Add GitHub ML run 20260927T155349000000Z-fbebcc43
Browse files
.gitattributes
CHANGED
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@@ -78,3 +78,4 @@ data/observations/2026/09/26/20260926T135007000000Z-052dc2ca.jsonl filter=lfs di
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data/observations/2026/09/26/20260926T143436000000Z-942e19b8.jsonl filter=lfs diff=lfs merge=lfs -text
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data/observations/2026/09/26/20260926T145046000000Z-e4445579.jsonl filter=lfs diff=lfs merge=lfs -text
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data/observations/2026/09/27/20260927T150859000000Z-f5fb8d48.jsonl filter=lfs diff=lfs merge=lfs -text
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data/observations/2026/09/26/20260926T143436000000Z-942e19b8.jsonl filter=lfs diff=lfs merge=lfs -text
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data/observations/2026/09/26/20260926T145046000000Z-e4445579.jsonl filter=lfs diff=lfs merge=lfs -text
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data/observations/2026/09/27/20260927T150859000000Z-f5fb8d48.jsonl filter=lfs diff=lfs merge=lfs -text
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data/observations/2026/09/27/20260927T155349000000Z-fbebcc43.jsonl filter=lfs diff=lfs merge=lfs -text
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coverage/20260927T155349000000Z-fbebcc43.json
ADDED
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@@ -0,0 +1,2812 @@
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}
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data/observations/2026/09/27/20260927T155349000000Z-fbebcc43.jsonl
ADDED
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state/historical-sample.json
CHANGED
|
@@ -2352,43 +2352,50 @@
|
|
| 2352 |
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|
| 2353 |
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|
| 2354 |
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| 2355 |
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| 2356 |
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|
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|
| 2359 |
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|
| 2360 |
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| 2361 |
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2026
|
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|
| 2362 |
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|
| 2363 |
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|
| 2364 |
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|
| 2365 |
"query": "\"causal inference\" in:description",
|
| 2366 |
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| 2367 |
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|
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|
| 2368 |
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|
| 2369 |
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| 2370 |
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|
| 2371 |
"query": "\"customer churn prediction\" in:description",
|
| 2372 |
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| 2373 |
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|
|
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|
| 2374 |
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|
| 2375 |
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|
| 2376 |
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|
| 2377 |
"query": "\"click-through rate prediction\" in:readme",
|
| 2378 |
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| 2379 |
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|
| 2380 |
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|
| 2381 |
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|
| 2382 |
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|
| 2383 |
"query": "\"collaborative filtering\" in:description",
|
| 2384 |
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|
| 2385 |
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2026
|
|
|
|
| 2386 |
]
|
| 2387 |
},
|
| 2388 |
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|
| 2389 |
"query": "\"credit scoring\" in:description",
|
| 2390 |
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| 2391 |
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2026
|
|
|
|
| 2392 |
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|
| 2393 |
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|
| 2394 |
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|
|
@@ -2716,79 +2723,92 @@
|
|
| 2716 |
"audio-speech-to-text": {
|
| 2717 |
"query": "\"speech to text\" in:readme",
|
| 2718 |
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| 2719 |
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2026
|
|
|
|
| 2720 |
]
|
| 2721 |
},
|
| 2722 |
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|
| 2723 |
"query": "\"speech translation\" in:readme",
|
| 2724 |
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|
| 2725 |
-
2026
|
|
|
|
| 2726 |
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|
| 2727 |
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|
| 2728 |
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|
| 2729 |
"query": "\"text to audio\" in:readme",
|
| 2730 |
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| 2731 |
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|
|
|
|
| 2732 |
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|
| 2733 |
},
|
| 2734 |
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|
| 2735 |
"query": "\"text to speech\" in:readme",
|
| 2736 |
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|
| 2737 |
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2026
|
|
|
|
| 2738 |
]
|
| 2739 |
},
|
| 2740 |
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|
| 2741 |
"query": "\"voice conversion\" in:readme",
|
| 2742 |
"years": [
|
| 2743 |
-
2026
|
|
|
|
| 2744 |
]
|
| 2745 |
},
|
| 2746 |
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|
| 2747 |
"query": "\"cell atlas\" in:description,readme",
|
| 2748 |
"years": [
|
| 2749 |
-
2026
|
|
|
|
| 2750 |
]
|
| 2751 |
},
|
| 2752 |
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|
| 2753 |
"query": "\"protein function prediction\" in:description,readme",
|
| 2754 |
"years": [
|
| 2755 |
-
2026
|
|
|
|
| 2756 |
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|
| 2757 |
},
|
| 2758 |
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|
| 2759 |
"query": "\"CRISPR screen\" in:description,readme",
|
| 2760 |
"years": [
|
| 2761 |
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2026
|
|
|
|
| 2762 |
]
|
| 2763 |
},
|
| 2764 |
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|
| 2765 |
"query": "\"DNA language model\" in:description",
|
| 2766 |
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|
| 2767 |
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|
|
|
|
| 2768 |
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|
| 2769 |
},
|
| 2770 |
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|
| 2771 |
"query": "\"gene regulatory network\" in:description,readme",
|
| 2772 |
"years": [
|
| 2773 |
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2026
|
|
|
|
| 2774 |
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|
| 2775 |
},
|
| 2776 |
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|
| 2777 |
"query": "\"genome assembly\" \"neural network\" in:description,readme",
|
| 2778 |
"years": [
|
| 2779 |
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2026
|
|
|
|
| 2780 |
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|
| 2781 |
},
|
| 2782 |
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|
| 2783 |
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|
| 2784 |
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|
| 2785 |
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|
|
|
|
| 2786 |
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|
| 2787 |
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|
| 2788 |
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|
| 2789 |
"query": "\"inverse folding\" in:readme",
|
| 2790 |
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|
| 2791 |
-
2026
|
|
|
|
| 2792 |
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|
| 2793 |
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|
| 2794 |
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|
|
@@ -2902,169 +2922,197 @@
|
|
| 2902 |
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|
| 2903 |
"query": "\"ADMET prediction\" in:description,readme",
|
| 2904 |
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|
| 2905 |
-
2026
|
|
|
|
| 2906 |
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|
| 2907 |
},
|
| 2908 |
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|
| 2909 |
"query": "\"binding affinity prediction\" in:description,readme",
|
| 2910 |
"years": [
|
| 2911 |
-
2026
|
|
|
|
| 2912 |
]
|
| 2913 |
},
|
| 2914 |
"chem-catalyst-discovery": {
|
| 2915 |
"query": "\"catalyst discovery\" in:description,readme",
|
| 2916 |
"years": [
|
| 2917 |
-
2026
|
|
|
|
| 2918 |
]
|
| 2919 |
},
|
| 2920 |
"chem-cheminformatics": {
|
| 2921 |
"query": "topic:cheminformatics",
|
| 2922 |
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|
| 2923 |
-
2026
|
|
|
|
| 2924 |
]
|
| 2925 |
},
|
| 2926 |
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|
| 2927 |
"query": "\"drug discovery\" in:description,readme",
|
| 2928 |
"years": [
|
| 2929 |
-
2026
|
|
|
|
| 2930 |
]
|
| 2931 |
},
|
| 2932 |
"chem-interatomic-potentials": {
|
| 2933 |
"query": "\"machine learning interatomic potential\" in:description,readme",
|
| 2934 |
"years": [
|
| 2935 |
-
2026
|
|
|
|
| 2936 |
]
|
| 2937 |
},
|
| 2938 |
"chem-molecular-docking": {
|
| 2939 |
"query": "\"molecular docking\" in:description,readme",
|
| 2940 |
"years": [
|
| 2941 |
-
2026
|
|
|
|
| 2942 |
]
|
| 2943 |
},
|
| 2944 |
"chem-molecular-dynamics": {
|
| 2945 |
"query": "\"molecular dynamics\" \"machine learning\" in:description,readme",
|
| 2946 |
"years": [
|
| 2947 |
-
2026
|
|
|
|
| 2948 |
]
|
| 2949 |
},
|
| 2950 |
"chem-molecular-generation": {
|
| 2951 |
"query": "\"molecular generation\" in:description,readme",
|
| 2952 |
"years": [
|
| 2953 |
-
2026
|
|
|
|
| 2954 |
]
|
| 2955 |
},
|
| 2956 |
"chem-molecular-property-benchmark": {
|
| 2957 |
"query": "MoleculeNet in:description,readme",
|
| 2958 |
"years": [
|
| 2959 |
-
2026
|
|
|
|
| 2960 |
]
|
| 2961 |
},
|
| 2962 |
"chem-molecular-spectroscopy": {
|
| 2963 |
"query": "molecular spectroscopy \"machine learning\" in:description,readme",
|
| 2964 |
"years": [
|
| 2965 |
-
2026
|
|
|
|
| 2966 |
]
|
| 2967 |
},
|
| 2968 |
"chem-property-prediction": {
|
| 2969 |
"query": "\"molecular property prediction\" in:description,readme",
|
| 2970 |
"years": [
|
| 2971 |
-
2026
|
|
|
|
| 2972 |
]
|
| 2973 |
},
|
| 2974 |
"chem-protein-ligand-docking": {
|
| 2975 |
"query": "\"protein-ligand docking\" in:description,readme",
|
| 2976 |
"years": [
|
| 2977 |
-
2026
|
|
|
|
| 2978 |
]
|
| 2979 |
},
|
| 2980 |
"chem-quantum-chemistry": {
|
| 2981 |
"query": "\"quantum chemistry\" \"machine learning\" in:description,readme",
|
| 2982 |
"years": [
|
| 2983 |
-
2026
|
|
|
|
| 2984 |
]
|
| 2985 |
},
|
| 2986 |
"chem-reaction-conditions": {
|
| 2987 |
"query": "\"reaction condition prediction\" in:description,readme",
|
| 2988 |
"years": [
|
| 2989 |
-
2026
|
|
|
|
| 2990 |
]
|
| 2991 |
},
|
| 2992 |
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|
| 2993 |
"query": "\"reaction prediction\" in:description,readme",
|
| 2994 |
"years": [
|
| 2995 |
-
2026
|
|
|
|
| 2996 |
]
|
| 2997 |
},
|
| 2998 |
"chem-reaction-yield": {
|
| 2999 |
"query": "\"reaction yield prediction\" in:description,readme",
|
| 3000 |
"years": [
|
| 3001 |
-
2026
|
|
|
|
| 3002 |
]
|
| 3003 |
},
|
| 3004 |
"chem-retrosynthesis": {
|
| 3005 |
"query": "\"computer aided synthesis planning\" in:description,readme",
|
| 3006 |
"years": [
|
| 3007 |
-
2026
|
|
|
|
| 3008 |
]
|
| 3009 |
},
|
| 3010 |
"chem-retrosynthesis-planning": {
|
| 3011 |
"query": "retrosynthesis in:description,readme",
|
| 3012 |
"years": [
|
| 3013 |
-
2026
|
|
|
|
| 3014 |
]
|
| 3015 |
},
|
| 3016 |
"chem-virtual-screening": {
|
| 3017 |
"query": "\"virtual screening\" in:description,readme",
|
| 3018 |
"years": [
|
| 3019 |
-
2026
|
|
|
|
| 3020 |
]
|
| 3021 |
},
|
| 3022 |
"efficiency.activation-checkpointing": {
|
| 3023 |
"query": "\"activation checkpointing\" in:description",
|
| 3024 |
"years": [
|
| 3025 |
-
2026
|
|
|
|
| 3026 |
]
|
| 3027 |
},
|
| 3028 |
"efficiency.automl": {
|
| 3029 |
"query": "AutoML in:description",
|
| 3030 |
"years": [
|
| 3031 |
-
2026
|
|
|
|
| 3032 |
]
|
| 3033 |
},
|
| 3034 |
"efficiency.communication-efficient-training": {
|
| 3035 |
"query": "\"communication efficient training\" in:description",
|
| 3036 |
"years": [
|
| 3037 |
-
2026
|
|
|
|
| 3038 |
]
|
| 3039 |
},
|
| 3040 |
"efficiency.compiler-optimization": {
|
| 3041 |
"query": "\"machine learning compiler\" in:description",
|
| 3042 |
"years": [
|
| 3043 |
-
2026
|
|
|
|
| 3044 |
]
|
| 3045 |
},
|
| 3046 |
"efficiency.distributed-training": {
|
| 3047 |
"query": "\"distributed training\" in:description",
|
| 3048 |
"years": [
|
| 3049 |
-
2026
|
|
|
|
| 3050 |
]
|
| 3051 |
},
|
| 3052 |
"efficiency.edge-ai": {
|
| 3053 |
"query": "\"edge AI\" in:description",
|
| 3054 |
"years": [
|
| 3055 |
-
2026
|
|
|
|
| 3056 |
]
|
| 3057 |
},
|
| 3058 |
"efficiency.energy-efficient-ml": {
|
| 3059 |
"query": "\"energy efficient machine learning\" in:description",
|
| 3060 |
"years": [
|
| 3061 |
-
2026
|
|
|
|
| 3062 |
]
|
| 3063 |
},
|
| 3064 |
"efficiency.flash-attention": {
|
| 3065 |
"query": "\"FlashAttention\" in:description",
|
| 3066 |
"years": [
|
| 3067 |
-
2026
|
|
|
|
| 3068 |
]
|
| 3069 |
},
|
| 3070 |
"efficiency.gpu-kernels": {
|
|
@@ -3190,49 +3238,57 @@
|
|
| 3190 |
"gap2026.archaeology-ml": {
|
| 3191 |
"query": "\"machine learning archaeology\" in:description,readme",
|
| 3192 |
"years": [
|
| 3193 |
-
2026
|
|
|
|
| 3194 |
]
|
| 3195 |
},
|
| 3196 |
"gap2026.climate-downscaling": {
|
| 3197 |
"query": "\"machine learning climate downscaling\" in:description,readme",
|
| 3198 |
"years": [
|
| 3199 |
-
2026
|
|
|
|
| 3200 |
]
|
| 3201 |
},
|
| 3202 |
"gap2026.clinical-ehr-representation": {
|
| 3203 |
"query": "\"electronic health record\" representation learning in:description,readme",
|
| 3204 |
"years": [
|
| 3205 |
-
2026
|
|
|
|
| 3206 |
]
|
| 3207 |
},
|
| 3208 |
"gap2026.clinical-risk-prediction": {
|
| 3209 |
"query": "\"clinical risk prediction\" machine learning in:description,readme",
|
| 3210 |
"years": [
|
| 3211 |
-
2026
|
|
|
|
| 3212 |
]
|
| 3213 |
},
|
| 3214 |
"gap2026.clinical-survival-ml": {
|
| 3215 |
"query": "\"survival analysis\" deep learning clinical in:description,readme",
|
| 3216 |
"years": [
|
| 3217 |
-
2026
|
|
|
|
| 3218 |
]
|
| 3219 |
},
|
| 3220 |
"gap2026.clinical-treatment-effect": {
|
| 3221 |
"query": "\"treatment effect estimation\" clinical in:description,readme",
|
| 3222 |
"years": [
|
| 3223 |
-
2026
|
|
|
|
| 3224 |
]
|
| 3225 |
},
|
| 3226 |
"gap2026.clinical-trial-matching": {
|
| 3227 |
"query": "\"clinical trial matching\" machine learning in:description,readme",
|
| 3228 |
"years": [
|
| 3229 |
-
2026
|
|
|
|
| 3230 |
]
|
| 3231 |
},
|
| 3232 |
"gap2026.econometrics-causal-forest": {
|
| 3233 |
"query": "\"causal forest\" econometrics in:description,readme",
|
| 3234 |
"years": [
|
| 3235 |
-
2026
|
|
|
|
| 3236 |
]
|
| 3237 |
},
|
| 3238 |
"gap2026.econometrics-double-ml": {
|
|
@@ -3364,49 +3420,57 @@
|
|
| 3364 |
"general.active-learning": {
|
| 3365 |
"query": "\"active learning\" in:description",
|
| 3366 |
"years": [
|
| 3367 |
-
2026
|
|
|
|
| 3368 |
]
|
| 3369 |
},
|
| 3370 |
"general.artificial-intelligence-description": {
|
| 3371 |
"query": "\"artificial intelligence\" in:description",
|
| 3372 |
"years": [
|
| 3373 |
-
2026
|
|
|
|
| 3374 |
]
|
| 3375 |
},
|
| 3376 |
"general.arxiv": {
|
| 3377 |
"query": "arxiv \"machine learning\" in:description,readme",
|
| 3378 |
"years": [
|
| 3379 |
-
2026
|
|
|
|
| 3380 |
]
|
| 3381 |
},
|
| 3382 |
"general.automated-machine-learning": {
|
| 3383 |
"query": "\"automated machine learning\" in:readme",
|
| 3384 |
"years": [
|
| 3385 |
-
2026
|
|
|
|
| 3386 |
]
|
| 3387 |
},
|
| 3388 |
"general.bayesian-deep-learning": {
|
| 3389 |
"query": "\"Bayesian deep learning\" in:description,readme",
|
| 3390 |
"years": [
|
| 3391 |
-
2026
|
|
|
|
| 3392 |
]
|
| 3393 |
},
|
| 3394 |
"general.bayesian-optimization": {
|
| 3395 |
"query": "\"Bayesian optimization\" in:description",
|
| 3396 |
"years": [
|
| 3397 |
-
2026
|
|
|
|
| 3398 |
]
|
| 3399 |
},
|
| 3400 |
"general.clustering": {
|
| 3401 |
"query": "\"clustering algorithm\" \"machine learning\" in:description,readme",
|
| 3402 |
"years": [
|
| 3403 |
-
2026
|
|
|
|
| 3404 |
]
|
| 3405 |
},
|
| 3406 |
"general.continual-learning": {
|
| 3407 |
"query": "\"continual learning\" in:description,readme",
|
| 3408 |
"years": [
|
| 3409 |
-
2026
|
|
|
|
| 3410 |
]
|
| 3411 |
},
|
| 3412 |
"general.contrastive-learning": {
|
|
@@ -3628,49 +3692,57 @@
|
|
| 3628 |
"geo.building-footprints": {
|
| 3629 |
"query": "\"building footprint extraction\" in:description,readme",
|
| 3630 |
"years": [
|
| 3631 |
-
2026
|
|
|
|
| 3632 |
]
|
| 3633 |
},
|
| 3634 |
"geo.change-detection": {
|
| 3635 |
"query": "\"remote sensing change detection\" in:description,readme",
|
| 3636 |
"years": [
|
| 3637 |
-
2026
|
|
|
|
| 3638 |
]
|
| 3639 |
},
|
| 3640 |
"geo.climate-mapping": {
|
| 3641 |
"query": "\"climate downscaling\" in:readme",
|
| 3642 |
"years": [
|
| 3643 |
-
2026
|
|
|
|
| 3644 |
]
|
| 3645 |
},
|
| 3646 |
"geo.crop-monitoring": {
|
| 3647 |
"query": "\"crop yield prediction\" in:description,readme",
|
| 3648 |
"years": [
|
| 3649 |
-
2026
|
|
|
|
| 3650 |
]
|
| 3651 |
},
|
| 3652 |
"geo.earth-observation-models": {
|
| 3653 |
"query": "\"Earth observation model\" in:description",
|
| 3654 |
"years": [
|
| 3655 |
-
2026
|
|
|
|
| 3656 |
]
|
| 3657 |
},
|
| 3658 |
"geo.environmental-monitoring": {
|
| 3659 |
"query": "\"environmental monitoring\" in:readme",
|
| 3660 |
"years": [
|
| 3661 |
-
2026
|
|
|
|
| 3662 |
]
|
| 3663 |
},
|
| 3664 |
"geo.flood-mapping": {
|
| 3665 |
"query": "\"flood mapping\" in:readme",
|
| 3666 |
"years": [
|
| 3667 |
-
2026
|
|
|
|
| 3668 |
]
|
| 3669 |
},
|
| 3670 |
"geo.foundation-models-earth": {
|
| 3671 |
"query": "\"Earth observation foundation model\" in:description,readme",
|
| 3672 |
"years": [
|
| 3673 |
-
2026
|
|
|
|
| 3674 |
]
|
| 3675 |
},
|
| 3676 |
"geo.geospatial-foundation-models": {
|
|
@@ -3790,49 +3862,57 @@
|
|
| 3790 |
"graph.e3-equivariance": {
|
| 3791 |
"query": "\"E(3)-equivariant\" in:readme",
|
| 3792 |
"years": [
|
| 3793 |
-
2026
|
|
|
|
| 3794 |
]
|
| 3795 |
},
|
| 3796 |
"graph.equivariant-networks": {
|
| 3797 |
"query": "\"equivariant neural network\" in:description",
|
| 3798 |
"years": [
|
| 3799 |
-
2026
|
|
|
|
| 3800 |
]
|
| 3801 |
},
|
| 3802 |
"graph.geometric-learning": {
|
| 3803 |
"query": "\"geometric deep learning\" in:readme",
|
| 3804 |
"years": [
|
| 3805 |
-
2026
|
|
|
|
| 3806 |
]
|
| 3807 |
},
|
| 3808 |
"graph.gnn-description": {
|
| 3809 |
"query": "\"graph neural network\" in:description",
|
| 3810 |
"years": [
|
| 3811 |
-
2026
|
|
|
|
| 3812 |
]
|
| 3813 |
},
|
| 3814 |
"graph.graph-anomaly-detection": {
|
| 3815 |
"query": "\"graph anomaly detection\" in:description,readme",
|
| 3816 |
"years": [
|
| 3817 |
-
2026
|
|
|
|
| 3818 |
]
|
| 3819 |
},
|
| 3820 |
"graph.graph-classification": {
|
| 3821 |
"query": "\"graph classification\" in:description",
|
| 3822 |
"years": [
|
| 3823 |
-
2026
|
|
|
|
| 3824 |
]
|
| 3825 |
},
|
| 3826 |
"graph.graph-contrastive-learning": {
|
| 3827 |
"query": "\"graph contrastive learning\" in:description",
|
| 3828 |
"years": [
|
| 3829 |
-
2026
|
|
|
|
| 3830 |
]
|
| 3831 |
},
|
| 3832 |
"graph.graph-diffusion": {
|
| 3833 |
"query": "\"graph diffusion model\" in:description",
|
| 3834 |
"years": [
|
| 3835 |
-
2026
|
|
|
|
| 3836 |
]
|
| 3837 |
},
|
| 3838 |
"graph.graph-foundation-model": {
|
|
@@ -3952,73 +4032,85 @@
|
|
| 3952 |
"llm-code-generation": {
|
| 3953 |
"query": "\"code generation\" \"language model\" in:readme",
|
| 3954 |
"years": [
|
| 3955 |
-
2026
|
|
|
|
| 3956 |
]
|
| 3957 |
},
|
| 3958 |
"llm-reasoning": {
|
| 3959 |
"query": "\"reasoning model\" in:readme",
|
| 3960 |
"years": [
|
| 3961 |
-
2026
|
|
|
|
| 3962 |
]
|
| 3963 |
},
|
| 3964 |
"llm-retrieval-augmented-generation": {
|
| 3965 |
"query": "\"retrieval-augmented generation\" in:readme",
|
| 3966 |
"years": [
|
| 3967 |
-
2026
|
|
|
|
| 3968 |
]
|
| 3969 |
},
|
| 3970 |
"llm-text-to-speech": {
|
| 3971 |
"query": "\"text-to-speech\" \"language model\" in:readme",
|
| 3972 |
"years": [
|
| 3973 |
-
2026
|
|
|
|
| 3974 |
]
|
| 3975 |
},
|
| 3976 |
"llm.agents": {
|
| 3977 |
"query": "topic:ai-agents",
|
| 3978 |
"years": [
|
| 3979 |
-
2026
|
|
|
|
| 3980 |
]
|
| 3981 |
},
|
| 3982 |
"llm.ai-agent": {
|
| 3983 |
"query": "\"AI agent\" in:description",
|
| 3984 |
"years": [
|
| 3985 |
-
2026
|
|
|
|
| 3986 |
]
|
| 3987 |
},
|
| 3988 |
"llm.alignment": {
|
| 3989 |
"query": "\"alignment\" in:description",
|
| 3990 |
"years": [
|
| 3991 |
-
2026
|
|
|
|
| 3992 |
]
|
| 3993 |
},
|
| 3994 |
"llm.diffusion": {
|
| 3995 |
"query": "topic:diffusion-model",
|
| 3996 |
"years": [
|
| 3997 |
-
2026
|
|
|
|
| 3998 |
]
|
| 3999 |
},
|
| 4000 |
"llm.diffusion-phrase": {
|
| 4001 |
"query": "\"diffusion model\" in:description",
|
| 4002 |
"years": [
|
| 4003 |
-
2026
|
|
|
|
| 4004 |
]
|
| 4005 |
},
|
| 4006 |
"llm.evaluation": {
|
| 4007 |
"query": "\"LLM evaluation\" in:readme",
|
| 4008 |
"years": [
|
| 4009 |
-
2026
|
|
|
|
| 4010 |
]
|
| 4011 |
},
|
| 4012 |
"llm.finetuning": {
|
| 4013 |
"query": "\"fine-tuning\" in:description",
|
| 4014 |
"years": [
|
| 4015 |
-
2026
|
|
|
|
| 4016 |
]
|
| 4017 |
},
|
| 4018 |
"llm.foundation-models": {
|
| 4019 |
"query": "\"foundation model\" in:description",
|
| 4020 |
"years": [
|
| 4021 |
-
2026
|
|
|
|
| 4022 |
]
|
| 4023 |
},
|
| 4024 |
"llm.generative-ai": {
|
|
@@ -4114,91 +4206,106 @@
|
|
| 4114 |
"materials-battery": {
|
| 4115 |
"query": "\"battery materials\" \"machine learning\" in:description,readme",
|
| 4116 |
"years": [
|
| 4117 |
-
2026
|
|
|
|
| 4118 |
]
|
| 4119 |
},
|
| 4120 |
"materials-crystal-generation": {
|
| 4121 |
"query": "\"crystal structure generation\" in:description,readme",
|
| 4122 |
"years": [
|
| 4123 |
-
2026
|
|
|
|
| 4124 |
]
|
| 4125 |
},
|
| 4126 |
"materials-crystal-graph": {
|
| 4127 |
"query": "\"crystal graph\" in:description,readme",
|
| 4128 |
"years": [
|
| 4129 |
-
2026
|
|
|
|
| 4130 |
]
|
| 4131 |
},
|
| 4132 |
"materials-dft-prediction": {
|
| 4133 |
"query": "\"density functional theory\" \"machine learning\" in:description,readme",
|
| 4134 |
"years": [
|
| 4135 |
-
2026
|
|
|
|
| 4136 |
]
|
| 4137 |
},
|
| 4138 |
"materials-informatics": {
|
| 4139 |
"query": "\"materials informatics\" in:description,readme",
|
| 4140 |
"years": [
|
| 4141 |
-
2026
|
|
|
|
| 4142 |
]
|
| 4143 |
},
|
| 4144 |
"materials-perovskite": {
|
| 4145 |
"query": "perovskite in:description \"machine learning\"",
|
| 4146 |
"years": [
|
| 4147 |
-
2026
|
|
|
|
| 4148 |
]
|
| 4149 |
},
|
| 4150 |
"materials-polymer-informatics": {
|
| 4151 |
"query": "polymer informatics in:description,readme",
|
| 4152 |
"years": [
|
| 4153 |
-
2026
|
|
|
|
| 4154 |
]
|
| 4155 |
},
|
| 4156 |
"medical.clinical-decision-support": {
|
| 4157 |
"query": "\"clinical decision support\" in:readme",
|
| 4158 |
"years": [
|
| 4159 |
-
2026
|
|
|
|
| 4160 |
]
|
| 4161 |
},
|
| 4162 |
"medical.clinical-nlp": {
|
| 4163 |
"query": "\"clinical natural language processing\" in:readme",
|
| 4164 |
"years": [
|
| 4165 |
-
2026
|
|
|
|
| 4166 |
]
|
| 4167 |
},
|
| 4168 |
"medical.clinical-time-series": {
|
| 4169 |
"query": "\"clinical time series\" in:description",
|
| 4170 |
"years": [
|
| 4171 |
-
2026
|
|
|
|
| 4172 |
]
|
| 4173 |
},
|
| 4174 |
"medical.clinical-trials": {
|
| 4175 |
"query": "\"clinical trial prediction\" in:description",
|
| 4176 |
"years": [
|
| 4177 |
-
2026
|
|
|
|
| 4178 |
]
|
| 4179 |
},
|
| 4180 |
"medical.dermatology": {
|
| 4181 |
"query": "dermatology AI dermoscopy in:description,readme",
|
| 4182 |
"years": [
|
| 4183 |
-
2026
|
|
|
|
| 4184 |
]
|
| 4185 |
},
|
| 4186 |
"medical.diagnosis-prediction": {
|
| 4187 |
"query": "\"diagnosis prediction\" in:description",
|
| 4188 |
"years": [
|
| 4189 |
-
2026
|
|
|
|
| 4190 |
]
|
| 4191 |
},
|
| 4192 |
"medical.drug-response": {
|
| 4193 |
"query": "\"drug response prediction\" in:description,readme",
|
| 4194 |
"years": [
|
| 4195 |
-
2026
|
|
|
|
| 4196 |
]
|
| 4197 |
},
|
| 4198 |
"medical.ecg": {
|
| 4199 |
"query": "ECG \"deep learning\" in:description,readme",
|
| 4200 |
"years": [
|
| 4201 |
-
2026
|
|
|
|
| 4202 |
]
|
| 4203 |
},
|
| 4204 |
"medical.eeg": {
|
|
@@ -4318,49 +4425,57 @@
|
|
| 4318 |
"multimodal.3d-generation": {
|
| 4319 |
"query": "\"3D generation\" in:readme",
|
| 4320 |
"years": [
|
| 4321 |
-
2026
|
|
|
|
| 4322 |
]
|
| 4323 |
},
|
| 4324 |
"multimodal.3d-human-pose": {
|
| 4325 |
"query": "\"3D human pose estimation\" in:readme",
|
| 4326 |
"years": [
|
| 4327 |
-
2026
|
|
|
|
| 4328 |
]
|
| 4329 |
},
|
| 4330 |
"multimodal.3d-object-detection": {
|
| 4331 |
"query": "\"3D object detection\" in:readme",
|
| 4332 |
"years": [
|
| 4333 |
-
2026
|
|
|
|
| 4334 |
]
|
| 4335 |
},
|
| 4336 |
"multimodal.3d-reconstruction": {
|
| 4337 |
"query": "\"3D reconstruction\" in:description",
|
| 4338 |
"years": [
|
| 4339 |
-
2026
|
|
|
|
| 4340 |
]
|
| 4341 |
},
|
| 4342 |
"multimodal.audio-visual-learning": {
|
| 4343 |
"query": "\"audio visual learning\" in:readme",
|
| 4344 |
"years": [
|
| 4345 |
-
2026
|
|
|
|
| 4346 |
]
|
| 4347 |
},
|
| 4348 |
"multimodal.embodied-world-models": {
|
| 4349 |
"query": "\"embodied world model\" in:description",
|
| 4350 |
"years": [
|
| 4351 |
-
2026
|
|
|
|
| 4352 |
]
|
| 4353 |
},
|
| 4354 |
"multimodal.gaussian-splatting": {
|
| 4355 |
"query": "\"Gaussian splatting\" in:description",
|
| 4356 |
"years": [
|
| 4357 |
-
2026
|
|
|
|
| 4358 |
]
|
| 4359 |
},
|
| 4360 |
"multimodal.image-text": {
|
| 4361 |
"query": "\"image-text\" in:description",
|
| 4362 |
"years": [
|
| 4363 |
-
2026
|
|
|
|
| 4364 |
]
|
| 4365 |
},
|
| 4366 |
"multimodal.image-to-3d": {
|
|
@@ -4468,91 +4583,106 @@
|
|
| 4468 |
"nlp-bm25-retrieval": {
|
| 4469 |
"query": "BM25 retrieval in:readme",
|
| 4470 |
"years": [
|
| 4471 |
-
2026
|
|
|
|
| 4472 |
]
|
| 4473 |
},
|
| 4474 |
"nlp-document-retrieval": {
|
| 4475 |
"query": "\"document retrieval\" in:readme",
|
| 4476 |
"years": [
|
| 4477 |
-
2026
|
|
|
|
| 4478 |
]
|
| 4479 |
},
|
| 4480 |
"nlp-entity-linking": {
|
| 4481 |
"query": "\"entity linking\" in:readme",
|
| 4482 |
"years": [
|
| 4483 |
-
2026
|
|
|
|
| 4484 |
]
|
| 4485 |
},
|
| 4486 |
"nlp-natural-language-inference": {
|
| 4487 |
"query": "\"natural language inference\" in:readme",
|
| 4488 |
"years": [
|
| 4489 |
-
2026
|
|
|
|
| 4490 |
]
|
| 4491 |
},
|
| 4492 |
"nlp-query-understanding": {
|
| 4493 |
"query": "\"query understanding\" in:readme",
|
| 4494 |
"years": [
|
| 4495 |
-
2026
|
|
|
|
| 4496 |
]
|
| 4497 |
},
|
| 4498 |
"nlp-sparse-retrieval": {
|
| 4499 |
"query": "\"sparse retrieval\" in:readme",
|
| 4500 |
"years": [
|
| 4501 |
-
2026
|
|
|
|
| 4502 |
]
|
| 4503 |
},
|
| 4504 |
"nlp-speech-language-modeling": {
|
| 4505 |
"query": "\"speech language model\" in:readme",
|
| 4506 |
"years": [
|
| 4507 |
-
2026
|
|
|
|
| 4508 |
]
|
| 4509 |
},
|
| 4510 |
"nlp.coreference-resolution": {
|
| 4511 |
"query": "\"coreference resolution\" in:readme",
|
| 4512 |
"years": [
|
| 4513 |
-
2026
|
|
|
|
| 4514 |
]
|
| 4515 |
},
|
| 4516 |
"nlp.cross-encoder-reranking": {
|
| 4517 |
"query": "\"cross-encoder\" \"reranking\" in:readme",
|
| 4518 |
"years": [
|
| 4519 |
-
2026
|
|
|
|
| 4520 |
]
|
| 4521 |
},
|
| 4522 |
"nlp.dense-retrieval": {
|
| 4523 |
"query": "\"dense retrieval\" in:readme",
|
| 4524 |
"years": [
|
| 4525 |
-
2026
|
|
|
|
| 4526 |
]
|
| 4527 |
},
|
| 4528 |
"nlp.dependency-parsing": {
|
| 4529 |
"query": "\"dependency parsing\" in:readme",
|
| 4530 |
"years": [
|
| 4531 |
-
2026
|
|
|
|
| 4532 |
]
|
| 4533 |
},
|
| 4534 |
"nlp.dialogue-systems": {
|
| 4535 |
"query": "\"dialogue system\" in:readme",
|
| 4536 |
"years": [
|
| 4537 |
-
2026
|
|
|
|
| 4538 |
]
|
| 4539 |
},
|
| 4540 |
"nlp.information-extraction": {
|
| 4541 |
"query": "\"information extraction\" in:description",
|
| 4542 |
"years": [
|
| 4543 |
-
2026
|
|
|
|
| 4544 |
]
|
| 4545 |
},
|
| 4546 |
"nlp.language-modeling": {
|
| 4547 |
"query": "\"language modeling\" in:description",
|
| 4548 |
"years": [
|
| 4549 |
-
2026
|
|
|
|
| 4550 |
]
|
| 4551 |
},
|
| 4552 |
"nlp.machine-translation": {
|
| 4553 |
"query": "\"machine translation\" in:description",
|
| 4554 |
"years": [
|
| 4555 |
-
2026
|
|
|
|
| 4556 |
]
|
| 4557 |
},
|
| 4558 |
"nlp.named-entity-recognition": {
|
|
@@ -4624,49 +4754,57 @@
|
|
| 4624 |
"recall.archaeological-remote-sensing": {
|
| 4625 |
"query": "archaeology \"remote sensing\" in:name,description,readme",
|
| 4626 |
"years": [
|
| 4627 |
-
2026
|
|
|
|
| 4628 |
]
|
| 4629 |
},
|
| 4630 |
"recall.case-outcome-prediction": {
|
| 4631 |
"query": "\"case outcome prediction\" in:name,description,readme",
|
| 4632 |
"years": [
|
| 4633 |
-
2026
|
|
|
|
| 4634 |
]
|
| 4635 |
},
|
| 4636 |
"recall.computational-linguistics": {
|
| 4637 |
"query": "\"computational linguistics\" model in:name,description,readme",
|
| 4638 |
"years": [
|
| 4639 |
-
2026
|
|
|
|
| 4640 |
]
|
| 4641 |
},
|
| 4642 |
"recall.computational-social-science": {
|
| 4643 |
"query": "\"computational social science\" in:name,description,readme",
|
| 4644 |
"years": [
|
| 4645 |
-
2026
|
|
|
|
| 4646 |
]
|
| 4647 |
},
|
| 4648 |
"recall.digital-humanities-nlp": {
|
| 4649 |
"query": "\"digital humanities\" NLP in:name,description,readme",
|
| 4650 |
"years": [
|
| 4651 |
-
2026
|
|
|
|
| 4652 |
]
|
| 4653 |
},
|
| 4654 |
"recall.disease-surveillance": {
|
| 4655 |
"query": "\"disease surveillance\" machine learning in:name,description,readme",
|
| 4656 |
"years": [
|
| 4657 |
-
2026
|
|
|
|
| 4658 |
]
|
| 4659 |
},
|
| 4660 |
"recall.educational-recommendation": {
|
| 4661 |
"query": "\"educational recommendation\" in:name,description,readme",
|
| 4662 |
"years": [
|
| 4663 |
-
2026
|
|
|
|
| 4664 |
]
|
| 4665 |
},
|
| 4666 |
"recall.epidemic-forecasting": {
|
| 4667 |
"query": "\"epidemic forecasting\" in:name,description,readme",
|
| 4668 |
"years": [
|
| 4669 |
-
2026
|
|
|
|
| 4670 |
]
|
| 4671 |
},
|
| 4672 |
"recall.epidemiological-modeling": {
|
|
@@ -4816,97 +4954,113 @@
|
|
| 4816 |
"recall26.audio-language-model": {
|
| 4817 |
"query": "\"audio language model\" in:description,readme",
|
| 4818 |
"years": [
|
| 4819 |
-
2026
|
|
|
|
| 4820 |
]
|
| 4821 |
},
|
| 4822 |
"recall26.class-imbalance": {
|
| 4823 |
"query": "\"class imbalance\" \"machine learning\" in:description,readme",
|
| 4824 |
"years": [
|
| 4825 |
-
2026
|
|
|
|
| 4826 |
]
|
| 4827 |
},
|
| 4828 |
"recall26.image-dehazing": {
|
| 4829 |
"query": "\"image dehazing\" in:description,readme",
|
| 4830 |
"years": [
|
| 4831 |
-
2026
|
|
|
|
| 4832 |
]
|
| 4833 |
},
|
| 4834 |
"recall26.image-denoising": {
|
| 4835 |
"query": "\"image denoising\" in:description,readme",
|
| 4836 |
"years": [
|
| 4837 |
-
2026
|
|
|
|
| 4838 |
]
|
| 4839 |
},
|
| 4840 |
"recall26.long-tailed-recognition": {
|
| 4841 |
"query": "\"long-tailed recognition\" in:description,readme",
|
| 4842 |
"years": [
|
| 4843 |
-
2026
|
|
|
|
| 4844 |
]
|
| 4845 |
},
|
| 4846 |
"recall26.low-light-enhancement": {
|
| 4847 |
"query": "\"low-light image enhancement\" in:description,readme",
|
| 4848 |
"years": [
|
| 4849 |
-
2026
|
|
|
|
| 4850 |
]
|
| 4851 |
},
|
| 4852 |
"recall26.sign-language": {
|
| 4853 |
"query": "\"sign language\" in:description,readme",
|
| 4854 |
"years": [
|
| 4855 |
-
2026
|
|
|
|
| 4856 |
]
|
| 4857 |
},
|
| 4858 |
"recall26.visual-grounding": {
|
| 4859 |
"query": "\"visual grounding\" in:description,readme",
|
| 4860 |
"years": [
|
| 4861 |
-
2026
|
|
|
|
| 4862 |
]
|
| 4863 |
},
|
| 4864 |
"rl.bandits": {
|
| 4865 |
"query": "\"multi-armed bandit\" in:description",
|
| 4866 |
"years": [
|
| 4867 |
-
2026
|
|
|
|
| 4868 |
]
|
| 4869 |
},
|
| 4870 |
"rl.continual": {
|
| 4871 |
"query": "\"continual reinforcement learning\" in:description,readme",
|
| 4872 |
"years": [
|
| 4873 |
-
2026
|
|
|
|
| 4874 |
]
|
| 4875 |
},
|
| 4876 |
"rl.core": {
|
| 4877 |
"query": "topic:reinforcement-learning",
|
| 4878 |
"years": [
|
| 4879 |
-
2026
|
|
|
|
| 4880 |
]
|
| 4881 |
},
|
| 4882 |
"rl.deep": {
|
| 4883 |
"query": "\"deep reinforcement learning\" in:description",
|
| 4884 |
"years": [
|
| 4885 |
-
2026
|
|
|
|
| 4886 |
]
|
| 4887 |
},
|
| 4888 |
"rl.deep-q-learning": {
|
| 4889 |
"query": "\"deep q-learning\" in:description",
|
| 4890 |
"years": [
|
| 4891 |
-
2026
|
|
|
|
| 4892 |
]
|
| 4893 |
},
|
| 4894 |
"rl.distributional": {
|
| 4895 |
"query": "\"distributional reinforcement learning\" in:description,readme",
|
| 4896 |
"years": [
|
| 4897 |
-
2026
|
|
|
|
| 4898 |
]
|
| 4899 |
},
|
| 4900 |
"rl.exploration": {
|
| 4901 |
"query": "\"reinforcement learning exploration\" in:description",
|
| 4902 |
"years": [
|
| 4903 |
-
2026
|
|
|
|
| 4904 |
]
|
| 4905 |
},
|
| 4906 |
"rl.games": {
|
| 4907 |
"query": "topic:game-ai",
|
| 4908 |
"years": [
|
| 4909 |
-
2026
|
|
|
|
| 4910 |
]
|
| 4911 |
},
|
| 4912 |
"rl.hierarchical": {
|
|
@@ -4996,49 +5150,57 @@
|
|
| 4996 |
"robotics.autonomous-driving": {
|
| 4997 |
"query": "\"autonomous driving\" in:description",
|
| 4998 |
"years": [
|
| 4999 |
-
2026
|
|
|
|
| 5000 |
]
|
| 5001 |
},
|
| 5002 |
"robotics.behavior-cloning": {
|
| 5003 |
"query": "\"behavior cloning\" in:description",
|
| 5004 |
"years": [
|
| 5005 |
-
2026
|
|
|
|
| 5006 |
]
|
| 5007 |
},
|
| 5008 |
"robotics.diffusion-policy": {
|
| 5009 |
"query": "\"diffusion policy\" in:description",
|
| 5010 |
"years": [
|
| 5011 |
-
2026
|
|
|
|
| 5012 |
]
|
| 5013 |
},
|
| 5014 |
"robotics.domain-randomization": {
|
| 5015 |
"query": "\"domain randomization\" in:description",
|
| 5016 |
"years": [
|
| 5017 |
-
2026
|
|
|
|
| 5018 |
]
|
| 5019 |
},
|
| 5020 |
"robotics.embodied-ai": {
|
| 5021 |
"query": "\"embodied AI\" in:description",
|
| 5022 |
"years": [
|
| 5023 |
-
2026
|
|
|
|
| 5024 |
]
|
| 5025 |
},
|
| 5026 |
"robotics.grasping": {
|
| 5027 |
"query": "\"robot grasping\" in:description",
|
| 5028 |
"years": [
|
| 5029 |
-
2026
|
|
|
|
| 5030 |
]
|
| 5031 |
},
|
| 5032 |
"robotics.imitation-learning": {
|
| 5033 |
"query": "\"imitation learning\" robotics in:description",
|
| 5034 |
"years": [
|
| 5035 |
-
2026
|
|
|
|
| 5036 |
]
|
| 5037 |
},
|
| 5038 |
"robotics.locomotion": {
|
| 5039 |
"query": "\"robot locomotion\" in:description",
|
| 5040 |
"years": [
|
| 5041 |
-
2026
|
|
|
|
| 5042 |
]
|
| 5043 |
},
|
| 5044 |
"robotics.manipulation": {
|
|
@@ -5134,49 +5296,57 @@
|
|
| 5134 |
"science.astronomy": {
|
| 5135 |
"query": "astronomy in:readme",
|
| 5136 |
"years": [
|
| 5137 |
-
2026
|
|
|
|
| 5138 |
]
|
| 5139 |
},
|
| 5140 |
"science.astrophysics": {
|
| 5141 |
"query": "astrophysics in:description",
|
| 5142 |
"years": [
|
| 5143 |
-
2026
|
|
|
|
| 5144 |
]
|
| 5145 |
},
|
| 5146 |
"science.climate-modeling": {
|
| 5147 |
"query": "\"climate modeling\" in:readme",
|
| 5148 |
"years": [
|
| 5149 |
-
2026
|
|
|
|
| 5150 |
]
|
| 5151 |
},
|
| 5152 |
"science.computational-mechanics": {
|
| 5153 |
"query": "\"computational mechanics\" \"machine learning\" in:description,readme",
|
| 5154 |
"years": [
|
| 5155 |
-
2026
|
|
|
|
| 5156 |
]
|
| 5157 |
},
|
| 5158 |
"science.cosmology": {
|
| 5159 |
"query": "cosmology in:description",
|
| 5160 |
"years": [
|
| 5161 |
-
2026
|
|
|
|
| 5162 |
]
|
| 5163 |
},
|
| 5164 |
"science.differentiable-simulation": {
|
| 5165 |
"query": "\"differentiable simulation\" in:readme",
|
| 5166 |
"years": [
|
| 5167 |
-
2026
|
|
|
|
| 5168 |
]
|
| 5169 |
},
|
| 5170 |
"science.exoplanets": {
|
| 5171 |
"query": "exoplanet in:description \"machine learning\"",
|
| 5172 |
"years": [
|
| 5173 |
-
2026
|
|
|
|
| 5174 |
]
|
| 5175 |
},
|
| 5176 |
"science.fluid-dynamics": {
|
| 5177 |
"query": "\"fluid dynamics\" in:description",
|
| 5178 |
"years": [
|
| 5179 |
-
2026
|
|
|
|
| 5180 |
]
|
| 5181 |
},
|
| 5182 |
"science.fusion-plasma": {
|
|
@@ -5290,49 +5460,57 @@
|
|
| 5290 |
"specialized.carbon-footprint-training": {
|
| 5291 |
"query": "\"carbon footprint\" \"deep learning\" training in:description,readme",
|
| 5292 |
"years": [
|
| 5293 |
-
2026
|
|
|
|
| 5294 |
]
|
| 5295 |
},
|
| 5296 |
"specialized.computational-pathology-ml": {
|
| 5297 |
"query": "\"computational pathology\" \"machine learning\" in:description,readme",
|
| 5298 |
"years": [
|
| 5299 |
-
2026
|
|
|
|
| 5300 |
]
|
| 5301 |
},
|
| 5302 |
"specialized.coreset-selection": {
|
| 5303 |
"query": "\"coreset selection\" in:description,readme",
|
| 5304 |
"years": [
|
| 5305 |
-
2026
|
|
|
|
| 5306 |
]
|
| 5307 |
},
|
| 5308 |
"specialized.curriculum-learning": {
|
| 5309 |
"query": "\"curriculum learning\" in:description,readme",
|
| 5310 |
"years": [
|
| 5311 |
-
2026
|
|
|
|
| 5312 |
]
|
| 5313 |
},
|
| 5314 |
"specialized.data-centric-ai": {
|
| 5315 |
"query": "\"data-centric AI\" in:description,readme",
|
| 5316 |
"years": [
|
| 5317 |
-
2026
|
|
|
|
| 5318 |
]
|
| 5319 |
},
|
| 5320 |
"specialized.data-shapley": {
|
| 5321 |
"query": "\"data Shapley\" in:description,readme",
|
| 5322 |
"years": [
|
| 5323 |
-
2026
|
|
|
|
| 5324 |
]
|
| 5325 |
},
|
| 5326 |
"specialized.dataset-distillation": {
|
| 5327 |
"query": "\"dataset distillation\" in:description,readme",
|
| 5328 |
"years": [
|
| 5329 |
-
2026
|
|
|
|
| 5330 |
]
|
| 5331 |
},
|
| 5332 |
"specialized.event-camera-deep-learning": {
|
| 5333 |
"query": "\"event camera\" \"deep learning\" in:description,readme",
|
| 5334 |
"years": [
|
| 5335 |
-
2026
|
|
|
|
| 5336 |
]
|
| 5337 |
},
|
| 5338 |
"specialized.fmri-decoding-ml": {
|
|
@@ -5380,49 +5558,57 @@
|
|
| 5380 |
"timeseries.anomaly-detection": {
|
| 5381 |
"query": "\"time series anomaly detection\" in:description",
|
| 5382 |
"years": [
|
| 5383 |
-
2026
|
|
|
|
| 5384 |
]
|
| 5385 |
},
|
| 5386 |
"timeseries.classification": {
|
| 5387 |
"query": "\"time series classification\" in:description",
|
| 5388 |
"years": [
|
| 5389 |
-
2026
|
|
|
|
| 5390 |
]
|
| 5391 |
},
|
| 5392 |
"timeseries.clustering": {
|
| 5393 |
"query": "\"time series clustering\" in:description,readme",
|
| 5394 |
"years": [
|
| 5395 |
-
2026
|
|
|
|
| 5396 |
]
|
| 5397 |
},
|
| 5398 |
"timeseries.energy-forecasting": {
|
| 5399 |
"query": "\"energy forecasting\" in:description",
|
| 5400 |
"years": [
|
| 5401 |
-
2026
|
|
|
|
| 5402 |
]
|
| 5403 |
},
|
| 5404 |
"timeseries.event-forecasting": {
|
| 5405 |
"query": "\"event forecasting\" in:description",
|
| 5406 |
"years": [
|
| 5407 |
-
2026
|
|
|
|
| 5408 |
]
|
| 5409 |
},
|
| 5410 |
"timeseries.forecasting": {
|
| 5411 |
"query": "\"time series forecasting\" in:description",
|
| 5412 |
"years": [
|
| 5413 |
-
2026
|
|
|
|
| 5414 |
]
|
| 5415 |
},
|
| 5416 |
"timeseries.foundation-models": {
|
| 5417 |
"query": "\"time series foundation model\" in:description",
|
| 5418 |
"years": [
|
| 5419 |
-
2026
|
|
|
|
| 5420 |
]
|
| 5421 |
},
|
| 5422 |
"timeseries.imputation": {
|
| 5423 |
"query": "\"time series imputation\" in:description,readme",
|
| 5424 |
"years": [
|
| 5425 |
-
2026
|
|
|
|
| 5426 |
]
|
| 5427 |
},
|
| 5428 |
"timeseries.long-horizon": {
|
|
@@ -5536,43 +5722,50 @@
|
|
| 5536 |
"trust.adversarial-robustness": {
|
| 5537 |
"query": "\"adversarial robustness\" in:description",
|
| 5538 |
"years": [
|
| 5539 |
-
2026
|
|
|
|
| 5540 |
]
|
| 5541 |
},
|
| 5542 |
"trust.ai-alignment": {
|
| 5543 |
"query": "\"AI alignment\" in:description",
|
| 5544 |
"years": [
|
| 5545 |
-
2026
|
|
|
|
| 5546 |
]
|
| 5547 |
},
|
| 5548 |
"trust.ai-safety": {
|
| 5549 |
"query": "\"AI safety\" in:description",
|
| 5550 |
"years": [
|
| 5551 |
-
2026
|
|
|
|
| 5552 |
]
|
| 5553 |
},
|
| 5554 |
"trust.algorithmic-fairness": {
|
| 5555 |
"query": "\"algorithmic fairness\" in:description",
|
| 5556 |
"years": [
|
| 5557 |
-
2026
|
|
|
|
| 5558 |
]
|
| 5559 |
},
|
| 5560 |
"trust.algorithmic-recourse": {
|
| 5561 |
"query": "\"algorithmic recourse\" in:description",
|
| 5562 |
"years": [
|
| 5563 |
-
2026
|
|
|
|
| 5564 |
]
|
| 5565 |
},
|
| 5566 |
"trust.causal-discovery": {
|
| 5567 |
"query": "\"causal discovery\" in:description",
|
| 5568 |
"years": [
|
| 5569 |
-
2026
|
|
|
|
| 5570 |
]
|
| 5571 |
},
|
| 5572 |
"trust.causal-effect-estimation": {
|
| 5573 |
"query": "\"causal effect estimation\" in:description",
|
| 5574 |
"years": [
|
| 5575 |
-
2026
|
|
|
|
| 5576 |
]
|
| 5577 |
},
|
| 5578 |
"trust.causal-mediation": {
|
|
@@ -5710,43 +5903,50 @@
|
|
| 5710 |
"vision.action-recognition": {
|
| 5711 |
"query": "\"action recognition\" in:description",
|
| 5712 |
"years": [
|
| 5713 |
-
2026
|
|
|
|
| 5714 |
]
|
| 5715 |
},
|
| 5716 |
"vision.change-detection": {
|
| 5717 |
"query": "\"change detection\" in:readme",
|
| 5718 |
"years": [
|
| 5719 |
-
2026
|
|
|
|
| 5720 |
]
|
| 5721 |
},
|
| 5722 |
"vision.depth-estimation": {
|
| 5723 |
"query": "\"depth estimation\" in:readme",
|
| 5724 |
"years": [
|
| 5725 |
-
2026
|
|
|
|
| 5726 |
]
|
| 5727 |
},
|
| 5728 |
"vision.face-recognition": {
|
| 5729 |
"query": "\"face recognition\" in:readme",
|
| 5730 |
"years": [
|
| 5731 |
-
2026
|
|
|
|
| 5732 |
]
|
| 5733 |
},
|
| 5734 |
"vision.image-captioning": {
|
| 5735 |
"query": "\"image captioning\" in:readme",
|
| 5736 |
"years": [
|
| 5737 |
-
2026
|
|
|
|
| 5738 |
]
|
| 5739 |
},
|
| 5740 |
"vision.image-classification": {
|
| 5741 |
"query": "\"image classification\" in:description",
|
| 5742 |
"years": [
|
| 5743 |
-
2026
|
|
|
|
| 5744 |
]
|
| 5745 |
},
|
| 5746 |
"vision.image-diffusion": {
|
| 5747 |
"query": "\"image diffusion model\" in:readme",
|
| 5748 |
"years": [
|
| 5749 |
-
2026
|
|
|
|
| 5750 |
]
|
| 5751 |
},
|
| 5752 |
"vision.image-restoration": {
|
|
@@ -5879,5 +6079,5 @@
|
|
| 5879 |
"end": "2026-09-24",
|
| 5880 |
"search_policy_version": 2,
|
| 5881 |
"start_year": 2008,
|
| 5882 |
-
"updated_at": "2026-09-
|
| 5883 |
}
|
|
|
|
| 2352 |
"applied.agricultural-ml": {
|
| 2353 |
"query": "\"agricultural machine learning\" in:description,readme",
|
| 2354 |
"years": [
|
| 2355 |
+
2026,
|
| 2356 |
+
2025
|
| 2357 |
]
|
| 2358 |
},
|
| 2359 |
"applied.algorithmic-trading": {
|
| 2360 |
"query": "\"algorithmic trading\" \"machine learning\" in:readme",
|
| 2361 |
"years": [
|
| 2362 |
+
2026,
|
| 2363 |
+
2025
|
| 2364 |
]
|
| 2365 |
},
|
| 2366 |
"applied.causal-inference": {
|
| 2367 |
"query": "\"causal inference\" in:description",
|
| 2368 |
"years": [
|
| 2369 |
+
2026,
|
| 2370 |
+
2025
|
| 2371 |
]
|
| 2372 |
},
|
| 2373 |
"applied.churn-prediction": {
|
| 2374 |
"query": "\"customer churn prediction\" in:description",
|
| 2375 |
"years": [
|
| 2376 |
+
2026,
|
| 2377 |
+
2025
|
| 2378 |
]
|
| 2379 |
},
|
| 2380 |
"applied.click-through-rate": {
|
| 2381 |
"query": "\"click-through rate prediction\" in:readme",
|
| 2382 |
"years": [
|
| 2383 |
+
2026,
|
| 2384 |
+
2025
|
| 2385 |
]
|
| 2386 |
},
|
| 2387 |
"applied.collaborative-filtering": {
|
| 2388 |
"query": "\"collaborative filtering\" in:description",
|
| 2389 |
"years": [
|
| 2390 |
+
2026,
|
| 2391 |
+
2025
|
| 2392 |
]
|
| 2393 |
},
|
| 2394 |
"applied.credit-scoring": {
|
| 2395 |
"query": "\"credit scoring\" in:description",
|
| 2396 |
"years": [
|
| 2397 |
+
2026,
|
| 2398 |
+
2025
|
| 2399 |
]
|
| 2400 |
},
|
| 2401 |
"applied.customer-lifetime-value": {
|
|
|
|
| 2723 |
"audio-speech-to-text": {
|
| 2724 |
"query": "\"speech to text\" in:readme",
|
| 2725 |
"years": [
|
| 2726 |
+
2026,
|
| 2727 |
+
2025
|
| 2728 |
]
|
| 2729 |
},
|
| 2730 |
"audio-speech-translation": {
|
| 2731 |
"query": "\"speech translation\" in:readme",
|
| 2732 |
"years": [
|
| 2733 |
+
2026,
|
| 2734 |
+
2025
|
| 2735 |
]
|
| 2736 |
},
|
| 2737 |
"audio-text-to-audio": {
|
| 2738 |
"query": "\"text to audio\" in:readme",
|
| 2739 |
"years": [
|
| 2740 |
+
2026,
|
| 2741 |
+
2025
|
| 2742 |
]
|
| 2743 |
},
|
| 2744 |
"audio-text-to-speech": {
|
| 2745 |
"query": "\"text to speech\" in:readme",
|
| 2746 |
"years": [
|
| 2747 |
+
2026,
|
| 2748 |
+
2025
|
| 2749 |
]
|
| 2750 |
},
|
| 2751 |
"audio-voice-conversion": {
|
| 2752 |
"query": "\"voice conversion\" in:readme",
|
| 2753 |
"years": [
|
| 2754 |
+
2026,
|
| 2755 |
+
2025
|
| 2756 |
]
|
| 2757 |
},
|
| 2758 |
"bio.cell-atlas": {
|
| 2759 |
"query": "\"cell atlas\" in:description,readme",
|
| 2760 |
"years": [
|
| 2761 |
+
2026,
|
| 2762 |
+
2025
|
| 2763 |
]
|
| 2764 |
},
|
| 2765 |
"bio.chemical-biology": {
|
| 2766 |
"query": "\"protein function prediction\" in:description,readme",
|
| 2767 |
"years": [
|
| 2768 |
+
2026,
|
| 2769 |
+
2025
|
| 2770 |
]
|
| 2771 |
},
|
| 2772 |
"bio.crispr": {
|
| 2773 |
"query": "\"CRISPR screen\" in:description,readme",
|
| 2774 |
"years": [
|
| 2775 |
+
2026,
|
| 2776 |
+
2025
|
| 2777 |
]
|
| 2778 |
},
|
| 2779 |
"bio.dna-language-models": {
|
| 2780 |
"query": "\"DNA language model\" in:description",
|
| 2781 |
"years": [
|
| 2782 |
+
2026,
|
| 2783 |
+
2025
|
| 2784 |
]
|
| 2785 |
},
|
| 2786 |
"bio.gene-regulatory-networks": {
|
| 2787 |
"query": "\"gene regulatory network\" in:description,readme",
|
| 2788 |
"years": [
|
| 2789 |
+
2026,
|
| 2790 |
+
2025
|
| 2791 |
]
|
| 2792 |
},
|
| 2793 |
"bio.genome-assembly": {
|
| 2794 |
"query": "\"genome assembly\" \"neural network\" in:description,readme",
|
| 2795 |
"years": [
|
| 2796 |
+
2026,
|
| 2797 |
+
2025
|
| 2798 |
]
|
| 2799 |
},
|
| 2800 |
"bio.genomics": {
|
| 2801 |
"query": "\"genomics\" in:description",
|
| 2802 |
"years": [
|
| 2803 |
+
2026,
|
| 2804 |
+
2025
|
| 2805 |
]
|
| 2806 |
},
|
| 2807 |
"bio.inverse-folding": {
|
| 2808 |
"query": "\"inverse folding\" in:readme",
|
| 2809 |
"years": [
|
| 2810 |
+
2026,
|
| 2811 |
+
2025
|
| 2812 |
]
|
| 2813 |
},
|
| 2814 |
"bio.metagenomics": {
|
|
|
|
| 2922 |
"chem-admet": {
|
| 2923 |
"query": "\"ADMET prediction\" in:description,readme",
|
| 2924 |
"years": [
|
| 2925 |
+
2026,
|
| 2926 |
+
2025
|
| 2927 |
]
|
| 2928 |
},
|
| 2929 |
"chem-binding-affinity": {
|
| 2930 |
"query": "\"binding affinity prediction\" in:description,readme",
|
| 2931 |
"years": [
|
| 2932 |
+
2026,
|
| 2933 |
+
2025
|
| 2934 |
]
|
| 2935 |
},
|
| 2936 |
"chem-catalyst-discovery": {
|
| 2937 |
"query": "\"catalyst discovery\" in:description,readme",
|
| 2938 |
"years": [
|
| 2939 |
+
2026,
|
| 2940 |
+
2025
|
| 2941 |
]
|
| 2942 |
},
|
| 2943 |
"chem-cheminformatics": {
|
| 2944 |
"query": "topic:cheminformatics",
|
| 2945 |
"years": [
|
| 2946 |
+
2026,
|
| 2947 |
+
2025
|
| 2948 |
]
|
| 2949 |
},
|
| 2950 |
"chem-drug-discovery": {
|
| 2951 |
"query": "\"drug discovery\" in:description,readme",
|
| 2952 |
"years": [
|
| 2953 |
+
2026,
|
| 2954 |
+
2025
|
| 2955 |
]
|
| 2956 |
},
|
| 2957 |
"chem-interatomic-potentials": {
|
| 2958 |
"query": "\"machine learning interatomic potential\" in:description,readme",
|
| 2959 |
"years": [
|
| 2960 |
+
2026,
|
| 2961 |
+
2025
|
| 2962 |
]
|
| 2963 |
},
|
| 2964 |
"chem-molecular-docking": {
|
| 2965 |
"query": "\"molecular docking\" in:description,readme",
|
| 2966 |
"years": [
|
| 2967 |
+
2026,
|
| 2968 |
+
2025
|
| 2969 |
]
|
| 2970 |
},
|
| 2971 |
"chem-molecular-dynamics": {
|
| 2972 |
"query": "\"molecular dynamics\" \"machine learning\" in:description,readme",
|
| 2973 |
"years": [
|
| 2974 |
+
2026,
|
| 2975 |
+
2025
|
| 2976 |
]
|
| 2977 |
},
|
| 2978 |
"chem-molecular-generation": {
|
| 2979 |
"query": "\"molecular generation\" in:description,readme",
|
| 2980 |
"years": [
|
| 2981 |
+
2026,
|
| 2982 |
+
2025
|
| 2983 |
]
|
| 2984 |
},
|
| 2985 |
"chem-molecular-property-benchmark": {
|
| 2986 |
"query": "MoleculeNet in:description,readme",
|
| 2987 |
"years": [
|
| 2988 |
+
2026,
|
| 2989 |
+
2025
|
| 2990 |
]
|
| 2991 |
},
|
| 2992 |
"chem-molecular-spectroscopy": {
|
| 2993 |
"query": "molecular spectroscopy \"machine learning\" in:description,readme",
|
| 2994 |
"years": [
|
| 2995 |
+
2026,
|
| 2996 |
+
2025
|
| 2997 |
]
|
| 2998 |
},
|
| 2999 |
"chem-property-prediction": {
|
| 3000 |
"query": "\"molecular property prediction\" in:description,readme",
|
| 3001 |
"years": [
|
| 3002 |
+
2026,
|
| 3003 |
+
2025
|
| 3004 |
]
|
| 3005 |
},
|
| 3006 |
"chem-protein-ligand-docking": {
|
| 3007 |
"query": "\"protein-ligand docking\" in:description,readme",
|
| 3008 |
"years": [
|
| 3009 |
+
2026,
|
| 3010 |
+
2025
|
| 3011 |
]
|
| 3012 |
},
|
| 3013 |
"chem-quantum-chemistry": {
|
| 3014 |
"query": "\"quantum chemistry\" \"machine learning\" in:description,readme",
|
| 3015 |
"years": [
|
| 3016 |
+
2026,
|
| 3017 |
+
2025
|
| 3018 |
]
|
| 3019 |
},
|
| 3020 |
"chem-reaction-conditions": {
|
| 3021 |
"query": "\"reaction condition prediction\" in:description,readme",
|
| 3022 |
"years": [
|
| 3023 |
+
2026,
|
| 3024 |
+
2025
|
| 3025 |
]
|
| 3026 |
},
|
| 3027 |
"chem-reaction-prediction": {
|
| 3028 |
"query": "\"reaction prediction\" in:description,readme",
|
| 3029 |
"years": [
|
| 3030 |
+
2026,
|
| 3031 |
+
2025
|
| 3032 |
]
|
| 3033 |
},
|
| 3034 |
"chem-reaction-yield": {
|
| 3035 |
"query": "\"reaction yield prediction\" in:description,readme",
|
| 3036 |
"years": [
|
| 3037 |
+
2026,
|
| 3038 |
+
2025
|
| 3039 |
]
|
| 3040 |
},
|
| 3041 |
"chem-retrosynthesis": {
|
| 3042 |
"query": "\"computer aided synthesis planning\" in:description,readme",
|
| 3043 |
"years": [
|
| 3044 |
+
2026,
|
| 3045 |
+
2025
|
| 3046 |
]
|
| 3047 |
},
|
| 3048 |
"chem-retrosynthesis-planning": {
|
| 3049 |
"query": "retrosynthesis in:description,readme",
|
| 3050 |
"years": [
|
| 3051 |
+
2026,
|
| 3052 |
+
2025
|
| 3053 |
]
|
| 3054 |
},
|
| 3055 |
"chem-virtual-screening": {
|
| 3056 |
"query": "\"virtual screening\" in:description,readme",
|
| 3057 |
"years": [
|
| 3058 |
+
2026,
|
| 3059 |
+
2025
|
| 3060 |
]
|
| 3061 |
},
|
| 3062 |
"efficiency.activation-checkpointing": {
|
| 3063 |
"query": "\"activation checkpointing\" in:description",
|
| 3064 |
"years": [
|
| 3065 |
+
2026,
|
| 3066 |
+
2025
|
| 3067 |
]
|
| 3068 |
},
|
| 3069 |
"efficiency.automl": {
|
| 3070 |
"query": "AutoML in:description",
|
| 3071 |
"years": [
|
| 3072 |
+
2026,
|
| 3073 |
+
2025
|
| 3074 |
]
|
| 3075 |
},
|
| 3076 |
"efficiency.communication-efficient-training": {
|
| 3077 |
"query": "\"communication efficient training\" in:description",
|
| 3078 |
"years": [
|
| 3079 |
+
2026,
|
| 3080 |
+
2025
|
| 3081 |
]
|
| 3082 |
},
|
| 3083 |
"efficiency.compiler-optimization": {
|
| 3084 |
"query": "\"machine learning compiler\" in:description",
|
| 3085 |
"years": [
|
| 3086 |
+
2026,
|
| 3087 |
+
2025
|
| 3088 |
]
|
| 3089 |
},
|
| 3090 |
"efficiency.distributed-training": {
|
| 3091 |
"query": "\"distributed training\" in:description",
|
| 3092 |
"years": [
|
| 3093 |
+
2026,
|
| 3094 |
+
2025
|
| 3095 |
]
|
| 3096 |
},
|
| 3097 |
"efficiency.edge-ai": {
|
| 3098 |
"query": "\"edge AI\" in:description",
|
| 3099 |
"years": [
|
| 3100 |
+
2026,
|
| 3101 |
+
2025
|
| 3102 |
]
|
| 3103 |
},
|
| 3104 |
"efficiency.energy-efficient-ml": {
|
| 3105 |
"query": "\"energy efficient machine learning\" in:description",
|
| 3106 |
"years": [
|
| 3107 |
+
2026,
|
| 3108 |
+
2025
|
| 3109 |
]
|
| 3110 |
},
|
| 3111 |
"efficiency.flash-attention": {
|
| 3112 |
"query": "\"FlashAttention\" in:description",
|
| 3113 |
"years": [
|
| 3114 |
+
2026,
|
| 3115 |
+
2025
|
| 3116 |
]
|
| 3117 |
},
|
| 3118 |
"efficiency.gpu-kernels": {
|
|
|
|
| 3238 |
"gap2026.archaeology-ml": {
|
| 3239 |
"query": "\"machine learning archaeology\" in:description,readme",
|
| 3240 |
"years": [
|
| 3241 |
+
2026,
|
| 3242 |
+
2025
|
| 3243 |
]
|
| 3244 |
},
|
| 3245 |
"gap2026.climate-downscaling": {
|
| 3246 |
"query": "\"machine learning climate downscaling\" in:description,readme",
|
| 3247 |
"years": [
|
| 3248 |
+
2026,
|
| 3249 |
+
2025
|
| 3250 |
]
|
| 3251 |
},
|
| 3252 |
"gap2026.clinical-ehr-representation": {
|
| 3253 |
"query": "\"electronic health record\" representation learning in:description,readme",
|
| 3254 |
"years": [
|
| 3255 |
+
2026,
|
| 3256 |
+
2025
|
| 3257 |
]
|
| 3258 |
},
|
| 3259 |
"gap2026.clinical-risk-prediction": {
|
| 3260 |
"query": "\"clinical risk prediction\" machine learning in:description,readme",
|
| 3261 |
"years": [
|
| 3262 |
+
2026,
|
| 3263 |
+
2025
|
| 3264 |
]
|
| 3265 |
},
|
| 3266 |
"gap2026.clinical-survival-ml": {
|
| 3267 |
"query": "\"survival analysis\" deep learning clinical in:description,readme",
|
| 3268 |
"years": [
|
| 3269 |
+
2026,
|
| 3270 |
+
2025
|
| 3271 |
]
|
| 3272 |
},
|
| 3273 |
"gap2026.clinical-treatment-effect": {
|
| 3274 |
"query": "\"treatment effect estimation\" clinical in:description,readme",
|
| 3275 |
"years": [
|
| 3276 |
+
2026,
|
| 3277 |
+
2025
|
| 3278 |
]
|
| 3279 |
},
|
| 3280 |
"gap2026.clinical-trial-matching": {
|
| 3281 |
"query": "\"clinical trial matching\" machine learning in:description,readme",
|
| 3282 |
"years": [
|
| 3283 |
+
2026,
|
| 3284 |
+
2025
|
| 3285 |
]
|
| 3286 |
},
|
| 3287 |
"gap2026.econometrics-causal-forest": {
|
| 3288 |
"query": "\"causal forest\" econometrics in:description,readme",
|
| 3289 |
"years": [
|
| 3290 |
+
2026,
|
| 3291 |
+
2025
|
| 3292 |
]
|
| 3293 |
},
|
| 3294 |
"gap2026.econometrics-double-ml": {
|
|
|
|
| 3420 |
"general.active-learning": {
|
| 3421 |
"query": "\"active learning\" in:description",
|
| 3422 |
"years": [
|
| 3423 |
+
2026,
|
| 3424 |
+
2025
|
| 3425 |
]
|
| 3426 |
},
|
| 3427 |
"general.artificial-intelligence-description": {
|
| 3428 |
"query": "\"artificial intelligence\" in:description",
|
| 3429 |
"years": [
|
| 3430 |
+
2026,
|
| 3431 |
+
2025
|
| 3432 |
]
|
| 3433 |
},
|
| 3434 |
"general.arxiv": {
|
| 3435 |
"query": "arxiv \"machine learning\" in:description,readme",
|
| 3436 |
"years": [
|
| 3437 |
+
2026,
|
| 3438 |
+
2025
|
| 3439 |
]
|
| 3440 |
},
|
| 3441 |
"general.automated-machine-learning": {
|
| 3442 |
"query": "\"automated machine learning\" in:readme",
|
| 3443 |
"years": [
|
| 3444 |
+
2026,
|
| 3445 |
+
2025
|
| 3446 |
]
|
| 3447 |
},
|
| 3448 |
"general.bayesian-deep-learning": {
|
| 3449 |
"query": "\"Bayesian deep learning\" in:description,readme",
|
| 3450 |
"years": [
|
| 3451 |
+
2026,
|
| 3452 |
+
2025
|
| 3453 |
]
|
| 3454 |
},
|
| 3455 |
"general.bayesian-optimization": {
|
| 3456 |
"query": "\"Bayesian optimization\" in:description",
|
| 3457 |
"years": [
|
| 3458 |
+
2026,
|
| 3459 |
+
2025
|
| 3460 |
]
|
| 3461 |
},
|
| 3462 |
"general.clustering": {
|
| 3463 |
"query": "\"clustering algorithm\" \"machine learning\" in:description,readme",
|
| 3464 |
"years": [
|
| 3465 |
+
2026,
|
| 3466 |
+
2025
|
| 3467 |
]
|
| 3468 |
},
|
| 3469 |
"general.continual-learning": {
|
| 3470 |
"query": "\"continual learning\" in:description,readme",
|
| 3471 |
"years": [
|
| 3472 |
+
2026,
|
| 3473 |
+
2025
|
| 3474 |
]
|
| 3475 |
},
|
| 3476 |
"general.contrastive-learning": {
|
|
|
|
| 3692 |
"geo.building-footprints": {
|
| 3693 |
"query": "\"building footprint extraction\" in:description,readme",
|
| 3694 |
"years": [
|
| 3695 |
+
2026,
|
| 3696 |
+
2025
|
| 3697 |
]
|
| 3698 |
},
|
| 3699 |
"geo.change-detection": {
|
| 3700 |
"query": "\"remote sensing change detection\" in:description,readme",
|
| 3701 |
"years": [
|
| 3702 |
+
2026,
|
| 3703 |
+
2025
|
| 3704 |
]
|
| 3705 |
},
|
| 3706 |
"geo.climate-mapping": {
|
| 3707 |
"query": "\"climate downscaling\" in:readme",
|
| 3708 |
"years": [
|
| 3709 |
+
2026,
|
| 3710 |
+
2025
|
| 3711 |
]
|
| 3712 |
},
|
| 3713 |
"geo.crop-monitoring": {
|
| 3714 |
"query": "\"crop yield prediction\" in:description,readme",
|
| 3715 |
"years": [
|
| 3716 |
+
2026,
|
| 3717 |
+
2025
|
| 3718 |
]
|
| 3719 |
},
|
| 3720 |
"geo.earth-observation-models": {
|
| 3721 |
"query": "\"Earth observation model\" in:description",
|
| 3722 |
"years": [
|
| 3723 |
+
2026,
|
| 3724 |
+
2025
|
| 3725 |
]
|
| 3726 |
},
|
| 3727 |
"geo.environmental-monitoring": {
|
| 3728 |
"query": "\"environmental monitoring\" in:readme",
|
| 3729 |
"years": [
|
| 3730 |
+
2026,
|
| 3731 |
+
2025
|
| 3732 |
]
|
| 3733 |
},
|
| 3734 |
"geo.flood-mapping": {
|
| 3735 |
"query": "\"flood mapping\" in:readme",
|
| 3736 |
"years": [
|
| 3737 |
+
2026,
|
| 3738 |
+
2025
|
| 3739 |
]
|
| 3740 |
},
|
| 3741 |
"geo.foundation-models-earth": {
|
| 3742 |
"query": "\"Earth observation foundation model\" in:description,readme",
|
| 3743 |
"years": [
|
| 3744 |
+
2026,
|
| 3745 |
+
2025
|
| 3746 |
]
|
| 3747 |
},
|
| 3748 |
"geo.geospatial-foundation-models": {
|
|
|
|
| 3862 |
"graph.e3-equivariance": {
|
| 3863 |
"query": "\"E(3)-equivariant\" in:readme",
|
| 3864 |
"years": [
|
| 3865 |
+
2026,
|
| 3866 |
+
2025
|
| 3867 |
]
|
| 3868 |
},
|
| 3869 |
"graph.equivariant-networks": {
|
| 3870 |
"query": "\"equivariant neural network\" in:description",
|
| 3871 |
"years": [
|
| 3872 |
+
2026,
|
| 3873 |
+
2025
|
| 3874 |
]
|
| 3875 |
},
|
| 3876 |
"graph.geometric-learning": {
|
| 3877 |
"query": "\"geometric deep learning\" in:readme",
|
| 3878 |
"years": [
|
| 3879 |
+
2026,
|
| 3880 |
+
2025
|
| 3881 |
]
|
| 3882 |
},
|
| 3883 |
"graph.gnn-description": {
|
| 3884 |
"query": "\"graph neural network\" in:description",
|
| 3885 |
"years": [
|
| 3886 |
+
2026,
|
| 3887 |
+
2025
|
| 3888 |
]
|
| 3889 |
},
|
| 3890 |
"graph.graph-anomaly-detection": {
|
| 3891 |
"query": "\"graph anomaly detection\" in:description,readme",
|
| 3892 |
"years": [
|
| 3893 |
+
2026,
|
| 3894 |
+
2025
|
| 3895 |
]
|
| 3896 |
},
|
| 3897 |
"graph.graph-classification": {
|
| 3898 |
"query": "\"graph classification\" in:description",
|
| 3899 |
"years": [
|
| 3900 |
+
2026,
|
| 3901 |
+
2025
|
| 3902 |
]
|
| 3903 |
},
|
| 3904 |
"graph.graph-contrastive-learning": {
|
| 3905 |
"query": "\"graph contrastive learning\" in:description",
|
| 3906 |
"years": [
|
| 3907 |
+
2026,
|
| 3908 |
+
2025
|
| 3909 |
]
|
| 3910 |
},
|
| 3911 |
"graph.graph-diffusion": {
|
| 3912 |
"query": "\"graph diffusion model\" in:description",
|
| 3913 |
"years": [
|
| 3914 |
+
2026,
|
| 3915 |
+
2025
|
| 3916 |
]
|
| 3917 |
},
|
| 3918 |
"graph.graph-foundation-model": {
|
|
|
|
| 4032 |
"llm-code-generation": {
|
| 4033 |
"query": "\"code generation\" \"language model\" in:readme",
|
| 4034 |
"years": [
|
| 4035 |
+
2026,
|
| 4036 |
+
2025
|
| 4037 |
]
|
| 4038 |
},
|
| 4039 |
"llm-reasoning": {
|
| 4040 |
"query": "\"reasoning model\" in:readme",
|
| 4041 |
"years": [
|
| 4042 |
+
2026,
|
| 4043 |
+
2025
|
| 4044 |
]
|
| 4045 |
},
|
| 4046 |
"llm-retrieval-augmented-generation": {
|
| 4047 |
"query": "\"retrieval-augmented generation\" in:readme",
|
| 4048 |
"years": [
|
| 4049 |
+
2026,
|
| 4050 |
+
2025
|
| 4051 |
]
|
| 4052 |
},
|
| 4053 |
"llm-text-to-speech": {
|
| 4054 |
"query": "\"text-to-speech\" \"language model\" in:readme",
|
| 4055 |
"years": [
|
| 4056 |
+
2026,
|
| 4057 |
+
2025
|
| 4058 |
]
|
| 4059 |
},
|
| 4060 |
"llm.agents": {
|
| 4061 |
"query": "topic:ai-agents",
|
| 4062 |
"years": [
|
| 4063 |
+
2026,
|
| 4064 |
+
2025
|
| 4065 |
]
|
| 4066 |
},
|
| 4067 |
"llm.ai-agent": {
|
| 4068 |
"query": "\"AI agent\" in:description",
|
| 4069 |
"years": [
|
| 4070 |
+
2026,
|
| 4071 |
+
2025
|
| 4072 |
]
|
| 4073 |
},
|
| 4074 |
"llm.alignment": {
|
| 4075 |
"query": "\"alignment\" in:description",
|
| 4076 |
"years": [
|
| 4077 |
+
2026,
|
| 4078 |
+
2025
|
| 4079 |
]
|
| 4080 |
},
|
| 4081 |
"llm.diffusion": {
|
| 4082 |
"query": "topic:diffusion-model",
|
| 4083 |
"years": [
|
| 4084 |
+
2026,
|
| 4085 |
+
2025
|
| 4086 |
]
|
| 4087 |
},
|
| 4088 |
"llm.diffusion-phrase": {
|
| 4089 |
"query": "\"diffusion model\" in:description",
|
| 4090 |
"years": [
|
| 4091 |
+
2026,
|
| 4092 |
+
2025
|
| 4093 |
]
|
| 4094 |
},
|
| 4095 |
"llm.evaluation": {
|
| 4096 |
"query": "\"LLM evaluation\" in:readme",
|
| 4097 |
"years": [
|
| 4098 |
+
2026,
|
| 4099 |
+
2025
|
| 4100 |
]
|
| 4101 |
},
|
| 4102 |
"llm.finetuning": {
|
| 4103 |
"query": "\"fine-tuning\" in:description",
|
| 4104 |
"years": [
|
| 4105 |
+
2026,
|
| 4106 |
+
2025
|
| 4107 |
]
|
| 4108 |
},
|
| 4109 |
"llm.foundation-models": {
|
| 4110 |
"query": "\"foundation model\" in:description",
|
| 4111 |
"years": [
|
| 4112 |
+
2026,
|
| 4113 |
+
2025
|
| 4114 |
]
|
| 4115 |
},
|
| 4116 |
"llm.generative-ai": {
|
|
|
|
| 4206 |
"materials-battery": {
|
| 4207 |
"query": "\"battery materials\" \"machine learning\" in:description,readme",
|
| 4208 |
"years": [
|
| 4209 |
+
2026,
|
| 4210 |
+
2025
|
| 4211 |
]
|
| 4212 |
},
|
| 4213 |
"materials-crystal-generation": {
|
| 4214 |
"query": "\"crystal structure generation\" in:description,readme",
|
| 4215 |
"years": [
|
| 4216 |
+
2026,
|
| 4217 |
+
2025
|
| 4218 |
]
|
| 4219 |
},
|
| 4220 |
"materials-crystal-graph": {
|
| 4221 |
"query": "\"crystal graph\" in:description,readme",
|
| 4222 |
"years": [
|
| 4223 |
+
2026,
|
| 4224 |
+
2025
|
| 4225 |
]
|
| 4226 |
},
|
| 4227 |
"materials-dft-prediction": {
|
| 4228 |
"query": "\"density functional theory\" \"machine learning\" in:description,readme",
|
| 4229 |
"years": [
|
| 4230 |
+
2026,
|
| 4231 |
+
2025
|
| 4232 |
]
|
| 4233 |
},
|
| 4234 |
"materials-informatics": {
|
| 4235 |
"query": "\"materials informatics\" in:description,readme",
|
| 4236 |
"years": [
|
| 4237 |
+
2026,
|
| 4238 |
+
2025
|
| 4239 |
]
|
| 4240 |
},
|
| 4241 |
"materials-perovskite": {
|
| 4242 |
"query": "perovskite in:description \"machine learning\"",
|
| 4243 |
"years": [
|
| 4244 |
+
2026,
|
| 4245 |
+
2025
|
| 4246 |
]
|
| 4247 |
},
|
| 4248 |
"materials-polymer-informatics": {
|
| 4249 |
"query": "polymer informatics in:description,readme",
|
| 4250 |
"years": [
|
| 4251 |
+
2026,
|
| 4252 |
+
2025
|
| 4253 |
]
|
| 4254 |
},
|
| 4255 |
"medical.clinical-decision-support": {
|
| 4256 |
"query": "\"clinical decision support\" in:readme",
|
| 4257 |
"years": [
|
| 4258 |
+
2026,
|
| 4259 |
+
2025
|
| 4260 |
]
|
| 4261 |
},
|
| 4262 |
"medical.clinical-nlp": {
|
| 4263 |
"query": "\"clinical natural language processing\" in:readme",
|
| 4264 |
"years": [
|
| 4265 |
+
2026,
|
| 4266 |
+
2025
|
| 4267 |
]
|
| 4268 |
},
|
| 4269 |
"medical.clinical-time-series": {
|
| 4270 |
"query": "\"clinical time series\" in:description",
|
| 4271 |
"years": [
|
| 4272 |
+
2026,
|
| 4273 |
+
2025
|
| 4274 |
]
|
| 4275 |
},
|
| 4276 |
"medical.clinical-trials": {
|
| 4277 |
"query": "\"clinical trial prediction\" in:description",
|
| 4278 |
"years": [
|
| 4279 |
+
2026,
|
| 4280 |
+
2025
|
| 4281 |
]
|
| 4282 |
},
|
| 4283 |
"medical.dermatology": {
|
| 4284 |
"query": "dermatology AI dermoscopy in:description,readme",
|
| 4285 |
"years": [
|
| 4286 |
+
2026,
|
| 4287 |
+
2025
|
| 4288 |
]
|
| 4289 |
},
|
| 4290 |
"medical.diagnosis-prediction": {
|
| 4291 |
"query": "\"diagnosis prediction\" in:description",
|
| 4292 |
"years": [
|
| 4293 |
+
2026,
|
| 4294 |
+
2025
|
| 4295 |
]
|
| 4296 |
},
|
| 4297 |
"medical.drug-response": {
|
| 4298 |
"query": "\"drug response prediction\" in:description,readme",
|
| 4299 |
"years": [
|
| 4300 |
+
2026,
|
| 4301 |
+
2025
|
| 4302 |
]
|
| 4303 |
},
|
| 4304 |
"medical.ecg": {
|
| 4305 |
"query": "ECG \"deep learning\" in:description,readme",
|
| 4306 |
"years": [
|
| 4307 |
+
2026,
|
| 4308 |
+
2025
|
| 4309 |
]
|
| 4310 |
},
|
| 4311 |
"medical.eeg": {
|
|
|
|
| 4425 |
"multimodal.3d-generation": {
|
| 4426 |
"query": "\"3D generation\" in:readme",
|
| 4427 |
"years": [
|
| 4428 |
+
2026,
|
| 4429 |
+
2025
|
| 4430 |
]
|
| 4431 |
},
|
| 4432 |
"multimodal.3d-human-pose": {
|
| 4433 |
"query": "\"3D human pose estimation\" in:readme",
|
| 4434 |
"years": [
|
| 4435 |
+
2026,
|
| 4436 |
+
2025
|
| 4437 |
]
|
| 4438 |
},
|
| 4439 |
"multimodal.3d-object-detection": {
|
| 4440 |
"query": "\"3D object detection\" in:readme",
|
| 4441 |
"years": [
|
| 4442 |
+
2026,
|
| 4443 |
+
2025
|
| 4444 |
]
|
| 4445 |
},
|
| 4446 |
"multimodal.3d-reconstruction": {
|
| 4447 |
"query": "\"3D reconstruction\" in:description",
|
| 4448 |
"years": [
|
| 4449 |
+
2026,
|
| 4450 |
+
2025
|
| 4451 |
]
|
| 4452 |
},
|
| 4453 |
"multimodal.audio-visual-learning": {
|
| 4454 |
"query": "\"audio visual learning\" in:readme",
|
| 4455 |
"years": [
|
| 4456 |
+
2026,
|
| 4457 |
+
2025
|
| 4458 |
]
|
| 4459 |
},
|
| 4460 |
"multimodal.embodied-world-models": {
|
| 4461 |
"query": "\"embodied world model\" in:description",
|
| 4462 |
"years": [
|
| 4463 |
+
2026,
|
| 4464 |
+
2025
|
| 4465 |
]
|
| 4466 |
},
|
| 4467 |
"multimodal.gaussian-splatting": {
|
| 4468 |
"query": "\"Gaussian splatting\" in:description",
|
| 4469 |
"years": [
|
| 4470 |
+
2026,
|
| 4471 |
+
2025
|
| 4472 |
]
|
| 4473 |
},
|
| 4474 |
"multimodal.image-text": {
|
| 4475 |
"query": "\"image-text\" in:description",
|
| 4476 |
"years": [
|
| 4477 |
+
2026,
|
| 4478 |
+
2025
|
| 4479 |
]
|
| 4480 |
},
|
| 4481 |
"multimodal.image-to-3d": {
|
|
|
|
| 4583 |
"nlp-bm25-retrieval": {
|
| 4584 |
"query": "BM25 retrieval in:readme",
|
| 4585 |
"years": [
|
| 4586 |
+
2026,
|
| 4587 |
+
2025
|
| 4588 |
]
|
| 4589 |
},
|
| 4590 |
"nlp-document-retrieval": {
|
| 4591 |
"query": "\"document retrieval\" in:readme",
|
| 4592 |
"years": [
|
| 4593 |
+
2026,
|
| 4594 |
+
2025
|
| 4595 |
]
|
| 4596 |
},
|
| 4597 |
"nlp-entity-linking": {
|
| 4598 |
"query": "\"entity linking\" in:readme",
|
| 4599 |
"years": [
|
| 4600 |
+
2026,
|
| 4601 |
+
2025
|
| 4602 |
]
|
| 4603 |
},
|
| 4604 |
"nlp-natural-language-inference": {
|
| 4605 |
"query": "\"natural language inference\" in:readme",
|
| 4606 |
"years": [
|
| 4607 |
+
2026,
|
| 4608 |
+
2025
|
| 4609 |
]
|
| 4610 |
},
|
| 4611 |
"nlp-query-understanding": {
|
| 4612 |
"query": "\"query understanding\" in:readme",
|
| 4613 |
"years": [
|
| 4614 |
+
2026,
|
| 4615 |
+
2025
|
| 4616 |
]
|
| 4617 |
},
|
| 4618 |
"nlp-sparse-retrieval": {
|
| 4619 |
"query": "\"sparse retrieval\" in:readme",
|
| 4620 |
"years": [
|
| 4621 |
+
2026,
|
| 4622 |
+
2025
|
| 4623 |
]
|
| 4624 |
},
|
| 4625 |
"nlp-speech-language-modeling": {
|
| 4626 |
"query": "\"speech language model\" in:readme",
|
| 4627 |
"years": [
|
| 4628 |
+
2026,
|
| 4629 |
+
2025
|
| 4630 |
]
|
| 4631 |
},
|
| 4632 |
"nlp.coreference-resolution": {
|
| 4633 |
"query": "\"coreference resolution\" in:readme",
|
| 4634 |
"years": [
|
| 4635 |
+
2026,
|
| 4636 |
+
2025
|
| 4637 |
]
|
| 4638 |
},
|
| 4639 |
"nlp.cross-encoder-reranking": {
|
| 4640 |
"query": "\"cross-encoder\" \"reranking\" in:readme",
|
| 4641 |
"years": [
|
| 4642 |
+
2026,
|
| 4643 |
+
2025
|
| 4644 |
]
|
| 4645 |
},
|
| 4646 |
"nlp.dense-retrieval": {
|
| 4647 |
"query": "\"dense retrieval\" in:readme",
|
| 4648 |
"years": [
|
| 4649 |
+
2026,
|
| 4650 |
+
2025
|
| 4651 |
]
|
| 4652 |
},
|
| 4653 |
"nlp.dependency-parsing": {
|
| 4654 |
"query": "\"dependency parsing\" in:readme",
|
| 4655 |
"years": [
|
| 4656 |
+
2026,
|
| 4657 |
+
2025
|
| 4658 |
]
|
| 4659 |
},
|
| 4660 |
"nlp.dialogue-systems": {
|
| 4661 |
"query": "\"dialogue system\" in:readme",
|
| 4662 |
"years": [
|
| 4663 |
+
2026,
|
| 4664 |
+
2025
|
| 4665 |
]
|
| 4666 |
},
|
| 4667 |
"nlp.information-extraction": {
|
| 4668 |
"query": "\"information extraction\" in:description",
|
| 4669 |
"years": [
|
| 4670 |
+
2026,
|
| 4671 |
+
2025
|
| 4672 |
]
|
| 4673 |
},
|
| 4674 |
"nlp.language-modeling": {
|
| 4675 |
"query": "\"language modeling\" in:description",
|
| 4676 |
"years": [
|
| 4677 |
+
2026,
|
| 4678 |
+
2025
|
| 4679 |
]
|
| 4680 |
},
|
| 4681 |
"nlp.machine-translation": {
|
| 4682 |
"query": "\"machine translation\" in:description",
|
| 4683 |
"years": [
|
| 4684 |
+
2026,
|
| 4685 |
+
2025
|
| 4686 |
]
|
| 4687 |
},
|
| 4688 |
"nlp.named-entity-recognition": {
|
|
|
|
| 4754 |
"recall.archaeological-remote-sensing": {
|
| 4755 |
"query": "archaeology \"remote sensing\" in:name,description,readme",
|
| 4756 |
"years": [
|
| 4757 |
+
2026,
|
| 4758 |
+
2025
|
| 4759 |
]
|
| 4760 |
},
|
| 4761 |
"recall.case-outcome-prediction": {
|
| 4762 |
"query": "\"case outcome prediction\" in:name,description,readme",
|
| 4763 |
"years": [
|
| 4764 |
+
2026,
|
| 4765 |
+
2025
|
| 4766 |
]
|
| 4767 |
},
|
| 4768 |
"recall.computational-linguistics": {
|
| 4769 |
"query": "\"computational linguistics\" model in:name,description,readme",
|
| 4770 |
"years": [
|
| 4771 |
+
2026,
|
| 4772 |
+
2025
|
| 4773 |
]
|
| 4774 |
},
|
| 4775 |
"recall.computational-social-science": {
|
| 4776 |
"query": "\"computational social science\" in:name,description,readme",
|
| 4777 |
"years": [
|
| 4778 |
+
2026,
|
| 4779 |
+
2025
|
| 4780 |
]
|
| 4781 |
},
|
| 4782 |
"recall.digital-humanities-nlp": {
|
| 4783 |
"query": "\"digital humanities\" NLP in:name,description,readme",
|
| 4784 |
"years": [
|
| 4785 |
+
2026,
|
| 4786 |
+
2025
|
| 4787 |
]
|
| 4788 |
},
|
| 4789 |
"recall.disease-surveillance": {
|
| 4790 |
"query": "\"disease surveillance\" machine learning in:name,description,readme",
|
| 4791 |
"years": [
|
| 4792 |
+
2026,
|
| 4793 |
+
2025
|
| 4794 |
]
|
| 4795 |
},
|
| 4796 |
"recall.educational-recommendation": {
|
| 4797 |
"query": "\"educational recommendation\" in:name,description,readme",
|
| 4798 |
"years": [
|
| 4799 |
+
2026,
|
| 4800 |
+
2025
|
| 4801 |
]
|
| 4802 |
},
|
| 4803 |
"recall.epidemic-forecasting": {
|
| 4804 |
"query": "\"epidemic forecasting\" in:name,description,readme",
|
| 4805 |
"years": [
|
| 4806 |
+
2026,
|
| 4807 |
+
2025
|
| 4808 |
]
|
| 4809 |
},
|
| 4810 |
"recall.epidemiological-modeling": {
|
|
|
|
| 4954 |
"recall26.audio-language-model": {
|
| 4955 |
"query": "\"audio language model\" in:description,readme",
|
| 4956 |
"years": [
|
| 4957 |
+
2026,
|
| 4958 |
+
2025
|
| 4959 |
]
|
| 4960 |
},
|
| 4961 |
"recall26.class-imbalance": {
|
| 4962 |
"query": "\"class imbalance\" \"machine learning\" in:description,readme",
|
| 4963 |
"years": [
|
| 4964 |
+
2026,
|
| 4965 |
+
2025
|
| 4966 |
]
|
| 4967 |
},
|
| 4968 |
"recall26.image-dehazing": {
|
| 4969 |
"query": "\"image dehazing\" in:description,readme",
|
| 4970 |
"years": [
|
| 4971 |
+
2026,
|
| 4972 |
+
2025
|
| 4973 |
]
|
| 4974 |
},
|
| 4975 |
"recall26.image-denoising": {
|
| 4976 |
"query": "\"image denoising\" in:description,readme",
|
| 4977 |
"years": [
|
| 4978 |
+
2026,
|
| 4979 |
+
2025
|
| 4980 |
]
|
| 4981 |
},
|
| 4982 |
"recall26.long-tailed-recognition": {
|
| 4983 |
"query": "\"long-tailed recognition\" in:description,readme",
|
| 4984 |
"years": [
|
| 4985 |
+
2026,
|
| 4986 |
+
2025
|
| 4987 |
]
|
| 4988 |
},
|
| 4989 |
"recall26.low-light-enhancement": {
|
| 4990 |
"query": "\"low-light image enhancement\" in:description,readme",
|
| 4991 |
"years": [
|
| 4992 |
+
2026,
|
| 4993 |
+
2025
|
| 4994 |
]
|
| 4995 |
},
|
| 4996 |
"recall26.sign-language": {
|
| 4997 |
"query": "\"sign language\" in:description,readme",
|
| 4998 |
"years": [
|
| 4999 |
+
2026,
|
| 5000 |
+
2025
|
| 5001 |
]
|
| 5002 |
},
|
| 5003 |
"recall26.visual-grounding": {
|
| 5004 |
"query": "\"visual grounding\" in:description,readme",
|
| 5005 |
"years": [
|
| 5006 |
+
2026,
|
| 5007 |
+
2025
|
| 5008 |
]
|
| 5009 |
},
|
| 5010 |
"rl.bandits": {
|
| 5011 |
"query": "\"multi-armed bandit\" in:description",
|
| 5012 |
"years": [
|
| 5013 |
+
2026,
|
| 5014 |
+
2025
|
| 5015 |
]
|
| 5016 |
},
|
| 5017 |
"rl.continual": {
|
| 5018 |
"query": "\"continual reinforcement learning\" in:description,readme",
|
| 5019 |
"years": [
|
| 5020 |
+
2026,
|
| 5021 |
+
2025
|
| 5022 |
]
|
| 5023 |
},
|
| 5024 |
"rl.core": {
|
| 5025 |
"query": "topic:reinforcement-learning",
|
| 5026 |
"years": [
|
| 5027 |
+
2026,
|
| 5028 |
+
2025
|
| 5029 |
]
|
| 5030 |
},
|
| 5031 |
"rl.deep": {
|
| 5032 |
"query": "\"deep reinforcement learning\" in:description",
|
| 5033 |
"years": [
|
| 5034 |
+
2026,
|
| 5035 |
+
2025
|
| 5036 |
]
|
| 5037 |
},
|
| 5038 |
"rl.deep-q-learning": {
|
| 5039 |
"query": "\"deep q-learning\" in:description",
|
| 5040 |
"years": [
|
| 5041 |
+
2026,
|
| 5042 |
+
2025
|
| 5043 |
]
|
| 5044 |
},
|
| 5045 |
"rl.distributional": {
|
| 5046 |
"query": "\"distributional reinforcement learning\" in:description,readme",
|
| 5047 |
"years": [
|
| 5048 |
+
2026,
|
| 5049 |
+
2025
|
| 5050 |
]
|
| 5051 |
},
|
| 5052 |
"rl.exploration": {
|
| 5053 |
"query": "\"reinforcement learning exploration\" in:description",
|
| 5054 |
"years": [
|
| 5055 |
+
2026,
|
| 5056 |
+
2025
|
| 5057 |
]
|
| 5058 |
},
|
| 5059 |
"rl.games": {
|
| 5060 |
"query": "topic:game-ai",
|
| 5061 |
"years": [
|
| 5062 |
+
2026,
|
| 5063 |
+
2025
|
| 5064 |
]
|
| 5065 |
},
|
| 5066 |
"rl.hierarchical": {
|
|
|
|
| 5150 |
"robotics.autonomous-driving": {
|
| 5151 |
"query": "\"autonomous driving\" in:description",
|
| 5152 |
"years": [
|
| 5153 |
+
2026,
|
| 5154 |
+
2025
|
| 5155 |
]
|
| 5156 |
},
|
| 5157 |
"robotics.behavior-cloning": {
|
| 5158 |
"query": "\"behavior cloning\" in:description",
|
| 5159 |
"years": [
|
| 5160 |
+
2026,
|
| 5161 |
+
2025
|
| 5162 |
]
|
| 5163 |
},
|
| 5164 |
"robotics.diffusion-policy": {
|
| 5165 |
"query": "\"diffusion policy\" in:description",
|
| 5166 |
"years": [
|
| 5167 |
+
2026,
|
| 5168 |
+
2025
|
| 5169 |
]
|
| 5170 |
},
|
| 5171 |
"robotics.domain-randomization": {
|
| 5172 |
"query": "\"domain randomization\" in:description",
|
| 5173 |
"years": [
|
| 5174 |
+
2026,
|
| 5175 |
+
2025
|
| 5176 |
]
|
| 5177 |
},
|
| 5178 |
"robotics.embodied-ai": {
|
| 5179 |
"query": "\"embodied AI\" in:description",
|
| 5180 |
"years": [
|
| 5181 |
+
2026,
|
| 5182 |
+
2025
|
| 5183 |
]
|
| 5184 |
},
|
| 5185 |
"robotics.grasping": {
|
| 5186 |
"query": "\"robot grasping\" in:description",
|
| 5187 |
"years": [
|
| 5188 |
+
2026,
|
| 5189 |
+
2025
|
| 5190 |
]
|
| 5191 |
},
|
| 5192 |
"robotics.imitation-learning": {
|
| 5193 |
"query": "\"imitation learning\" robotics in:description",
|
| 5194 |
"years": [
|
| 5195 |
+
2026,
|
| 5196 |
+
2025
|
| 5197 |
]
|
| 5198 |
},
|
| 5199 |
"robotics.locomotion": {
|
| 5200 |
"query": "\"robot locomotion\" in:description",
|
| 5201 |
"years": [
|
| 5202 |
+
2026,
|
| 5203 |
+
2025
|
| 5204 |
]
|
| 5205 |
},
|
| 5206 |
"robotics.manipulation": {
|
|
|
|
| 5296 |
"science.astronomy": {
|
| 5297 |
"query": "astronomy in:readme",
|
| 5298 |
"years": [
|
| 5299 |
+
2026,
|
| 5300 |
+
2025
|
| 5301 |
]
|
| 5302 |
},
|
| 5303 |
"science.astrophysics": {
|
| 5304 |
"query": "astrophysics in:description",
|
| 5305 |
"years": [
|
| 5306 |
+
2026,
|
| 5307 |
+
2025
|
| 5308 |
]
|
| 5309 |
},
|
| 5310 |
"science.climate-modeling": {
|
| 5311 |
"query": "\"climate modeling\" in:readme",
|
| 5312 |
"years": [
|
| 5313 |
+
2026,
|
| 5314 |
+
2025
|
| 5315 |
]
|
| 5316 |
},
|
| 5317 |
"science.computational-mechanics": {
|
| 5318 |
"query": "\"computational mechanics\" \"machine learning\" in:description,readme",
|
| 5319 |
"years": [
|
| 5320 |
+
2026,
|
| 5321 |
+
2025
|
| 5322 |
]
|
| 5323 |
},
|
| 5324 |
"science.cosmology": {
|
| 5325 |
"query": "cosmology in:description",
|
| 5326 |
"years": [
|
| 5327 |
+
2026,
|
| 5328 |
+
2025
|
| 5329 |
]
|
| 5330 |
},
|
| 5331 |
"science.differentiable-simulation": {
|
| 5332 |
"query": "\"differentiable simulation\" in:readme",
|
| 5333 |
"years": [
|
| 5334 |
+
2026,
|
| 5335 |
+
2025
|
| 5336 |
]
|
| 5337 |
},
|
| 5338 |
"science.exoplanets": {
|
| 5339 |
"query": "exoplanet in:description \"machine learning\"",
|
| 5340 |
"years": [
|
| 5341 |
+
2026,
|
| 5342 |
+
2025
|
| 5343 |
]
|
| 5344 |
},
|
| 5345 |
"science.fluid-dynamics": {
|
| 5346 |
"query": "\"fluid dynamics\" in:description",
|
| 5347 |
"years": [
|
| 5348 |
+
2026,
|
| 5349 |
+
2025
|
| 5350 |
]
|
| 5351 |
},
|
| 5352 |
"science.fusion-plasma": {
|
|
|
|
| 5460 |
"specialized.carbon-footprint-training": {
|
| 5461 |
"query": "\"carbon footprint\" \"deep learning\" training in:description,readme",
|
| 5462 |
"years": [
|
| 5463 |
+
2026,
|
| 5464 |
+
2025
|
| 5465 |
]
|
| 5466 |
},
|
| 5467 |
"specialized.computational-pathology-ml": {
|
| 5468 |
"query": "\"computational pathology\" \"machine learning\" in:description,readme",
|
| 5469 |
"years": [
|
| 5470 |
+
2026,
|
| 5471 |
+
2025
|
| 5472 |
]
|
| 5473 |
},
|
| 5474 |
"specialized.coreset-selection": {
|
| 5475 |
"query": "\"coreset selection\" in:description,readme",
|
| 5476 |
"years": [
|
| 5477 |
+
2026,
|
| 5478 |
+
2025
|
| 5479 |
]
|
| 5480 |
},
|
| 5481 |
"specialized.curriculum-learning": {
|
| 5482 |
"query": "\"curriculum learning\" in:description,readme",
|
| 5483 |
"years": [
|
| 5484 |
+
2026,
|
| 5485 |
+
2025
|
| 5486 |
]
|
| 5487 |
},
|
| 5488 |
"specialized.data-centric-ai": {
|
| 5489 |
"query": "\"data-centric AI\" in:description,readme",
|
| 5490 |
"years": [
|
| 5491 |
+
2026,
|
| 5492 |
+
2025
|
| 5493 |
]
|
| 5494 |
},
|
| 5495 |
"specialized.data-shapley": {
|
| 5496 |
"query": "\"data Shapley\" in:description,readme",
|
| 5497 |
"years": [
|
| 5498 |
+
2026,
|
| 5499 |
+
2025
|
| 5500 |
]
|
| 5501 |
},
|
| 5502 |
"specialized.dataset-distillation": {
|
| 5503 |
"query": "\"dataset distillation\" in:description,readme",
|
| 5504 |
"years": [
|
| 5505 |
+
2026,
|
| 5506 |
+
2025
|
| 5507 |
]
|
| 5508 |
},
|
| 5509 |
"specialized.event-camera-deep-learning": {
|
| 5510 |
"query": "\"event camera\" \"deep learning\" in:description,readme",
|
| 5511 |
"years": [
|
| 5512 |
+
2026,
|
| 5513 |
+
2025
|
| 5514 |
]
|
| 5515 |
},
|
| 5516 |
"specialized.fmri-decoding-ml": {
|
|
|
|
| 5558 |
"timeseries.anomaly-detection": {
|
| 5559 |
"query": "\"time series anomaly detection\" in:description",
|
| 5560 |
"years": [
|
| 5561 |
+
2026,
|
| 5562 |
+
2025
|
| 5563 |
]
|
| 5564 |
},
|
| 5565 |
"timeseries.classification": {
|
| 5566 |
"query": "\"time series classification\" in:description",
|
| 5567 |
"years": [
|
| 5568 |
+
2026,
|
| 5569 |
+
2025
|
| 5570 |
]
|
| 5571 |
},
|
| 5572 |
"timeseries.clustering": {
|
| 5573 |
"query": "\"time series clustering\" in:description,readme",
|
| 5574 |
"years": [
|
| 5575 |
+
2026,
|
| 5576 |
+
2025
|
| 5577 |
]
|
| 5578 |
},
|
| 5579 |
"timeseries.energy-forecasting": {
|
| 5580 |
"query": "\"energy forecasting\" in:description",
|
| 5581 |
"years": [
|
| 5582 |
+
2026,
|
| 5583 |
+
2025
|
| 5584 |
]
|
| 5585 |
},
|
| 5586 |
"timeseries.event-forecasting": {
|
| 5587 |
"query": "\"event forecasting\" in:description",
|
| 5588 |
"years": [
|
| 5589 |
+
2026,
|
| 5590 |
+
2025
|
| 5591 |
]
|
| 5592 |
},
|
| 5593 |
"timeseries.forecasting": {
|
| 5594 |
"query": "\"time series forecasting\" in:description",
|
| 5595 |
"years": [
|
| 5596 |
+
2026,
|
| 5597 |
+
2025
|
| 5598 |
]
|
| 5599 |
},
|
| 5600 |
"timeseries.foundation-models": {
|
| 5601 |
"query": "\"time series foundation model\" in:description",
|
| 5602 |
"years": [
|
| 5603 |
+
2026,
|
| 5604 |
+
2025
|
| 5605 |
]
|
| 5606 |
},
|
| 5607 |
"timeseries.imputation": {
|
| 5608 |
"query": "\"time series imputation\" in:description,readme",
|
| 5609 |
"years": [
|
| 5610 |
+
2026,
|
| 5611 |
+
2025
|
| 5612 |
]
|
| 5613 |
},
|
| 5614 |
"timeseries.long-horizon": {
|
|
|
|
| 5722 |
"trust.adversarial-robustness": {
|
| 5723 |
"query": "\"adversarial robustness\" in:description",
|
| 5724 |
"years": [
|
| 5725 |
+
2026,
|
| 5726 |
+
2025
|
| 5727 |
]
|
| 5728 |
},
|
| 5729 |
"trust.ai-alignment": {
|
| 5730 |
"query": "\"AI alignment\" in:description",
|
| 5731 |
"years": [
|
| 5732 |
+
2026,
|
| 5733 |
+
2025
|
| 5734 |
]
|
| 5735 |
},
|
| 5736 |
"trust.ai-safety": {
|
| 5737 |
"query": "\"AI safety\" in:description",
|
| 5738 |
"years": [
|
| 5739 |
+
2026,
|
| 5740 |
+
2025
|
| 5741 |
]
|
| 5742 |
},
|
| 5743 |
"trust.algorithmic-fairness": {
|
| 5744 |
"query": "\"algorithmic fairness\" in:description",
|
| 5745 |
"years": [
|
| 5746 |
+
2026,
|
| 5747 |
+
2025
|
| 5748 |
]
|
| 5749 |
},
|
| 5750 |
"trust.algorithmic-recourse": {
|
| 5751 |
"query": "\"algorithmic recourse\" in:description",
|
| 5752 |
"years": [
|
| 5753 |
+
2026,
|
| 5754 |
+
2025
|
| 5755 |
]
|
| 5756 |
},
|
| 5757 |
"trust.causal-discovery": {
|
| 5758 |
"query": "\"causal discovery\" in:description",
|
| 5759 |
"years": [
|
| 5760 |
+
2026,
|
| 5761 |
+
2025
|
| 5762 |
]
|
| 5763 |
},
|
| 5764 |
"trust.causal-effect-estimation": {
|
| 5765 |
"query": "\"causal effect estimation\" in:description",
|
| 5766 |
"years": [
|
| 5767 |
+
2026,
|
| 5768 |
+
2025
|
| 5769 |
]
|
| 5770 |
},
|
| 5771 |
"trust.causal-mediation": {
|
|
|
|
| 5903 |
"vision.action-recognition": {
|
| 5904 |
"query": "\"action recognition\" in:description",
|
| 5905 |
"years": [
|
| 5906 |
+
2026,
|
| 5907 |
+
2025
|
| 5908 |
]
|
| 5909 |
},
|
| 5910 |
"vision.change-detection": {
|
| 5911 |
"query": "\"change detection\" in:readme",
|
| 5912 |
"years": [
|
| 5913 |
+
2026,
|
| 5914 |
+
2025
|
| 5915 |
]
|
| 5916 |
},
|
| 5917 |
"vision.depth-estimation": {
|
| 5918 |
"query": "\"depth estimation\" in:readme",
|
| 5919 |
"years": [
|
| 5920 |
+
2026,
|
| 5921 |
+
2025
|
| 5922 |
]
|
| 5923 |
},
|
| 5924 |
"vision.face-recognition": {
|
| 5925 |
"query": "\"face recognition\" in:readme",
|
| 5926 |
"years": [
|
| 5927 |
+
2026,
|
| 5928 |
+
2025
|
| 5929 |
]
|
| 5930 |
},
|
| 5931 |
"vision.image-captioning": {
|
| 5932 |
"query": "\"image captioning\" in:readme",
|
| 5933 |
"years": [
|
| 5934 |
+
2026,
|
| 5935 |
+
2025
|
| 5936 |
]
|
| 5937 |
},
|
| 5938 |
"vision.image-classification": {
|
| 5939 |
"query": "\"image classification\" in:description",
|
| 5940 |
"years": [
|
| 5941 |
+
2026,
|
| 5942 |
+
2025
|
| 5943 |
]
|
| 5944 |
},
|
| 5945 |
"vision.image-diffusion": {
|
| 5946 |
"query": "\"image diffusion model\" in:readme",
|
| 5947 |
"years": [
|
| 5948 |
+
2026,
|
| 5949 |
+
2025
|
| 5950 |
]
|
| 5951 |
},
|
| 5952 |
"vision.image-restoration": {
|
|
|
|
| 6079 |
"end": "2026-09-24",
|
| 6080 |
"search_policy_version": 2,
|
| 6081 |
"start_year": 2008,
|
| 6082 |
+
"updated_at": "2026-09-27T15:53:49Z"
|
| 6083 |
}
|