all_domains list | all_methods list | all_novelty_signals list | all_query_ids list | archived bool | candidate_status string | candidate_rule_version string | candidate_eligible bool | candidate_reason string | candidate_evidence list | created_at string | description string | domains list | evidence_signals list | evidence_tier string | evidence_version string | first_observed_at string | fork bool | forks int64 | github_id int64 | homepage string | language string | license string | methods list | name string | novelty_signals list | observation_count int64 | observed_at string | paper_ids list | pushed_at string | query_ids list | readme_blob_sha string | readme_checked_at string | readme_evidence_version string | readme_etag string | readme_sections list | readme_signals list | readme_status string | readme_observed_at string | readme_repository_name_at_fetch string | selection_reason string | selection_signals list | selection_status string | selection_version string | stars int64 | topics list | updated_at string | url string | extra_json string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
[
"earth-observation",
"generative-modeling",
"geospatial",
"remote-sensing",
"science-and-engineering"
] | [
"deep-learning",
"machine-learning"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"geo.topic-remote-sensing"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-03T11:30:54Z | [Pattern Recognit. Lett.] This is the official code of the paper "Cloud removal using SAR and optical images via attention mechanism-based GAN" | [
"earth-observation",
"generative-modeling",
"geospatial",
"remote-sensing",
"science-and-engineering"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-02T16:44:32Z | false | 3 | 766,476,917 | null | Python | null | [
"deep-learning",
"machine-learning"
] | Shuaizhang7/AttentionGAN-for-Cloud-removal | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 2 | 2026-10-03T15:45:52Z | [] | 2025-01-17T15:15:39Z | [
"geo.topic-remote-sensing"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 28 | [
"cloud-removal",
"gan",
"remote-sensing"
] | 2026-08-20T12:21:14Z | https://github.com/Shuaizhang7/AttentionGAN-for-Cloud-removal | null |
[
"machine-learning",
"reliable-ml",
"robust-ml",
"trustworthy-ml"
] | [
"out-of-distribution-detection",
"transformer",
"uncertainty-estimation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"probable.evaluation-ood-readme",
"trust.out-of-distribution-detection"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-03T15:19:11Z | [ICML 2026] Code for Paper How Out-of-Distribution Detection Learning Theory Enhances Transformer: Learnability and Reliability | [
"machine-learning",
"reliable-ml"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-26T14:50:46Z | false | 0 | 766,549,490 | null | Python | null | [
"out-of-distribution-detection",
"transformer",
"uncertainty-estimation"
] | yjzscode/GROD-OOD-Detection-with-Transformers | [
"description",
"query-match",
"repository-metadata"
] | 4 | 2026-10-12T20:31:19Z | [] | 2026-05-25T14:27:20Z | [
"probable.evaluation-ood-readme"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 3 | [] | 2026-05-25T14:27:25Z | https://github.com/yjzscode/GROD-OOD-Detection-with-Transformers | null |
[
"generative-ai",
"language",
"linguistics",
"natural-language-processing"
] | [
"language-model",
"language-modeling",
"pretraining"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.language-models",
"recall.computational-linguistics"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-04T06:49:53Z | [ACL'25] Official Code for LlamaDuo: LLMOps Pipeline for Seamless Migration from Service LLMs to Small-Scale Local LLMs | [
"generative-ai",
"language",
"natural-language-processing"
] | [
"large-language-model",
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T16:30:43Z | false | 30 | 766,805,752 | https://huggingface.co/papers/2408.13467 | Python | Apache-2.0 | [
"language-model",
"pretraining"
] | deep-diver/llamaduo | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-08T18:15:14Z | [
"2408.13467"
] | 2025-07-13T02:59:22Z | [
"llm.language-models"
] | 60a8798e45640358b9e93bd9ecc763333ed6c545 | 2026-09-30T18:16:34Z | gh-ml-readme-evidence-v2 | "60a8798e45640358b9e93bd9ecc763333ed6c545" | [
"citation",
"dataset",
"other",
"overview",
"results"
] | [
"method-contribution",
"ml-method-context",
"paper-reference"
] | ok | 2026-09-30T18:16:34Z | deep-diver/llamaduo | readme-supported-paper-method-implementation | [
"method-contribution",
"ml-context-only",
"ml-method-context",
"paper-reference"
] | include | ml-contribution-v5 | 318 | [
"large-language-models",
"machine-learning"
] | 2026-09-27T05:44:37Z | https://github.com/deep-diver/llamaduo | null |
[
"machine-learning"
] | [
"adaptation",
"test-time-adaptation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"specialized.test-time-adaptation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-04T08:24:35Z | Official implementation of "Test-Time Zero-Shot Temporal Action Localization", CVPR 2024 | [
"machine-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 3 | 766,841,753 | https://benedettaliberatori.github.io/T3AL/ | Python | null | [
"adaptation",
"test-time-adaptation"
] | benedettaliberatori/T3AL | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-01T17:35:19Z | [] | 2024-09-11T08:38:10Z | [
"specialized.test-time-adaptation"
] | d363d22b5582f1f774ecde7f652502306f976fa2 | 2026-09-30T18:16:34Z | gh-ml-readme-evidence-v2 | "d363d22b5582f1f774ecde7f652502306f976fa2" | [
"citation",
"installation",
"other",
"results"
] | [
"method-contribution",
"ml-method-context",
"paper-reference"
] | ok | 2026-09-30T18:16:34Z | benedettaliberatori/T3AL | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-method-context",
"paper-reference"
] | include | ml-contribution-v5 | 80 | [] | 2026-09-13T11:31:06Z | https://github.com/benedettaliberatori/T3AL | null |
[
"3d",
"computer-vision",
"generative-ai",
"generative-modeling",
"multimodal"
] | [
"3d-generation",
"diffusion",
"image-to-3d",
"text-to-3d"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.image-to-3d",
"multimodal.text-to-3d"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-04T18:49:21Z | Official PyTorch implementation of "A Unified Approach for Text- and Image-guided 4D Scene Generation", [CVPR 2024] | [
"3d",
"computer-vision",
"generative-ai",
"generative-modeling",
"multimodal"
] | [
"deep-learning",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 9 | 767,127,345 | https://research.nvidia.com/labs/nxp/dream-in-4d/ | Python | NOASSERTION | [
"3d-generation",
"diffusion",
"text-to-3d"
] | NVlabs/dream-in-4d | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-01T17:35:19Z | [] | 2024-04-23T03:24:46Z | [
"multimodal.text-to-3d"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 94 | [
"deep-learning",
"diffusion-models",
"pytorch",
"text-to-4d"
] | 2026-07-10T13:11:42Z | https://github.com/NVlabs/dream-in-4d | null |
[
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-05T05:16:04Z | Official PyTorch implementation of DiffuseMix : Label-Preserving Data Augmentation with Diffusion Models (CVPR'2024) | [
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"classifier",
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-02T18:08:49.004055Z | false | 9 | 767,332,139 | https://diffusemix.github.io | Python | null | [
"diffusion"
] | khawar-islam/diffuseMix | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 2 | 2026-10-11T16:48:52Z | [] | 2026-01-13T03:42:38Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 137 | [
"cutmix",
"data-augmentation",
"diffusion-models",
"generative-data-augmentation",
"image-classification",
"mixup",
"synthetic-data",
"transfer-learning"
] | 2026-10-02T14:55:40Z | https://github.com/khawar-islam/diffuseMix | null |
[
"forecasting",
"general-ml",
"time-series",
"time-series-and-forecasting"
] | [
"deep-learning",
"forecasting",
"machine-learning",
"sequence-modeling",
"time-series-forecasting"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"applied.time-series-forecasting",
"general.topic-deep-learning",
"general.topic-machine-learning",
"timeseries.forecasting",
"timeseries.topic"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-05T06:37:26Z | [ICLR 2024] Official implementation of "TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting" | [
"time-series",
"time-series-and-forecasting"
] | [
"deep-learning",
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:27:52Z | false | 239 | 767,358,941 | https://openreview.net/pdf?id=7oLshfEIC2 | Python | Apache-2.0 | [
"sequence-modeling"
] | kwuking/TimeMixer | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 15 | 2026-10-19T17:46:36Z | [] | 2025-10-05T00:30:08Z | [
"timeseries.topic"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 1,991 | [
"deep-learning",
"machine-learning",
"time-series",
"time-series-analysis",
"time-series-forecasting"
] | 2026-10-18T14:56:46Z | https://github.com/kwuking/TimeMixer | null |
[
"machine-learning"
] | [
"adaptation",
"test-time-adaptation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"specialized.test-time-adaptation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-05T06:40:21Z | [CVPR 2024] Official implementation of "MedBN: Robust Test Time Adaptation against Malicious Test Samples" | [
"machine-learning"
] | [
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 2 | 767,359,999 | null | Python | null | [
"adaptation",
"test-time-adaptation"
] | ml-postech/MedBN-robust-test-time-adaptation | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-01T17:35:19Z | [] | 2024-09-05T12:47:36Z | [
"specialized.test-time-adaptation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 16 | [] | 2026-06-19T06:24:01Z | https://github.com/ml-postech/MedBN-robust-test-time-adaptation | null |
[
"efficient-ml",
"generative-ai",
"generative-modeling",
"machine-learning-systems",
"model-compression"
] | [
"diffusion",
"quantization"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.quantization",
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-06T12:11:38Z | Code for paper 'QNCD: Quantization Noise Correction for Diffusion Models' | [
"efficient-ml",
"generative-modeling",
"machine-learning-systems",
"model-compression"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-12T20:31:19Z | false | 0 | 768,067,763 | null | Python | MIT | [
"diffusion",
"quantization"
] | huanpengchu/QNCD | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-13T18:05:26Z | [] | 2024-08-23T09:39:54Z | [
"efficiency.quantization"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 8 | [] | 2025-04-25T17:59:35Z | https://github.com/huanpengchu/QNCD | null |
[
"computer-vision",
"efficient-ml",
"model-compression"
] | [
"distillation",
"knowledge-distillation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-06T15:33:29Z | This repository contains the official implementation of the paper titled "Multiple Teachers-Meticulous Student: A Domain Adaptive Meta-Knowledge Distillation Model for Medical Image Classification". | [
"computer-vision",
"efficient-ml",
"model-compression"
] | [
"classifier"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 1 | 768,165,048 | null | Python | MIT | [
"distillation",
"knowledge-distillation"
] | kiananvari/MTMS-A-Domain-Adaptive-Meta-Knowledge-Distillation-Model-for-Medical-Image-Classification | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-01T17:50:46Z | [] | 2026-04-21T03:37:43Z | [
"efficiency.knowledge-distillation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 9 | [] | 2026-04-21T03:37:46Z | https://github.com/kiananvari/MTMS-A-Domain-Adaptive-Meta-Knowledge-Distillation-Model-for-Medical-Image-Classification | null |
[
"generative-ai",
"language",
"multimodal-learning",
"natural-language-processing"
] | [
"language-model",
"pretraining"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.large-language-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-06T18:23:16Z | This is the official implementation of ICLR 2024 paper "VDC: Versatile Data Cleanser based on Visual-Linguistic Inconsistency by Multimodal Large Language Models". | [
"generative-ai",
"language",
"multimodal-learning",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-21T15:48:06Z | false | 1 | 768,246,379 | https://versatile-data-cleanser.github.io/ | Python | MIT | [
"language-model",
"pretraining"
] | zihao-ai/vdc | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-21T15:48:06Z | [] | 2025-02-24T09:11:49Z | [
"llm.large-language-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 19 | [] | 2025-10-06T09:25:04Z | https://github.com/zihao-ai/vdc | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-07T00:16:37Z | This is the official Pytorch implementation of the paper "Three-dimensional latent diffusion model for brain tumour segmentation" | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-13T18:05:26Z | false | 0 | 768,365,411 | null | Python | null | [
"diffusion"
] | nicoloesch/PatchLDMSeg | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-13T18:05:26Z | [] | 2024-03-07T05:13:01Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2024-03-07T05:07:13Z | https://github.com/nicoloesch/PatchLDMSeg | null |
[
"deep-learning",
"representation-learning"
] | [
"representation-learning",
"self-supervised-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.self-supervised-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-07T04:26:34Z | The official implementation of CVPR 2025 paper "Invisible Backdoor Attack against Self-supervised Learning" | [
"deep-learning",
"representation-learning"
] | [
"self-supervised-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 0 | 768,433,505 | null | Python | null | [
"representation-learning",
"self-supervised-learning"
] | Zhang-Henry/INACTIVE | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-02T16:44:32Z | [] | 2025-07-05T03:42:10Z | [
"general.self-supervised-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 20 | [] | 2026-09-23T02:25:29Z | https://github.com/Zhang-Henry/INACTIVE | null |
[
"generative-ai",
"language",
"multimodal-learning",
"natural-language-processing",
"safety",
"speech-and-audio"
] | [
"alignment",
"language-model",
"preference-optimization",
"pretraining"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.large-language-model",
"llm.preference-optimization"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-07T08:32:59Z | [ECCV’24] Official Implementation for CAT: Enhancing Multimodal Large Language Model to Answer Questions in Dynamic Audio-Visual Scenarios | [
"generative-ai",
"language",
"multimodal-learning",
"natural-language-processing",
"speech-and-audio"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 1 | 768,519,722 | null | Python | Apache-2.0 | [
"language-model",
"pretraining"
] | rikeilong/Bay-CAT | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-21T15:48:06Z | [] | 2024-09-04T03:50:50Z | [
"llm.large-language-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 59 | [] | 2026-05-03T06:56:01Z | https://github.com/rikeilong/Bay-CAT | null |
[
"forecasting",
"generative-ai",
"language",
"natural-language-processing",
"time-series",
"time-series-and-forecasting"
] | [
"forecasting",
"language-model",
"long-context-modeling",
"pretraining",
"sequence-modeling"
] | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"llm.large-language-model",
"llm.long-context",
"timeseries.forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-07T09:14:40Z | Official implementation for "AutoTimes: Autoregressive Time Series Forecasters via Large Language Models" | [
"generative-ai",
"language",
"natural-language-processing",
"time-series-and-forecasting"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 6 | 768,537,827 | https://arxiv.org/abs/2402.02370 | null | MIT | [
"language-model",
"pretraining"
] | kwuking/AutoTimes | [
"description",
"license-metadata",
"paper-reference",
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"3d-perception",
"depth-estimation",
"diffusion"
] | bytedance/DEADiff | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-10-22T16:09:07Z | [] | 2026-09-16T19:55:07Z | [
"vision.depth-estimation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 278 | [] | 2026-10-13T07:25:40Z | https://github.com/bytedance/DEADiff | null |
[
"reinforcement-learning"
] | [
"mixture-of-experts",
"reinforcement-learning"
] | [
"description",
"github-topics",
"license-metadata",
"repository-metadata"
] | [] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-10T23:39:15Z | [ICLR 2024] Official code of the paper "Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts. | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T18:03:22.392067Z | false | 6 | 770,110,230 | null | Python | MIT | [
"mixture-of-experts",
"reinforcement-learning"
] | AhmedMagdyHendawy/MOORE | [
"description",
"github-topics",
"license-metadata",
"repository-metadata"
] | 1 | 2026-09-30T18:03:22.392067Z | [] | 2024-11-18T13:49:53Z | [] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 51 | [
"deeplearning",
"mixture-of-experts",
"multi-task-learning",
"reinforcement-learning"
] | 2026-08-06T04:33:27Z | https://github.com/AhmedMagdyHendawy/MOORE | {"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["mixture-of-experts"]} |
[
"computer-vision",
"generative-ai",
"graph-learning",
"language",
"multimodal",
"multimodal-learning",
"vision"
] | [
"contrastive-learning",
"fine-tuning",
"generative-modeling",
"graph-generation",
"instruction-tuning",
"multimodal-learning",
"vision-language",
"vision-language-model"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.graph-generation",
"llm.instruction-tuning",
"multimodal.vision-language",
"vision.vision-language"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-11T07:51:14Z | [ECCV 2024] Official PyTorch implementation code for realizing the technical part of Mixture of All Intelligence (MoAI) to improve performance of numerous zero-shot vision language tasks. | [
"computer-vision",
"generative-ai",
"graph-learning",
"multimodal-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 31 | 770,241,208 | null | Python | MIT | [
"generative-modeling",
"graph-generation"
] | ByungKwanLee/MoAI | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 9 | 2026-10-15T15:20:52Z | [
"2403.07508"
] | 2024-03-28T15:27:43Z | [
"graph.graph-generation"
] | ec07bf4c074ad885135d14a36e9a9eeb90a4bb03 | 2026-09-30T18:16:34Z | gh-ml-readme-evidence-v2 | "ec07bf4c074ad885135d14a36e9a9eeb90a4bb03" | [
"citation",
"dataset",
"method",
"other"
] | [
"method-contribution",
"ml-method-context",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-09-30T18:16:34Z | ByungKwanLee/MoAI | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-method-context",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 329 | [] | 2026-09-28T07:54:59Z | https://github.com/ByungKwanLee/MoAI | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-11T09:27:12Z | Official implementation of the paper "Training Graph Neural Networks Subject to a aTight Lipschitz Constraint" (Transactions on Machine Learning Research, 2024). | [
"deep-learning",
"graph-learning"
] | [
"machine-learning",
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-14T16:10:46Z | false | 0 | 770,281,008 | https://openreview.net/forum?id=KLojVqdj2y | Python | BSD-3-Clause | [
"graph-neural-network",
"message-passing",
"neural-network"
] | simona-juvina/lipschitz-gnn | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-14T16:10:46Z | [] | 2026-05-20T18:28:06Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-09-17T21:01:22Z | https://github.com/simona-juvina/lipschitz-gnn | null |
[
"generative-ai",
"tabular-and-structured-data"
] | [
"generative-model"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.generative-ai"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-11T10:17:56Z | Code for our paper titled "Generative AI-augmented Offshore Jacket Design: Integrated Approach for Mixed Tabular Data Generation under Scarcity and Imbalance" | [
"generative-ai",
"tabular-and-structured-data"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-14T16:10:46Z | false | 1 | 770,303,627 | null | Jupyter Notebook | null | [
"generative-model"
] | Panagiotou/Offshore_Jacket_Design_Augmentation | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-14T16:10:46Z | [] | 2025-06-12T09:39:58Z | [
"llm.generative-ai"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-context-only",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 2 | [] | 2025-06-12T09:40:58Z | https://github.com/Panagiotou/Offshore_Jacket_Design_Augmentation | null |
[
"deep-learning",
"representation-learning"
] | [
"representation-learning",
"self-supervised-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.self-supervised-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-11T11:59:06Z | This is the official code repository of our IEEE FG 2024 paper "RS-rPPG: Robust Self-Supervised Learning for rPPG" and IEEE TCSVT paper "RS+rPPG: Robust Strongly Self-Supervised Learning for rPPG" | [
"deep-learning",
"representation-learning"
] | [
"self-supervised-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 0 | 770,350,389 | null | Python | MIT | [
"representation-learning",
"self-supervised-learning"
] | marukosan93/RS-rPPG | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-02T16:44:32Z | [] | 2025-08-29T10:20:52Z | [
"general.self-supervised-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 30 | [] | 2026-04-26T14:03:16Z | https://github.com/marukosan93/RS-rPPG | null |
[
"deep-learning",
"generative-ai",
"language",
"representation-learning"
] | [
"language-model",
"multi-task-learning",
"transfer-learning",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.multi-task-learning",
"llm.transformers"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-11T13:13:35Z | Toolkit for attaching, training, saving and loading of new heads for transformer models | [
"deep-learning",
"representation-learning"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-02T16:44:32Z | false | 26 | 770,385,357 | https://transformer-heads.readthedocs.io/en/latest/ | Jupyter Notebook | MIT | [
"multi-task-learning",
"transfer-learning",
"transformer"
] | center-for-humans-and-machines/transformer-heads | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-22T16:09:07Z | [] | 2026-02-12T14:34:11Z | [
"general.multi-task-learning"
] | 95c8d74b676c4e04b7f81854ee845925e3ee23c4 | 2026-10-02T18:21:54Z | gh-ml-readme-evidence-v2 | "95c8d74b676c4e04b7f81854ee845925e3ee23c4" | [
"installation",
"method",
"other",
"usage"
] | [
"ml-method-context",
"model-training-artifact",
"paper-reference"
] | ok | 2026-10-02T18:21:54Z | center-for-humans-and-machines/transformer-heads | specific-method-with-novelty-claim | [
"contribution-language",
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue",
"model-training-artifact",
"paper-reference"
] | include | ml-contribution-v5 | 301 | [] | 2026-09-23T17:16:21Z | https://github.com/center-for-humans-and-machines/transformer-heads | null |
[
"efficient-ml",
"model-compression"
] | [
"distillation",
"knowledge-distillation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-11T20:32:35Z | Official code repo fro CVPR 2024 paper: Aligning Logits Generatively for Principled Black-Box Knowledge Distillation | [
"efficient-ml",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 0 | 770,598,909 | null | Python | MIT | [
"distillation",
"knowledge-distillation"
] | HAIV-Lab/MEKD | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-01T17:50:46Z | [] | 2025-08-10T08:05:41Z | [
"efficiency.knowledge-distillation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 3 | [] | 2025-08-10T08:05:44Z | https://github.com/HAIV-Lab/MEKD | null |
[
"embodied-ai",
"generative-modeling",
"robotics",
"robotics-and-control"
] | [
"diffusion",
"diffusion-policy",
"imitation-learning"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"robotics.diffusion-policy"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-11T20:37:05Z | [RA-L/ICRA2025] Official implementation for paper "Diverse Controllable Diffusion Policy with Signal Temporal Logic." | [
"embodied-ai",
"generative-modeling",
"robotics",
"robotics-and-control"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 3 | 770,600,833 | https://mengyuest.github.io/pSTL-diffusion-policy/ | Python | null | [
"diffusion",
"diffusion-policy",
"imitation-learning"
] | mengyuest/pSTL-diffusion-policy | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 4 | 2026-09-30T16:30:43Z | [] | 2024-10-17T18:54:29Z | [
"robotics.diffusion-policy"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 37 | [
"autonomous-driving",
"diffusion-models",
"robotics",
"signal-temporal-logic"
] | 2026-09-08T02:50:38Z | https://github.com/mengyuest/pSTL-diffusion-policy | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-12T05:17:17Z | Official PyTorch Implementation of "Self-Guided Generation of Minority Samples Using Diffusion Models" (ECCV 2024) | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-12T20:31:19Z | false | 1 | 770,754,964 | null | Python | MIT | [
"diffusion"
] | soobin-um/sg-minority | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-12T20:31:19Z | [] | 2025-05-05T14:11:34Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 13 | [] | 2026-05-02T03:20:41Z | https://github.com/soobin-um/sg-minority | null |
[
"deep-learning",
"representation-learning"
] | [
"contrastive-learning",
"self-supervised-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-12T09:42:13Z | This is the code repo for our ICLR 2024 paper "Backdoor Contrastive Learning via Bi-level Trigger Optimization". | [
"deep-learning",
"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-29T17:49:51Z | false | 0 | 770,860,935 | null | Python | null | [
"contrastive-learning",
"self-supervised-learning"
] | SWY666/SSL-backdoor-BLTO | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-29T17:49:51Z | [] | 2026-03-20T02:12:48Z | [
"general.contrastive-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 9 | [] | 2026-07-08T16:20:58Z | https://github.com/SWY666/SSL-backdoor-BLTO | null |
[
"deep-learning",
"forecasting",
"generative-ai",
"language",
"representation-learning",
"time-series",
"time-series-and-forecasting"
] | [
"contrastive-learning",
"fine-tuning",
"forecasting",
"parameter-efficient-fine-tuning",
"self-supervised-learning",
"sequence-modeling"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning",
"llm.finetuning",
"timeseries.forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-12T09:55:07Z | Official implementation of "CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning" (AAAI 2025) | [
"deep-learning",
"representation-learning",
"time-series-and-forecasting"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T16:30:43Z | false | 16 | 770,867,085 | null | Python | Apache-2.0 | [
"contrastive-learning",
"self-supervised-learning"
] | Hank0626/CALF | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 9 | 2026-10-29T17:49:51Z | [] | 2025-03-14T08:39:37Z | [
"general.contrastive-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 166 | [] | 2026-10-23T07:00:31Z | https://github.com/Hank0626/CALF | null |
[
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"forecasting",
"multivariate-forecasting",
"sequence-modeling"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"timeseries.forecasting",
"timeseries.multivariate"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-12T16:40:21Z | Official implementation of the paper "Deep Coupling Network for Multivariate Time Series Forecasting" | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 0 | 771,077,075 | null | null | null | [
"forecasting",
"sequence-modeling"
] | aikunyi/DeepCN | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-18T14:44:29Z | [] | 2024-03-12T16:40:21Z | [
"timeseries.forecasting"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2024-03-12T16:40:21Z | https://github.com/aikunyi/DeepCN | null |
[
"computer-vision"
] | [
"restoration",
"super-resolution"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"vision.image-restoration",
"vision.image-super-resolution"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-13T02:00:49Z | [CVPR 2024] Official implementation of "Beyond Image Super-Resolution for Image Recognition with Task-Driven Perceptual Loss" | [
"computer-vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-30T16:30:43Z | false | 9 | 771,275,610 | null | Python | null | [
"restoration",
"super-resolution"
] | JaehaKim97/SR4IR | [
"description",
"query-match",
"repository-metadata"
] | 8 | 2026-10-22T16:09:07Z | [] | 2026-08-14T01:40:08Z | [
"vision.image-super-resolution"
] | 27b39bc47cebd0551570255342976f4af0b7fe91 | 2026-09-30T18:16:34Z | gh-ml-readme-evidence-v2 | "27b39bc47cebd0551570255342976f4af0b7fe91" | [
"abstract",
"citation",
"other"
] | [
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-09-30T18:16:34Z | JaehaKim97/SR4IR | readme-supported-paper-method-implementation | [
"contribution-language",
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 67 | [] | 2026-10-09T14:57:41Z | https://github.com/JaehaKim97/SR4IR | null |
[
"deep-learning",
"language",
"multimodal",
"representation-learning",
"video"
] | [
"continual-learning",
"lifelong-learning",
"video-question-answering",
"video-understanding"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.continual-learning",
"multimodal.video-question-answering"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-13T04:26:47Z | Official code for DAM: Dynamic Adapter Merging for Continual Video QA Learning | [
"deep-learning",
"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 1 | 771,316,423 | null | Python | Apache-2.0 | [
"continual-learning",
"lifelong-learning"
] | klauscc/DAM | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-15T15:20:52Z | [] | 2024-04-25T20:15:11Z | [
"general.continual-learning"
] | bb27230e18105bce0cd8bf5bf3d4da5ec0f809c0 | 2026-09-30T18:16:34Z | gh-ml-readme-evidence-v2 | "bb27230e18105bce0cd8bf5bf3d4da5ec0f809c0" | [
"citation",
"installation",
"other"
] | [
"method-contribution",
"ml-method-context",
"model-training-artifact",
"paper-reference"
] | ok | 2026-09-30T18:16:34Z | klauscc/DAM | readme-supported-paper-method-implementation | [
"method-contribution",
"ml-method-context",
"model-training-artifact",
"paper-reference"
] | include | ml-contribution-v5 | 15 | [] | 2026-05-20T17:09:36Z | https://github.com/klauscc/DAM | null |
[
"3d",
"computer-vision",
"generative-modeling",
"multimodal"
] | [
"3d-reconstruction",
"diffusion",
"neural-reconstruction"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.3d-reconstruction"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-13T07:00:18Z | [CVPR 2024] This repository includes the official implementation our paper "MicroDiffusion: Implicit Representation-Guided Diffusion for 3D Reconstruction from Limited 2D Microscopy Projections" | [
"3d",
"computer-vision",
"generative-modeling",
"multimodal"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-30T16:30:43Z | false | 0 | 771,365,988 | null | Python | null | [
"3d-reconstruction",
"diffusion",
"neural-reconstruction"
] | UCSC-VLAA/MicroDiffusion | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-02T16:59:40Z | [] | 2024-05-13T19:38:31Z | [
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[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-16T19:34:33Z | Official code of RetFormer: Embracing Point Cloud Transformer with Retentive Network paper. | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-17T13:17:44Z | false | 0 | 773,113,839 | null | Python | MIT | [
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] | gopi-erabati/RetFormer | [
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] | 2 | 2026-10-24T14:31:13Z | [] | 2024-08-28T15:39:47Z | [
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] | include | ml-contribution-v5 | 0 | [] | 2024-08-28T15:39:51Z | https://github.com/gopi-erabati/RetFormer | null |
[
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] | include | ml-contribution-v5 | 21 | [] | 2026-05-24T14:20:30Z | https://github.com/Rooooyy/HILL | null |
[
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T16:30:43Z | false | 21 | 773,384,846 | https://ecodepth-iitd.github.io/ | Python | NOASSERTION | [
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] | Aradhye2002/EcoDepth | [
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] | 6 | 2026-10-22T16:09:07Z | [] | 2025-11-20T14:24:04Z | [
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] | include | ml-contribution-v5 | 220 | [
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] | 2026-08-05T08:48:36Z | https://github.com/Aradhye2002/EcoDepth | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-17T23:58:15Z | Official implementation for "Instructing Large Language Models to Identify and Ignore Irrelevant Conditions" (NAACL 2024 main) | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-21T15:48:06Z | false | 0 | 773,547,176 | https://wzy6642.github.io/I3C.github.io/ | Python | Apache-2.0 | [
"language-model",
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] | wzy6642/I3C-Select | [
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"repository-metadata"
] | 1 | 2026-10-21T15:48:06Z | [] | 2024-05-25T19:27:57Z | [
"llm.large-language-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 8 | [
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] | 2026-08-29T09:45:06Z | https://github.com/wzy6642/I3C-Select | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-18T07:30:54Z | [CVPR 2024] Official Code for "AiOS: All-in-One-Stage Expressive Human Pose and Shape Estimation | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 12 | 773,672,526 | https://smplcap.github.io/AiOS/ | Python | NOASSERTION | [
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] | MotrixLab/AiOS | [
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] | 5 | 2026-10-01T17:35:19Z | [] | 2026-02-13T05:54:31Z | [
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] | 60912eaa2e97aceb7d6c4549954be4fc5aa81204 | 2026-09-30T18:16:34Z | gh-ml-readme-evidence-v2 | "60912eaa2e97aceb7d6c4549954be4fc5aa81204" | [
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] | [
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] | ok | 2026-09-30T18:16:34Z | MotrixLab/AiOS | readme-supported-paper-method-implementation | [
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] | include | ml-contribution-v5 | 347 | [] | 2026-08-10T08:31:28Z | https://github.com/MotrixLab/AiOS | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-18T11:19:48Z | [ECCV 2024] Official Code for "WHAC: World-grounded Humans and Cameras" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 3 | 773,770,778 | https://wqyin.github.io/projects/WHAC/ | Python | NOASSERTION | [
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] | MotrixLab/WHAC | [
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] | 5 | 2026-10-01T17:35:19Z | [] | 2026-02-12T08:54:02Z | [
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] | 3a93dd212d771e1fc7e3dd0878e571ff2eaca7db | 2026-09-30T18:16:34Z | gh-ml-readme-evidence-v2 | "3a93dd212d771e1fc7e3dd0878e571ff2eaca7db" | [
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] | [
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] | ok | 2026-09-30T18:16:34Z | MotrixLab/WHAC | readme-supported-paper-method-implementation | [
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] | include | ml-contribution-v5 | 155 | [] | 2026-09-30T01:13:09Z | https://github.com/MotrixLab/WHAC | null |
[
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] | [
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-18T12:59:58Z | Implementation details of our paper "Effective Layer Pruning Through Similarity Metric Perspective" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-20T15:59:06Z | false | 2 | 773,815,943 | null | Python | MIT | [
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] | IanPons/CKA-Layer-Pruning | [
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] | 1 | 2026-10-20T15:59:06Z | [] | 2024-11-24T15:01:53Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 2 | [] | 2025-12-01T07:24:52Z | https://github.com/IanPons/CKA-Layer-Pruning | null |
[
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-19T05:35:08Z | Official code for ICLR 2024 paper "Do Generated Data Always Help Contrastive Learning?" | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-29T17:49:51Z | false | 1 | 774,194,710 | null | Python | null | [
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] | PKU-ML/adainf | [
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] | 1 | 2026-10-29T17:49:51Z | [] | 2024-04-04T03:56:54Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 31 | [] | 2025-06-06T01:59:17Z | https://github.com/PKU-ML/adainf | null |
[
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"repository-metadata"
] | [
"general.contrastive-learning",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-19T08:20:01Z | Official Code for ICLR 2024 Paper: Non-negative Contrastive Learning | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T16:30:43Z | false | 8 | 774,256,721 | null | Python | null | [
"contrastive-learning",
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] | PKU-ML/non_neg | [
"description",
"query-match",
"repository-metadata"
] | 8 | 2026-10-29T17:49:51Z | [] | 2024-04-20T15:18:11Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 48 | [] | 2026-09-02T04:03:41Z | https://github.com/PKU-ML/non_neg | null |
[
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] | [
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"repository-metadata"
] | [
"multimodal.text-to-image",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-19T22:51:44Z | [ECCV 2024] Official PyTorch implementation of "Getting it Right: Improving Spatial Consistency in Text-to-Image Models" | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-01T17:35:19Z | false | 5 | 774,641,156 | https://spright-t2i.github.io/ | Python | Apache-2.0 | [
"diffusion",
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] | SPRIGHT-T2I/SPRIGHT | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-04T15:58:58Z | [
"2404.01197"
] | 2024-07-05T01:23:53Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 106 | [
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"text-to-image"
] | 2026-07-23T20:04:44Z | https://github.com/SPRIGHT-T2I/SPRIGHT | null |
[
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"language",
"machine-learning-systems",
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] | [
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] | [
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"query-match",
"repository-metadata"
] | [
"multimodal.video-question-answering"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-20T06:39:00Z | Official implementation of CVPR 2024 paper "vid-TLDR: Training Free Token merging for Light-weight Video Transformer". | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 3 | 774,770,936 | null | Python | MIT | [
"pruning",
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"video-understanding"
] | mlvlab/vid-TLDR | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-02T16:44:32Z | [] | 2025-10-21T08:19:07Z | [
"multimodal.video-question-answering"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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"ml-method-cue",
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 56 | [
"computer-vision",
"cvpr2024",
"efficient-vision-transformers",
"token-merging",
"token-pruning",
"video-transformer"
] | 2026-09-23T10:47:32Z | https://github.com/mlvlab/vid-TLDR | null |
[
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] | [
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] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-20T09:15:01Z | Official Implementation of "MoDiTalker: Motion-Disentangled Diffusion Model for High-Fidelity Talking Head Generation" (AAAI 2025) | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-11T16:48:52Z | false | 11 | 774,830,319 | null | Python | null | [
"diffusion"
] | cvlab-kaist/MoDiTalker | [
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"repository-metadata"
] | 1 | 2026-10-11T16:48:52Z | [] | 2025-01-14T05:11:58Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 176 | [] | 2026-09-05T13:29:37Z | https://github.com/cvlab-kaist/MoDiTalker | null |
[
"generative-ai",
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] | [
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] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-20T13:02:06Z | Code of our CVPR2024 paper - DiffusionMTL: Learning Multi-Task Denoising Diffusion Model from Partially Annotated Data | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-11T16:48:52Z | false | 3 | 774,926,560 | null | Python | null | [
"diffusion"
] | prismformore/DiffusionMTL | [
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"repository-metadata"
] | 1 | 2026-10-11T16:48:52Z | [] | 2024-03-25T01:33:08Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 61 | [] | 2026-09-27T02:54:20Z | https://github.com/prismformore/DiffusionMTL | null |
[
"reinforcement-learning",
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] | [
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"reinforcement-learning",
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] | [
"description",
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"repository-metadata"
] | [
"rl.safe"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-20T13:25:14Z | An implementation of our paper on safe and robust safe reinforcement learning using Student-t processes. | [
"reinforcement-learning",
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] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 0 | 774,937,318 | null | Python | null | [
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] | onetimedev/safe-robust-srl-tp | [
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] | 2 | 2026-10-02T16:44:32Z | [] | 2024-11-21T15:10:51Z | [
"rl.safe"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2024-11-22T11:00:44Z | https://github.com/onetimedev/safe-robust-srl-tp | null |
[
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] | [
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] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-21T08:18:59Z | [CVPR 2024] This is the official implementation of "ExtDM: Distribution Extrapolation Diffusion Model for Video Prediction" | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-12T20:31:19Z | false | 1 | 775,349,897 | null | Python | null | [
"diffusion"
] | nku-zhichengzhang/ExtDM | [
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"repository-metadata"
] | 1 | 2026-10-12T20:31:19Z | [] | 2025-06-24T04:12:12Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 58 | [] | 2026-07-21T01:58:57Z | https://github.com/nku-zhichengzhang/ExtDM | null |
[
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] | [
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] | [
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"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-21T09:45:31Z | Official code for the paper: Physics-Informed Graph Neural Networks for Water Distribution Systems | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T16:30:43Z | false | 4 | 775,386,528 | null | Python | null | [
"graph-neural-network",
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] | inaamashraf/physics_informed_gnns_for_wds | [
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"repository-metadata"
] | 3 | 2026-10-12T20:31:19Z | [] | 2024-07-09T14:22:28Z | [
"graph.gnn-description"
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-21T10:08:42Z | [CVPR'24][TPAMI'26] Official implementation of paper "FreeKD: Knowledge Distillation via Semantic Frequency Prompt". | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 6 | 775,396,742 | null | Python | Apache-2.0 | [
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] | 2 | 2026-10-01T17:35:19Z | [] | 2024-04-20T05:13:21Z | [
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] | 2026-08-10T08:21:40Z | https://github.com/Gumpest/FreeKD | null |
[
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] | [] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-22T17:14:38Z | This repository is the official code source of PPFlow: Target-Aware Peptide Design with Torsional Flow Matching (ICML2024) | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T20:57:28.489717Z | false | 12 | 776,096,726 | null | Python | null | [
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] | EDAPINENUT/ppflow | [
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] | 1 | 2026-09-26T20:57:28.489717Z | [] | 2025-05-17T08:32:36Z | [] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 95 | [
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] | 2026-09-18T09:34:45Z | https://github.com/EDAPINENUT/ppflow | {"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["flow-matching"]} |
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] | [
"specialized.test-time-adaptation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-23T07:41:27Z | [CVPR 2024] Official implementation of "MedBN: Robust Test Time Adaptation against Malicious Test Samples" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 0 | 776,327,991 | null | Python | null | [
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] | hyejin-s/MedBN-robust-test-time-adpatation | [
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] | 1 | 2026-09-30T16:47:43Z | [] | 2024-09-05T12:47:17Z | [
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] | include | ml-contribution-v5 | 1 | [] | 2024-09-05T12:47:21Z | https://github.com/hyejin-s/MedBN-robust-test-time-adpatation | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-23T20:20:18Z | Official code for LREC-COLING2024 paper "Pruning before Fine-tuning: A Retraining-free Compression Framework for Pre-trained Language Models" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-20T15:59:06Z | false | 0 | 776,550,735 | null | Python | null | [
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] | applewpj/P-pruning | [
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] | 2 | 2026-10-29T17:49:51Z | [] | 2024-07-09T08:06:09Z | [
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] | include | ml-contribution-v5 | 4 | [] | 2026-05-31T05:52:14Z | https://github.com/applewpj/P-pruning | null |
[
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] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-25T08:30:19Z | [ECCV 2024] Official PyTorch implementation of FRDiff : Feature Reuse for Universal Training-free Acceleration of Diffusion Models | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-12T20:31:19Z | false | 2 | 777,116,917 | https://jungwon-lee.github.io/Project_FRDiff/ | Jupyter Notebook | null | [
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] | 1 | 2026-10-12T20:31:19Z | [] | 2024-03-25T14:18:18Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 5 | [
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] | 2026-05-16T16:20:15Z | https://github.com/Jungwon-Lee/FRDiff | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-25T12:46:40Z | Code for paper:"TracKGE: Transformer with Relation-pattern Adaptive Contrastive Learning for Knowledge Graph Embedding". | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 0 | 777,220,048 | null | Python | null | [
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] | JieJava/TracKGE | [
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] | 3 | 2026-10-30T17:16:29Z | [] | 2024-05-09T10:44:01Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 1 | [] | 2025-04-27T09:09:01Z | https://github.com/JieJava/TracKGE | null |
[
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] | [
"description",
"query-match",
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] | [
"efficiency.pruning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-26T08:20:35Z | The code for paper: "Evolving Filter Criteria for Randomly Initialized Network Pruning in Image Classification" | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-21T15:48:06Z | false | 0 | 777,634,343 | null | Python | null | [
"neural-network-pruning",
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] | cxrasdfg/evofc | [
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] | 1 | 2026-10-21T15:48:06Z | [] | 2024-03-26T08:36:41Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2024-03-26T08:36:45Z | https://github.com/cxrasdfg/evofc | null |
[
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"repository-metadata"
] | [
"multimodal.motion-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-26T09:42:03Z | Code for ICLR 2024 paper "Duolando: Follower GPT with Off-Policy Reinforcement Learning for Dance Accompaniment" | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 6 | 777,667,978 | null | Python | null | [
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] | lisiyao21/Duolando | [
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] | 4 | 2026-10-01T17:35:19Z | [] | 2024-03-28T03:29:05Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 114 | [] | 2026-09-08T20:57:59Z | https://github.com/lisiyao21/Duolando | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-26T23:28:49Z | Official implementation of the paper "Global Convergence Guarantees for Federated Policy Gradient Methods with Adversaries" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-01T17:35:19Z | false | 0 | 778,018,098 | null | Python | null | [
"policy-gradient",
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] | LucasCJYSDL/ResNHARPG | [
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] | 2 | 2026-10-03T15:45:52Z | [] | 2024-11-30T01:46:44Z | [
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] | include | ml-contribution-v5 | 2 | [] | 2025-03-19T22:09:17Z | https://github.com/LucasCJYSDL/ResNHARPG | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-27T05:09:55Z | [PLOS ONE] Official implementation of the paper "SignFormer-GCN : Continuous Sign Language Translation Using Spatio-Temporal Graph Convolutional Networks" | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-30T16:30:43Z | false | 3 | 778,111,209 | null | Python | MIT | [
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] | rabeya-akter/SignLanguageTranslation | [
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] | 3 | 2026-10-05T20:10:04Z | [] | 2024-10-16T04:40:40Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 8 | [
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"german-sign-language-translation",
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"stgcn",
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] | 2026-09-25T18:39:21Z | https://github.com/rabeya-akter/SignLanguageTranslation | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-27T07:49:14Z | This is the official pytorch implementation for the paper: Towards Accurate Post-training Quantization for Diffusion Models.(CVPR24 Poster Highlight) | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 2 | 778,164,696 | null | Python | Apache-2.0 | [
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] | ChangyuanWang17/APQ-DM | [
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] | 6 | 2026-10-12T20:31:19Z | [] | 2024-06-04T02:06:18Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 37 | [] | 2026-08-14T02:49:56Z | https://github.com/ChangyuanWang17/APQ-DM | null |
[
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] | [
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] | [
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"query-match",
"repository-metadata"
] | [
"llm.diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-27T13:49:10Z | [CVPR 2024 Highlight] Official PyTorch implementation of "MindBridge: A Cross-Subject Brain Decoding Framework" | [
"generative-ai",
"generative-modeling",
"vision"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 12 | 778,315,577 | https://littlepure2333.github.io/MindBridge/ | Python | null | [
"diffusion"
] | littlepure2333/MindBridge | [
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] | 5 | 2026-09-30T16:30:43Z | [] | 2024-12-30T16:10:46Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 123 | [
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] | 2026-08-20T21:18:21Z | https://github.com/littlepure2333/MindBridge | null |
[
"generative-ai",
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] | [
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] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-28T00:19:56Z | [CVPR 2024] Official implementation of "Towards Realistic Scene Generation with LiDAR Diffusion Models" | [
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] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-30T16:30:43Z | false | 21 | 778,560,533 | https://lidar-diffusion.github.io/ | Python | MIT | [
"diffusion"
] | hancyran/LiDAR-Diffusion | [
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] | 3 | 2026-10-11T16:48:52Z | [] | 2024-05-21T11:12:27Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 290 | [
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"text-to-lidar"
] | 2026-09-06T10:09:06Z | https://github.com/hancyran/LiDAR-Diffusion | null |
[
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] | [
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] | [
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"license-metadata",
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] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-28T14:45:52Z | Implementation code for paper "CausalDiff: Causality-Inspired Disentanglement via Diffusion Model for Adversarial Defense" | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-12T20:31:19Z | false | 2 | 778,856,991 | null | Python | MIT | [
"diffusion"
] | ZhangMingKun1/CausalDiff | [
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] | 1 | 2026-10-12T20:31:19Z | [] | 2025-02-11T11:55:58Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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] | include | ml-contribution-v5 | 10 | [] | 2025-10-23T13:18:54Z | https://github.com/ZhangMingKun1/CausalDiff | null |
[
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"repository-metadata"
] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-28T16:39:08Z | This is the official code for the CVPR 2024 Publication: Tiger: Time-Varying Denoising Model for 3D Point Cloud Generation with Diffusion Process | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 5 | 778,907,933 | null | Python | null | [
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] | Zhiyuan-R/Tiger-Diffusion | [
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] | 1 | 2026-10-23T15:59:01Z | [] | 2024-07-06T15:54:25Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 36 | [] | 2026-08-06T08:38:38Z | https://github.com/Zhiyuan-R/Tiger-Diffusion | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-29T02:23:53Z | [ICML'24 Oral] The official code of "DiJiang: Efficient Large Language Models through Compact Kernelization", a novel DCT-based linear attention mechanism. | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-21T15:48:06Z | false | 6 | 779,086,143 | https://arxiv.org/abs/2403.19928 | Python | null | [
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] | YuchuanTian/DiJiang | [
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] | 2 | 2026-10-21T15:48:06Z | [
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] | 2024-06-14T17:06:26Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 105 | [] | 2026-09-26T17:58:32Z | https://github.com/YuchuanTian/DiJiang | null |
[
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] | [
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-29T05:47:25Z | This is the official implementation of the paper "Graph-Based Prediction and Planning Policy Network (GP3Net) for Scalable Self-Driving in Dynamic Environments using Deep Reinforcement Learning" - AAAI Conference on Artificial Intelligence 2024 | [
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[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-30T13:24:55Z | official repository of aiXcoder-7B Code Large Language Model | [
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[
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"efficiency.speculative-decoding"
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"... | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-01T16:53:18Z | [NeurIPS 2024 Best Paper Award][GPT beats diffusion🔥] [scaling laws in visual generation📈] Official impl. of "Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction". An *ultra-simple, user-friendly yet state-of-the-art* codebase for autoregressive image generation! | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:27:52Z | false | 573 | 780,522,250 | null | Jupyter Notebook | MIT | [
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