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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
[
"multimodal",
"reinforcement-learning",
"robotics",
"robotics-and-control"
] | [
"environment-modeling",
"world-model"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.world-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-04T10:51:18Z | Official implementation of the paper "Next Embedding Prediction Makes World Models Stronger" | [
"multimodal",
"reinforcement-learning",
"robotics",
"robotics-and-control"
] | [
"representation-learning",
"embedding"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T18:08:49.004055Z | false | 2 | 1,172,471,781 | https://corl-team.github.io/nedreamer/ | Python | MIT | [
"environment-modeling",
"world-model"
] | corl-team/nedreamer | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-06T17:10:23Z | [] | 2026-08-24T13:53:23Z | [
"multimodal.world-models"
] | 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 | 41 | [
"embodied-ai",
"model-based-rl",
"pytorch",
"representation-learning",
"world-models"
] | 2026-09-22T10:05:19Z | https://github.com/corl-team/nedreamer | null |
[
"interpretability-and-safety",
"language",
"safety"
] | [
"alignment",
"reinforcement-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.alignment"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-04T12:58:58Z | [ECCV 2026] Official code of "Representation Alignment for Just Image Transformers is not Easier than You Think" | [
"interpretability-and-safety",
"language",
"safety"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 3 | 1,172,561,487 | null | Python | MIT | [
"alignment",
"reinforcement-learning"
] | kaist-cvml/PixelREPA | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-09-25T15:43:40Z | [] | 2026-06-18T11:43:44Z | [
"llm.alignment"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 55 | [] | 2026-09-09T11:38:17Z | https://github.com/kaist-cvml/PixelREPA | null |
[
"generative-ai",
"generative-modeling",
"scientific-computing",
"vision"
] | [
"bayesian-optimization",
"diffusion",
"scientific-machine-learning",
"surrogate-modeling"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion",
"probable.scientific-surrogate-modeling"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-04T19:18:13Z | ♠️ [ICML 2026] Official Implementation of SPADE: Support-Proximity Augmented Diffusion Estimation for Offline Black-Box Optimization 🧬 🤖 | [
"generative-modeling",
"scientific-computing"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 0 | 1,172,869,261 | https://new-ground-ai.github.io/SPADE/ | Python | MIT | [
"bayesian-optimization",
"diffusion",
"scientific-machine-learning",
"surrogate-modeling"
] | New-Ground-AI/SPADE | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 9 | 2026-10-19T17:46:36Z | [] | 2026-10-13T05:19:21Z | [
"probable.scientific-surrogate-modeling"
] | 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 | 7 | [
"bayesian-optimization",
"bbo",
"black-box-optimization",
"blackbox-optimization",
"design-bench",
"diffusion-model",
"diffusion-models",
"icml-2026",
"offline-bbo",
"offline-black-box-optimization",
"offline-optimization",
"uncertainty-estimation"
] | 2026-10-13T12:45:07Z | https://github.com/New-Ground-AI/SPADE | null |
[
"computer-vision",
"embodied-ai",
"robotics",
"robotics-and-control"
] | [
"manipulation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"robotics.manipulation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-04T20:38:38Z | Official implementation of the Vision-Based-Hand-Shadowing-for-Robotic-Manipulation-via-Inverse-Kinematics paper | [
"computer-vision",
"embodied-ai",
"robotics",
"robotics-and-control"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-03T15:45:52Z | false | 1 | 1,172,921,056 | null | Python | null | [
"manipulation"
] | chichonnade/Vision-Based-Hand-Shadowing | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-03T15:45:52Z | [] | 2026-05-11T19:35:36Z | [
"robotics.manipulation"
] | 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 | 1 | [] | 2026-09-14T07:38:11Z | https://github.com/chichonnade/Vision-Based-Hand-Shadowing | null |
[
"machine-learning"
] | [
"adaptation",
"test-time-adaptation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"specialized.test-time-adaptation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-04T21:09:12Z | Official implementation of "REFUSE: A Normalization-free Paradigm with Entropy-regularized Feature Recalibration for Test-Time Adaptation" (ECCV 2026 submission) | [
"machine-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 0 | 1,172,939,824 | null | Python | MIT | [
"adaptation",
"test-time-adaptation"
] | refuse-tta/REFUSE | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-08T18:15:14Z | [] | 2026-03-04T21:10:59Z | [
"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 | 0 | [] | 2026-09-21T10:55:47Z | https://github.com/refuse-tta/REFUSE | null |
[
"deep-learning",
"earth-science",
"generative-modeling",
"geospatial-science",
"remote-sensing",
"representation-learning",
"science-and-engineering",
"time-series-and-forecasting"
] | [
"diffusion",
"foundation-model",
"geospatial-learning",
"remote-sensing",
"representation-learning",
"self-supervised-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.self-supervised-learning",
"science.remote-sensing"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-04T21:37:16Z | [ICLR ML4RS 2026] Official implementation for the paper "Sat-JEPA-Diff: Bridging Self-Supervised Learning and Generative Diffusion for Remote Sensing", [IEEE GRSL] Official implementation for the paper "Sat-JEPA-Diff: Caption-Guided Zero-RGB Satellite Image Forecasting via Self-Supervised Diffusion" | [
"earth-science",
"generative-modeling",
"geospatial-science",
"remote-sensing",
"science-and-engineering",
"time-series-and-forecasting"
] | [
"self-supervised-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 1 | 1,172,955,484 | null | Python | MIT | [
"diffusion",
"foundation-model",
"geospatial-learning",
"remote-sensing",
"self-supervised-learning"
] | VU-AIML/SAT-JEPA-DIFF | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-28T17:27:44Z | [] | 2026-07-10T06:59:07Z | [
"science.remote-sensing"
] | 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 | 12 | [] | 2026-09-21T11:47:01Z | https://github.com/VU-AIML/SAT-JEPA-DIFF | null |
[
"efficient-ml",
"general-ml",
"machine-learning-systems",
"model-compression",
"natural-language-processing"
] | [
"language-model",
"neural-network-pruning",
"paper-implementation",
"pruning",
"structured-pruning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning",
"general.arxiv",
"recall.name.language-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-05T00:29:00Z | The official implementation of the paper "Demystifying When Pruning Works via Representation Hierarchies" (ICML 2026). | [
"machine-learning-systems",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T16:16:53Z | false | 2 | 1,173,043,096 | null | Python | null | [
"language-model",
"pruning"
] | CASE-Lab-UMD/Pruning-on-Representations | [
"description",
"query-match",
"repository-metadata"
] | 6 | 2026-10-25T14:09:27Z | [] | 2026-10-02T20:31:46Z | [
"recall.name.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 | 11 | [] | 2026-10-02T20:31:52Z | https://github.com/CASE-Lab-UMD/Pruning-on-Representations | null |
[
"deep-learning",
"efficient-ml",
"language",
"time-series-and-forecasting"
] | [
"conditional-computation",
"mixture-of-experts",
"sparse-activation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.mixture-of-experts"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-05T02:04:23Z | Official source code for paper 《Mixture-of-Experts with Physics-Guided Gating Improves Short-Range Forecasting of Sudden Tropical Cyclone Track Turns》 | [
"deep-learning",
"efficient-ml",
"language",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-06T17:10:23Z | false | 0 | 1,173,089,802 | null | Python | null | [
"conditional-computation",
"mixture-of-experts",
"sparse-activation"
] | wangchong96/Physics-Guided-Sudden-Tropical-Cyclone-Track-Turns-Forecasting | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-06T17:10:23Z | [] | 2026-04-03T14:24:03Z | [
"efficiency.mixture-of-experts"
] | 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 | 0 | [] | 2026-03-05T02:05:44Z | https://github.com/wangchong96/Physics-Guided-Sudden-Tropical-Cyclone-Track-Turns-Forecasting | null |
[
"audio",
"deep-learning",
"efficient-ml",
"language",
"speech"
] | [
"conditional-computation",
"distillation",
"evaluation",
"inference",
"mixture-of-experts",
"representation",
"sparse-activation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"audio-speaker-verification",
"efficiency.mixture-of-experts"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-05T06:43:46Z | Official implementation of the Interspeech 2026 paper "Speaker Identity in Non-Verbal Vocalizations: Conditional Distillation and Mixture of Experts Approach" | [
"audio",
"speech"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-25T15:00:52Z | false | 0 | 1,173,244,450 | null | Python | null | [
"distillation",
"evaluation",
"inference",
"mixture-of-experts",
"representation"
] | wiizzz/nonverbal-sv | [
"description",
"query-match",
"repository-metadata"
] | 5 | 2026-10-13T18:05:26Z | [] | 2026-09-26T19:42:48Z | [
"audio-speaker-verification"
] | 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 | 1 | [] | 2026-10-13T16:22:37Z | https://github.com/wiizzz/nonverbal-sv | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-05T15:22:53Z | Code for paper "LG-HoleNet: A Lightweight Graph Neural Network for Through-Hole Recognition via Differential Geometric Attributes". | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 0 | 1,173,629,122 | null | Python | null | [
"graph-neural-network",
"message-passing",
"neural-network"
] | Miss-Hedgehog/LGHoleNet | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-23T15:59:01Z | [] | 2026-03-05T15:33:00Z | [
"graph.gnn-description"
] | 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 | 2 | [] | 2026-10-22T11:36:42Z | https://github.com/Miss-Hedgehog/LGHoleNet | null |
[
"reinforcement-learning"
] | [
"batch-reinforcement-learning",
"offline-reinforcement-learning",
"reinforcement-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.offline"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-06T09:19:01Z | code for our paper "MangoBench: A Benchmark for Multi-Agent Goal-Conditioned Offline Reinforcement Learning" | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 1 | 1,174,288,523 | null | Python | MIT | [
"batch-reinforcement-learning",
"offline-reinforcement-learning",
"reinforcement-learning"
] | SYSU-SAIL/mangobench-locomotion | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-09-26T10:28:13Z | [] | 2026-03-16T02:28:14Z | [
"rl.offline"
] | 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 | 7 | [] | 2026-08-27T06:45:51Z | https://github.com/SYSU-SAIL/mangobench-locomotion | null |
[
"computer-vision",
"multimodal",
"reinforcement-learning",
"robotics"
] | [
"computer-vision",
"environment-modeling",
"world-model"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.world-models",
"vision.topic-computer-vision"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-06T10:39:43Z | [ECCV 2026] Official implementation of paper LiveWorld: Simulating Out-of-Sight Dynamics in Generative Video World Models | [
"computer-vision",
"multimodal",
"reinforcement-learning",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T16:16:53Z | false | 4 | 1,174,347,442 | https://zichengduan.github.io/LiveWorld/index.html | Python | Apache-2.0 | [
"environment-modeling",
"world-model"
] | ZichengDuan/LiveWorld | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-10-06T17:10:23Z | [] | 2026-08-07T06:39:42Z | [
"multimodal.world-models"
] | 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 | 147 | [
"aigc",
"computer-vision",
"generative-ai",
"video-generation",
"wan",
"world-models"
] | 2026-10-05T23:57:59Z | https://github.com/ZichengDuan/LiveWorld | null |
[
"multi-agent-systems",
"reinforcement-learning"
] | [
"centralized-training",
"multi-agent-reinforcement-learning",
"reinforcement-learning"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"rl.multiagent"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-07T04:52:55Z | Official implementation of "Learning to Assist: Physics-Grounded Human-Human Control via Multi-Agent Reinforcement Learning" (CVPR 2026) | [
"multi-agent-systems",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 0 | 1,174,997,079 | https://yutoshibata07.github.io/AssistMimic/ | Python | null | [
"centralized-training",
"multi-agent-reinforcement-learning",
"reinforcement-learning"
] | YutoShibata07/AssistMimic | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 4 | 2026-10-03T15:45:52Z | [] | 2026-07-20T07:08:12Z | [
"rl.multiagent"
] | 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 | 10 | [
"cvpr2026",
"humanoid",
"isaac-gym",
"motion-imitation",
"multi-agent",
"physics-simulation",
"reinforcement-learning"
] | 2026-10-02T07:30:16Z | https://github.com/YutoShibata07/AssistMimic | null |
[
"sequence-modeling",
"time-series",
"time-series-and-forecasting"
] | [
"sequence-modeling",
"state-space-model"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"timeseries.state-space-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-08T12:25:58Z | Official implementation of the paper: "Beyond Attention: Leveraging Selective State-Space Models and Volatility-Aware Gating for Cryptocurrency Forecasting". | [
"sequence-modeling",
"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 1,175,958,958 | null | Jupyter Notebook | MIT | [
"sequence-modeling",
"state-space-model"
] | aminebatsi/VA-MPM | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-04T15:58:58Z | [] | 2026-03-11T09:08:30Z | [
"timeseries.state-space-models"
] | 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 | [] | 2026-03-11T12:10:15Z | https://github.com/aminebatsi/VA-MPM | null |
[
"computer-vision",
"general-ml",
"generative-ai",
"generative-modeling"
] | [
"deep-learning",
"diffusion",
"distillation",
"generative-modeling",
"lora",
"low-rank-adaptation"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"general.topic-deep-learning",
"recall.name.diffusion-model",
"vision.image-diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-09T07:08:36Z | [CVPR 2026] Official PyTorch implementation of WaDi: Weight Direction-aware Distillation for One-step Image Synthesis | [
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"deep-learning",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 13 | 1,176,591,086 | https://gudaochangsheng.github.io/WaDi-Page/ | Python | null | [
"diffusion",
"distillation",
"generative-modeling",
"lora",
"low-rank-adaptation"
] | gudaochangsheng/WaDi | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 8 | 2026-10-16T15:36:56Z | [] | 2026-05-26T02:30:57Z | [
"vision.image-diffusion"
] | 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 | 155 | [
"adaptation",
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] | 2026-10-10T07:28:55Z | https://github.com/gudaochangsheng/WaDi | null |
[
"computer-vision"
] | [
"representation-learning",
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] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.self-supervised-vision"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-09T16:39:33Z | [ECCV26] Official PyTorch implementation of "Understanding Geometric Representations in Self-Supervised Vision Transformers via Subspace Intervention" | [
"computer-vision"
] | [
"self-supervised-learning",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 0 | 1,177,051,602 | null | Python | MIT | [
"representation-learning",
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] | Zhou-Weichen/Geosubprobe | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-09-26T14:34:36Z | [] | 2026-09-20T15:55:29Z | [
"vision.self-supervised-vision"
] | 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 | 19 | [] | 2026-09-20T15:55:33Z | https://github.com/Zhou-Weichen/Geosubprobe | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-09T22:32:35Z | This repository contains the official experimental code for the paper "A Completion-Aware Framework for Impactful and Robust Counterfactual Explainability in Graph Neural Networks", accepted at ECML PKDD 2026. | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-24T14:31:13Z | false | 0 | 1,177,299,535 | null | Python | null | [
"graph-neural-network",
"message-passing",
"neural-network"
] | philipposg/DR-CFGNN2 | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-24T14:31:13Z | [] | 2026-06-03T13:35:25Z | [
"graph.gnn-description"
] | 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 | 0 | [] | 2026-06-03T13:40:29Z | https://github.com/philipposg/DR-CFGNN2 | null |
[
"generative-ai",
"multimodal",
"video"
] | [
"text-to-video",
"video-generation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.text-to-video"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-10T02:43:52Z | Official code implementation of the CVPR 2026: "Less is More: Data-Efficient Adaptation for Controllable Text-to-Video Generation" | [
"generative-ai",
"multimodal",
"video"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 1 | 1,177,432,131 | null | Python | MIT | [
"text-to-video",
"video-generation"
] | csh-apprentice/Less_Is_More | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-03T15:45:52Z | [] | 2026-05-09T20:50:47Z | [
"multimodal.text-to-video"
] | 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 | 6 | [] | 2026-08-15T06:25:44Z | https://github.com/csh-apprentice/Less_Is_More | null |
[
"multimodal",
"reinforcement-learning",
"robotics"
] | [
"environment-modeling",
"world-model"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.world-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-10T05:09:29Z | [ECCV2026] Official implementation of RAE-NWM: Navigation World Model in Dense Visual Representation Space. | [
"multimodal",
"reinforcement-learning",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 3 | 1,177,514,224 | null | Python | MIT | [
"environment-modeling",
"world-model"
] | 20robo/raenwm | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-18T13:47:34Z | [] | 2026-10-05T02:25:04Z | [
"multimodal.world-models"
] | 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 | 40 | [] | 2026-10-15T04:38:51Z | https://github.com/20robo/raenwm | 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 | [] | 2026-03-11T03:08:37Z | The official PyTorch implementation of the paper "Distillation-SAM: Knowledge distillation based auto-prompt embedding learning for surgical image segmentation, TMI 2026". | [
"computer-vision",
"efficient-ml",
"model-compression"
] | [
"embedding"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 1 | 1,178,448,645 | null | Python | Apache-2.0 | [
"distillation",
"knowledge-distillation"
] | tjy828/DistillSAM | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-09T17:31:36Z | [] | 2026-05-19T16:08:40Z | [
"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 | 4 | [] | 2026-09-14T05:35:08Z | https://github.com/tjy828/DistillSAM | null |
[
"general-ml",
"generative-ai",
"generative-modeling",
"language"
] | [
"diffusion",
"language-model",
"transformer"
] | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"llm.transformers",
"recall.name.diffusion-model",
"recall.name.transformer-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-11T03:18:04Z | [NeurIPS 2026] Official Implementation of "When Latents Forget Pixels: Restoring Fidelity in Diffusion Transformer Super-Resolution" | [
"general-ml",
"generative-modeling"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-02T15:56:46Z | false | 0 | 1,178,454,039 | https://arxiv.org/abs/2608.09133 | Python | null | [
"diffusion",
"transformer"
] | Rain-sy/PGSR | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | 5 | 2026-10-09T16:23:55Z | [] | 2026-09-25T02:35:03Z | [
"recall.name.transformer-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 | 6 | [] | 2026-09-30T23:16:25Z | https://github.com/Rain-sy/PGSR | null |
[
"data-centric-ai",
"generative-ai",
"generative-modeling",
"language"
] | [
"diffusion",
"language-model",
"synthetic-data"
] | [
"description",
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"query-match",
"repository-metadata"
] | [
"llm.synthetic-data",
"llm.synthetic-data-generation",
"recall.name.diffusion-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-11T14:50:02Z | Official implementation of "SNPgen: Phenotype-Supervised Genotype Representation and Synthetic Data Generation via Latent Diffusion" (arXiv 2026) | [
"generative-ai",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-06T17:10:23Z | false | 1 | 1,178,965,033 | https://arxiv.org/abs/2603.10873 | Python | MIT | [
"diffusion"
] | ht-diva/SNPgen | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 5 | 2026-10-26T16:16:45Z | [] | 2026-10-07T14:41:57Z | [
"recall.name.diffusion-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 | 2 | [] | 2026-10-11T09:17:35Z | https://github.com/ht-diva/SNPgen | null |
[
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"github-topics",
"license-metadata",
"repository-metadata"
] | [] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-12T05:46:47Z | [ECCV 2026] Official implementation of "MemRoPE: Training-Free Infinite Video Generation via Evolving Memory Tokens" | [
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-30T18:03:22.392067Z | false | 3 | 1,179,532,389 | https://memrope.github.io/ | Python | Apache-2.0 | [
"diffusion"
] | YoungRaeKimm/MemRoPE | [
"description",
"github-topics",
"license-metadata",
"repository-metadata"
] | 1 | 2026-09-30T18:03:22.392067Z | [] | 2026-06-24T05:12:00Z | [] | 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 | [
"diffusion-models"
] | 2026-09-30T11:14:51Z | https://github.com/YoungRaeKimm/MemRoPE | {"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["diffusion-models"]} |
[
"distributed-ml",
"privacy-and-federated-learning"
] | [
"collaborative-learning",
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] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-13T06:40:53Z | Official implementation of CVPR'26 paper 'Domain-Skewed Federated Learning with Feature Decoupling and Calibration'. | [
"distributed-ml",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 3 | 1,180,550,035 | null | Python | Apache-2.0 | [
"collaborative-learning",
"federated-learning"
] | mala-lab/F2DC | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-21T15:48:06Z | [] | 2026-06-14T10:52:37Z | [
"trust.federated-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 | 7 | [
"domain-shift",
"federated-learning"
] | 2026-07-13T16:31:14Z | https://github.com/mala-lab/F2DC | null |
[
"computer-vision",
"trustworthy-ml"
] | [
"adversarial-robustness",
"robust-optimization"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"trust.adversarial-robustness"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-13T08:23:10Z | [CVPR 2026] Official implementation for "ReMoE: Region-Mixture Experts for Adversarially-Robust Vision Transformers" | [
"computer-vision",
"trustworthy-ml"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 0 | 1,180,618,672 | null | Python | null | [
"adversarial-robustness",
"robust-optimization"
] | zhongskr0114/ReMoE | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-09-30T16:47:43Z | [] | 2026-03-13T09:12:43Z | [
"trust.adversarial-robustness"
] | 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 | [] | 2026-03-13T09:13:11Z | https://github.com/zhongskr0114/ReMoE | null |
[
"deep-learning",
"efficient-ml",
"language"
] | [
"conditional-computation",
"mixture-of-experts",
"sparse-activation"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.mixture-of-experts"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-14T10:58:18Z | Conduit is a Mixture of Experts (MoE) model developed to research novel routing algorithms, architecture fault tolerance, and gating techniques. | [
"deep-learning",
"efficient-ml",
"language"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 0 | 1,181,591,080 | null | Python | MIT | [
"conditional-computation",
"mixture-of-experts",
"sparse-activation"
] | mAiello00/Conduit | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2026-05-14T21:07:26Z | [
"efficiency.mixture-of-experts"
] | ac89d5a92b0f9e4b59bdf249a4bad1877721073e | 2026-10-05T21:36:53Z | gh-ml-readme-evidence-v2 | "ac89d5a92b0f9e4b59bdf249a4bad1877721073e" | [
"citation",
"method",
"other",
"overview",
"results"
] | [] | ok | 2026-10-05T21:36:53Z | mAiello00/Conduit | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 1 | [
"moe"
] | 2026-05-14T21:07:32Z | https://github.com/mAiello00/Conduit | null |
[
"computer-vision",
"machine-learning",
"machine-learning-systems",
"robust-ml",
"trustworthy-ml"
] | [
"adaptation",
"distribution-shift-detection",
"domain-generalization",
"pruning",
"test-time-adaptation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"specialized.test-time-adaptation",
"trust.distribution-shift"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-14T13:04:01Z | This repository is the code of "Pretrained Feature Contributions Can Reverse Under Distribution Shifts", a extension of our CVPR 2025 paper "Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning." | [
"computer-vision",
"machine-learning",
"machine-learning-systems"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 2 | 1,181,663,260 | null | Python | null | [
"adaptation",
"pruning",
"test-time-adaptation"
] | wangkunyu241/SGP | [
"description",
"query-match",
"repository-metadata"
] | 4 | 2026-10-07T18:07:52Z | [] | 2026-09-06T02:29:43Z | [
"specialized.test-time-adaptation"
] | 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 | [] | 2026-09-06T02:29:47Z | https://github.com/wangkunyu241/SGP | null |
[
"multimodal",
"reinforcement-learning",
"robotics"
] | [
"environment-modeling",
"world-model"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.world-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-14T16:15:42Z | Official implementation of the Nature Communications paper: "A Brain-Inspired World Model for Embodied Navigation via Multi-Scale Spatiotemporal Graphs" | [
"multimodal",
"reinforcement-learning",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-06T17:10:23Z | false | 0 | 1,181,787,188 | null | Python | null | [
"environment-modeling",
"world-model"
] | offroadmsp/BIW-Nav | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-06T17:10:23Z | [] | 2026-03-26T14:34:50Z | [
"multimodal.world-models"
] | 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 | 2 | [] | 2026-09-29T04:33:43Z | https://github.com/offroadmsp/BIW-Nav | null |
[
"computational-science",
"efficient-ml",
"model-compression",
"physics",
"science-and-engineering",
"scientific-computing"
] | [
"model-compression",
"neural-network",
"neural-operator",
"neural-operators",
"partial-differential-equations",
"surrogate-modeling"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"efficiency.model-compression",
"gap2026.physics-neural-pde-solver",
"science.neural-operators"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-15T18:18:01Z | Novel neural operator merging MLP-Mixer + multi-resolution pyramids for PDE solving. 67% lower L2 error, 7x model compression. WIAS Berlin research. | [
"computational-science",
"physics",
"science-and-engineering"
] | [
"deep-learning",
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-25T16:01:25Z | false | 0 | 1,182,595,210 | null | Jupyter Notebook | null | [
"neural-network",
"neural-operator",
"neural-operators",
"surrogate-modeling"
] | alaryum/Multi-Head-MLP-Mixer | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 4 | 2026-09-29T17:30:08Z | [] | 2026-03-18T23:22:15Z | [
"science.neural-operators"
] | 0578b10cbd87d5daac4fe13b020738aa3ae515fd | 2026-09-26T15:53:33Z | gh-ml-readme-evidence-v2 | "0578b10cbd87d5daac4fe13b020738aa3ae515fd" | [
"installation",
"method",
"other",
"usage"
] | [
"ml-method-context",
"model-training-artifact"
] | ok | 2026-09-26T15:53:33Z | alaryum/Multi-Head-MLP-Mixer | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue",
"model-training-artifact"
] | include | ml-contribution-v5 | 0 | [
"cnn-model",
"deep-learning",
"mlp-mixer",
"physics-informed-neural-networks",
"pytorch"
] | 2026-03-18T23:22:18Z | https://github.com/alaryum/Multi-Head-MLP-Mixer | null |
[
"information-retrieval",
"linguistics",
"natural-language-processing"
] | [
"language-modeling",
"rag",
"retrieval-augmented-generation"
] | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"nlp.rag",
"recall.computational-linguistics"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-16T06:39:39Z | [ACL'26 Main] Official code for "LegalGraphRAG: Multi-Agent Graph Retrieval-Augmented Generation for Reliable Legal Reasoning". | [
"information-retrieval",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 9 | 1,182,983,194 | https://arxiv.org/abs/2605.28120 | Python | null | [
"rag",
"retrieval-augmented-generation"
] | XMUDeepLIT/LegalGraphRAG | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | 4 | 2026-09-26T10:12:50Z | [] | 2026-08-03T01:04:43Z | [
"nlp.rag"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 59 | [
"graphrag",
"legal",
"legalgraph",
"legalgraphrag",
"legalrag",
"llms",
"rag"
] | 2026-09-24T12:16:29Z | https://github.com/XMUDeepLIT/LegalGraphRAG | null |
[
"computer-vision",
"embodied-ai",
"language",
"multimodal",
"multimodal-learning",
"robotics",
"vision"
] | [
"contrastive-learning",
"robot-learning",
"vision-language-action",
"vision-language-model"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.vision-language",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-16T09:36:59Z | Official real-robot code for ICML 2026 paper "Neural Implicit Action Fields: From Discrete Waypoints to Continuous Functions for Vision-Language-Action Models". | [
"computer-vision",
"language",
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 0 | 1,183,115,049 | null | Python | null | [
"contrastive-learning",
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] | liuhaoyunBUPT/NIAF.real | [
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] | 2 | 2026-10-19T17:46:36Z | [] | 2026-07-01T09:17:05Z | [
"multimodal.vision-language"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 4 | [] | 2026-10-06T07:39:47Z | https://github.com/liuhaoyunBUPT/NIAF.real | null |
[
"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 | [] | 2026-03-16T11:19:05Z | RCDT code for Paper "Conditional Sequence Modeling for Safe Reinforcement Learning" | [
"reinforcement-learning",
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] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 0 | 1,183,197,105 | null | Python | null | [
"constrained-reinforcement-learning",
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] | ZJUBai/RCDT | [
"description",
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] | 4 | 2026-09-26T14:34:36Z | [] | 2026-05-21T10:57:10Z | [
"rl.safe"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 1 | [] | 2026-05-21T10:57:26Z | https://github.com/ZJUBai/RCDT | null |
[
"3d",
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"deep-learning",
"efficient-ml",
"graphics",
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] | [
"efficiency.mixture-of-experts",
"multimodal.gaussian-splatting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-16T11:54:38Z | Official code of "MoE-GS: Mixture of Experts for Dynamic Gaussian Splatting (ICLR 2026)" | [
"3d",
"computer-vision",
"graphics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 1 | 1,183,225,087 | http://paper.pnu-cvsp.com/MoE-GS/ | Python | NOASSERTION | [
"gaussian-splatting",
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] | cvsp-lab/MoE-GS | [
"description",
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"query-match",
"repository-metadata"
] | 5 | 2026-10-10T16:53:26Z | [] | 2026-07-09T15:38:37Z | [
"multimodal.gaussian-splatting"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 15 | [] | 2026-10-10T11:14:28Z | https://github.com/cvsp-lab/MoE-GS | null |
[
"computer-vision",
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"multimodal",
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] | [
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] | [
"description",
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"repository-metadata"
] | [
"multimodal.vision-language",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-16T20:25:19Z | Official code repo of ATV-Pruning [CVPR 2026] -- Mostly Text, Smart Visuals: Asymmetric Text-Visual Pruning for Large Vision-Language Models | [
"computer-vision",
"language",
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"multimodal",
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"natural-language-processing",
"vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-14T16:10:46Z | false | 0 | 1,183,649,599 | null | Python | null | [
"contrastive-learning",
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] | LezJ/ATV-Pruning | [
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] | 2 | 2026-10-19T17:46:36Z | [] | 2026-05-19T13:31:08Z | [
"multimodal.vision-language"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 2 | [] | 2026-05-19T14:10:44Z | https://github.com/LezJ/ATV-Pruning | null |
[
"computer-vision",
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] | [
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] | [
"description",
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"repository-metadata"
] | [
"specialized.test-time-adaptation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-17T07:09:34Z | Official code for "Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation" (CVPR 2026) | [
"computer-vision",
"machine-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T10:12:50Z | false | 0 | 1,184,023,113 | null | Python | null | [
"adaptation",
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] | jihun1998/DiTTA | [
"description",
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] | 2 | 2026-10-07T18:07:52Z | [] | 2026-08-12T10:33:05Z | [
"specialized.test-time-adaptation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 3 | [] | 2026-09-28T02:38:42Z | https://github.com/jihun1998/DiTTA | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
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] | [
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"query-match",
"repository-metadata"
] | [
"llm.alignment",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-17T08:48:58Z | [ECCV 2026] Official PyTorch implementation of RefAlign: Representation Alignment for Reference-to-Video Generation | [
"computer-vision",
"generative-modeling",
"interpretability-and-safety",
"language",
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] | [
"representation-learning",
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 19 | 1,184,096,749 | https://gudaochangsheng.github.io/RefAlign-Page/ | Python | NOASSERTION | [
"alignment",
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] | gudaochangsheng/RefAlign | [
"description",
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"query-match",
"repository-metadata"
] | 7 | 2026-10-28T17:27:44Z | [] | 2026-08-25T03:13:36Z | [
"llm.alignment"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 351 | [
"controllable-generation",
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"reference-to-video",
"repa",
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"representation-learning",
"subject-to-video",
"text-image-generation",
"video-generation"
] | 2026-10-24T08:40:04Z | https://github.com/gudaochangsheng/RefAlign | null |
[
"information-retrieval",
"natural-language-processing",
"search",
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] | [
"rag"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"applied.search-ranking"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-17T12:22:33Z | Code for paper "CONTROLNET: A Firewall for RAG-based LLM System" | [
"information-retrieval",
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"search",
"search-ranking"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-24T14:31:13Z | false | 0 | 1,184,265,028 | null | Python | null | [
"rag"
] | ZJUICSR/ControlNET | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-24T14:31:13Z | [] | 2026-03-17T12:27:07Z | [
"applied.search-ranking"
] | 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 | 1 | [] | 2026-05-20T13:57:32Z | https://github.com/ZJUICSR/ControlNET | null |
[
"computer-vision",
"sequence-modeling",
"time-series"
] | [
"mamba",
"sequence-modeling",
"state-space-model"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"timeseries.state-space-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-17T19:28:11Z | Official implementation of the paper "MFil-Mamba: Multi-Filter Scanning for Spatial Redundancy-Aware Visual State Space Models" | [
"computer-vision",
"sequence-modeling",
"time-series"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 1 | 1,184,635,788 | null | Python | MIT | [
"mamba",
"sequence-modeling",
"state-space-model"
] | puskal-khadka/MFil-Mamba | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-03T15:45:52Z | [] | 2026-03-24T01:53:56Z | [
"timeseries.state-space-models"
] | 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 | 7 | [
"mamba",
"state-space-model",
"vision-mamba"
] | 2026-07-10T06:48:45Z | https://github.com/puskal-khadka/MFil-Mamba | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"multimodal",
"vision"
] | [
"diffusion",
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] | [
"description",
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"repository-metadata"
] | [
"llm.diffusion-phrase",
"multimodal.text-to-image",
"vision.image-diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-18T06:43:54Z | [CVPR-2026] Official implementations of "TINA: Text-Free Inversion Attack for Unlearned Text-to-Image Diffusion Models". | [
"generative-ai",
"generative-modeling",
"multimodal",
"vision"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 2 | 1,185,017,931 | null | Python | null | [
"diffusion",
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"text-to-image"
] | iLearn-Lab/CVPR26-TINA | [
"description",
"query-match",
"repository-metadata"
] | 8 | 2026-10-19T17:46:36Z | [] | 2026-04-09T11:36:57Z | [
"multimodal.text-to-image"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 9 | [] | 2026-09-21T13:05:51Z | https://github.com/iLearn-Lab/CVPR26-TINA | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"image-processing",
"low-level-vision",
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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 | [] | 2026-03-19T01:35:31Z | This repository is the official implementation of the paper "From haze degradation to haze evolution: an anisotropic-diffusion-inspired generative framework for image dehazing." https://link.springer.com/article/10.1007/s10044-026-01738-4 | [
"computer-vision",
"generative-modeling",
"image-processing",
"low-level-vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-02T15:56:46Z | false | 0 | 1,185,830,616 | null | Python | null | [
"diffusion",
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] | miraclebai/LAD | [
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] | 6 | 2026-10-10T16:53:26Z | [] | 2026-09-26T03:44:59Z | [
"recall26.image-dehazing"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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] | include | ml-contribution-v5 | 0 | [] | 2026-09-26T03:54:59Z | https://github.com/miraclebai/LAD | null |
[
"3d-vision",
"computer-vision",
"generative-modeling"
] | [
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"diffusion"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.depth-estimation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-19T05:45:58Z | Official implementation of CVPR2026 peper "Iris: Bringing Real-World Priors into Diffusion Model for Monocular Depth Estimation" | [
"3d-vision",
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"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 3 | 1,185,962,527 | null | Python | Apache-2.0 | [
"3d-perception",
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] | Strike-1999/Iris | [
"description",
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"query-match",
"repository-metadata"
] | 2 | 2026-09-25T15:43:40Z | [] | 2026-04-04T03:16:30Z | [
"vision.depth-estimation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
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] | include | ml-contribution-v5 | 3 | [] | 2026-07-09T02:19:45Z | https://github.com/Strike-1999/Iris | null |
[
"computer-vision"
] | [
"image-classification"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"vision.image-classification"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-19T16:58:04Z | [ECCV 2026] Official code of "Revisiting Autoregressive Models for Generative Image Classification" | [
"computer-vision"
] | [
"classifier"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 0 | 1,186,473,238 | null | Python | null | [
"image-classification"
] | yandex-research/ar-classifier | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-09-25T15:43:40Z | [] | 2026-09-01T07:19:37Z | [
"vision.image-classification"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 14 | [] | 2026-09-01T07:22:08Z | https://github.com/yandex-research/ar-classifier | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
"message-passing",
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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 | [] | 2026-03-20T01:52:47Z | The official implementation of the paper : "Learning Continuous Solvent Effects from Transient Flow Data: A Graph Neural Network Benchmark on Catechol Rearrangement" | [
"deep-learning",
"graph-learning"
] | [
"deep-learning",
"machine-learning",
"neural-network",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-24T14:31:13Z | false | 0 | 1,186,791,418 | https://arxiv.org/abs/2512.19530 | Python | MIT | [
"graph-neural-network",
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"transformer"
] | StarxSky/catechol-benchmark | [
"description",
"github-topics",
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"query-match",
"repository-metadata"
] | 1 | 2026-10-24T14:31:13Z | [] | 2026-03-20T01:57:19Z | [
"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 | 1 | [
"chemical",
"deep-learning",
"machine-learning",
"pinn",
"transformer"
] | 2026-03-20T02:03:12Z | https://github.com/StarxSky/catechol-benchmark | null |
[
"artificial-intelligence",
"machine-learning"
] | [
"interpretability",
"mechanistic-interpretability"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"specialized.mechanistic-interpretability"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-20T06:06:22Z | This repository contains the official code accompanying the paper: Dual Path Attribution (DPA): Efficient Attribution for SwiGLU-Transformers through Layer-Wise Target Propagation. | [
"artificial-intelligence",
"machine-learning"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-15T15:20:52Z | false | 0 | 1,186,917,972 | null | Jupyter Notebook | MIT | [
"interpretability",
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] | lab-paper-code/LTP | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-15T15:20:52Z | [] | 2026-08-24T09:07:24Z | [
"specialized.mechanistic-interpretability"
] | 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 | 1 | [] | 2026-08-24T09:10:07Z | https://github.com/lab-paper-code/LTP | null |
[
"computer-vision",
"health-and-biomedicine",
"language",
"medical-imaging",
"multimodal",
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"vision"
] | [
"contrastive-learning",
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] | [
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"repository-metadata"
] | [
"medical.medical-image-segmentation",
"multimodal.vision-language"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-20T09:41:47Z | This is the official implementation of the paper: "A Separable Optimization Framework with Dual-Granularity Aligned Vision-Language Mamba for Medical Image Segmentation". | [
"computer-vision",
"language",
"multimodal",
"multimodal-learning",
"vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-09T17:31:36Z | false | 0 | 1,187,056,860 | null | Python | null | [
"contrastive-learning",
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] | shentu66/Sep-DGM | [
"description",
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] | 2 | 2026-10-20T15:59:06Z | [] | 2026-06-07T12:11:52Z | [
"multimodal.vision-language"
] | 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 | [] | 2026-06-07T12:11:56Z | https://github.com/shentu66/Sep-DGM | null |
[
"computer-vision",
"deep-learning",
"general-ml",
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] | [
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] | [
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"repository-metadata"
] | [
"general.contrastive-learning",
"recall.name.foundation-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-20T15:39:05Z | [CVPR'26] Official implementation of our paper "Diffusion-Based Makeup Transfer with Facial Region-Aware Makeup Features" | [
"computer-vision",
"general-ml",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-25T15:00:52Z | false | 0 | 1,187,322,402 | null | Python | null | [
"diffusion",
"foundation-model"
] | zaczgao/Facial_Region-Aware_Makeup | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 3 | 2026-09-28T17:46:51Z | [] | 2026-09-23T12:50:04Z | [
"recall.name.foundation-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 | 8 | [
"diffusion",
"image-generation",
"image-to-image",
"makeup-transfer",
"text-to-image"
] | 2026-09-23T12:50:08Z | https://github.com/zaczgao/Facial_Region-Aware_Makeup | null |
[
"biology",
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"computational-biology"
] | [
"molecular-dynamics",
"scientific-machine-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"bio.molecular-dynamics"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-22T05:03:59Z | Official implementation for ProAR: Probabilistic Autoregressive Modeling for Molecular Dynamics (AAAI 26') | [
"biology",
"chemistry",
"computational-biology"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 0 | 1,188,453,594 | null | Python | Apache-2.0 | [
"molecular-dynamics",
"scientific-machine-learning"
] | kevincheng7/ProAR | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-15T15:20:52Z | [] | 2026-03-28T06:29:18Z | [
"bio.molecular-dynamics"
] | 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 | 8 | [] | 2026-08-07T01:08:54Z | https://github.com/kevincheng7/ProAR | null |
[
"multimodal",
"reinforcement-learning",
"robotics"
] | [
"environment-modeling",
"world-model"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.world-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-22T20:48:57Z | Official implementation of the ICLR 2026 2nd workshop on world models paper: PhysLang | [
"multimodal",
"reinforcement-learning",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-06T17:10:23Z | false | 0 | 1,188,979,688 | null | Python | Apache-2.0 | [
"environment-modeling",
"world-model"
] | Codernob/PhysLang | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-06T17:10:23Z | [] | 2026-05-26T09:52:03Z | [
"multimodal.world-models"
] | 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 | [] | 2026-05-26T09:52:07Z | https://github.com/Codernob/PhysLang | null |
[
"embodied-ai",
"robotics",
"robotics-and-control"
] | [
"manipulation"
] | [
"description",
"license-metadata",
"model-weights",
"query-match",
"repository-metadata"
] | [
"robotics.manipulation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-23T13:17:55Z | [CVPR 2026] Beyond Success: Refining Elegant Robot Manipulation via Just-in-Time Intervention — official code, benchmark, and pretrained checkpoints. See the first version of code in https://github.com/Mwuqiu/drm | [
"embodied-ai",
"robotics",
"robotics-and-control"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 1,189,581,687 | null | Python | MIT | [
"manipulation"
] | Mwuqiu/BeyondSuccess | [
"description",
"license-metadata",
"model-weights",
"query-match",
"repository-metadata"
] | 2 | 2026-10-19T16:36:56Z | [] | 2026-10-16T14:08:43Z | [
"robotics.manipulation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-10-16T20:34:42Z | https://github.com/Mwuqiu/BeyondSuccess | null |
[
"machine-learning",
"reinforcement-learning"
] | [
"curriculum-learning",
"reinforcement-learning",
"training-strategy"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"specialized.curriculum-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-24T03:01:39Z | Official implementation of the paper "Balancing Tripartite Interests in Cloud Service Composition and Optimal Selection via Curriculum-based Reinforcement Learning". | [
"machine-learning",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T10:12:50Z | false | 4 | 1,190,169,683 | null | Python | null | [
"curriculum-learning",
"reinforcement-learning",
"training-strategy"
] | 1Happ-cyber/COHER | [
"description",
"query-match",
"repository-metadata"
] | 5 | 2026-10-28T17:27:44Z | [] | 2026-07-17T01:32:03Z | [
"specialized.curriculum-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 | 28 | [] | 2026-09-29T12:02:45Z | https://github.com/1Happ-cyber/COHER | null |
[
"generative-ai",
"language"
] | [
"language-model",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.transformers"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-25T13:50:15Z | [CVPR 2026 Workshop] Official code and models for Plain Mask Transformer (PMT). | [
"generative-ai",
"language"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T16:16:53Z | false | 3 | 1,191,693,237 | null | Jupyter Notebook | MIT | [
"language-model",
"transformer"
] | tue-mps/pmt | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-09-26T14:34:36Z | [] | 2026-09-04T13:31:26Z | [
"llm.transformers"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 67 | [] | 2026-09-17T09:10:49Z | https://github.com/tue-mps/pmt | null |
[
"generative-ai",
"graph-learning",
"interpretability-and-safety",
"language",
"natural-language-processing"
] | [
"language-model",
"pretraining"
] | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"llm.language-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-26T16:44:28Z | Code for ICML 2026 paper experiments studying how deep sequence models tend to memorize through a learned geometric structure. | [
"generative-ai",
"graph-learning",
"interpretability-and-safety",
"language",
"natural-language-processing"
] | [
"large-language-model",
"representation-learning",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-24T14:31:13Z | false | 0 | 1,192,877,552 | https://arxiv.org/abs/2510.26745 | Python | null | [
"language-model",
"pretraining"
] | Shahriarnz14/Geometric_Memory | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-10-24T14:31:13Z | [] | 2026-05-29T01:18:24Z | [
"llm.language-models"
] | 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 | [
"geometric-memory",
"graph-learning",
"icml",
"icml-2026",
"large-language-models",
"mechanistic-interpretability",
"memorization",
"path-finding",
"representation-learning",
"sequence-models",
"spectral-bias",
"transformers"
] | 2026-09-27T05:32:38Z | https://github.com/Shahriarnz14/Geometric_Memory | null |
[
"deep-learning",
"efficient-ml",
"language"
] | [
"conditional-computation",
"mixture-of-experts",
"sparse-activation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.mixture-of-experts"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-28T02:11:13Z | Official code for the paper--- PatchMoE-CFC: Sparse Mixture-of-Experts with Closed-form Continuous Networks for Adaptive Time-Scale Modeling | [
"deep-learning",
"efficient-ml",
"language"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 0 | 1,194,169,618 | null | Python | null | [
"conditional-computation",
"mixture-of-experts",
"sparse-activation"
] | liujian123223/PatchMoE-CFC | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2026-03-28T02:16:28Z | [
"efficiency.mixture-of-experts"
] | 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 | 0 | [] | 2026-03-28T02:16:31Z | https://github.com/liujian123223/PatchMoE-CFC | null |
[
"natural-language-processing",
"reinforcement-learning"
] | [
"batch-reinforcement-learning",
"offline-reinforcement-learning",
"reinforcement-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.offline"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-30T03:29:18Z | Official code for 'Unveiling Markov Heads in Pretrained Language Models for Offline Reinforcement Learning' (ICML 2025) | [
"natural-language-processing",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 0 | 1,195,767,497 | null | Python | MIT | [
"batch-reinforcement-learning",
"offline-reinforcement-learning",
"reinforcement-learning"
] | Qiushui-Xu/Markov-Head-Offline-RL | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-09-26T14:50:46Z | [] | 2026-03-30T03:37:52Z | [
"rl.offline"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-03-30T03:37:55Z | https://github.com/Qiushui-Xu/Markov-Head-Offline-RL | null |
[
"information-retrieval",
"language",
"linguistics",
"natural-language-processing"
] | [
"language-modeling",
"rag",
"retrieval-augmented-generation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm-retrieval-augmented-generation",
"nlp.rag",
"recall.computational-linguistics"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-30T10:31:12Z | [ACL'26 Main Oral] Official code for "LogicPoison: Logical Attacks on Graph Retrieval-Augmented Generation". | [
"information-retrieval",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 5 | 1,196,060,213 | https://aclanthology.org/2026.acl-long.252/ | Python | MIT | [
"rag",
"retrieval-augmented-generation"
] | Jord8061/logicPoison | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-09-26T10:12:50Z | [] | 2026-08-25T09:29:03Z | [
"nlp.rag"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 45 | [] | 2026-09-19T05:34:03Z | https://github.com/Jord8061/logicPoison | null |
[
"computer-vision",
"foundation-models",
"generative-ai",
"generative-modeling"
] | [
"diffusion",
"multimodal",
"pretraining"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.foundation-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-03-31T14:17:54Z | Official implementation of paper "One Noise to Fool Them All: Universal Adversarial Defenses Against Image Editing", accepted at The 5th Workshop of Adversarial Machine Learning on Computer Vision: Foundation Models + X, IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) 2025 | [
"computer-vision",
"foundation-models",
"generative-ai",
"generative-modeling"
] | [
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-18T14:44:29Z | false | 1 | 1,197,358,837 | null | Python | MIT | [
"diffusion",
"multimodal",
"pretraining"
] | Parth-Badgujar/diffusion-immunization | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-18T14:44:29Z | [] | 2026-03-31T14:18:17Z | [
"llm.foundation-models"
] | 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 | 2 | [] | 2026-09-21T10:50:22Z | https://github.com/Parth-Badgujar/diffusion-immunization | null |
[
"computer-vision",
"general-ml",
"multimodal",
"natural-language-processing"
] | [
"distillation",
"foundation-model",
"language-model",
"multimodal-learning",
"vision-language"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"recall.name.foundation-model",
"recall.name.language-model",
"vision.vision-language"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-01T18:15:19Z | [NeurIPS 2026] Official PyTorch implementation of IGLOSS Image Generation for Lidar Open-vocabulary Semantic Segmentation | [
"computer-vision",
"general-ml",
"natural-language-processing"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-02T15:56:46Z | false | 0 | 1,198,752,895 | null | Python | null | [
"distillation",
"foundation-model",
"language-model"
] | valeoai/IGLOSS | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 2 | 2026-10-08T17:03:06Z | [] | 2026-09-25T16:28:55Z | [
"recall.name.foundation-model",
"recall.name.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 | 7 | [
"2d-to-3d",
"distillation",
"image-generation",
"lidar-point-cloud",
"open-vocabulary-segmentation",
"text-to-image-generation"
] | 2026-10-04T09:36:25Z | https://github.com/valeoai/IGLOSS | null |
[
"imitation-learning",
"reinforcement-learning"
] | [
"imitation-learning",
"inverse-reinforcement-learning",
"reinforcement-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.inverse"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-01T21:43:03Z | Stores here is the official implementation of the paper "DeFi Intent Discovery via Maximum Entropy Inverse Reinforcement Learning" in IJCNN 2026. | [
"imitation-learning",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 0 | 1,198,899,634 | http://ezharjan.github.io/ChemoIRL | Python | MIT | [
"imitation-learning",
"inverse-reinforcement-learning",
"reinforcement-learning"
] | Ezharjan/ChemoIRL | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-09-26T14:50:46Z | [] | 2026-07-08T23:19:21Z | [
"rl.inverse"
] | 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 | [] | 2026-04-01T22:31:41Z | https://github.com/Ezharjan/ChemoIRL | null |
[
"embodied-ai",
"robotics",
"robotics-and-control"
] | [
"manipulation",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"robotics.manipulation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-02T08:28:04Z | [ICLR 2026] Official Implementation for "Cortical Policy: A Dual-Stream View Transformer for Robotic Manipulation" | [
"embodied-ai",
"robotics",
"robotics-and-control"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 0 | 1,199,315,452 | null | Python | Apache-2.0 | [
"manipulation",
"transformer"
] | iLearn-Lab/ICLR26-Cortical_Policy | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-02T16:59:40Z | [] | 2026-04-05T07:27:13Z | [
"robotics.manipulation"
] | 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 | 3 | [] | 2026-04-30T15:55:19Z | https://github.com/iLearn-Lab/ICLR26-Cortical_Policy | null |
[
"computer-vision",
"deep-learning",
"efficient-ml",
"language"
] | [
"conditional-computation",
"mixture-of-experts",
"sparse-activation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.mixture-of-experts"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-05T14:25:22Z | Official code for CVPR 2026 Findings Paper: FA-MoE: Improving Medical Image Generation through Frequency-Aware Mixture of Experts | [
"computer-vision",
"deep-learning",
"efficient-ml",
"language"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 1,202,047,105 | null | Python | MIT | [
"conditional-computation",
"mixture-of-experts",
"sparse-activation"
] | Jean-Yifan-Sun/FA-MoE | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-04T15:58:58Z | [] | 2026-04-05T14:30:19Z | [
"efficiency.mixture-of-experts"
] | 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 | 2 | [] | 2026-07-16T01:15:17Z | https://github.com/Jean-Yifan-Sun/FA-MoE | null |
[
"earth-science",
"generative-modeling",
"geospatial-science",
"remote-sensing",
"science-and-engineering"
] | [
"diffusion",
"foundation-model",
"geospatial-learning",
"remote-sensing"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"science.remote-sensing"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-06T02:52:07Z | [TGRS 2026] Official implementation of the paper "A Spatio-Temporal Hierarchical Diffusion Framework for Training-Free Perceptual Remote Sensing Image Compression" | [
"earth-science",
"generative-modeling",
"geospatial-science",
"remote-sensing",
"science-and-engineering"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-28T17:27:44Z | false | 0 | 1,202,437,126 | https://sthcd.github.io | Python | BSD-3-Clause | [
"diffusion",
"foundation-model",
"geospatial-learning",
"remote-sensing"
] | STHCD/STHCD | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-28T17:27:44Z | [] | 2026-09-15T09:42:53Z | [
"science.remote-sensing"
] | 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 | 2 | [] | 2026-09-16T00:06:05Z | https://github.com/STHCD/STHCD | null |
[
"machine-learning",
"multimodal-learning"
] | [
"adaptation",
"test-time-adaptation"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"specialized.test-time-adaptation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-06T14:26:23Z | The official code of "Pretrain-then-Adapt: Uncertainty-Aware Test-Time Adaptation for Text-based Person Search" [SIGIR 2026] | [
"machine-learning",
"multimodal-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T10:12:50Z | false | 0 | 1,202,910,649 | https://arxiv.org/abs/2604.08598 | Python | MIT | [
"adaptation",
"test-time-adaptation"
] | nkuzjh/UATTA | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 2 | 2026-10-07T18:07:52Z | [] | 2026-07-22T13:15:00Z | [
"specialized.test-time-adaptation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 8 | [
"multimodal-retrieval",
"sigir2026",
"test-time-adaptation",
"text-based-person-search",
"uncertainty"
] | 2026-09-23T11:45:06Z | https://github.com/nkuzjh/UATTA | null |
[
"natural-language-processing",
"reinforcement-learning"
] | [
"hierarchical-reinforcement-learning",
"options",
"reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"rl.hierarchical"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-07T08:23:20Z | [ACL 2026 Main Conference] Official Implementation of "Hierarchical Reinforcement Learning with Augmented Step-Level Transitions for LLM Agents" | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 0 | 1,203,641,794 | null | Python | null | [
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] | TonyStark042/STEP-HRL | [
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] | 6 | 2026-10-01T16:56:07Z | [] | 2026-09-29T03:52:21Z | [
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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 | [] | 2026-10-01T00:58:26Z | https://github.com/TonyStark042/STEP-HRL | null |
[
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"health-and-biomedicine",
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] | [
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"repository-metadata"
] | [
"medical.medical-image-segmentation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-07T12:07:15Z | Official Pytorch implementation of the paper MAMIS: Modality-adaptive medical image segmentation via ensemble supervised contrastive learning published in Biomedical Signal Processing and Control. | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-09T17:31:36Z | false | 0 | 1,203,813,947 | null | Python | null | [
"contrastive-learning",
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] | ZXS-Labs/MAMIS | [
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] | 1 | 2026-10-09T17:31:36Z | [] | 2026-06-01T02:46:10Z | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-06-01T02:46:13Z | https://github.com/ZXS-Labs/MAMIS | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-08T00:05:44Z | [ICML 2026] Source code for paper Reviving Error Correction in Modern Deep Time-Series Forecasting | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 0 | 1,204,360,971 | null | Shell | MIT | [
"forecasting",
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] | 2 | 2026-09-25T15:43:40Z | [] | 2026-05-04T06:20:47Z | [
"timeseries.forecasting"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 6 | [] | 2026-09-11T06:39:43Z | https://github.com/DA2I2-SLM/UEC-STD | null |
[
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] | [
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] | [
"applied.plant-disease-detection"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-08T06:49:45Z | Triplet-MHA ViT: A novel vision transformer network for plant disease detection | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-24T14:31:13Z | false | 0 | 1,204,605,462 | https://doi.org/10.1016/j.eswa.2025.127743 | Python | NOASSERTION | [
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] | mah-iasri/triplet-mha-vit | [
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] | 1 | 2026-10-24T14:31:13Z | [] | 2026-04-14T14:53:51Z | [
"applied.plant-disease-detection"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
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"ml-method-cue"
] | include | ml-contribution-v5 | 1 | [] | 2026-04-19T15:24:51Z | https://github.com/mah-iasri/triplet-mha-vit | null |
[
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] | [
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] | [
"graph.graph-transformer",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-08T14:34:40Z | Official implementation for paper "EHRFormer: A Next-Visit Diagnosis Prediction Model Using Electronic Health Records via Temporal Graph Transformer" | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 0 | 1,204,998,610 | null | Python | null | [
"dynamic-graph-learning",
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] | shiyuxing1995422-dev/EHRFormer | [
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] | 6 | 2026-10-20T15:59:06Z | [] | 2026-04-08T14:54:28Z | [
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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 | [] | 2026-09-21T12:49:11Z | https://github.com/shiyuxing1995422-dev/EHRFormer | null |
[
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] | [
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"query-match",
"repository-metadata"
] | [
"multimodal.world-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-09T06:29:40Z | Official implementation of the paper: Sub-JEPA: Subspace Gaussian Regularization for Stable End-to-End World Models. | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 2 | 1,205,638,229 | https://kaizhao.net/sub-jepa | Python | Apache-2.0 | [
"environment-modeling",
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] | intcomp/Sub-JEPA | [
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] | 2 | 2026-10-06T17:10:23Z | [] | 2026-07-27T12:43:36Z | [
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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 | 67 | [] | 2026-10-01T14:00:46Z | https://github.com/intcomp/Sub-JEPA | null |
[
"computer-vision",
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] | [
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] | [
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"repository-metadata"
] | [
"vision.image-retrieval"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-09T08:36:06Z | The official implementation of [CVPR-2026 Findings] Asymmetric Collaborative Distillation for Asymmetric Image Retrieval. | [
"computer-vision",
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 0 | 1,205,740,874 | null | Python | null | [
"distillation",
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] | SCY-X/Asymmetric_Collaborative_Distillation | [
"description",
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] | 2 | 2026-09-26T10:12:50Z | [] | 2026-06-04T06:44:02Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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] | include | ml-contribution-v5 | 8 | [
"asymmetric-image-retrieval",
"image-retrieval",
"knowledge-distillation"
] | 2026-07-21T13:55:08Z | https://github.com/SCY-X/Asymmetric_Collaborative_Distillation | null |
[
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] | [
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] | [
"specialized.mechanistic-interpretability"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-10T08:49:52Z | Official code for "The Mechanics of Interference: Defusing Distractors in RAG via Sparse Autoencoder Interventions" — Findings of ACL 2026 | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-14T16:10:46Z | false | 0 | 1,206,784,089 | null | Python | MIT | [
"interpretability",
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] | christian-giannetti/mechanics-of-interference | [
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] | 1 | 2026-10-14T16:10:46Z | [] | 2026-07-02T17:06:31Z | [
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] | include | ml-contribution-v5 | 2 | [] | 2026-08-11T08:27:51Z | https://github.com/christian-giannetti/mechanics-of-interference | null |
[
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"query-match",
"repository-metadata"
] | [
"specialized.test-time-adaptation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-11T14:51:22Z | [ACL 2026] Official code for "Generative Giants, Retrieval Weaklings: Why do Multimodal Large Language Models Fail at Multimodal Retrieval?" | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 0 | 1,207,896,006 | null | Python | NOASSERTION | [
"adaptation",
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] | Heinz217/mllm-retrieval-analysis | [
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] | 1 | 2026-10-08T18:15:14Z | [] | 2026-05-12T07:45:00Z | [
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] | include | ml-contribution-v5 | 10 | [] | 2026-09-03T05:34:55Z | https://github.com/Heinz217/mllm-retrieval-analysis | null |
[
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] | [
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] | [
"vision.image-diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-13T08:47:22Z | [ICML2026] Official Implementation of "Balancing Fidelity and Diversity in Diffusion Models via Symmetric Attention Decomposition: Hopfield Perspective" | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-16T15:36:56Z | false | 0 | 1,209,259,705 | https://hyeon-cho.github.io/Balancing/ | Python | null | [
"diffusion",
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] | hyeon-cho/Attention-Symmetric-Decomposition | [
"description",
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] | 1 | 2026-10-16T15:36:56Z | [] | 2026-07-06T08:38:48Z | [
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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 | 7 | [
"attention-mechanism",
"diffusion-models",
"hopfield-network"
] | 2026-09-29T14:11:30Z | https://github.com/hyeon-cho/Attention-Symmetric-Decomposition | null |
[
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] | [
"efficiency.kv-cache",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-13T20:42:59Z | Official implementation of Paper "System-Aware 4-Bit KV-Cache Quantization for Real-World LLM Serving" | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 3 | 1,209,838,820 | null | Shell | MIT | [
"quantization"
] | togethercomputer/saw-int4 | [
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] | 8 | 2026-10-22T16:09:07Z | [] | 2026-04-17T23:05:36Z | [
"efficiency.quantization"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 30 | [] | 2026-07-20T11:16:31Z | https://github.com/togethercomputer/saw-int4 | null |
[
"generative-ai",
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] | [
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"query-match",
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] | [
"llm.diffusion-phrase",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-14T07:09:55Z | Official implementation for "Causal Motion Diffusion Models for Autoregressive Motion Generation" (CVPR 2026) | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-01T16:56:07Z | false | 1 | 1,210,192,147 | https://yu1ut.com/CMDM-HP/ | Python | CC0-1.0 | [
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] | 3 | 2026-10-18T13:47:34Z | [] | 2026-10-12T02:16:20Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 6 | [] | 2026-10-13T11:13:39Z | https://github.com/lycorp-jp/CMDM | null |
[
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] | [
"efficiency.knowledge-distillation",
"medical.medical-image-segmentation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-14T13:58:27Z | Official code for MICCAI2025 paper: "UM-SAM: Unsupervised Medical Image Segmentation using Knowledge Distillation from Segment Anything Model" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 0 | 1,210,526,165 | null | Python | null | [
"distillation",
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] | Fujia1998/UM-SAM | [
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] | 2 | 2026-10-10T16:53:26Z | [] | 2026-04-14T14:23:42Z | [
"efficiency.knowledge-distillation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
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] | include | ml-contribution-v5 | 0 | [] | 2026-04-14T14:23:46Z | https://github.com/Fujia1998/UM-SAM | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-transformer",
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"transformer"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.graph-transformer"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-15T12:21:15Z | SARE-GT: Structure-Aware Robust Enhancement Graph Transformer for Corporate Credit Rating Official implementation of the paper | [
"deep-learning",
"graph-learning"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-26T10:12:50Z | false | 0 | 1,211,462,925 | null | Python | null | [
"graph-transformer",
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] | yumingjs/SARE-GT-IJCNN2026 | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-09-27T16:09:59Z | [] | 2026-04-15T12:24:51Z | [
"graph.graph-transformer"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 1 | [] | 2026-04-15T13:13:01Z | https://github.com/yumingjs/SARE-GT-IJCNN2026 | null |
[
"generative-ai"
] | [
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] | [
"description",
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"repository-metadata"
] | [
"recall.name.diffusion-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-17T14:38:07Z | This repository contains the official implementation of the paper Wavelet-Constrained Trajectory Distillation for Efficient Transferable Targeted Attacks | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-26T16:16:45Z | false | 0 | 1,213,586,232 | null | Python | null | [
"diffusion",
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] | xuejianhuang/WCTD | [
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] | 1 | 2026-10-26T16:16:45Z | [] | 2026-10-08T13:57:40Z | [
"recall.name.diffusion-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 2 | [] | 2026-10-11T09:31:34Z | https://github.com/xuejianhuang/WCTD | null |
[
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] | [
"recall26.low-light-enhancement"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-17T16:50:12Z | Official implementation of the CVPR 2026 paper "Bi-Bridge: Bidirectional Diffusion Bridges for Low-Light Image Enhancement". | [
"computer-vision",
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"low-level-vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T10:12:50Z | false | 1 | 1,213,690,092 | null | Python | null | [
"diffusion",
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] | qq1332427275/Bi-Bridge | [
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] | 3 | 2026-10-09T17:31:36Z | [] | 2026-06-07T08:53:00Z | [
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"contribution-language",
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] | include | ml-contribution-v5 | 8 | [] | 2026-08-22T02:59:14Z | https://github.com/qq1332427275/Bi-Bridge | null |
[
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] | [
"timeseries.state-space-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-17T18:24:13Z | Official code for arXiv:2604.11807 - Physics-Informed State Space Models for Off-Grid Solar Forecasting | [
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] | 3 | 2026-10-03T15:45:52Z | [] | 2026-04-21T04:44:53Z | [
"timeseries.state-space-models"
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"ml-method-cue",
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] | include | ml-contribution-v5 | 5 | [
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"nasa-power",
"physics-informed",
"solar-forecasting",
"state-space-model",
"ultra-lightweight"
] | 2026-06-02T08:08:38Z | https://github.com/Moh97746/PISSM-Solar-Forecasting | null |
[
"computer-vision",
"efficient-ml",
"generative-modeling",
"machine-learning-systems",
"model-compression"
] | [
"diffusion",
"neural-network-pruning",
"pruning",
"restoration",
"structured-pruning",
"super-resolution"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning",
"vision.image-super-resolution"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-19T04:17:19Z | [CVPR 2026 Findings] Official implementation of the paper "TinySR: Pruning Diffusion for Real-World Image Super-Resolution". | [
"computer-vision",
"generative-modeling",
"machine-learning-systems"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 4 | 1,214,802,301 | null | Python | Apache-2.0 | [
"diffusion",
"pruning",
"restoration",
"super-resolution"
] | Microtreei/TinySR | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-10-24T14:31:13Z | [] | 2026-05-25T09:55:14Z | [
"vision.image-super-resolution"
] | 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 | 46 | [] | 2026-10-23T16:28:09Z | https://github.com/Microtreei/TinySR | null |
[
"efficient-ml",
"generative-ai",
"language",
"linguistics",
"machine-learning-systems",
"model-compression",
"natural-language-processing"
] | [
"language-model",
"language-modeling",
"large-language-model",
"neural-network-pruning",
"pretraining",
"pruning",
"structured-pruning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning",
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"nlp.language-modeling",
"recall.computational-linguistics",
"recall.name.language-model",
"recall.name.large-language-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-19T14:17:22Z | [ACL 2026 Outstanding Paper Award] Official implementation of the paper "From Local to Global: Revisiting Structured Pruning Paradigms for Large Language Models" | [
"machine-learning-systems",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-18T13:47:34Z | false | 0 | 1,215,128,316 | null | Python | Apache-2.0 | [
"language-model",
"pruning"
] | uncc-efficient-ai/GISP | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-25T14:09:27Z | [] | 2026-10-02T20:05:46Z | [
"recall.name.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 | 4 | [] | 2026-10-11T07:29:00Z | https://github.com/uncc-efficient-ai/GISP | null |
[
"anomaly-detection",
"generative-modeling",
"graph-learning"
] | [
"anomaly-detection",
"diffusion",
"graph-anomaly-detection"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.graph-anomaly-detection"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-20T05:52:58Z | Official PyTorch implementation for the paper ARLDM: Autoencoder Reconstruction-based Latent1 Diffusion Model for Graph Anomaly Detection. | [
"anomaly-detection",
"generative-modeling",
"graph-learning"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 0 | 1,215,647,499 | null | Python | null | [
"anomaly-detection",
"diffusion",
"graph-anomaly-detection"
] | weihus/ARLDM | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-09-27T16:09:59Z | [] | 2026-04-20T06:23:26Z | [
"graph.graph-anomaly-detection"
] | 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 | 1 | [] | 2026-05-18T14:56:47Z | https://github.com/weihus/ARLDM | null |
[
"natural-language-processing",
"representation-learning",
"time-series",
"time-series-and-forecasting"
] | [
"representation-learning",
"self-supervised-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"timeseries.temporal-representation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-20T15:47:56Z | Code for our Informer-LLM paper on time series forecasting with Informer and large language models. | [
"natural-language-processing",
"representation-learning",
"time-series",
"time-series-and-forecasting"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T16:16:53Z | false | 0 | 1,216,133,708 | null | Python | MIT | [
"representation-learning",
"self-supervised-learning"
] | YaruShan/Informer-LLM | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-09-26T14:34:36Z | [] | 2026-04-20T16:02:22Z | [
"timeseries.temporal-representation"
] | 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 | 1 | [] | 2026-04-20T16:02:26Z | https://github.com/YaruShan/Informer-LLM | null |
[
"deep-learning",
"efficient-ml",
"language",
"natural-language-processing"
] | [
"conditional-computation",
"distillation",
"mamba",
"mixture-of-experts",
"sparse-activation"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"efficiency.mixture-of-experts"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-20T22:25:19Z | Novel architecture integrating Mamba SSM, Mixture-of-Experts, and Chain-of-Thought distillation for text classification | [
"deep-learning",
"efficient-ml",
"language",
"natural-language-processing"
] | [
"deep-learning",
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"classifier"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 0 | 1,216,424,230 | null | Jupyter Notebook | null | [
"conditional-computation",
"distillation",
"mamba",
"mixture-of-experts",
"sparse-activation"
] | Zara-iqbal-01/Mamba-MoE-CoT | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2026-04-20T22:33:03Z | [
"efficiency.mixture-of-experts"
] | db8df1373b6e14e9654ec5e0a7ccf6dff67dcade | 2026-10-05T21:36:53Z | gh-ml-readme-evidence-v2 | "db8df1373b6e14e9654ec5e0a7ccf6dff67dcade" | [
"method",
"other",
"overview"
] | [
"method-contribution",
"ml-method-context"
] | ok | 2026-10-05T21:36:53Z | Zara-iqbal-01/Mamba-MoE-CoT | specific-method-with-novelty-claim | [
"method-contribution",
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [
"chain-of-thought",
"deep-learning",
"llm",
"mamba",
"mixture-of-experts",
"nlp",
"pytorch",
"sentiment-analysis",
"state-space-models"
] | 2026-04-20T22:33:06Z | https://github.com/Zara-iqbal-01/Mamba-MoE-CoT | null |
[
"health-and-biomedicine",
"information-retrieval",
"natural-language-processing"
] | [
"lexical-retrieval",
"rag",
"sparse-retrieval"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"nlp-sparse-retrieval"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-21T03:56:54Z | Code for our paper"When Retrieval Doesn't Help: A Large-Scale Study of Biomedical RAG" | [
"health-and-biomedicine",
"information-retrieval",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-19T17:46:36Z | false | 0 | 1,216,594,408 | null | Python | null | [
"lexical-retrieval",
"rag",
"sparse-retrieval"
] | erfan-nourbakhsh/BioMedicalRAG | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-19T17:46:36Z | [] | 2026-04-21T04:08:50Z | [
"nlp-sparse-retrieval"
] | 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 | 4 | [] | 2026-09-22T04:07:11Z | https://github.com/erfan-nourbakhsh/BioMedicalRAG | null |
[
"deep-learning",
"efficient-ml",
"language",
"robust-ml",
"time-series-and-forecasting",
"trustworthy-ml"
] | [
"conditional-computation",
"distribution-shift-detection",
"domain-generalization",
"mixture-of-experts",
"sparse-activation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.mixture-of-experts",
"trust.distribution-shift"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-25T06:07:45Z | This is the official PyTorch implementation for the paper "A Spatial-Temporal Distribution Shift Aware Mixture-of-Experts Model for Urban Atmospheric Forecasting". | [
"deep-learning",
"efficient-ml",
"language",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 0 | 1,220,636,209 | null | Python | MIT | [
"conditional-computation",
"mixture-of-experts",
"sparse-activation"
] | Lucas-Fu/DiS-MoE | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-19T16:36:56Z | [] | 2026-10-08T04:17:14Z | [
"efficiency.mixture-of-experts"
] | 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 | 1 | [] | 2026-10-08T04:17:18Z | https://github.com/Lucas-Fu/DiS-MoE | null |
[
"computer-vision"
] | [
"action-recognition",
"contrastive-learning",
"video-understanding"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.action-recognition"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-26T12:12:29Z | Official Code for Paper WeCo-OSAR: Weighted Contrastive Learning for Open-set Skeleton-based Action Recognition with pseudo-OOD Samples | [
"computer-vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-09T17:31:36Z | false | 0 | 1,221,582,202 | null | Python | NOASSERTION | [
"action-recognition",
"contrastive-learning",
"video-understanding"
] | cyoungx/WeCo-OSAR | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-09T17:31:36Z | [] | 2026-07-09T01:00:19Z | [
"vision.action-recognition"
] | 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 | 1 | [] | 2026-07-14T12:25:52Z | https://github.com/cyoungx/WeCo-OSAR | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-28T08:08:24Z | This repository will have the code to work with graph neural network algorithms, it will also be used to develop novel algorithms. | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-25T15:11:36Z | false | 0 | 1,223,327,807 | null | Jupyter Notebook | null | [
"graph-neural-network",
"message-passing",
"neural-network"
] | cs24d0002-jpg/graph_neural_networks | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-25T15:11:36Z | [] | 2026-06-11T04:38:21Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-06-11T04:38:25Z | https://github.com/cs24d0002-jpg/graph_neural_networks | null |
[
"computer-vision",
"efficient-ml",
"model-compression"
] | [
"distillation",
"knowledge-distillation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-30T04:34:28Z | Implementation of our paper "Recall-Based Knowledge Distillation for Data Distribution Based Catastrophic Forgetting in Semantic Segmentation" | [
"computer-vision",
"efficient-ml",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-10T16:53:26Z | false | 0 | 1,225,230,532 | null | Python | null | [
"distillation",
"knowledge-distillation"
] | Sam343-mir/Recall_based_KD | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-10T16:53:26Z | [] | 2026-04-30T04:57:22Z | [
"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 | 1 | [] | 2026-04-30T16:13:56Z | https://github.com/Sam343-mir/Recall_based_KD | null |
[
"computer-vision",
"generative-modeling",
"multimodal-learning"
] | [
"diffusion",
"promptable-segmentation",
"segmentation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.promptable-segmentation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-30T07:25:45Z | Official implementation repository of IJCB2026 paper IdentiFace: Multi-Modal Iterative Diffusion Framework for Identifiable Suspect Face Generation in Crime Investigations | [
"computer-vision",
"generative-modeling",
"multimodal-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 1,225,346,450 | null | Jupyter Notebook | MIT | [
"diffusion",
"promptable-segmentation",
"segmentation"
] | uglasses/IdentiFace | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-04T15:58:58Z | [] | 2026-09-02T07:44:55Z | [
"vision.promptable-segmentation"
] | 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 | [] | 2026-09-02T07:47:35Z | https://github.com/uglasses/IdentiFace | null |
[
"multimodal",
"reinforcement-learning",
"robotics"
] | [
"environment-modeling",
"world-model"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.world-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-30T22:05:04Z | A new generation of world models. | [
"multimodal",
"reinforcement-learning",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 0 | 1,226,011,421 | null | HTML | null | [
"environment-modeling",
"world-model"
] | universemodel/universemodel.github.io | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-08T18:15:14Z | [] | 2026-04-30T22:30:26Z | [
"multimodal.world-models"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-04-30T22:30:30Z | https://github.com/universemodel/universemodel.github.io | null |
[
"biology",
"chemistry",
"computational-biology",
"computational-chemistry",
"materials-science"
] | [
"molecular-dynamics",
"scientific-machine-learning",
"simulation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"bio.molecular-dynamics",
"chem-molecular-dynamics"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-30T22:56:47Z | Official code for the ICML2026 paper "Large-Scale Molecular Dynamics Simulation: Direct Interatomic Modeling with Dilated Message Passing" | [
"biology",
"chemistry",
"computational-biology"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-10T16:53:26Z | false | 0 | 1,226,036,892 | null | Python | null | [
"molecular-dynamics",
"scientific-machine-learning"
] | WanyuGroup/ICML2026-DKMP | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-17T13:17:44Z | [] | 2026-05-19T10:58:54Z | [
"bio.molecular-dynamics"
] | 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 | 0 | [] | 2026-05-28T11:52:33Z | https://github.com/WanyuGroup/ICML2026-DKMP | null |
[
"forecasting",
"general-ml",
"time-series",
"time-series-and-forecasting"
] | [
"forecasting",
"machine-learning",
"sequence-modeling",
"transformer"
] | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"general.machine-learning-readme",
"recall.name.machine-learning",
"recall.name.transformer-model",
"timeseries.forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-01T08:06:21Z | [ICML 2026] Official implementation of "CombinationTS: A Modular Framework for Understanding Time-Series Forecasting Models" | [
"general-ml",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 0 | 1,226,298,457 | https://arxiv.org/abs/2605.01231 | Python | null | [
"machine-learning"
] | BenchCouncil/CombinationTS | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | 6 | 2026-10-14T15:02:21Z | [] | 2026-09-27T16:24:45Z | [
"general.machine-learning-readme",
"recall.name.machine-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 | 6 | [] | 2026-10-06T03:27:09Z | https://github.com/BenchCouncil/CombinationTS | null |
[
"multi-agent-systems",
"reinforcement-learning"
] | [
"centralized-training",
"multi-agent-reinforcement-learning",
"reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"rl.multiagent"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-01T09:26:32Z | This repository is the official implementation of the paper: **"Optimistic $\epsilon$-Greedy Exploration for Cooperative Multi-Agent Reinforcement Learning"**. | [
"multi-agent-systems",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 0 | 1,226,349,396 | null | Python | null | [
"centralized-training",
"multi-agent-reinforcement-learning",
"reinforcement-learning"
] | qxqxtxdy/OptimisticExploration | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2026-05-01T09:29:07Z | [
"rl.multiagent"
] | 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 | [] | 2026-05-01T09:29:11Z | https://github.com/qxqxtxdy/OptimisticExploration | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"recall.name.diffusion-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-01T12:42:11Z | [ICML 2026] official code release for our paper "Recovering Hidden Reward in Diffusion-Based Policies". | [
"generative-ai",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-22T14:58:59Z | false | 0 | 1,226,466,741 | null | Python | null | [
"diffusion"
] | sotaagi/EnergyFlow | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-22T14:58:59Z | [] | 2026-10-02T08:52:29Z | [
"recall.name.diffusion-model"
] | 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 | [] | 2026-10-11T07:18:23Z | https://github.com/sotaagi/EnergyFlow | null |
[
"chemistry",
"generative-modeling",
"organic-chemistry"
] | [
"flow-matching",
"generative-modeling",
"planning",
"reaction-prediction"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"chem-retrosynthesis-planning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-01T16:48:55Z | Official implementation for Order Matters in Retrosynthesis: Structure-aware Generation via Reaction-Center-Guided Discrete Flow Matching, ICML2026 | [
"chemistry",
"generative-modeling",
"organic-chemistry"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 0 | 1,226,639,024 | null | Python | MIT | [
"flow-matching",
"generative-modeling",
"planning",
"reaction-prediction"
] | LOGO-CUHKSZ/RetroDiT | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-09-29T17:30:08Z | [] | 2026-08-19T06:31:34Z | [
"chem-retrosynthesis-planning"
] | 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 | 4 | [] | 2026-08-19T14:32:48Z | https://github.com/LOGO-CUHKSZ/RetroDiT | null |
[
"general-ml",
"generative-ai",
"language"
] | [
"inference-scaling",
"model-architecture",
"test-time-compute",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.model-architecture",
"llm.test-time-compute"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-01T20:50:29Z | Context-Anchored Recurrent Transformer - a new AI model architecture | [
"general-ml"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 1,226,790,004 | null | Python | MIT | [
"model-architecture",
"transformer"
] | ccapps42/CART | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-07T18:07:52Z | [] | 2026-06-02T11:30:03Z | [
"general.model-architecture"
] | eba0e3bc9f84456f72ba974bf4c0cbdade70d650 | 2026-10-04T17:18:56Z | gh-ml-readme-evidence-v2 | "eba0e3bc9f84456f72ba974bf4c0cbdade70d650" | [
"citation",
"method",
"other",
"overview"
] | [
"ml-method-context",
"model-training-artifact",
"paper-reference"
] | ok | 2026-10-04T17:18:56Z | ccapps42/CART | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue",
"model-training-artifact",
"paper-reference"
] | include | ml-contribution-v5 | 1 | [] | 2026-07-16T12:19:59Z | https://github.com/ccapps42/CART | null |
[
"multimodal",
"reinforcement-learning",
"robotics"
] | [
"environment-modeling",
"world-model"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.world-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-02T03:40:29Z | Official implementation of "Identifiable Token Correspondence for World Models" (ICML'26) | [
"multimodal",
"reinforcement-learning",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 2 | 1,226,976,346 | null | Python | MIT | [
"environment-modeling",
"world-model"
] | snu-mllab/Identifiable-Token-Correspondence | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-08T18:15:14Z | [] | 2026-06-26T13:57:21Z | [
"multimodal.world-models"
] | 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 | 6 | [] | 2026-10-04T20:53:16Z | https://github.com/snu-mllab/Identifiable-Token-Correspondence | 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 | [] | 2026-05-02T09:35:53Z | The official implementation of the paper "Toward Extremely Efficient Infrared Small Target Detection via Universal U-Net-based Knowledge Distillation" in PyTorch. | [
"efficient-ml",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 0 | 1,227,156,768 | null | Python | Apache-2.0 | [
"distillation",
"knowledge-distillation"
] | RuiminHuang/LW-DSTransNet | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-09T17:31:36Z | [] | 2026-07-14T08:18:26Z | [
"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 | 4 | [] | 2026-07-14T08:18:30Z | https://github.com/RuiminHuang/LW-DSTransNet | null |
[
"generative-ai",
"generative-modeling",
"multimodal",
"vision"
] | [
"diffusion",
"image-generation",
"text-to-image"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.text-to-image"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-02T19:44:54Z | Official code for "Responsible Text-to-Image Diffusion" (ICML 2026) | [
"generative-ai",
"generative-modeling",
"multimodal",
"vision"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-19T17:46:36Z | false | 0 | 1,227,515,849 | null | Python | MIT | [
"diffusion",
"image-generation",
"text-to-image"
] | Moslem-Sh21/responsible-t2i-diffusion | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-19T17:46:36Z | [] | 2026-05-02T20:00:27Z | [
"multimodal.text-to-image"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-05-02T20:00:30Z | https://github.com/Moslem-Sh21/responsible-t2i-diffusion | null |
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