5658 |
]
|
| 5659 |
},
|
| 5660 |
"science.fluid-dynamics": {
|
| 5661 |
"query": "\"fluid dynamics\" in:description",
|
| 5662 |
"years": [
|
| 5663 |
2026,
|
| 5664 |
-
2025
|
|
|
|
| 5665 |
]
|
| 5666 |
},
|
| 5667 |
"science.fusion-plasma": {
|
|
@@ -5794,56 +5962,64 @@
|
|
| 5794 |
"query": "\"carbon footprint\" \"deep learning\" training in:description,readme",
|
| 5795 |
"years": [
|
| 5796 |
2026,
|
| 5797 |
-
2025
|
|
|
|
| 5798 |
]
|
| 5799 |
},
|
| 5800 |
"specialized.computational-pathology-ml": {
|
| 5801 |
"query": "\"computational pathology\" \"machine learning\" in:description,readme",
|
| 5802 |
"years": [
|
| 5803 |
2026,
|
| 5804 |
-
2025
|
|
|
|
| 5805 |
]
|
| 5806 |
},
|
| 5807 |
"specialized.coreset-selection": {
|
| 5808 |
"query": "\"coreset selection\" in:description,readme",
|
| 5809 |
"years": [
|
| 5810 |
2026,
|
| 5811 |
-
2025
|
|
|
|
| 5812 |
]
|
| 5813 |
},
|
| 5814 |
"specialized.curriculum-learning": {
|
| 5815 |
"query": "\"curriculum learning\" in:description,readme",
|
| 5816 |
"years": [
|
| 5817 |
2026,
|
| 5818 |
-
2025
|
|
|
|
| 5819 |
]
|
| 5820 |
},
|
| 5821 |
"specialized.data-centric-ai": {
|
| 5822 |
"query": "\"data-centric AI\" in:description,readme",
|
| 5823 |
"years": [
|
| 5824 |
2026,
|
| 5825 |
-
2025
|
|
|
|
| 5826 |
]
|
| 5827 |
},
|
| 5828 |
"specialized.data-shapley": {
|
| 5829 |
"query": "\"data Shapley\" in:description,readme",
|
| 5830 |
"years": [
|
| 5831 |
2026,
|
| 5832 |
-
2025
|
|
|
|
| 5833 |
]
|
| 5834 |
},
|
| 5835 |
"specialized.dataset-distillation": {
|
| 5836 |
"query": "\"dataset distillation\" in:description,readme",
|
| 5837 |
"years": [
|
| 5838 |
2026,
|
| 5839 |
-
2025
|
|
|
|
| 5840 |
]
|
| 5841 |
},
|
| 5842 |
"specialized.event-camera-deep-learning": {
|
| 5843 |
"query": "\"event camera\" \"deep learning\" in:description,readme",
|
| 5844 |
"years": [
|
| 5845 |
2026,
|
| 5846 |
-
2025
|
|
|
|
| 5847 |
]
|
| 5848 |
},
|
| 5849 |
"specialized.fmri-decoding-ml": {
|
|
@@ -5899,56 +6075,64 @@
|
|
| 5899 |
"query": "\"time series anomaly detection\" in:description",
|
| 5900 |
"years": [
|
| 5901 |
2026,
|
| 5902 |
-
2025
|
|
|
|
| 5903 |
]
|
| 5904 |
},
|
| 5905 |
"timeseries.classification": {
|
| 5906 |
"query": "\"time series classification\" in:description",
|
| 5907 |
"years": [
|
| 5908 |
2026,
|
| 5909 |
-
2025
|
|
|
|
| 5910 |
]
|
| 5911 |
},
|
| 5912 |
"timeseries.clustering": {
|
| 5913 |
"query": "\"time series clustering\" in:description,readme",
|
| 5914 |
"years": [
|
| 5915 |
2026,
|
| 5916 |
-
2025
|
|
|
|
| 5917 |
]
|
| 5918 |
},
|
| 5919 |
"timeseries.energy-forecasting": {
|
| 5920 |
"query": "\"energy forecasting\" in:description",
|
| 5921 |
"years": [
|
| 5922 |
2026,
|
| 5923 |
-
2025
|
|
|
|
| 5924 |
]
|
| 5925 |
},
|
| 5926 |
"timeseries.event-forecasting": {
|
| 5927 |
"query": "\"event forecasting\" in:description",
|
| 5928 |
"years": [
|
| 5929 |
2026,
|
| 5930 |
-
2025
|
|
|
|
| 5931 |
]
|
| 5932 |
},
|
| 5933 |
"timeseries.forecasting": {
|
| 5934 |
"query": "\"time series forecasting\" in:description",
|
| 5935 |
"years": [
|
| 5936 |
2026,
|
| 5937 |
-
2025
|
|
|
|
| 5938 |
]
|
| 5939 |
},
|
| 5940 |
"timeseries.foundation-models": {
|
| 5941 |
"query": "\"time series foundation model\" in:description",
|
| 5942 |
"years": [
|
| 5943 |
2026,
|
| 5944 |
-
2025
|
|
|
|
| 5945 |
]
|
| 5946 |
},
|
| 5947 |
"timeseries.imputation": {
|
| 5948 |
"query": "\"time series imputation\" in:description,readme",
|
| 5949 |
"years": [
|
| 5950 |
2026,
|
| 5951 |
-
2025
|
|
|
|
| 5952 |
]
|
| 5953 |
},
|
| 5954 |
"timeseries.long-horizon": {
|
|
@@ -6081,56 +6265,64 @@
|
|
| 6081 |
"query": "\"adversarial robustness\" in:description",
|
| 6082 |
"years": [
|
| 6083 |
2026,
|
| 6084 |
-
2025
|
|
|
|
| 6085 |
]
|
| 6086 |
},
|
| 6087 |
"trust.ai-alignment": {
|
| 6088 |
"query": "\"AI alignment\" in:description",
|
| 6089 |
"years": [
|
| 6090 |
2026,
|
| 6091 |
-
2025
|
|
|
|
| 6092 |
]
|
| 6093 |
},
|
| 6094 |
"trust.ai-safety": {
|
| 6095 |
"query": "\"AI safety\" in:description",
|
| 6096 |
"years": [
|
| 6097 |
2026,
|
| 6098 |
-
2025
|
|
|
|
| 6099 |
]
|
| 6100 |
},
|
| 6101 |
"trust.algorithmic-fairness": {
|
| 6102 |
"query": "\"algorithmic fairness\" in:description",
|
| 6103 |
"years": [
|
| 6104 |
2026,
|
| 6105 |
-
2025
|
|
|
|
| 6106 |
]
|
| 6107 |
},
|
| 6108 |
"trust.algorithmic-recourse": {
|
| 6109 |
"query": "\"algorithmic recourse\" in:description",
|
| 6110 |
"years": [
|
| 6111 |
2026,
|
| 6112 |
-
2025
|
|
|
|
| 6113 |
]
|
| 6114 |
},
|
| 6115 |
"trust.causal-discovery": {
|
| 6116 |
"query": "\"causal discovery\" in:description",
|
| 6117 |
"years": [
|
| 6118 |
2026,
|
| 6119 |
-
2025
|
|
|
|
| 6120 |
]
|
| 6121 |
},
|
| 6122 |
"trust.causal-effect-estimation": {
|
| 6123 |
"query": "\"causal effect estimation\" in:description",
|
| 6124 |
"years": [
|
| 6125 |
2026,
|
| 6126 |
-
2025
|
|
|
|
| 6127 |
]
|
| 6128 |
},
|
| 6129 |
"trust.causal-mediation": {
|
| 6130 |
"query": "\"causal mediation analysis\" in:description",
|
| 6131 |
"years": [
|
| 6132 |
2026,
|
| 6133 |
-
2025
|
|
|
|
| 6134 |
]
|
| 6135 |
},
|
| 6136 |
"trust.causal-ml": {
|
|
@@ -6284,56 +6476,64 @@
|
|
| 6284 |
"query": "\"action recognition\" in:description",
|
| 6285 |
"years": [
|
| 6286 |
2026,
|
| 6287 |
-
2025
|
|
|
|
| 6288 |
]
|
| 6289 |
},
|
| 6290 |
"vision.change-detection": {
|
| 6291 |
"query": "\"change detection\" in:readme",
|
| 6292 |
"years": [
|
| 6293 |
2026,
|
| 6294 |
-
2025
|
|
|
|
| 6295 |
]
|
| 6296 |
},
|
| 6297 |
"vision.depth-estimation": {
|
| 6298 |
"query": "\"depth estimation\" in:readme",
|
| 6299 |
"years": [
|
| 6300 |
2026,
|
| 6301 |
-
2025
|
|
|
|
| 6302 |
]
|
| 6303 |
},
|
| 6304 |
"vision.face-recognition": {
|
| 6305 |
"query": "\"face recognition\" in:readme",
|
| 6306 |
"years": [
|
| 6307 |
2026,
|
| 6308 |
-
2025
|
|
|
|
| 6309 |
]
|
| 6310 |
},
|
| 6311 |
"vision.image-captioning": {
|
| 6312 |
"query": "\"image captioning\" in:readme",
|
| 6313 |
"years": [
|
| 6314 |
2026,
|
| 6315 |
-
2025
|
|
|
|
| 6316 |
]
|
| 6317 |
},
|
| 6318 |
"vision.image-classification": {
|
| 6319 |
"query": "\"image classification\" in:description",
|
| 6320 |
"years": [
|
| 6321 |
2026,
|
| 6322 |
-
2025
|
|
|
|
| 6323 |
]
|
| 6324 |
},
|
| 6325 |
"vision.image-diffusion": {
|
| 6326 |
"query": "\"image diffusion model\" in:readme",
|
| 6327 |
"years": [
|
| 6328 |
2026,
|
| 6329 |
-
2025
|
|
|
|
| 6330 |
]
|
| 6331 |
},
|
| 6332 |
"vision.image-restoration": {
|
| 6333 |
"query": "\"image restoration\" in:readme",
|
| 6334 |
"years": [
|
| 6335 |
2026,
|
| 6336 |
-
2025
|
|
|
|
| 6337 |
]
|
| 6338 |
},
|
| 6339 |
"vision.image-retrieval": {
|
|
@@ -6479,5 +6679,5 @@
|
|
| 6479 |
"end": "2026-09-24",
|
| 6480 |
"search_policy_version": 2,
|
| 6481 |
"start_year": 2008,
|
| 6482 |
-
"updated_at": "2026-09-
|
| 6483 |
}
|
|
|
|
| 2354 |
"query": "\"agricultural machine learning\" in:description,readme",
|
| 2355 |
"years": [
|
| 2356 |
2026,
|
| 2357 |
+
2025,
|
| 2358 |
+
2024
|
| 2359 |
]
|
| 2360 |
},
|
| 2361 |
"applied.algorithmic-trading": {
|
| 2362 |
"query": "\"algorithmic trading\" \"machine learning\" in:readme",
|
| 2363 |
"years": [
|
| 2364 |
2026,
|
| 2365 |
+
2025,
|
| 2366 |
+
2024
|
| 2367 |
]
|
| 2368 |
},
|
| 2369 |
"applied.causal-inference": {
|
| 2370 |
"query": "\"causal inference\" in:description",
|
| 2371 |
"years": [
|
| 2372 |
2026,
|
| 2373 |
+
2025,
|
| 2374 |
+
2024
|
| 2375 |
]
|
| 2376 |
},
|
| 2377 |
"applied.churn-prediction": {
|
| 2378 |
"query": "\"customer churn prediction\" in:description",
|
| 2379 |
"years": [
|
| 2380 |
2026,
|
| 2381 |
+
2025,
|
| 2382 |
+
2024
|
| 2383 |
]
|
| 2384 |
},
|
| 2385 |
"applied.click-through-rate": {
|
| 2386 |
"query": "\"click-through rate prediction\" in:readme",
|
| 2387 |
"years": [
|
| 2388 |
2026,
|
| 2389 |
+
2025,
|
| 2390 |
+
2024
|
| 2391 |
]
|
| 2392 |
},
|
| 2393 |
"applied.collaborative-filtering": {
|
| 2394 |
"query": "\"collaborative filtering\" in:description",
|
| 2395 |
"years": [
|
| 2396 |
2026,
|
| 2397 |
+
2025,
|
| 2398 |
+
2024
|
| 2399 |
]
|
| 2400 |
},
|
| 2401 |
"applied.credit-scoring": {
|
| 2402 |
"query": "\"credit scoring\" in:description",
|
| 2403 |
"years": [
|
| 2404 |
2026,
|
| 2405 |
+
2025,
|
| 2406 |
+
2024
|
| 2407 |
]
|
| 2408 |
},
|
| 2409 |
"applied.customer-lifetime-value": {
|
| 2410 |
"query": "\"customer lifetime value\" in:description",
|
| 2411 |
"years": [
|
| 2412 |
2026,
|
| 2413 |
+
2025,
|
| 2414 |
+
2024
|
| 2415 |
]
|
| 2416 |
},
|
| 2417 |
"applied.customer-segmentation": {
|
|
|
|
| 2831 |
"query": "\"protein function prediction\" in:description,readme",
|
| 2832 |
"years": [
|
| 2833 |
2026,
|
| 2834 |
+
2025,
|
| 2835 |
+
2024
|
| 2836 |
]
|
| 2837 |
},
|
| 2838 |
"bio.crispr": {
|
| 2839 |
"query": "\"CRISPR screen\" in:description,readme",
|
| 2840 |
"years": [
|
| 2841 |
2026,
|
| 2842 |
+
2025,
|
| 2843 |
+
2024
|
| 2844 |
]
|
| 2845 |
},
|
| 2846 |
"bio.dna-language-models": {
|
| 2847 |
"query": "\"DNA language model\" in:description",
|
| 2848 |
"years": [
|
| 2849 |
2026,
|
| 2850 |
+
2025,
|
| 2851 |
+
2024
|
| 2852 |
]
|
| 2853 |
},
|
| 2854 |
"bio.gene-regulatory-networks": {
|
| 2855 |
"query": "\"gene regulatory network\" in:description,readme",
|
| 2856 |
"years": [
|
| 2857 |
2026,
|
| 2858 |
+
2025,
|
| 2859 |
+
2024
|
| 2860 |
]
|
| 2861 |
},
|
| 2862 |
"bio.genome-assembly": {
|
| 2863 |
"query": "\"genome assembly\" \"neural network\" in:description,readme",
|
| 2864 |
"years": [
|
| 2865 |
2026,
|
| 2866 |
+
2025,
|
| 2867 |
+
2024
|
| 2868 |
]
|
| 2869 |
},
|
| 2870 |
"bio.genomics": {
|
| 2871 |
"query": "\"genomics\" in:description",
|
| 2872 |
"years": [
|
| 2873 |
2026,
|
| 2874 |
+
2025,
|
| 2875 |
+
2024
|
| 2876 |
]
|
| 2877 |
},
|
| 2878 |
"bio.inverse-folding": {
|
| 2879 |
"query": "\"inverse folding\" in:readme",
|
| 2880 |
"years": [
|
| 2881 |
2026,
|
| 2882 |
+
2025,
|
| 2883 |
+
2024
|
| 2884 |
]
|
| 2885 |
},
|
| 2886 |
"bio.metagenomics": {
|
| 2887 |
"query": "\"metagenomic classification\" in:description,readme",
|
| 2888 |
"years": [
|
| 2889 |
2026,
|
| 2890 |
+
2025,
|
| 2891 |
+
2024
|
| 2892 |
]
|
| 2893 |
},
|
| 2894 |
"bio.microbiome": {
|
|
|
|
| 3094 |
"query": "molecular spectroscopy \"machine learning\" in:description,readme",
|
| 3095 |
"years": [
|
| 3096 |
2026,
|
| 3097 |
+
2025,
|
| 3098 |
+
2024
|
| 3099 |
]
|
| 3100 |
},
|
| 3101 |
"chem-property-prediction": {
|
| 3102 |
"query": "\"molecular property prediction\" in:description,readme",
|
| 3103 |
"years": [
|
| 3104 |
2026,
|
| 3105 |
+
2025,
|
| 3106 |
+
2024
|
| 3107 |
]
|
| 3108 |
},
|
| 3109 |
"chem-protein-ligand-docking": {
|
| 3110 |
"query": "\"protein-ligand docking\" in:description,readme",
|
| 3111 |
"years": [
|
| 3112 |
2026,
|
| 3113 |
+
2025,
|
| 3114 |
+
2024
|
| 3115 |
]
|
| 3116 |
},
|
| 3117 |
"chem-quantum-chemistry": {
|
| 3118 |
"query": "\"quantum chemistry\" \"machine learning\" in:description,readme",
|
| 3119 |
"years": [
|
| 3120 |
2026,
|
| 3121 |
+
2025,
|
| 3122 |
+
2024
|
| 3123 |
]
|
| 3124 |
},
|
| 3125 |
"chem-reaction-conditions": {
|
| 3126 |
"query": "\"reaction condition prediction\" in:description,readme",
|
| 3127 |
"years": [
|
| 3128 |
2026,
|
| 3129 |
+
2025,
|
| 3130 |
+
2024
|
| 3131 |
]
|
| 3132 |
},
|
| 3133 |
"chem-reaction-prediction": {
|
| 3134 |
"query": "\"reaction prediction\" in:description,readme",
|
| 3135 |
"years": [
|
| 3136 |
2026,
|
| 3137 |
+
2025,
|
| 3138 |
+
2024
|
| 3139 |
]
|
| 3140 |
},
|
| 3141 |
"chem-reaction-yield": {
|
| 3142 |
"query": "\"reaction yield prediction\" in:description,readme",
|
| 3143 |
"years": [
|
| 3144 |
2026,
|
| 3145 |
+
2025,
|
| 3146 |
+
2024
|
| 3147 |
]
|
| 3148 |
},
|
| 3149 |
"chem-retrosynthesis": {
|
| 3150 |
"query": "\"computer aided synthesis planning\" in:description,readme",
|
| 3151 |
"years": [
|
| 3152 |
2026,
|
| 3153 |
+
2025,
|
| 3154 |
+
2024
|
| 3155 |
]
|
| 3156 |
},
|
| 3157 |
"chem-retrosynthesis-planning": {
|
| 3158 |
"query": "retrosynthesis in:description,readme",
|
| 3159 |
"years": [
|
| 3160 |
2026,
|
| 3161 |
+
2025,
|
| 3162 |
+
2024
|
| 3163 |
]
|
| 3164 |
},
|
| 3165 |
"chem-virtual-screening": {
|
| 3166 |
"query": "\"virtual screening\" in:description,readme",
|
| 3167 |
"years": [
|
| 3168 |
2026,
|
| 3169 |
+
2025,
|
| 3170 |
+
2024
|
| 3171 |
]
|
| 3172 |
},
|
| 3173 |
"efficiency.activation-checkpointing": {
|
| 3174 |
"query": "\"activation checkpointing\" in:description",
|
| 3175 |
"years": [
|
| 3176 |
2026,
|
| 3177 |
+
2025,
|
| 3178 |
+
2024
|
| 3179 |
]
|
| 3180 |
},
|
| 3181 |
"efficiency.automl": {
|
| 3182 |
"query": "AutoML in:description",
|
| 3183 |
"years": [
|
| 3184 |
2026,
|
| 3185 |
+
2025,
|
| 3186 |
+
2024
|
| 3187 |
]
|
| 3188 |
},
|
| 3189 |
"efficiency.communication-efficient-training": {
|
| 3190 |
"query": "\"communication efficient training\" in:description",
|
| 3191 |
"years": [
|
| 3192 |
2026,
|
| 3193 |
+
2025,
|
| 3194 |
+
2024
|
| 3195 |
]
|
| 3196 |
},
|
| 3197 |
"efficiency.compiler-optimization": {
|
| 3198 |
"query": "\"machine learning compiler\" in:description",
|
| 3199 |
"years": [
|
| 3200 |
2026,
|
| 3201 |
+
2025,
|
| 3202 |
+
2024
|
| 3203 |
]
|
| 3204 |
},
|
| 3205 |
"efficiency.distributed-training": {
|
| 3206 |
"query": "\"distributed training\" in:description",
|
| 3207 |
"years": [
|
| 3208 |
2026,
|
| 3209 |
+
2025,
|
| 3210 |
+
2024
|
| 3211 |
]
|
| 3212 |
},
|
| 3213 |
"efficiency.edge-ai": {
|
| 3214 |
"query": "\"edge AI\" in:description",
|
| 3215 |
"years": [
|
| 3216 |
2026,
|
| 3217 |
+
2025,
|
| 3218 |
+
2024
|
| 3219 |
]
|
| 3220 |
},
|
| 3221 |
"efficiency.energy-efficient-ml": {
|
| 3222 |
"query": "\"energy efficient machine learning\" in:description",
|
| 3223 |
"years": [
|
| 3224 |
2026,
|
| 3225 |
+
2025,
|
| 3226 |
+
2024
|
| 3227 |
]
|
| 3228 |
},
|
| 3229 |
"efficiency.flash-attention": {
|
| 3230 |
"query": "\"FlashAttention\" in:description",
|
| 3231 |
"years": [
|
| 3232 |
2026,
|
| 3233 |
+
2025,
|
| 3234 |
+
2024
|
| 3235 |
]
|
| 3236 |
},
|
| 3237 |
"efficiency.gpu-kernels": {
|
| 3238 |
"query": "\"GPU kernel\" in:description",
|
| 3239 |
"years": [
|
| 3240 |
2026,
|
| 3241 |
+
2025,
|
| 3242 |
+
2024
|
| 3243 |
]
|
| 3244 |
},
|
| 3245 |
"efficiency.gradient-optimization": {
|
|
|
|
| 3379 |
"query": "\"machine learning archaeology\" in:description,readme",
|
| 3380 |
"years": [
|
| 3381 |
2026,
|
| 3382 |
+
2025,
|
| 3383 |
+
2024
|
| 3384 |
]
|
| 3385 |
},
|
| 3386 |
"gap2026.climate-downscaling": {
|
| 3387 |
"query": "\"machine learning climate downscaling\" in:description,readme",
|
| 3388 |
"years": [
|
| 3389 |
2026,
|
| 3390 |
+
2025,
|
| 3391 |
+
2024
|
| 3392 |
]
|
| 3393 |
},
|
| 3394 |
"gap2026.clinical-ehr-representation": {
|
| 3395 |
"query": "\"electronic health record\" representation learning in:description,readme",
|
| 3396 |
"years": [
|
| 3397 |
2026,
|
| 3398 |
+
2025,
|
| 3399 |
+
2024
|
| 3400 |
]
|
| 3401 |
},
|
| 3402 |
"gap2026.clinical-risk-prediction": {
|
| 3403 |
"query": "\"clinical risk prediction\" machine learning in:description,readme",
|
| 3404 |
"years": [
|
| 3405 |
2026,
|
| 3406 |
+
2025,
|
| 3407 |
+
2024
|
| 3408 |
]
|
| 3409 |
},
|
| 3410 |
"gap2026.clinical-survival-ml": {
|
| 3411 |
"query": "\"survival analysis\" deep learning clinical in:description,readme",
|
| 3412 |
"years": [
|
| 3413 |
2026,
|
| 3414 |
+
2025,
|
| 3415 |
+
2024
|
| 3416 |
]
|
| 3417 |
},
|
| 3418 |
"gap2026.clinical-treatment-effect": {
|
| 3419 |
"query": "\"treatment effect estimation\" clinical in:description,readme",
|
| 3420 |
"years": [
|
| 3421 |
2026,
|
| 3422 |
+
2025,
|
| 3423 |
+
2024
|
| 3424 |
]
|
| 3425 |
},
|
| 3426 |
"gap2026.clinical-trial-matching": {
|
| 3427 |
"query": "\"clinical trial matching\" machine learning in:description,readme",
|
| 3428 |
"years": [
|
| 3429 |
2026,
|
| 3430 |
+
2025,
|
| 3431 |
+
2024
|
| 3432 |
]
|
| 3433 |
},
|
| 3434 |
"gap2026.econometrics-causal-forest": {
|
| 3435 |
"query": "\"causal forest\" econometrics in:description,readme",
|
| 3436 |
"years": [
|
| 3437 |
2026,
|
| 3438 |
+
2025,
|
| 3439 |
+
2024
|
| 3440 |
]
|
| 3441 |
},
|
| 3442 |
"gap2026.econometrics-double-ml": {
|
| 3443 |
"query": "\"double machine learning\" econometrics in:description,readme",
|
| 3444 |
"years": [
|
| 3445 |
2026,
|
| 3446 |
+
2025,
|
| 3447 |
+
2024
|
| 3448 |
]
|
| 3449 |
},
|
| 3450 |
"gap2026.econometrics-heterogeneous-effects": {
|
|
|
|
| 3591 |
"query": "\"active learning\" in:description",
|
| 3592 |
"years": [
|
| 3593 |
2026,
|
| 3594 |
+
2025,
|
| 3595 |
+
2024
|
| 3596 |
]
|
| 3597 |
},
|
| 3598 |
"general.artificial-intelligence-description": {
|
| 3599 |
"query": "\"artificial intelligence\" in:description",
|
| 3600 |
"years": [
|
| 3601 |
2026,
|
| 3602 |
+
2025,
|
| 3603 |
+
2024
|
| 3604 |
]
|
| 3605 |
},
|
| 3606 |
"general.arxiv": {
|
| 3607 |
"query": "arxiv \"machine learning\" in:description,readme",
|
| 3608 |
"years": [
|
| 3609 |
2026,
|
| 3610 |
+
2025,
|
| 3611 |
+
2024
|
| 3612 |
]
|
| 3613 |
},
|
| 3614 |
"general.automated-machine-learning": {
|
| 3615 |
"query": "\"automated machine learning\" in:readme",
|
| 3616 |
"years": [
|
| 3617 |
2026,
|
| 3618 |
+
2025,
|
| 3619 |
+
2024
|
| 3620 |
]
|
| 3621 |
},
|
| 3622 |
"general.bayesian-deep-learning": {
|
| 3623 |
"query": "\"Bayesian deep learning\" in:description,readme",
|
| 3624 |
"years": [
|
| 3625 |
2026,
|
| 3626 |
+
2025,
|
| 3627 |
+
2024
|
| 3628 |
]
|
| 3629 |
},
|
| 3630 |
"general.bayesian-optimization": {
|
| 3631 |
"query": "\"Bayesian optimization\" in:description",
|
| 3632 |
"years": [
|
| 3633 |
2026,
|
| 3634 |
+
2025,
|
| 3635 |
+
2024
|
| 3636 |
]
|
| 3637 |
},
|
| 3638 |
"general.clustering": {
|
| 3639 |
"query": "\"clustering algorithm\" \"machine learning\" in:description,readme",
|
| 3640 |
"years": [
|
| 3641 |
2026,
|
| 3642 |
+
2025,
|
| 3643 |
+
2024
|
| 3644 |
]
|
| 3645 |
},
|
| 3646 |
"general.continual-learning": {
|
| 3647 |
"query": "\"continual learning\" in:description,readme",
|
| 3648 |
"years": [
|
| 3649 |
2026,
|
| 3650 |
+
2025,
|
| 3651 |
+
2024
|
| 3652 |
]
|
| 3653 |
},
|
| 3654 |
"general.contrastive-learning": {
|
| 3655 |
"query": "\"contrastive learning\" in:description,readme",
|
| 3656 |
"years": [
|
| 3657 |
2026,
|
| 3658 |
+
2025,
|
| 3659 |
+
2024
|
| 3660 |
]
|
| 3661 |
},
|
| 3662 |
"general.decision-trees": {
|
|
|
|
| 3908 |
"query": "\"building footprint extraction\" in:description,readme",
|
| 3909 |
"years": [
|
| 3910 |
2026,
|
| 3911 |
+
2025,
|
| 3912 |
+
2024
|
| 3913 |
]
|
| 3914 |
},
|
| 3915 |
"geo.change-detection": {
|
| 3916 |
"query": "\"remote sensing change detection\" in:description,readme",
|
| 3917 |
"years": [
|
| 3918 |
2026,
|
| 3919 |
+
2025,
|
| 3920 |
+
2024
|
| 3921 |
]
|
| 3922 |
},
|
| 3923 |
"geo.climate-mapping": {
|
| 3924 |
"query": "\"climate downscaling\" in:readme",
|
| 3925 |
"years": [
|
| 3926 |
2026,
|
| 3927 |
+
2025,
|
| 3928 |
+
2024
|
| 3929 |
]
|
| 3930 |
},
|
| 3931 |
"geo.crop-monitoring": {
|
| 3932 |
"query": "\"crop yield prediction\" in:description,readme",
|
| 3933 |
"years": [
|
| 3934 |
2026,
|
| 3935 |
+
2025,
|
| 3936 |
+
2024
|
| 3937 |
]
|
| 3938 |
},
|
| 3939 |
"geo.earth-observation-models": {
|
| 3940 |
"query": "\"Earth observation model\" in:description",
|
| 3941 |
"years": [
|
| 3942 |
2026,
|
| 3943 |
+
2025,
|
| 3944 |
+
2024
|
| 3945 |
]
|
| 3946 |
},
|
| 3947 |
"geo.environmental-monitoring": {
|
| 3948 |
"query": "\"environmental monitoring\" in:readme",
|
| 3949 |
"years": [
|
| 3950 |
2026,
|
| 3951 |
+
2025,
|
| 3952 |
+
2024
|
| 3953 |
]
|
| 3954 |
},
|
| 3955 |
"geo.flood-mapping": {
|
| 3956 |
"query": "\"flood mapping\" in:readme",
|
| 3957 |
"years": [
|
| 3958 |
2026,
|
| 3959 |
+
2025,
|
| 3960 |
+
2024
|
| 3961 |
]
|
| 3962 |
},
|
| 3963 |
"geo.foundation-models-earth": {
|
| 3964 |
"query": "\"Earth observation foundation model\" in:description,readme",
|
| 3965 |
"years": [
|
| 3966 |
2026,
|
| 3967 |
+
2025,
|
| 3968 |
+
2024
|
| 3969 |
]
|
| 3970 |
},
|
| 3971 |
"geo.geospatial-foundation-models": {
|
| 3972 |
"query": "\"geospatial foundation model\" in:readme",
|
| 3973 |
"years": [
|
| 3974 |
2026,
|
| 3975 |
+
2025,
|
| 3976 |
+
2024
|
| 3977 |
]
|
| 3978 |
},
|
| 3979 |
"geo.geospatial-knowledge-graph": {
|
|
|
|
| 4106 |
"query": "\"E(3)-equivariant\" in:readme",
|
| 4107 |
"years": [
|
| 4108 |
2026,
|
| 4109 |
+
2025,
|
| 4110 |
+
2024
|
| 4111 |
]
|
| 4112 |
},
|
| 4113 |
"graph.equivariant-networks": {
|
| 4114 |
"query": "\"equivariant neural network\" in:description",
|
| 4115 |
"years": [
|
| 4116 |
2026,
|
| 4117 |
+
2025,
|
| 4118 |
+
2024
|
| 4119 |
]
|
| 4120 |
},
|
| 4121 |
"graph.geometric-learning": {
|
| 4122 |
"query": "\"geometric deep learning\" in:readme",
|
| 4123 |
"years": [
|
| 4124 |
2026,
|
| 4125 |
+
2025,
|
| 4126 |
+
2024
|
| 4127 |
]
|
| 4128 |
},
|
| 4129 |
"graph.gnn-description": {
|
| 4130 |
"query": "\"graph neural network\" in:description",
|
| 4131 |
"years": [
|
| 4132 |
2026,
|
| 4133 |
+
2025,
|
| 4134 |
+
2024
|
| 4135 |
]
|
| 4136 |
},
|
| 4137 |
"graph.graph-anomaly-detection": {
|
| 4138 |
"query": "\"graph anomaly detection\" in:description,readme",
|
| 4139 |
"years": [
|
| 4140 |
2026,
|
| 4141 |
+
2025,
|
| 4142 |
+
2024
|
| 4143 |
]
|
| 4144 |
},
|
| 4145 |
"graph.graph-classification": {
|
| 4146 |
"query": "\"graph classification\" in:description",
|
| 4147 |
"years": [
|
| 4148 |
2026,
|
| 4149 |
+
2025,
|
| 4150 |
+
2024
|
| 4151 |
]
|
| 4152 |
},
|
| 4153 |
"graph.graph-contrastive-learning": {
|
| 4154 |
"query": "\"graph contrastive learning\" in:description",
|
| 4155 |
"years": [
|
| 4156 |
2026,
|
| 4157 |
+
2025,
|
| 4158 |
+
2024
|
| 4159 |
]
|
| 4160 |
},
|
| 4161 |
"graph.graph-diffusion": {
|
| 4162 |
"query": "\"graph diffusion model\" in:description",
|
| 4163 |
"years": [
|
| 4164 |
2026,
|
| 4165 |
+
2025,
|
| 4166 |
+
2024
|
| 4167 |
]
|
| 4168 |
},
|
| 4169 |
"graph.graph-foundation-model": {
|
| 4170 |
"query": "\"graph foundation model\" in:description",
|
| 4171 |
"years": [
|
| 4172 |
2026,
|
| 4173 |
+
2025,
|
| 4174 |
+
2024
|
| 4175 |
]
|
| 4176 |
},
|
| 4177 |
"graph.graph-generation": {
|
|
|
|
| 4304 |
"query": "\"code generation\" \"language model\" in:readme",
|
| 4305 |
"years": [
|
| 4306 |
2026,
|
| 4307 |
+
2025,
|
| 4308 |
+
2024
|
| 4309 |
]
|
| 4310 |
},
|
| 4311 |
"llm-reasoning": {
|
| 4312 |
"query": "\"reasoning model\" in:readme",
|
| 4313 |
"years": [
|
| 4314 |
2026,
|
| 4315 |
+
2025,
|
| 4316 |
+
2024
|
| 4317 |
]
|
| 4318 |
},
|
| 4319 |
"llm-retrieval-augmented-generation": {
|
| 4320 |
"query": "\"retrieval-augmented generation\" in:readme",
|
| 4321 |
"years": [
|
| 4322 |
2026,
|
| 4323 |
+
2025,
|
| 4324 |
+
2024
|
| 4325 |
]
|
| 4326 |
},
|
| 4327 |
"llm-text-to-speech": {
|
| 4328 |
"query": "\"text-to-speech\" \"language model\" in:readme",
|
| 4329 |
"years": [
|
| 4330 |
2026,
|
| 4331 |
+
2025,
|
| 4332 |
+
2024
|
| 4333 |
]
|
| 4334 |
},
|
| 4335 |
"llm.agents": {
|
| 4336 |
"query": "topic:ai-agents",
|
| 4337 |
"years": [
|
| 4338 |
2026,
|
| 4339 |
+
2025,
|
| 4340 |
+
2024
|
| 4341 |
]
|
| 4342 |
},
|
| 4343 |
"llm.ai-agent": {
|
| 4344 |
"query": "\"AI agent\" in:description",
|
| 4345 |
"years": [
|
| 4346 |
2026,
|
| 4347 |
+
2025,
|
| 4348 |
+
2024
|
| 4349 |
]
|
| 4350 |
},
|
| 4351 |
"llm.alignment": {
|
| 4352 |
"query": "\"alignment\" in:description",
|
| 4353 |
"years": [
|
| 4354 |
2026,
|
| 4355 |
+
2025,
|
| 4356 |
+
2024
|
| 4357 |
]
|
| 4358 |
},
|
| 4359 |
"llm.diffusion": {
|
| 4360 |
"query": "topic:diffusion-model",
|
| 4361 |
"years": [
|
| 4362 |
2026,
|
| 4363 |
+
2025,
|
| 4364 |
+
2024
|
| 4365 |
]
|
| 4366 |
},
|
| 4367 |
"llm.diffusion-phrase": {
|
| 4368 |
"query": "\"diffusion model\" in:description",
|
| 4369 |
"years": [
|
| 4370 |
2026,
|
| 4371 |
+
2025,
|
| 4372 |
+
2024
|
| 4373 |
]
|
| 4374 |
},
|
| 4375 |
"llm.evaluation": {
|
| 4376 |
"query": "\"LLM evaluation\" in:readme",
|
| 4377 |
"years": [
|
| 4378 |
2026,
|
| 4379 |
+
2025,
|
| 4380 |
+
2024
|
| 4381 |
]
|
| 4382 |
},
|
| 4383 |
"llm.finetuning": {
|
| 4384 |
"query": "\"fine-tuning\" in:description",
|
| 4385 |
"years": [
|
| 4386 |
2026,
|
| 4387 |
+
2025,
|
| 4388 |
+
2024
|
| 4389 |
]
|
| 4390 |
},
|
| 4391 |
"llm.foundation-models": {
|
| 4392 |
"query": "\"foundation model\" in:description",
|
| 4393 |
"years": [
|
| 4394 |
2026,
|
| 4395 |
+
2025,
|
| 4396 |
+
2024
|
| 4397 |
]
|
| 4398 |
},
|
| 4399 |
"llm.generative-ai": {
|
| 4400 |
"query": "\"generative AI\" in:description",
|
| 4401 |
"years": [
|
| 4402 |
2026,
|
| 4403 |
+
2025,
|
| 4404 |
+
2024
|
| 4405 |
]
|
| 4406 |
},
|
| 4407 |
"llm.instruction-tuning": {
|
|
|
|
| 4506 |
"query": "\"battery materials\" \"machine learning\" in:description,readme",
|
| 4507 |
"years": [
|
| 4508 |
2026,
|
| 4509 |
+
2025,
|
| 4510 |
+
2024
|
| 4511 |
]
|
| 4512 |
},
|
| 4513 |
"materials-crystal-generation": {
|
| 4514 |
"query": "\"crystal structure generation\" in:description,readme",
|
| 4515 |
"years": [
|
| 4516 |
2026,
|
| 4517 |
+
2025,
|
| 4518 |
+
2024
|
| 4519 |
]
|
| 4520 |
},
|
| 4521 |
"materials-crystal-graph": {
|
| 4522 |
"query": "\"crystal graph\" in:description,readme",
|
| 4523 |
"years": [
|
| 4524 |
2026,
|
| 4525 |
+
2025,
|
| 4526 |
+
2024
|
| 4527 |
]
|
| 4528 |
},
|
| 4529 |
"materials-dft-prediction": {
|
| 4530 |
"query": "\"density functional theory\" \"machine learning\" in:description,readme",
|
| 4531 |
"years": [
|
| 4532 |
2026,
|
| 4533 |
+
2025,
|
| 4534 |
+
2024
|
| 4535 |
]
|
| 4536 |
},
|
| 4537 |
"materials-informatics": {
|
| 4538 |
"query": "\"materials informatics\" in:description,readme",
|
| 4539 |
"years": [
|
| 4540 |
2026,
|
| 4541 |
+
2025,
|
| 4542 |
+
2024
|
| 4543 |
]
|
| 4544 |
},
|
| 4545 |
"materials-perovskite": {
|
| 4546 |
"query": "perovskite in:description \"machine learning\"",
|
| 4547 |
"years": [
|
| 4548 |
2026,
|
| 4549 |
+
2025,
|
| 4550 |
+
2024
|
| 4551 |
]
|
| 4552 |
},
|
| 4553 |
"materials-polymer-informatics": {
|
| 4554 |
"query": "polymer informatics in:description,readme",
|
| 4555 |
"years": [
|
| 4556 |
2026,
|
| 4557 |
+
2025,
|
| 4558 |
+
2024
|
| 4559 |
]
|
| 4560 |
},
|
| 4561 |
"medical.clinical-decision-support": {
|
| 4562 |
"query": "\"clinical decision support\" in:readme",
|
| 4563 |
"years": [
|
| 4564 |
2026,
|
| 4565 |
+
2025,
|
| 4566 |
+
2024
|
| 4567 |
]
|
| 4568 |
},
|
| 4569 |
"medical.clinical-nlp": {
|
| 4570 |
"query": "\"clinical natural language processing\" in:readme",
|
| 4571 |
"years": [
|
| 4572 |
2026,
|
| 4573 |
+
2025,
|
| 4574 |
+
2024
|
| 4575 |
]
|
| 4576 |
},
|
| 4577 |
"medical.clinical-time-series": {
|
| 4578 |
"query": "\"clinical time series\" in:description",
|
| 4579 |
"years": [
|
| 4580 |
2026,
|
| 4581 |
+
2025,
|
| 4582 |
+
2024
|
| 4583 |
]
|
| 4584 |
},
|
| 4585 |
"medical.clinical-trials": {
|
| 4586 |
"query": "\"clinical trial prediction\" in:description",
|
| 4587 |
"years": [
|
| 4588 |
2026,
|
| 4589 |
+
2025,
|
| 4590 |
+
2024
|
| 4591 |
]
|
| 4592 |
},
|
| 4593 |
"medical.dermatology": {
|
| 4594 |
"query": "dermatology AI dermoscopy in:description,readme",
|
| 4595 |
"years": [
|
| 4596 |
2026,
|
| 4597 |
+
2025,
|
| 4598 |
+
2024
|
| 4599 |
]
|
| 4600 |
},
|
| 4601 |
"medical.diagnosis-prediction": {
|
| 4602 |
"query": "\"diagnosis prediction\" in:description",
|
| 4603 |
"years": [
|
| 4604 |
2026,
|
| 4605 |
+
2025,
|
| 4606 |
+
2024
|
| 4607 |
]
|
| 4608 |
},
|
| 4609 |
"medical.drug-response": {
|
| 4610 |
"query": "\"drug response prediction\" in:description,readme",
|
| 4611 |
"years": [
|
| 4612 |
2026,
|
| 4613 |
+
2025,
|
| 4614 |
+
2024
|
| 4615 |
]
|
| 4616 |
},
|
| 4617 |
"medical.ecg": {
|
| 4618 |
"query": "ECG \"deep learning\" in:description,readme",
|
| 4619 |
"years": [
|
| 4620 |
2026,
|
| 4621 |
+
2025,
|
| 4622 |
+
2024
|
| 4623 |
]
|
| 4624 |
},
|
| 4625 |
"medical.eeg": {
|
| 4626 |
"query": "EEG \"deep learning\" in:description,readme",
|
| 4627 |
"years": [
|
| 4628 |
2026,
|
| 4629 |
+
2025,
|
| 4630 |
+
2024
|
| 4631 |
]
|
| 4632 |
},
|
| 4633 |
"medical.ehr": {
|
|
|
|
| 4760 |
"query": "\"3D generation\" in:readme",
|
| 4761 |
"years": [
|
| 4762 |
2026,
|
| 4763 |
+
2025,
|
| 4764 |
+
2024
|
| 4765 |
]
|
| 4766 |
},
|
| 4767 |
"multimodal.3d-human-pose": {
|
| 4768 |
"query": "\"3D human pose estimation\" in:readme",
|
| 4769 |
"years": [
|
| 4770 |
2026,
|
| 4771 |
+
2025,
|
| 4772 |
+
2024
|
| 4773 |
]
|
| 4774 |
},
|
| 4775 |
"multimodal.3d-object-detection": {
|
| 4776 |
"query": "\"3D object detection\" in:readme",
|
| 4777 |
"years": [
|
| 4778 |
2026,
|
| 4779 |
+
2025,
|
| 4780 |
+
2024
|
| 4781 |
]
|
| 4782 |
},
|
| 4783 |
"multimodal.3d-reconstruction": {
|
| 4784 |
"query": "\"3D reconstruction\" in:description",
|
| 4785 |
"years": [
|
| 4786 |
2026,
|
| 4787 |
+
2025,
|
| 4788 |
+
2024
|
| 4789 |
]
|
| 4790 |
},
|
| 4791 |
"multimodal.audio-visual-learning": {
|
| 4792 |
"query": "\"audio visual learning\" in:readme",
|
| 4793 |
"years": [
|
| 4794 |
2026,
|
| 4795 |
+
2025,
|
| 4796 |
+
2024
|
| 4797 |
]
|
| 4798 |
},
|
| 4799 |
"multimodal.embodied-world-models": {
|
| 4800 |
"query": "\"embodied world model\" in:description",
|
| 4801 |
"years": [
|
| 4802 |
2026,
|
| 4803 |
+
2025,
|
| 4804 |
+
2024
|
| 4805 |
]
|
| 4806 |
},
|
| 4807 |
"multimodal.gaussian-splatting": {
|
| 4808 |
"query": "\"Gaussian splatting\" in:description",
|
| 4809 |
"years": [
|
| 4810 |
2026,
|
| 4811 |
+
2025,
|
| 4812 |
+
2024
|
| 4813 |
]
|
| 4814 |
},
|
| 4815 |
"multimodal.image-text": {
|
| 4816 |
"query": "\"image-text\" in:description",
|
| 4817 |
"years": [
|
| 4818 |
2026,
|
| 4819 |
+
2025,
|
| 4820 |
+
2024
|
| 4821 |
]
|
| 4822 |
},
|
| 4823 |
"multimodal.image-to-3d": {
|
| 4824 |
"query": "\"image to 3D\" in:readme",
|
| 4825 |
"years": [
|
| 4826 |
2026,
|
| 4827 |
+
2025,
|
| 4828 |
+
2024
|
| 4829 |
]
|
| 4830 |
},
|
| 4831 |
"multimodal.image-to-video": {
|
|
|
|
| 4944 |
"query": "BM25 retrieval in:readme",
|
| 4945 |
"years": [
|
| 4946 |
2026,
|
| 4947 |
+
2025,
|
| 4948 |
+
2024
|
| 4949 |
]
|
| 4950 |
},
|
| 4951 |
"nlp-document-retrieval": {
|
| 4952 |
"query": "\"document retrieval\" in:readme",
|
| 4953 |
"years": [
|
| 4954 |
2026,
|
| 4955 |
+
2025,
|
| 4956 |
+
2024
|
| 4957 |
]
|
| 4958 |
},
|
| 4959 |
"nlp-entity-linking": {
|
| 4960 |
"query": "\"entity linking\" in:readme",
|
| 4961 |
"years": [
|
| 4962 |
2026,
|
| 4963 |
+
2025,
|
| 4964 |
+
2024
|
| 4965 |
]
|
| 4966 |
},
|
| 4967 |
"nlp-natural-language-inference": {
|
| 4968 |
"query": "\"natural language inference\" in:readme",
|
| 4969 |
"years": [
|
| 4970 |
2026,
|
| 4971 |
+
2025,
|
| 4972 |
+
2024
|
| 4973 |
]
|
| 4974 |
},
|
| 4975 |
"nlp-query-understanding": {
|
| 4976 |
"query": "\"query understanding\" in:readme",
|
| 4977 |
"years": [
|
| 4978 |
2026,
|
| 4979 |
+
2025,
|
| 4980 |
+
2024
|
| 4981 |
]
|
| 4982 |
},
|
| 4983 |
"nlp-sparse-retrieval": {
|
| 4984 |
"query": "\"sparse retrieval\" in:readme",
|
| 4985 |
"years": [
|
| 4986 |
2026,
|
| 4987 |
+
2025,
|
| 4988 |
+
2024
|
| 4989 |
]
|
| 4990 |
},
|
| 4991 |
"nlp-speech-language-modeling": {
|
| 4992 |
"query": "\"speech language model\" in:readme",
|
| 4993 |
"years": [
|
| 4994 |
2026,
|
| 4995 |
+
2025,
|
| 4996 |
+
2024
|
| 4997 |
]
|
| 4998 |
},
|
| 4999 |
"nlp.coreference-resolution": {
|
| 5000 |
"query": "\"coreference resolution\" in:readme",
|
| 5001 |
"years": [
|
| 5002 |
2026,
|
| 5003 |
+
2025,
|
| 5004 |
+
2024
|
| 5005 |
]
|
| 5006 |
},
|
| 5007 |
"nlp.cross-encoder-reranking": {
|
| 5008 |
"query": "\"cross-encoder\" \"reranking\" in:readme",
|
| 5009 |
"years": [
|
| 5010 |
2026,
|
| 5011 |
+
2025,
|
| 5012 |
+
2024
|
| 5013 |
]
|
| 5014 |
},
|
| 5015 |
"nlp.dense-retrieval": {
|
| 5016 |
"query": "\"dense retrieval\" in:readme",
|
| 5017 |
"years": [
|
| 5018 |
2026,
|
| 5019 |
+
2025,
|
| 5020 |
+
2024
|
| 5021 |
]
|
| 5022 |
},
|
| 5023 |
"nlp.dependency-parsing": {
|
| 5024 |
"query": "\"dependency parsing\" in:readme",
|
| 5025 |
"years": [
|
| 5026 |
2026,
|
| 5027 |
+
2025,
|
| 5028 |
+
2024
|
| 5029 |
]
|
| 5030 |
},
|
| 5031 |
"nlp.dialogue-systems": {
|
| 5032 |
"query": "\"dialogue system\" in:readme",
|
| 5033 |
"years": [
|
| 5034 |
2026,
|
| 5035 |
+
2025,
|
| 5036 |
+
2024
|
| 5037 |
]
|
| 5038 |
},
|
| 5039 |
"nlp.information-extraction": {
|
| 5040 |
"query": "\"information extraction\" in:description",
|
| 5041 |
"years": [
|
| 5042 |
2026,
|
| 5043 |
+
2025,
|
| 5044 |
+
2024
|
| 5045 |
]
|
| 5046 |
},
|
| 5047 |
"nlp.language-modeling": {
|
| 5048 |
"query": "\"language modeling\" in:description",
|
| 5049 |
"years": [
|
| 5050 |
2026,
|
| 5051 |
+
2025,
|
| 5052 |
+
2024
|
| 5053 |
]
|
| 5054 |
},
|
| 5055 |
"nlp.machine-translation": {
|
| 5056 |
"query": "\"machine translation\" in:description",
|
| 5057 |
"years": [
|
| 5058 |
2026,
|
| 5059 |
+
2025,
|
| 5060 |
+
2024
|
| 5061 |
]
|
| 5062 |
},
|
| 5063 |
"nlp.named-entity-recognition": {
|
| 5064 |
"query": "\"named entity recognition\" in:readme",
|
| 5065 |
"years": [
|
| 5066 |
2026,
|
| 5067 |
+
2025,
|
| 5068 |
+
2024
|
| 5069 |
]
|
| 5070 |
},
|
| 5071 |
"nlp.neural-reranking": {
|
|
|
|
| 5142 |
"query": "archaeology \"remote sensing\" in:name,description,readme",
|
| 5143 |
"years": [
|
| 5144 |
2026,
|
| 5145 |
+
2025,
|
| 5146 |
+
2024
|
| 5147 |
]
|
| 5148 |
},
|
| 5149 |
"recall.case-outcome-prediction": {
|
| 5150 |
"query": "\"case outcome prediction\" in:name,description,readme",
|
| 5151 |
"years": [
|
| 5152 |
2026,
|
| 5153 |
+
2025,
|
| 5154 |
+
2024
|
| 5155 |
]
|
| 5156 |
},
|
| 5157 |
"recall.computational-linguistics": {
|
| 5158 |
"query": "\"computational linguistics\" model in:name,description,readme",
|
| 5159 |
"years": [
|
| 5160 |
2026,
|
| 5161 |
+
2025,
|
| 5162 |
+
2024
|
| 5163 |
]
|
| 5164 |
},
|
| 5165 |
"recall.computational-social-science": {
|
| 5166 |
"query": "\"computational social science\" in:name,description,readme",
|
| 5167 |
"years": [
|
| 5168 |
2026,
|
| 5169 |
+
2025,
|
| 5170 |
+
2024
|
| 5171 |
]
|
| 5172 |
},
|
| 5173 |
"recall.digital-humanities-nlp": {
|
| 5174 |
"query": "\"digital humanities\" NLP in:name,description,readme",
|
| 5175 |
"years": [
|
| 5176 |
2026,
|
| 5177 |
+
2025,
|
| 5178 |
+
2024
|
| 5179 |
]
|
| 5180 |
},
|
| 5181 |
"recall.disease-surveillance": {
|
| 5182 |
"query": "\"disease surveillance\" machine learning in:name,description,readme",
|
| 5183 |
"years": [
|
| 5184 |
2026,
|
| 5185 |
+
2025,
|
| 5186 |
+
2024
|
| 5187 |
]
|
| 5188 |
},
|
| 5189 |
"recall.educational-recommendation": {
|
| 5190 |
"query": "\"educational recommendation\" in:name,description,readme",
|
| 5191 |
"years": [
|
| 5192 |
2026,
|
| 5193 |
+
2025,
|
| 5194 |
+
2024
|
| 5195 |
]
|
| 5196 |
},
|
| 5197 |
"recall.epidemic-forecasting": {
|
| 5198 |
"query": "\"epidemic forecasting\" in:name,description,readme",
|
| 5199 |
"years": [
|
| 5200 |
2026,
|
| 5201 |
+
2025,
|
| 5202 |
+
2024
|
| 5203 |
]
|
| 5204 |
},
|
| 5205 |
"recall.epidemiological-modeling": {
|
| 5206 |
"query": "\"epidemiological modeling\" machine learning in:name,description,readme",
|
| 5207 |
"years": [
|
| 5208 |
2026,
|
| 5209 |
+
2025,
|
| 5210 |
+
2024
|
| 5211 |
]
|
| 5212 |
},
|
| 5213 |
"recall.grid-stability-prediction": {
|
|
|
|
| 5375 |
"query": "\"audio language model\" in:description,readme",
|
| 5376 |
"years": [
|
| 5377 |
2026,
|
| 5378 |
+
2025,
|
| 5379 |
+
2024
|
| 5380 |
]
|
| 5381 |
},
|
| 5382 |
"recall26.class-imbalance": {
|
| 5383 |
"query": "\"class imbalance\" \"machine learning\" in:description,readme",
|
| 5384 |
"years": [
|
| 5385 |
2026,
|
| 5386 |
+
2025,
|
| 5387 |
+
2024
|
| 5388 |
]
|
| 5389 |
},
|
| 5390 |
"recall26.image-dehazing": {
|
| 5391 |
"query": "\"image dehazing\" in:description,readme",
|
| 5392 |
"years": [
|
| 5393 |
2026,
|
| 5394 |
+
2025,
|
| 5395 |
+
2024
|
| 5396 |
]
|
| 5397 |
},
|
| 5398 |
"recall26.image-denoising": {
|
| 5399 |
"query": "\"image denoising\" in:description,readme",
|
| 5400 |
"years": [
|
| 5401 |
2026,
|
| 5402 |
+
2025,
|
| 5403 |
+
2024
|
| 5404 |
]
|
| 5405 |
},
|
| 5406 |
"recall26.long-tailed-recognition": {
|
| 5407 |
"query": "\"long-tailed recognition\" in:description,readme",
|
| 5408 |
"years": [
|
| 5409 |
2026,
|
| 5410 |
+
2025,
|
| 5411 |
+
2024
|
| 5412 |
]
|
| 5413 |
},
|
| 5414 |
"recall26.low-light-enhancement": {
|
| 5415 |
"query": "\"low-light image enhancement\" in:description,readme",
|
| 5416 |
"years": [
|
| 5417 |
2026,
|
| 5418 |
+
2025,
|
| 5419 |
+
2024
|
| 5420 |
]
|
| 5421 |
},
|
| 5422 |
"recall26.sign-language": {
|
| 5423 |
"query": "\"sign language\" in:description,readme",
|
| 5424 |
"years": [
|
| 5425 |
2026,
|
| 5426 |
+
2025,
|
| 5427 |
+
2024
|
| 5428 |
]
|
| 5429 |
},
|
| 5430 |
"recall26.visual-grounding": {
|
| 5431 |
"query": "\"visual grounding\" in:description,readme",
|
| 5432 |
"years": [
|
| 5433 |
2026,
|
| 5434 |
+
2025,
|
| 5435 |
+
2024
|
| 5436 |
]
|
| 5437 |
},
|
| 5438 |
"rl.bandits": {
|
| 5439 |
"query": "\"multi-armed bandit\" in:description",
|
| 5440 |
"years": [
|
| 5441 |
2026,
|
| 5442 |
+
2025,
|
| 5443 |
+
2024
|
| 5444 |
]
|
| 5445 |
},
|
| 5446 |
"rl.continual": {
|
| 5447 |
"query": "\"continual reinforcement learning\" in:description,readme",
|
| 5448 |
"years": [
|
| 5449 |
2026,
|
| 5450 |
+
2025,
|
| 5451 |
+
2024
|
| 5452 |
]
|
| 5453 |
},
|
| 5454 |
"rl.core": {
|
| 5455 |
"query": "topic:reinforcement-learning",
|
| 5456 |
"years": [
|
| 5457 |
2026,
|
| 5458 |
+
2025,
|
| 5459 |
+
2024
|
| 5460 |
]
|
| 5461 |
},
|
| 5462 |
"rl.deep": {
|
| 5463 |
"query": "\"deep reinforcement learning\" in:description",
|
| 5464 |
"years": [
|
| 5465 |
2026,
|
| 5466 |
+
2025,
|
| 5467 |
+
2024
|
| 5468 |
]
|
| 5469 |
},
|
| 5470 |
"rl.deep-q-learning": {
|
| 5471 |
"query": "\"deep q-learning\" in:description",
|
| 5472 |
"years": [
|
| 5473 |
2026,
|
| 5474 |
+
2025,
|
| 5475 |
+
2024
|
| 5476 |
]
|
| 5477 |
},
|
| 5478 |
"rl.distributional": {
|
| 5479 |
"query": "\"distributional reinforcement learning\" in:description,readme",
|
| 5480 |
"years": [
|
| 5481 |
2026,
|
| 5482 |
+
2025,
|
| 5483 |
+
2024
|
| 5484 |
]
|
| 5485 |
},
|
| 5486 |
"rl.exploration": {
|
| 5487 |
"query": "\"reinforcement learning exploration\" in:description",
|
| 5488 |
"years": [
|
| 5489 |
2026,
|
| 5490 |
+
2025,
|
| 5491 |
+
2024
|
| 5492 |
]
|
| 5493 |
},
|
| 5494 |
"rl.games": {
|
| 5495 |
"query": "topic:game-ai",
|
| 5496 |
"years": [
|
| 5497 |
2026,
|
| 5498 |
+
2025,
|
| 5499 |
+
2024
|
| 5500 |
]
|
| 5501 |
},
|
| 5502 |
"rl.hierarchical": {
|
| 5503 |
"query": "\"hierarchical reinforcement learning\" in:description",
|
| 5504 |
"years": [
|
| 5505 |
2026,
|
| 5506 |
+
2025,
|
| 5507 |
+
2024
|
| 5508 |
]
|
| 5509 |
},
|
| 5510 |
"rl.imitation-learning": {
|
|
|
|
| 5602 |
"query": "\"autonomous driving\" in:description",
|
| 5603 |
"years": [
|
| 5604 |
2026,
|
| 5605 |
+
2025,
|
| 5606 |
+
2024
|
| 5607 |
]
|
| 5608 |
},
|
| 5609 |
"robotics.behavior-cloning": {
|
| 5610 |
"query": "\"behavior cloning\" in:description",
|
| 5611 |
"years": [
|
| 5612 |
2026,
|
| 5613 |
+
2025,
|
| 5614 |
+
2024
|
| 5615 |
]
|
| 5616 |
},
|
| 5617 |
"robotics.diffusion-policy": {
|
| 5618 |
"query": "\"diffusion policy\" in:description",
|
| 5619 |
"years": [
|
| 5620 |
2026,
|
| 5621 |
+
2025,
|
| 5622 |
+
2024
|
| 5623 |
]
|
| 5624 |
},
|
| 5625 |
"robotics.domain-randomization": {
|
| 5626 |
"query": "\"domain randomization\" in:description",
|
| 5627 |
"years": [
|
| 5628 |
2026,
|
| 5629 |
+
2025,
|
| 5630 |
+
2024
|
| 5631 |
]
|
| 5632 |
},
|
| 5633 |
"robotics.embodied-ai": {
|
| 5634 |
"query": "\"embodied AI\" in:description",
|
| 5635 |
"years": [
|
| 5636 |
2026,
|
| 5637 |
+
2025,
|
| 5638 |
+
2024
|
| 5639 |
]
|
| 5640 |
},
|
| 5641 |
"robotics.grasping": {
|
| 5642 |
"query": "\"robot grasping\" in:description",
|
| 5643 |
"years": [
|
| 5644 |
2026,
|
| 5645 |
+
2025,
|
| 5646 |
+
2024
|
| 5647 |
]
|
| 5648 |
},
|
| 5649 |
"robotics.imitation-learning": {
|
| 5650 |
"query": "\"imitation learning\" robotics in:description",
|
| 5651 |
"years": [
|
| 5652 |
2026,
|
| 5653 |
+
2025,
|
| 5654 |
+
2024
|
| 5655 |
]
|
| 5656 |
},
|
| 5657 |
"robotics.locomotion": {
|
| 5658 |
"query": "\"robot locomotion\" in:description",
|
| 5659 |
"years": [
|
| 5660 |
2026,
|
| 5661 |
+
2025,
|
| 5662 |
+
2024
|
| 5663 |
]
|
| 5664 |
},
|
| 5665 |
"robotics.manipulation": {
|
| 5666 |
"query": "\"robot manipulation\" in:description",
|
| 5667 |
"years": [
|
| 5668 |
2026,
|
| 5669 |
+
2025,
|
| 5670 |
+
2024
|
| 5671 |
]
|
| 5672 |
},
|
| 5673 |
"robotics.navigation": {
|
|
|
|
| 5772 |
"query": "astronomy in:readme",
|
| 5773 |
"years": [
|
| 5774 |
2026,
|
| 5775 |
+
2025,
|
| 5776 |
+
2024
|
| 5777 |
]
|
| 5778 |
},
|
| 5779 |
"science.astrophysics": {
|
| 5780 |
"query": "astrophysics in:description",
|
| 5781 |
"years": [
|
| 5782 |
2026,
|
| 5783 |
+
2025,
|
| 5784 |
+
2024
|
| 5785 |
]
|
| 5786 |
},
|
| 5787 |
"science.climate-modeling": {
|
| 5788 |
"query": "\"climate modeling\" in:readme",
|
| 5789 |
"years": [
|
| 5790 |
2026,
|
| 5791 |
+
2025,
|
| 5792 |
+
2024
|
| 5793 |
]
|
| 5794 |
},
|
| 5795 |
"science.computational-mechanics": {
|
| 5796 |
"query": "\"computational mechanics\" \"machine learning\" in:description,readme",
|
| 5797 |
"years": [
|
| 5798 |
2026,
|
| 5799 |
+
2025,
|
| 5800 |
+
2024
|
| 5801 |
]
|
| 5802 |
},
|
| 5803 |
"science.cosmology": {
|
| 5804 |
"query": "cosmology in:description",
|
| 5805 |
"years": [
|
| 5806 |
2026,
|
| 5807 |
+
2025,
|
| 5808 |
+
2024
|
| 5809 |
]
|
| 5810 |
},
|
| 5811 |
"science.differentiable-simulation": {
|
| 5812 |
"query": "\"differentiable simulation\" in:readme",
|
| 5813 |
"years": [
|
| 5814 |
2026,
|
| 5815 |
+
2025,
|
| 5816 |
+
2024
|
| 5817 |
]
|
| 5818 |
},
|
| 5819 |
"science.exoplanets": {
|
| 5820 |
"query": "exoplanet in:description \"machine learning\"",
|
| 5821 |
"years": [
|
| 5822 |
2026,
|
| 5823 |
+
2025,
|
| 5824 |
+
2024
|
| 5825 |
]
|
| 5826 |
},
|
| 5827 |
"science.fluid-dynamics": {
|
| 5828 |
"query": "\"fluid dynamics\" in:description",
|
| 5829 |
"years": [
|
| 5830 |
2026,
|
| 5831 |
+
2025,
|
| 5832 |
+
2024
|
| 5833 |
]
|
| 5834 |
},
|
| 5835 |
"science.fusion-plasma": {
|
|
|
|
| 5962 |
"query": "\"carbon footprint\" \"deep learning\" training in:description,readme",
|
| 5963 |
"years": [
|
| 5964 |
2026,
|
| 5965 |
+
2025,
|
| 5966 |
+
2024
|
| 5967 |
]
|
| 5968 |
},
|
| 5969 |
"specialized.computational-pathology-ml": {
|
| 5970 |
"query": "\"computational pathology\" \"machine learning\" in:description,readme",
|
| 5971 |
"years": [
|
| 5972 |
2026,
|
| 5973 |
+
2025,
|
| 5974 |
+
2024
|
| 5975 |
]
|
| 5976 |
},
|
| 5977 |
"specialized.coreset-selection": {
|
| 5978 |
"query": "\"coreset selection\" in:description,readme",
|
| 5979 |
"years": [
|
| 5980 |
2026,
|
| 5981 |
+
2025,
|
| 5982 |
+
2024
|
| 5983 |
]
|
| 5984 |
},
|
| 5985 |
"specialized.curriculum-learning": {
|
| 5986 |
"query": "\"curriculum learning\" in:description,readme",
|
| 5987 |
"years": [
|
| 5988 |
2026,
|
| 5989 |
+
2025,
|
| 5990 |
+
2024
|
| 5991 |
]
|
| 5992 |
},
|
| 5993 |
"specialized.data-centric-ai": {
|
| 5994 |
"query": "\"data-centric AI\" in:description,readme",
|
| 5995 |
"years": [
|
| 5996 |
2026,
|
| 5997 |
+
2025,
|
| 5998 |
+
2024
|
| 5999 |
]
|
| 6000 |
},
|
| 6001 |
"specialized.data-shapley": {
|
| 6002 |
"query": "\"data Shapley\" in:description,readme",
|
| 6003 |
"years": [
|
| 6004 |
2026,
|
| 6005 |
+
2025,
|
| 6006 |
+
2024
|
| 6007 |
]
|
| 6008 |
},
|
| 6009 |
"specialized.dataset-distillation": {
|
| 6010 |
"query": "\"dataset distillation\" in:description,readme",
|
| 6011 |
"years": [
|
| 6012 |
2026,
|
| 6013 |
+
2025,
|
| 6014 |
+
2024
|
| 6015 |
]
|
| 6016 |
},
|
| 6017 |
"specialized.event-camera-deep-learning": {
|
| 6018 |
"query": "\"event camera\" \"deep learning\" in:description,readme",
|
| 6019 |
"years": [
|
| 6020 |
2026,
|
| 6021 |
+
2025,
|
| 6022 |
+
2024
|
| 6023 |
]
|
| 6024 |
},
|
| 6025 |
"specialized.fmri-decoding-ml": {
|
|
|
|
| 6075 |
"query": "\"time series anomaly detection\" in:description",
|
| 6076 |
"years": [
|
| 6077 |
2026,
|
| 6078 |
+
2025,
|
| 6079 |
+
2024
|
| 6080 |
]
|
| 6081 |
},
|
| 6082 |
"timeseries.classification": {
|
| 6083 |
"query": "\"time series classification\" in:description",
|
| 6084 |
"years": [
|
| 6085 |
2026,
|
| 6086 |
+
2025,
|
| 6087 |
+
2024
|
| 6088 |
]
|
| 6089 |
},
|
| 6090 |
"timeseries.clustering": {
|
| 6091 |
"query": "\"time series clustering\" in:description,readme",
|
| 6092 |
"years": [
|
| 6093 |
2026,
|
| 6094 |
+
2025,
|
| 6095 |
+
2024
|
| 6096 |
]
|
| 6097 |
},
|
| 6098 |
"timeseries.energy-forecasting": {
|
| 6099 |
"query": "\"energy forecasting\" in:description",
|
| 6100 |
"years": [
|
| 6101 |
2026,
|
| 6102 |
+
2025,
|
| 6103 |
+
2024
|
| 6104 |
]
|
| 6105 |
},
|
| 6106 |
"timeseries.event-forecasting": {
|
| 6107 |
"query": "\"event forecasting\" in:description",
|
| 6108 |
"years": [
|
| 6109 |
2026,
|
| 6110 |
+
2025,
|
| 6111 |
+
2024
|
| 6112 |
]
|
| 6113 |
},
|
| 6114 |
"timeseries.forecasting": {
|
| 6115 |
"query": "\"time series forecasting\" in:description",
|
| 6116 |
"years": [
|
| 6117 |
2026,
|
| 6118 |
+
2025,
|
| 6119 |
+
2024
|
| 6120 |
]
|
| 6121 |
},
|
| 6122 |
"timeseries.foundation-models": {
|
| 6123 |
"query": "\"time series foundation model\" in:description",
|
| 6124 |
"years": [
|
| 6125 |
2026,
|
| 6126 |
+
2025,
|
| 6127 |
+
2024
|
| 6128 |
]
|
| 6129 |
},
|
| 6130 |
"timeseries.imputation": {
|
| 6131 |
"query": "\"time series imputation\" in:description,readme",
|
| 6132 |
"years": [
|
| 6133 |
2026,
|
| 6134 |
+
2025,
|
| 6135 |
+
2024
|
| 6136 |
]
|
| 6137 |
},
|
| 6138 |
"timeseries.long-horizon": {
|
|
|
|
| 6265 |
"query": "\"adversarial robustness\" in:description",
|
| 6266 |
"years": [
|
| 6267 |
2026,
|
| 6268 |
+
2025,
|
| 6269 |
+
2024
|
| 6270 |
]
|
| 6271 |
},
|
| 6272 |
"trust.ai-alignment": {
|
| 6273 |
"query": "\"AI alignment\" in:description",
|
| 6274 |
"years": [
|
| 6275 |
2026,
|
| 6276 |
+
2025,
|
| 6277 |
+
2024
|
| 6278 |
]
|
| 6279 |
},
|
| 6280 |
"trust.ai-safety": {
|
| 6281 |
"query": "\"AI safety\" in:description",
|
| 6282 |
"years": [
|
| 6283 |
2026,
|
| 6284 |
+
2025,
|
| 6285 |
+
2024
|
| 6286 |
]
|
| 6287 |
},
|
| 6288 |
"trust.algorithmic-fairness": {
|
| 6289 |
"query": "\"algorithmic fairness\" in:description",
|
| 6290 |
"years": [
|
| 6291 |
2026,
|
| 6292 |
+
2025,
|
| 6293 |
+
2024
|
| 6294 |
]
|
| 6295 |
},
|
| 6296 |
"trust.algorithmic-recourse": {
|
| 6297 |
"query": "\"algorithmic recourse\" in:description",
|
| 6298 |
"years": [
|
| 6299 |
2026,
|
| 6300 |
+
2025,
|
| 6301 |
+
2024
|
| 6302 |
]
|
| 6303 |
},
|
| 6304 |
"trust.causal-discovery": {
|
| 6305 |
"query": "\"causal discovery\" in:description",
|
| 6306 |
"years": [
|
| 6307 |
2026,
|
| 6308 |
+
2025,
|
| 6309 |
+
2024
|
| 6310 |
]
|
| 6311 |
},
|
| 6312 |
"trust.causal-effect-estimation": {
|
| 6313 |
"query": "\"causal effect estimation\" in:description",
|
| 6314 |
"years": [
|
| 6315 |
2026,
|
| 6316 |
+
2025,
|
| 6317 |
+
2024
|
| 6318 |
]
|
| 6319 |
},
|
| 6320 |
"trust.causal-mediation": {
|
| 6321 |
"query": "\"causal mediation analysis\" in:description",
|
| 6322 |
"years": [
|
| 6323 |
2026,
|
| 6324 |
+
2025,
|
| 6325 |
+
2024
|
| 6326 |
]
|
| 6327 |
},
|
| 6328 |
"trust.causal-ml": {
|
|
|
|
| 6476 |
"query": "\"action recognition\" in:description",
|
| 6477 |
"years": [
|
| 6478 |
2026,
|
| 6479 |
+
2025,
|
| 6480 |
+
2024
|
| 6481 |
]
|
| 6482 |
},
|
| 6483 |
"vision.change-detection": {
|
| 6484 |
"query": "\"change detection\" in:readme",
|
| 6485 |
"years": [
|
| 6486 |
2026,
|
| 6487 |
+
2025,
|
| 6488 |
+
2024
|
| 6489 |
]
|
| 6490 |
},
|
| 6491 |
"vision.depth-estimation": {
|
| 6492 |
"query": "\"depth estimation\" in:readme",
|
| 6493 |
"years": [
|
| 6494 |
2026,
|
| 6495 |
+
2025,
|
| 6496 |
+
2024
|
| 6497 |
]
|
| 6498 |
},
|
| 6499 |
"vision.face-recognition": {
|
| 6500 |
"query": "\"face recognition\" in:readme",
|
| 6501 |
"years": [
|
| 6502 |
2026,
|
| 6503 |
+
2025,
|
| 6504 |
+
2024
|
| 6505 |
]
|
| 6506 |
},
|
| 6507 |
"vision.image-captioning": {
|
| 6508 |
"query": "\"image captioning\" in:readme",
|
| 6509 |
"years": [
|
| 6510 |
2026,
|
| 6511 |
+
2025,
|
| 6512 |
+
2024
|
| 6513 |
]
|
| 6514 |
},
|
| 6515 |
"vision.image-classification": {
|
| 6516 |
"query": "\"image classification\" in:description",
|
| 6517 |
"years": [
|
| 6518 |
2026,
|
| 6519 |
+
2025,
|
| 6520 |
+
2024
|
| 6521 |
]
|
| 6522 |
},
|
| 6523 |
"vision.image-diffusion": {
|
| 6524 |
"query": "\"image diffusion model\" in:readme",
|
| 6525 |
"years": [
|
| 6526 |
2026,
|
| 6527 |
+
2025,
|
| 6528 |
+
2024
|
| 6529 |
]
|
| 6530 |
},
|
| 6531 |
"vision.image-restoration": {
|
| 6532 |
"query": "\"image restoration\" in:readme",
|
| 6533 |
"years": [
|
| 6534 |
2026,
|
| 6535 |
+
2025,
|
| 6536 |
+
2024
|
| 6537 |
]
|
| 6538 |
},
|
| 6539 |
"vision.image-retrieval": {
|
|
|
|
| 6679 |
"end": "2026-09-24",
|
| 6680 |
"search_policy_version": 2,
|
| 6681 |
"start_year": 2008,
|
| 6682 |
+
"updated_at": "2026-09-30T16:30:43Z"
|
| 6683 |
}
|