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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-14T17:05:48Z | Official implementation of "Watch Your Step: Information Injection in Diffusion Models via Shadow Timestep Embedding" (ICML 2026). | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model",
"embedding"
] | ml_related_text | gh-ml-relevance-v1 | 2026-11-02T21:04:38Z | false | 0 | 1,239,006,853 | null | Python | Apache-2.0 | [
"diffusion"
] | OldDreamInWind/STE-Diffusion | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-11-02T21:04:38Z | [] | 2026-06-01T22:59:53Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-06-01T22:59:58Z | https://github.com/OldDreamInWind/STE-Diffusion | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion",
"lora"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-14T19:25:22Z | Official Implementation of LoRAverse: A Submodular Framework to Retrieve Diverse Adapters for Diffusion Models (ICCV 2025) | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-11-02T21:04:38Z | false | 0 | 1,239,117,251 | https://loraverse-vt.github.io/ | Python | MIT | [
"diffusion",
"lora"
] | mertsonmezer/LoRAverse | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-11-02T21:04:38Z | [] | 2026-05-14T21:30:04Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 0 | [
"lora",
"stable-diffusion",
"submodular-optimization"
] | 2026-05-14T21:31:22Z | https://github.com/mertsonmezer/LoRAverse | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-classification",
"graph-neural-network",
"graph-representation-learning",
"message-passing",
"neural-network"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description",
"graph.graph-classification"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-14T21:36:18Z | Official repository for SumGCN: A novel Graph Neural Network architecture based on the anti-Laplacian operator. Includes theoretical foundations, source code, and full reproducibility for node/graph classification experiments and ADHD detection. | [
"deep-learning",
"graph-learning"
] | [
"neural-network",
"classifier"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 0 | 1,239,203,796 | null | Jupyter Notebook | MIT | [
"graph-neural-network",
"message-passing",
"neural-network"
] | MauricioAguasFonseca/SumGCN-Research | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-25T15:11:36Z | [] | 2026-06-01T14:48:30Z | [
"graph.gnn-description"
] | 97cb62fa26127ea4a0deaba006e5126244af723e | 2026-09-26T15:53:33Z | gh-ml-readme-evidence-v2 | "97cb62fa26127ea4a0deaba006e5126244af723e" | [
"other"
] | [
"ml-method-context",
"official-implementation-claim"
] | ok | 2026-09-26T15:53:33Z | MauricioAguasFonseca/SumGCN-Research | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue",
"official-implementation-claim"
] | include | ml-contribution-v5 | 0 | [] | 2026-06-01T15:19:12Z | https://github.com/MauricioAguasFonseca/SumGCN-Research | null |
[
"generative-ai",
"language"
] | [
"distillation",
"language-model",
"reasoning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm-reasoning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-15T16:48:46Z | NeurIPS 2026: Official implementation of “VISD: Enhancing Video Reasoning via Structured Self-Distillation”. | [
"generative-ai",
"language"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-06T16:04:50Z | false | 0 | 1,239,990,702 | null | Python | null | [
"distillation",
"language-model",
"reasoning"
] | Koreyoshi01/VISD | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-10-20T14:47:52Z | [] | 2026-10-01T11:39:16Z | [
"llm-reasoning"
] | 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 | 26 | [] | 2026-10-05T13:10:06Z | https://github.com/Koreyoshi01/VISD | null |
[
"reinforcement-learning"
] | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-18T04:49:46Z | Official Implementation of NeurIPS 25 Paper "Confounding Robust Deep Reinforcement Learning: A Causal Approach" | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-11-02T21:04:38Z | false | 0 | 1,242,048,402 | null | Python | MIT | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | drmeerkat/Confounding-Robust-Deep-Reinforcement-Learning | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-11-02T21:04:38Z | [] | 2026-05-18T04:53:39Z | [
"rl.deep"
] | 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 | [] | 2026-05-18T04:53:43Z | https://github.com/drmeerkat/Confounding-Robust-Deep-Reinforcement-Learning | null |
[
"generative-modeling",
"health-and-biomedicine"
] | [] | [
"description",
"license-metadata",
"repository-metadata"
] | [] | false | unknown | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-18T05:21:37Z | This is the official implementation of "Learning Latent Protein Languages for Autoregressive Generation" paper. | [
"generative-modeling",
"health-and-biomedicine"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-06T18:33:52Z | false | 0 | 1,242,067,542 | null | Python | MIT | [] | mahdip72/latent_protein_languages | [
"description",
"license-metadata",
"repository-metadata"
] | 3 | 2026-10-08T19:45:04Z | [
"2610.03978"
] | 2026-10-06T17:39:59Z | [] | 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 | [] | 2026-10-06T17:40:04Z | https://github.com/mahdip72/latent_protein_languages | {"discovery_source":"hf_daily_papers","paper_evidence":"unverified","queryless":true} |
[
"reinforcement-learning"
] | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-18T06:40:13Z | Official Implementation of NeurIPS 25 Paper "Confounding Robust Deep Reinforcement Learning: A Causal Approach" | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-11-01T17:09:41Z | false | 0 | 1,242,123,571 | null | Python | MIT | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | CausalAILab/Confounding-Robust-Deep-Reinforcement-Learning | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-11-01T17:09:41Z | [] | 2026-06-08T01:55:36Z | [
"rl.deep"
] | 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 | [] | 2026-10-08T07:56:33Z | https://github.com/CausalAILab/Confounding-Robust-Deep-Reinforcement-Learning | null |
[
"multimodal",
"reinforcement-learning",
"robotics"
] | [
"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 | [] | 2026-05-18T09:38:45Z | Official implementation of the ICML 2026 paper "Structure Abstraction and Generalization in a Hippocampal-Entorhinal Inspired World Model". | [
"multimodal",
"reinforcement-learning",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 1 | 1,242,287,166 | https://hpc-mec-worldmodel.github.io/ | Jupyter Notebook | MIT | [
"environment-modeling",
"world-model"
] | senngadaisuki/hpc-mec-worldmodel | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-08T18:15:14Z | [] | 2026-07-03T01:55:02Z | [
"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 | 3 | [
"brain-inspired",
"cognitive-modeling",
"computational-neuroscience",
"entorhinal-cortex",
"hippocampus",
"icml2026",
"structure-learning",
"world-models"
] | 2026-08-01T01:03:46Z | https://github.com/senngadaisuki/hpc-mec-worldmodel | null |
[
"multimodal",
"reinforcement-learning",
"robotics"
] | [
"environment-modeling",
"world-model"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"multimodal.world-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-18T09:39:34Z | Official implementation of the ICML 2026 paper "DiLA: Disentangled Latent Action World Models". | [
"multimodal",
"reinforcement-learning",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 1 | 1,242,287,943 | https://disentangled-latent-action-world-models.github.io/ | Jupyter Notebook | null | [
"environment-modeling",
"world-model"
] | senngadaisuki/disentangled-latent-action-world-models | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 1 | 2026-10-08T18:15:14Z | [] | 2026-09-15T14:06:55Z | [
"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 | 19 | [
"cross-embodiment",
"disentanglement",
"human2robot",
"icml-2026",
"latent-action-models",
"visual-planning",
"world-models"
] | 2026-10-06T20:19:53Z | https://github.com/senngadaisuki/disentangled-latent-action-world-models | null |
[
"time-series",
"time-series-and-forecasting"
] | [
"mamba",
"sequence-modeling"
] | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"timeseries.topic"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-18T17:03:00Z | [KDD 2026] Official implementation of "STM3: Mixture of Multiscale Mamba for Long-Term Spatio-Temporal Time-Series Prediction" | [
"time-series",
"time-series-and-forecasting"
] | [
"deep-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-11-02T21:04:38Z | false | 0 | 1,242,695,611 | https://arxiv.org/abs/2508.12247 | Python | null | [
"mamba",
"sequence-modeling"
] | IfReasonable/STM3_KDD26 | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-11-02T21:04:38Z | [] | 2026-09-16T03:33:43Z | [
"timeseries.topic"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 6 | [
"deep-learning",
"mamba",
"spatio-temporal",
"time-series"
] | 2026-10-15T02:21:28Z | https://github.com/IfReasonable/STM3_KDD26 | 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 | [] | 2026-05-19T04:51:00Z | Official Implementation of ICML 26 Paper "Causal Flow Q-Learning for Robust Offline Reinforcement Learning" | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 0 | 1,243,141,273 | null | Python | MIT | [
"batch-reinforcement-learning",
"offline-reinforcement-learning",
"reinforcement-learning"
] | CausalAILab/Causal-Flow-Q-Learning-for-Robust-Offline-Reinforcement-Learning | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-09-26T14:50:46Z | [] | 2026-05-19T04:58:13Z | [
"rl.offline"
] | 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 | [] | 2026-05-19T04:58:17Z | https://github.com/CausalAILab/Causal-Flow-Q-Learning-for-Robust-Offline-Reinforcement-Learning | null |
[
"computer-vision",
"general-ml",
"generative-ai",
"generative-modeling"
] | [
"flow-matching",
"generative-modeling",
"machine-learning",
"video-generation"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.machine-learning-readme",
"recall.name.machine-learning",
"vision.video-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-19T06:28:10Z | Official implementation of ICML 2026 paper Temporal-aware Flow Matching for Video Generation with Temporally Coherent Motion | [
"general-ml",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T13:50:07Z | false | 0 | 1,243,208,019 | https://pzrain.github.io/tfm/ | Python | Apache-2.0 | [
"flow-matching",
"machine-learning"
] | pzrain/TFM | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-09-28T17:46:51Z | [] | 2026-09-23T12:04:25Z | [
"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",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 4 | [
"flowmatching",
"videogeneration"
] | 2026-09-23T17:19:11Z | https://github.com/pzrain/TFM | 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 | [] | 2026-05-19T11:15:58Z | Official page for implementation of S2Q following "Retaining Suboptimal Actions to Follow Shifting Optima in Multi-Agent Reinforcement Learning" which has been accepted to ICLR 2026 | [
"multi-agent-systems",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 0 | 1,243,464,832 | https://hyeon1996.github.io/ | HTML | null | [
"centralized-training",
"multi-agent-reinforcement-learning",
"reinforcement-learning"
] | hyeon1996/hyeon1996.github.io | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2026-05-19T11:44:12Z | [
"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 | 0 | [] | 2026-05-19T11:44:16Z | https://github.com/hyeon1996/hyeon1996.github.io | null |
[
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"forecasting",
"multivariate-forecasting"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"timeseries.multivariate"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-20T03:08:52Z | Official PyTorch implementation of our TPAMI paper: DisenTS: Disentangled Channel Evolving Pattern Modeling for Multivariate Time Series Forecasting | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 1,244,175,680 | null | Python | Apache-2.0 | [
"forecasting",
"multivariate-forecasting"
] | icantnamemyself/DisenTS | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-04T15:58:58Z | [] | 2026-05-20T03:12:24Z | [
"timeseries.multivariate"
] | 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 | [] | 2026-05-20T03:12:29Z | https://github.com/icantnamemyself/DisenTS | null |
[
"automl",
"classical-ml",
"general-ml",
"tabular-ml"
] | [
"ensemble-learning",
"few-shot-learning",
"gradient-boosting",
"meta-learning",
"paper-implementation"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"general.arxiv",
"general.meta-learning",
"probable.classical-lightgbm-readme"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-20T03:14:39Z | Official code for MetaEns (ICML 2026): label-free selection of small, diverse outlier-detection ensembles via meta-learning. | [
"automl",
"general-ml"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-18T13:47:34Z | false | 2 | 1,244,179,169 | https://arxiv.org/abs/2605.16567 | Python | MIT | [
"ensemble-learning",
"few-shot-learning",
"meta-learning"
] | ph-phuc/MetaEns | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 6 | 2026-10-29T17:49:51Z | [] | 2026-10-09T14:46:24Z | [
"general.meta-learning"
] | 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 | [
"anomaly-detection",
"ensemble-learning",
"icml-2026",
"meta-learning",
"model-selection",
"outlier-detection",
"unsupervised-learning"
] | 2026-10-11T10:03:38Z | https://github.com/ph-phuc/MetaEns | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-20T06:20:10Z | Official implementation of [GP2F: Cross-Domain Graph Prompting with Adaptive Fusion of Pre-trained Graph Neural Networks](https://arxiv.org/abs/2602.11629), accepted by ICML 2026. | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-25T15:11:36Z | false | 0 | 1,244,295,579 | null | Python | null | [
"graph-neural-network",
"message-passing",
"neural-network"
] | hedongxiao-tju/GP2F | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-10-25T15:11:36Z | [] | 2026-05-20T06:20:59Z | [
"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 | [] | 2026-05-29T06:18:52Z | https://github.com/hedongxiao-tju/GP2F | null |
[
"embodied-ai",
"generative-modeling",
"interpretability-and-safety",
"robotics"
] | [
"diffusion",
"diffusion-policy",
"imitation-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"robotics.diffusion-policy"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-20T09:47:15Z | [ICML 2026 Spotlight] Official implementation of "PACT: Self-Evolving Physical Safety Alignment for Diffusion Policies in Embodied Manipulation". | [
"embodied-ai",
"generative-modeling",
"interpretability-and-safety",
"robotics"
] | [
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 1 | 1,244,472,355 | https://ethan-iai.github.io/pact/ | Python | Apache-2.0 | [
"diffusion",
"diffusion-policy",
"imitation-learning"
] | thu-ml/PACT | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-09-25T16:01:25Z | [] | 2026-06-16T02:31:42Z | [
"robotics.diffusion-policy"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 14 | [] | 2026-08-27T20:14:53Z | https://github.com/thu-ml/PACT | null |
[
"machine-learning-systems",
"natural-language-processing",
"sequence-modeling",
"time-series"
] | [
"mamba",
"quantization",
"sequence-modeling",
"state-space-model",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"timeseries.state-space-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-21T04:25:35Z | TESM (Token-Entangled State Machine) - A novel LLM architecture based on state entanglement theory, combining State Space Models with local entanglement mechanisms for efficient long-sequence modeling | [
"machine-learning-systems",
"natural-language-processing",
"sequence-modeling",
"time-series"
] | [
"deep-learning",
"large-language-model",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 0 | 1,245,271,340 | https://github.com/WANGLILIN360/tesm | Python | MIT | [
"mamba",
"quantization",
"sequence-modeling",
"state-space-model",
"transformer"
] | WANGLILIN360/tesm | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2026-06-08T01:10:30Z | [
"timeseries.state-space-models"
] | 725fb42c6bc55251a697beedff3481ce89820a8e | 2026-10-05T21:36:53Z | gh-ml-readme-evidence-v2 | "725fb42c6bc55251a697beedff3481ce89820a8e" | [
"other"
] | [] | ok | 2026-10-05T21:36:53Z | WANGLILIN360/tesm | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 1 | [
"cuda",
"deep-learning",
"entanglement",
"int2-quantization",
"large-language-model",
"llm",
"mamba",
"nlp",
"pytorch",
"quantum-entanglement",
"sequence-modeling",
"ssm",
"state-space-model",
"token-entanglement",
"transformer-alternative",
"triton"
] | 2026-06-03T04:29:36Z | https://github.com/WANGLILIN360/tesm | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-21T07:16:54Z | [NeurIPS 2026] An official implementation of Reward Score Matching: Unifying Reward-based Fine-tuning for Flow and Diffusion Models | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-30T17:16:29Z | false | 0 | 1,245,390,819 | https://arxiv.org/abs/2604.17415 | Python | MIT | [
"diffusion"
] | jaylee2000/rsm | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 2 | 2026-11-01T17:09:41Z | [] | 2026-06-19T23:42:50Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 18 | [
"diffusion-rl"
] | 2026-10-28T03:19:09Z | https://github.com/jaylee2000/rsm | null |
[
"machine-learning"
] | [
"deep-kernel-learning",
"kernel-methods"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"specialized26.deep-kernel-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-21T12:37:23Z | Code for paper “Continuous Bayesian Machine Learning for High-Dimensional Analog and Mixed-signal Circuit Yield Analysis and Optimization” | [
"machine-learning"
] | [
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T22:33:16Z | false | 0 | 1,245,659,859 | null | Python | Apache-2.0 | [
"deep-kernel-learning",
"kernel-methods"
] | JingKou129/SBYA | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-09-26T22:33:16Z | [] | 2026-05-21T13:18:53Z | [
"specialized26.deep-kernel-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-05-22T02:34:26Z | https://github.com/JingKou129/SBYA | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-21T12:52:20Z | Repository containing code to our ICML26 paper "Certifying Graph Neural Networks Against Label and Structure Poisoning" | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-25T15:11:36Z | false | 1 | 1,245,673,075 | null | Python | MIT | [
"graph-neural-network",
"message-passing",
"neural-network"
] | p-CXTN/ST-GPA | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-25T15:11:36Z | [] | 2026-08-30T09:22:26Z | [
"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 | 1 | [] | 2026-08-30T17:53:47Z | https://github.com/p-CXTN/ST-GPA | null |
[
"general-ml"
] | [
"novel-method",
"transformer"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.novel-method"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-21T20:50:51Z | Soft Clustering and Weighted Transformer-Based Novel Method for Missing Data Imputation | [
"general-ml"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 0 | 1,246,064,184 | null | Python | null | [
"novel-method",
"transformer"
] | caferbudak/Soft-Clustering-and-Weighted-Transformer-Based-Novel-Method-for-Missing-Data-Imputation- | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-09-27T16:09:59Z | [] | 2026-05-21T22:00:55Z | [
"general.novel-method"
] | 20a1f05f06dc08b64fd8edc287fc783b789dc604 | 2026-09-26T15:53:33Z | gh-ml-readme-evidence-v2 | "20a1f05f06dc08b64fd8edc287fc783b789dc604" | [
"dataset",
"other"
] | [
"ml-method-context",
"paper-reference"
] | ok | 2026-09-26T15:53:33Z | caferbudak/Soft-Clustering-and-Weighted-Transformer-Based-Novel-Method-for-Missing-Data-Imputation- | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue",
"paper-reference"
] | include | ml-contribution-v5 | 0 | [] | 2026-05-21T22:00:59Z | https://github.com/caferbudak/Soft-Clustering-and-Weighted-Transformer-Based-Novel-Method-for-Missing-Data-Imputation- | null |
[
"generative-ai",
"generative-modeling",
"natural-language-processing"
] | [
"diffusion",
"language-model"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"recall.name.diffusion-model",
"recall.name.language-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-22T00:59:44Z | [NeurIPS 2026] Official implementation of Contrastive Distribution Matching for Amortized Sequential Monte Carlo in Discrete Diffusion | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-01T19:00:27.900159Z | false | 1 | 1,246,194,140 | https://cdm-smc.github.io/ | Python | NOASSERTION | [
"diffusion"
] | KAIST-Visual-AI-Group/CDM | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-20T14:47:52Z | [] | 2026-10-01T14:58:40Z | [
"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"
] | include | ml-contribution-v5 | 15 | [
"diffusion-models",
"generative-ai",
"inference"
] | 2026-10-01T14:58:47Z | https://github.com/KAIST-Visual-AI-Group/CDM | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"multimodal",
"vision"
] | [
"diffusion",
"generative-modeling",
"image-generation",
"text-to-image"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"multimodal.text-to-image",
"vision.image-diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-22T15:50:04Z | Official implementation for "Cert-LAS: Toward Certified Model Ownership Verification for Text-to-Image Diffusion Models via Layer-Adaptive Smoothing" (ICML 2026). | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-16T15:36:56Z | false | 0 | 1,246,831,619 | null | Python | Apache-2.0 | [
"diffusion"
] | Leyi-Qi/Cert-LAS | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-11-02T21:04:38Z | [] | 2026-06-19T09:52:28Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 1 | [] | 2026-06-19T09:52:32Z | https://github.com/Leyi-Qi/Cert-LAS | null |
[
"applied-mathematics",
"applied-physics",
"computational-science",
"generative-ai",
"generative-modeling"
] | [
"data-assimilation",
"diffusion",
"inverse-problems",
"reconstruction"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"science.inverse-problems"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-23T14:05:03Z | Official code for ICML 2026 paper "Stage-wise Distortion–Perception Traversal in Zero-shot Inverse Problems with Diffusion Models". | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 0 | 1,247,582,384 | null | Python | null | [
"diffusion"
] | weigerzan/MAP_RPS | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-11-02T21:04:38Z | [] | 2026-05-26T14:13:01Z | [
"llm.diffusion-phrase"
] | 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 | [] | 2026-10-12T20:23:15Z | https://github.com/weigerzan/MAP_RPS | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-23T14:42:01Z | implementation for neurosemantic signature-a novel graph neural network framework that integrates spectral temporal EEG feature with high level linguistic features derived from same subjects. | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-25T15:11:36Z | false | 0 | 1,247,607,475 | null | Jupyter Notebook | GPL-3.0 | [
"graph-neural-network",
"message-passing",
"neural-network"
] | kash78675/kkt-alzheimer-neuro-rep | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-25T15:11:36Z | [] | 2026-05-26T09:02:10Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
"contribution-language",
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-05-26T09:02:15Z | https://github.com/kash78675/kkt-alzheimer-neuro-rep | null |
[
"multi-agent-systems",
"reinforcement-learning"
] | [
"centralized-training",
"multi-agent-reinforcement-learning",
"reinforcement-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.multiagent"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-23T16:58:39Z | Code for paper "HPS: Hyperspherical Parameter Sharing for Efficient Multi-Agent Reinforcement Learning" | [
"multi-agent-systems",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 1 | 1,247,699,938 | null | Python | Apache-2.0 | [
"centralized-training",
"multi-agent-reinforcement-learning",
"reinforcement-learning"
] | stimulus1594/HPS | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-03T15:45:52Z | [] | 2026-05-26T15:05:51Z | [
"rl.multiagent"
] | 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 | 13 | [] | 2026-08-24T08:30:39Z | https://github.com/stimulus1594/HPS | null |
[
"deep-learning",
"graph-learning",
"trustworthy-ml"
] | [
"calibration",
"graph-neural-network",
"message-passing",
"neural-network",
"uncertainty-quantification"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description",
"trust.uncertainty-quantification"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-24T13:07:35Z | This repository contains the official implementation for the paper: X-EviProbe: Post-hoc Parameter-free Evidential Uncertainty Quantification for Frozen Graph Neural Networks | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-07T18:07:52Z | false | 0 | 1,248,297,342 | null | HTML | NOASSERTION | [
"graph-neural-network",
"message-passing",
"neural-network"
] | chenghuaguo/X-EviProbe | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-25T15:11:36Z | [] | 2026-05-24T13:15:05Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-05-24T13:15:30Z | https://github.com/chenghuaguo/X-EviProbe | null |
[
"generative-ai",
"generative-modeling",
"language",
"multimodal",
"multimodal-learning",
"natural-language-processing"
] | [
"diffusion",
"language-model",
"language-modeling",
"large-language-model",
"multimodal-learning",
"pretraining"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.language-model",
"llm.large-language-model",
"nlp.language-modeling",
"recall.name.diffusion-model",
"recall.name.language-model",
"recall.name.large-language-model",
"recall.name.multimodal-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-25T08:23:03Z | Official implementation of paper “Visual-Redundancy-Controlled Parallel Decoding for Diffusion-Based Multimodal Large Language Models” | [
"generative-modeling",
"multimodal-learning",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T15:56:46Z | false | 2 | 1,248,977,394 | null | Python | Apache-2.0 | [
"diffusion",
"language-model"
] | infiniteYuanyl/VRCD | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-10-09T16:23:55Z | [] | 2026-09-25T09:11:27Z | [
"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 | 9 | [] | 2026-10-06T16:10:44Z | https://github.com/infiniteYuanyl/VRCD | null |
[
"deep-learning",
"graph-learning",
"representation-learning"
] | [
"contrastive-learning",
"graph-contrastive-learning",
"self-supervised-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning",
"graph.graph-contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-25T09:06:30Z | Official PyTorch implementation of SPGCL in 'Revisiting Positive Samples in Graph Contrastive Learning: From the Perspective of Message Passing' (ICML 2026). | [
"graph-learning",
"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 0 | 1,249,013,174 | null | Python | MIT | [
"contrastive-learning",
"graph-contrastive-learning",
"self-supervised-learning"
] | hedongxiao-tju/SPGCL | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-09-26T10:28:13Z | [] | 2026-05-29T00:55:19Z | [
"graph.graph-contrastive-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 7 | [] | 2026-09-13T06:01:38Z | https://github.com/hedongxiao-tju/SPGCL | null |
[
"language",
"multimodal",
"natural-language-processing",
"video"
] | [
"distillation",
"video-question-answering",
"video-understanding"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.video-question-answering"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-25T11:56:28Z | The official code of the paper PEGS: Progressive Event Grounding with Self-Distillation for Video Question Answering | [
"language",
"multimodal",
"natural-language-processing",
"video"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 0 | 1,249,147,302 | null | Python | null | [
"distillation",
"video-question-answering",
"video-understanding"
] | XizeWu/PEGS | [
"description",
"query-match",
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T14:50:46Z | false | 1 | 1,249,946,507 | null | Python | null | [
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] | lmy1001/LARA | [
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] | 2 | 2026-10-21T15:48:06Z | [] | 2026-08-03T07:43:51Z | [
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[
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] | 2 | 2026-10-10T16:53:26Z | [] | 2026-05-26T07:26:48Z | [
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[
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] | include | ml-contribution-v5 | 9 | [] | 2026-10-20T03:33:03Z | https://github.com/Iiiiiiirene/LearniBridge | null |
[
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] | include | ml-contribution-v5 | 1 | [] | 2026-07-28T14:57:40Z | https://github.com/siowmeng/TraCeS | null |
[
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-05T18:59:49Z | false | 1 | 1,250,740,026 | https://wuyushuwys.github.io/OmniMem/ | Python | null | [
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] | include | ml-contribution-v5 | 12 | [] | 2026-10-16T21:56:41Z | https://github.com/wuyushuwys/OmniMem | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-27T08:27:19Z | Official implementation of "Understanding and Mitigating Token-Pruning-Induced Vulnerabilities in VLMs" (ICML 2026). | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T14:34:36Z | false | 0 | 1,251,088,306 | null | null | MIT | [
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] | liongliong/SAP | [
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] | 2 | 2026-11-02T21:04:38Z | [] | 2026-06-12T09:04:05Z | [
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] | include | ml-contribution-v5 | 8 | [] | 2026-10-08T06:59:40Z | https://github.com/liongliong/SAP | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-27T14:30:25Z | [ICML 2026] Official implementation of SLOT-Align: Distribution Alignment for One-Shot Federated Learning via Optimal Transport | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T10:12:50Z | false | 0 | 1,251,391,830 | null | Python | Apache-2.0 | [
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] | 1 | 2026-09-26T10:12:50Z | [] | 2026-09-20T18:20:06Z | [
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] | include | ml-contribution-v5 | 3 | [] | 2026-09-25T12:49:21Z | https://github.com/daniebera/SLOT-Align | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-27T16:59:20Z | Official implementation for the ICML 2026 paper “Policy-Driven World Model Adaptation for Robust Offline Model-based Reinforcement Learning”. | [
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] | 5 | 2026-10-08T18:15:14Z | [] | 2026-08-04T06:13:29Z | [
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] | include | ml-contribution-v5 | 3 | [] | 2026-09-11T03:52:21Z | https://github.com/Agentic-Intelligence-Lab/ROMBRL | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-28T02:55:52Z | Code for paper "ClothTransformer: Unified Latent-Space Transformers for Scalable Cloth Simulation (NeurIPS 2026)" | [
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] | 1 | 2026-10-02T15:56:46Z | [] | 2026-09-26T11:48:11Z | [
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] | include | ml-contribution-v5 | 29 | [] | 2026-09-26T11:48:15Z | https://github.com/YuCrazing/ClothTransformer | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-28T04:03:23Z | Code for paper "MGGCL: Motif-Guided Graph Contrastive Learning for Recommendation" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 0 | 1,251,957,457 | null | null | null | [
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] | 4 | 2026-09-26T10:28:13Z | [] | 2026-05-28T04:24:39Z | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-05-28T04:24:43Z | https://github.com/LiPang-PhD/MGGCL-code | null |
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[
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[
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 1 | 1,253,142,881 | null | Python | null | [
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] | 4 | 2026-09-25T16:01:25Z | [] | 2026-07-15T14:33:59Z | [
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] | include | ml-contribution-v5 | 5 | [] | 2026-08-11T22:16:04Z | https://github.com/yeshenpy/Nevo-CRL | null |
[
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[
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[
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] | [] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-29T11:19:29Z | [ICML 2026 Oral] Official implementation of Rex: A Family of Reversible Exponential (Stochastic) Runge-Kutta Solvers | [
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[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-30T11:30:07Z | The code for paper "SILC_G: Semantic-aware Integration with Learning and Contrastive Graph Neural Networks". | [
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] | 1 | 2026-10-25T15:11:36Z | [] | 2026-09-08T12:43:25Z | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-09-08T12:44:38Z | https://github.com/tony-soochow/SILC_G | null |
[
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] | [
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] | [] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-30T15:35:43Z | [SIGGRAPH 2026 Posters] - BlendAnything: A Blender Plugin for Cross-Topology Motion Blending - Official implementation | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-30T18:03:22.392067Z | false | 2 | 1,254,457,383 | https://mmlab-cv.github.io/BlendAnything/ | Python | NOASSERTION | [
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] | include | ml-contribution-v5 | 10 | [
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] | 2026-10-01T14:48:07Z | https://github.com/mmlab-cv/BlendAnything | {"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["diffusion-models"]} |
[
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] | [
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] | true | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-01T09:23:22Z | Official implementation of the paper: Sub-JEPA: Subspace Gaussian Regularization for Stable End-to-End World Models. | [
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[
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T09:41:18Z | false | 13 | 1,256,286,501 | null | Python | MIT | [
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] | dcharatan/millivid | [
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] | 9 | 2026-10-07T17:02:17Z | [] | 2026-09-24T16:46:06Z | [
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] | include | ml-contribution-v5 | 115 | [] | 2026-09-30T09:07:27Z | https://github.com/dcharatan/millivid | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-02T06:09:26Z | Official implementation of "When Gradient Boosting Meets Adapter: Exploring Weak Learners for Parameter-Efficient Fine-tuning of LLMs", accepted at KDD 2026. | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 0 | 1,256,835,948 | null | null | Apache-2.0 | [
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] | yifeiacc/XGBLoRA | [
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] | 2 | 2026-10-28T17:27:44Z | [] | 2026-06-02T06:49:32Z | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-06-02T06:49:37Z | https://github.com/yifeiacc/XGBLoRA | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-02T07:30:10Z | This is the official code repository for our paper "Understanding Concept-Driven Diffusion Model with Uncertainty" | [
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"diffusion"
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] | 1 | 2026-11-02T21:04:38Z | [] | 2026-06-02T07:30:11Z | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-06-02T07:30:11Z | https://github.com/xukun12138/UCDDMU | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-02T17:46:19Z | News-Informed Cross-sectional Temporal Meta-learning (NICTM), our new novel method for Financial Forecasting | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 0 | 1,257,462,261 | null | Python | null | [
"meta-learning",
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] | varunbly/NICTM | [
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] | 3 | 2026-09-27T16:09:59Z | [] | 2026-06-26T11:23:18Z | [
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] | [] | ok | 2026-09-26T20:25:07Z | varunbly/NICTM | specific-method-with-novelty-claim | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-06-26T11:26:31Z | https://github.com/varunbly/NICTM | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-05T15:42:53Z | This repository contains the official implementation and code associated with the paper: "End-to-End Optimization of AI Pipelines for Mammography: A Sequential Assessment from Data Filtering to Vision-Language Models." | [
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] | 2 | 2026-10-23T15:59:01Z | [] | 2026-06-05T15:53:31Z | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-06-05T15:53:40Z | https://github.com/Sofya-Z/mammography-ai-pipeline | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-06T14:53:51Z | Official implementation of the paper: Enhanced Medical Image Segmentation Using Mixer-Block Integration with Channel Attention Mechanism | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 0 | 1,261,337,835 | null | Python | null | [
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] | JaouadT/Mixer-Unet | [
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] | 1 | 2026-10-08T18:15:14Z | [] | 2026-09-19T19:46:10Z | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-09-19T19:46:14Z | https://github.com/JaouadT/Mixer-Unet | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-06T19:04:12Z | Code for paper INFER: Inductive Feature Engineering via Hierarchical Reinforcement Learning | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 0 | 1,261,493,771 | null | Python | null | [
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] | 4 | 2026-09-26T10:28:13Z | [] | 2026-06-06T19:07:42Z | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-06-06T19:07:47Z | https://github.com/infer2026/INFER | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-07T13:15:54Z | Official Implementation of paper "AFFormer: Adaptive Feature Fusion Transformer for V2X Cooperative Perception Under Channel Impairments". | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-24T14:31:13Z | false | 1 | 1,262,018,003 | null | Python | MIT | [
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] | icclab5188/AFFormer | [
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] | 1 | 2026-10-24T14:31:13Z | [] | 2026-06-07T13:28:50Z | [
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] | include | ml-contribution-v5 | 1 | [] | 2026-08-30T04:01:04Z | https://github.com/icclab5188/AFFormer | null |
[
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] | [
"multimodal.vision-language",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-08T03:58:05Z | Official simulation code for ICML 2026 paper "Neural Implicit Action Fields: From Discrete Waypoints to Continuous Functions for Vision-Language-Action Models". | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 0 | 1,262,510,180 | null | Python | MIT | [
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] | liuhaoyunBUPT/NIAF.sim | [
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] | 2 | 2026-10-23T15:59:01Z | [] | 2026-06-30T12:12:34Z | [
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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 | [] | 2026-06-30T12:12:42Z | https://github.com/liuhaoyunBUPT/NIAF.sim | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-08T18:56:46Z | A new direct preference optimization algorithm that simultaneously align LLM with multiple positive and negative responses | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 0 | 1,263,236,917 | null | Python | null | [
"alignment",
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] | yaochenzhu/Mult_DPO | [
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] | 5 | 2026-10-20T15:59:06Z | [] | 2026-06-08T18:57:57Z | [
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] | 3755cd6bdeb0a160ea985ab9f888910fe866b564 | 2026-09-26T20:25:07Z | gh-ml-readme-evidence-v2 | "3755cd6bdeb0a160ea985ab9f888910fe866b564" | [
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"method-tied-novelty-claim",
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] | include | ml-contribution-v5 | 4 | [] | 2026-06-15T15:03:09Z | https://github.com/yaochenzhu/Mult_DPO | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-09T19:45:34Z | A new kind of world model: the PSD coupling kernel K(T,T') of admissible possible worlds. Diagonal = prediction, off-diagonal = counterfactual coupling. Built in public. | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-09T17:31:36Z | false | 1 | 1,264,339,108 | null | Python | MIT | [
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] | fabio-rovai/worldkernel | [
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] | 1 | 2026-10-09T17:31:36Z | [] | 2026-08-11T10:44:54Z | [
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] | include | ml-contribution-v5 | 0 | [
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[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-10T07:43:44Z | Official implementation of "Diffusion World Model: Future Modeling Beyond Step-by-Step Rollout for Offline Reinforcement Learning" (arXiv:2402.03570) | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 0 | 1,264,808,491 | null | Python | null | [
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] | quantumiracle/DiffusionWorldModel | [
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] | 4 | 2026-10-08T18:15:14Z | [] | 2026-06-10T08:19:39Z | [
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] | include | ml-contribution-v5 | 2 | [] | 2026-07-23T00:40:00Z | https://github.com/quantumiracle/DiffusionWorldModel | null |
[
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] | [
"general.neural-processes"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-11T04:29:06Z | Official code for "Spectral Convolutional Conditional Neural Processes" (NeurIPS 2025) | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T16:16:53Z | false | 0 | 1,265,777,975 | null | Python | MIT | [
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] | peiman-m/SConvCNP | [
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] | 4 | 2026-10-14T16:10:46Z | [] | 2026-06-11T04:29:09Z | [
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] | include | ml-contribution-v5 | 0 | [
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] | include | ml-contribution-v5 | 31 | [] | 2026-09-18T02:42:06Z | https://github.com/nilskiKonjIzDunava/fprm | null |
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-12T17:14:55Z | Code for our paper: A Gradient Perspective on RLVR Stability and Winner Advantage Policy Optimization | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-13T00:34:05Z | Official Implementation for ICML2026 "Temporal Difference Learning for Diffusion Models" | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-30T17:16:29Z | false | 0 | 1,267,896,643 | null | Python | MIT | [
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] | include | ml-contribution-v5 | 6 | [] | 2026-09-16T08:12:45Z | https://github.com/StephenYing/Temporal_Difference_Learning_for_Diffusion_Models | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-13T09:38:29Z | Official implementation of "LaGuardia: Language-Guided Adaptive Distillation from Pathology Foundation Models" (MICCAI 2026) | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T16:16:53Z | false | 0 | 1,268,232,183 | null | Python | Apache-2.0 | [
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] | include | ml-contribution-v5 | 2 | [
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] | 2026-07-20T14:22:07Z | https://github.com/hvcl/LaGuadia | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-16T03:13:17Z | [EMNLP 2026] An official Implementation of "Beyond the Current Observation: Evaluating Multimodal Large Language Models in Controllable Non-Markov Games" | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 0 | 1,270,768,211 | https://internlm.github.io/RNGBench/ | Python | null | [
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] | InternLM/RNGBench | [
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] | 1 | 2026-10-23T15:59:01Z | [] | 2026-10-14T09:14:18Z | [
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] | include | ml-contribution-v5 | 42 | [] | 2026-09-28T14:27:06Z | https://github.com/InternLM/RNGBench | null |
[
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 0 | 1,270,778,262 | null | Python | null | [
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] | dunnolab/harmony | [
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] | 4 | 2026-10-15T15:20:52Z | [] | 2026-06-30T21:44:17Z | [
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] | include | ml-contribution-v5 | 17 | [] | 2026-09-11T16:54:45Z | https://github.com/dunnolab/harmony | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-16T05:53:00Z | This repository contains the official implementation of our MICCAI 2026 paper "ProMoE-FL: Prototype-conditioned Mixture of Experts for Multimodal Federated Learning with Missing Modalities" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-06T17:10:23Z | false | 0 | 1,270,866,205 | null | Python | MIT | [
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] | bhattarailab/ProMoE-FL | [
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] | 3 | 2026-10-19T16:36:56Z | [] | 2026-10-07T13:46:39Z | [
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] | include | ml-contribution-v5 | 2 | [] | 2026-10-15T04:07:08Z | https://github.com/bhattarailab/ProMoE-FL | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-16T12:42:56Z | Fair benchmark + task-queue for model-based RL abstraction research (paper B: can a new world-model abstraction beat TD-MPC2?) | [
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] | SuuTTT/wm-bench | [
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] | 1 | 2026-10-12T20:31:19Z | [] | 2026-06-16T12:43:02Z | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-06-16T12:43:07Z | https://github.com/SuuTTT/wm-bench | null |
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-17T11:32:56Z | Official PyTorch Implementation of Paper "The Surprising Effectiveness of Video Diffusion Models for Hand Motion Reconstruction" | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 7 | 1,272,197,527 | null | null | null | [
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] | 4 | 2026-10-30T17:16:29Z | [] | 2026-07-13T05:13:20Z | [
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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 | 194 | [] | 2026-10-25T11:26:29Z | https://github.com/NTUYWANG103/ViDiHand | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-18T06:35:40Z | The official code for paper of A morphological difference and statistically sparse transformer-based deep neural network for medical image segmentation | [
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] | hntoncy/MD-SSFormer | [
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] | 1 | 2026-10-08T18:15:14Z | [] | 2026-06-27T10:08:39Z | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-06-27T10:08:43Z | https://github.com/hntoncy/MD-SSFormer | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-18T14:52:49Z | [ECCV 2026] Official code for paper: ELDiff: When Evidential Learning Meets Text-to-Image Diffusion | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-21T15:48:06Z | false | 0 | 1,273,468,804 | null | Python | null | [
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] | QingtaoPan/ELDiff | [
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] | 1 | 2026-10-21T15:48:06Z | [] | 2026-06-23T20:52:00Z | [
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] | include | ml-contribution-v5 | 2 | [] | 2026-06-29T18:21:45Z | https://github.com/QingtaoPan/ELDiff | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-18T15:33:33Z | [ICML 2026] Source code for our paper "Finding DoRI: Discovery of Retained Images in Diffusion Models" | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-18T14:44:29Z | false | 0 | 1,273,506,215 | https://arxiv.org/abs/2507.16880 | Python | MIT | [
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] | sprintml/finding_dori | [
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] | 1 | 2026-10-18T14:44:29Z | [] | 2026-07-02T09:05:54Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 1 | [] | 2026-07-31T03:21:01Z | https://github.com/sprintml/finding_dori | null |
[
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] | [
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] | [
"timeseries.state-space-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-19T10:22:18Z | Official implementation of "Demographic-Conditioned State Space Models for ECG-Based Age Estimation", accepted at MICCAI 2026. | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 1,274,251,551 | null | Python | MIT | [
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] | B-Bracke/demographic-ssm-ecg-age | [
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] | 1 | 2026-10-04T15:58:58Z | [] | 2026-06-19T10:23:49Z | [
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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 | [] | 2026-06-19T12:35:20Z | https://github.com/B-Bracke/demographic-ssm-ecg-age | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-19T11:46:15Z | [ECCV 2026] Official implementation of MixTTA: Low-Rank Cross-Channel Mixing for Reliable Test-Time Adaptation | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T10:12:50Z | false | 0 | 1,274,317,622 | null | Python | BSD-3-Clause | [
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] | include | ml-contribution-v5 | 2 | [] | 2026-10-11T08:58:09Z | https://github.com/delta6189/MixTTA | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-19T15:20:26Z | Source code for paper: "Multi-Modal Hypergraph Self-Supervised Learning for Brain Disease Diagnosis" | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-06T17:10:23Z | false | 0 | 1,274,499,866 | null | Python | null | [
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] | 1 | 2026-10-06T17:10:23Z | [] | 2026-06-20T05:10:14Z | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-06-20T05:10:17Z | https://github.com/Yizhao-dong/BrainMHSL | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-20T04:39:19Z | Souce code for paper "SPARK: Adaptive Parametric Knowledge Participation for Faithful Retrieval-Augmented Generation" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-18T13:47:34Z | false | 1 | 1,274,956,243 | null | Python | MIT | [
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] | 1 | 2026-10-18T13:47:34Z | [] | 2026-10-15T11:11:21Z | [
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] | include | ml-contribution-v5 | 10 | [] | 2026-10-15T11:11:25Z | https://github.com/OpenBMB/SPARK | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-20T08:15:44Z | Official Implementation of ECCV 2026 page: "CulinaryCut: A Physics-aware Vision-Language-Action Benchmark for Food Cutting via Material Point Method" | [
"computer-vision",
"language",
"multimodal",
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 0 | 1,275,079,720 | null | HTML | null | [
"contrastive-learning",
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] | deepvelop99/culinary.github.io | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-25T15:11:36Z | [] | 2026-07-07T13:30:06Z | [
"multimodal.vision-language"
] | 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 | 1 | [] | 2026-07-07T13:31:38Z | https://github.com/deepvelop99/culinary.github.io | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"multimodal",
"vision"
] | [
"diffusion",
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] | [
"description",
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"repository-metadata"
] | [
"multimodal.text-to-image",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-20T16:06:34Z | [ECCV-2026] Official implementation for "Analyzing and Improving Training-Free Fast Sampling of Text-to-Image Diffusion Models". | [
"generative-ai",
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] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 0 | 1,275,382,990 | null | Python | Apache-2.0 | [
"diffusion",
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] | zju-pi/TORS | [
"description",
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"query-match",
"repository-metadata"
] | 5 | 2026-10-21T15:48:06Z | [] | 2026-06-26T04:05:37Z | [
"multimodal.text-to-image"
] | 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 | 4 | [] | 2026-09-22T07:13:28Z | https://github.com/zju-pi/TORS | null |
[
"generative-ai",
"generative-modeling",
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] | [
"diffusion",
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] | [
"description",
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"repository-metadata"
] | [
"llm.transformers",
"recall.name.diffusion-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-21T05:46:24Z | [ECCV 2026] Official implementation of "Fill2SR: Repurposing Inpainting Diffusion Transformers for Real-World Super-Resolution". | [
"generative-ai",
"generative-modeling"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-23T14:45:14Z | false | 0 | 1,275,774,494 | https://xingfu-yi.github.io/Fill2SR/ | Python | Apache-2.0 | [
"diffusion"
] | Xingfu-Yi/Fill2SR | [
"description",
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"query-match",
"repository-metadata"
] | 2 | 2026-10-29T16:41:07Z | [] | 2026-10-11T02:08:18Z | [
"recall.name.diffusion-model"
] | 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 | 7 | [] | 2026-10-29T09:14:08Z | https://github.com/Xingfu-Yi/Fill2SR | null |
[
"machine-learning"
] | [
"adaptation",
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] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"specialized.test-time-adaptation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-21T06:45:33Z | [MICCAI 2026] Official implementation of ECFR: Entropy-Consistency Flow Rectification in Test-Time Adaptation for Medical Foundation Models. | [
"machine-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-10T16:53:26Z | false | 0 | 1,275,807,220 | null | Python | MIT | [
"adaptation",
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] | EndoluminalSurgicalVision-IMR/ECFR | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-10T16:53:26Z | [] | 2026-06-21T10:12:42Z | [
"specialized.test-time-adaptation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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] | include | ml-contribution-v5 | 0 | [] | 2026-06-21T12:57:37Z | https://github.com/EndoluminalSurgicalVision-IMR/ECFR | null |
[
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] | [
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] | [
"medical.medical-imaging",
"timeseries.state-space-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-22T14:27:36Z | Official implementation of "SpikeMamba: Spike-Driven State Space Models for Energy-Efficient Biomedical Sequence Modeling" (MICCAI 2026) | [
"health-and-biomedicine",
"medical-imaging"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 2 | 1,277,043,339 | null | Python | Apache-2.0 | [
"image-analysis"
] | Brain-Inspired-Computing-Lab/SpikeMamba | [
"description",
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] | 6 | 2026-10-24T13:28:22Z | [] | 2026-10-13T08:41:04Z | [
"medical.medical-imaging"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 2 | [] | 2026-10-21T17:09:35Z | https://github.com/Brain-Inspired-Computing-Lab/SpikeMamba | null |
[
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] | [
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"probable.classical-xgboost-readme"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-23T04:46:51Z | Blockchain-empowered lightweight LightGBM IDS for IoMT networks. Couples real-time ML intrusion detection with Pure Chain smart-contract alert logging and automated firewall enforcement into a verifiable detection-to-response pipeline. Official code for our IEEE IoT Journal paper. | [
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] | [
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-29T16:41:07Z | false | 0 | 1,277,657,332 | null | Jupyter Notebook | MIT | [
"ensemble-learning",
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] | Subroto43/lightgbm-blockchain-iomt-ids | [
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"repository-metadata"
] | 3 | 2026-11-02T20:01:20Z | [] | 2026-10-13T06:56:22Z | [
"probable.classical-xgboost-readme"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-10-13T06:56:25Z | https://github.com/Subroto43/lightgbm-blockchain-iomt-ids | null |
[
"generative-ai",
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] | [
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] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.preference-optimization"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-23T05:26:35Z | Official code for "Beyond Linear Shortcuts: Rectifying Diffusion Preference Optimization with Intrinsic Generative Geometry", ECCV 2026 | [
"generative-ai",
"generative-modeling",
"language",
"safety"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 0 | 1,277,683,833 | null | null | null | [
"alignment",
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] | wangpin-aier/TCPO | [
"description",
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"repository-metadata"
] | 1 | 2026-10-23T15:59:01Z | [] | 2026-06-30T04:41:09Z | [
"llm.preference-optimization"
] | 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 | [] | 2026-07-04T15:34:28Z | https://github.com/wangpin-aier/TCPO | null |
[
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] | [
"efficiency.knowledge-distillation",
"general.bayesian-deep-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-23T14:35:13Z | Official code release for 'SGD-based Knowledge Distillation with Bayesian Teachers: Theory and Guidelines' (ICLR 2026). | [
"efficient-ml",
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-03T15:45:52Z | false | 0 | 1,278,177,498 | null | Python | null | [
"distillation",
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] | itaimorad/bayesian-kd | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-10T16:53:26Z | [] | 2026-06-23T14:53:18Z | [
"efficiency.knowledge-distillation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-06-23T14:53:22Z | https://github.com/itaimorad/bayesian-kd | null |
[
"computer-vision",
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] | [
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] | [
"description",
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"repository-metadata"
] | [
"efficiency.knowledge-distillation",
"recall26.visual-grounding"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-23T14:43:51Z | [ECCV 2026] Official code for "Token-level Response-visual Attention Guidance for Multimodal LLM Knowledge Distillation" | [
"computer-vision",
"multimodal-learning",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 1 | 1,278,185,167 | null | null | null | [
"distillation",
"knowledge-distillation",
"referring-expression-comprehension",
"vision-language-learning",
"visual-grounding"
] | jhjangjh/TRAG | [
"description",
"query-match",
"repository-metadata"
] | 6 | 2026-10-15T15:20:52Z | [] | 2026-07-07T08:48:20Z | [
"recall26.visual-grounding"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 7 | [] | 2026-07-22T06:20:04Z | https://github.com/jhjangjh/TRAG | null |
[
"reinforcement-learning"
] | [
"policy-gradient",
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"self-attention"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.policy-gradient"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-23T16:32:04Z | This repository contains the code for our paper "Policy Gradient with Self-Attention for Model-Free Distributed Nonlinear Multi-Agent Games", published and presented at IEEE/RSJ IROS, 2026 | [
"reinforcement-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 0 | 1,278,280,013 | null | Python | NOASSERTION | [
"policy-gradient",
"policy-optimization",
"self-attention"
] | EduardoSebastianRodriguez/Self-Attention-for-Multi-agent-Games | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2026-07-07T09:34:25Z | [
"rl.policy-gradient"
] | 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 | 0 | [] | 2026-07-07T09:34:39Z | https://github.com/EduardoSebastianRodriguez/Self-Attention-for-Multi-agent-Games | null |
[
"computer-vision",
"generative-modeling"
] | [
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] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.image-super-resolution"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-24T07:08:21Z | [ECCV 2026] Official code repository for "FreqOrtho-SR: Frequency-Guided Orthogonal Expert Learning for Real-World Image Super-Resolution" | [
"computer-vision",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 2 | 1,278,874,815 | null | Python | Apache-2.0 | [
"diffusion",
"mixture-of-experts",
"restoration",
"super-resolution"
] | sonhm3029/FreqOrtho-SR | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-31T16:49:26Z | [] | 2026-08-27T08:09:00Z | [
"vision.image-super-resolution"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 16 | [
"diffusion",
"mixture-of-experts",
"orthogonal-projection",
"super-resolution"
] | 2026-10-09T07:18:25Z | https://github.com/sonhm3029/FreqOrtho-SR | null |
[
"computer-vision",
"multi-agent-systems",
"reinforcement-learning"
] | [
"action-recognition",
"centralized-training",
"multi-agent-reinforcement-learning",
"reinforcement-learning",
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] | [
"description",
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"query-match",
"repository-metadata"
] | [
"rl.multiagent",
"vision.action-recognition"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-25T07:09:09Z | Code for paper "Potentially Optimal Joint Actions Recognition for Cooperative Multi-Agent Reinforcement Learning" | [
"computer-vision",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 1,279,988,119 | null | Python | MIT | [
"action-recognition",
"reinforcement-learning",
"video-understanding"
] | tiev-tongji/potentially-optimal-joint-actions-recognition-marl | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-08T18:15:14Z | [] | 2026-06-25T07:23:30Z | [
"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 | 0 | [] | 2026-06-25T07:23:36Z | https://github.com/tiev-tongji/potentially-optimal-joint-actions-recognition-marl | null |
[
"multimodal",
"reinforcement-learning",
"robotics",
"robotics-and-control"
] | [
"environment-modeling",
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] | [
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"github-topics",
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"query-match",
"repository-metadata"
] | [
"multimodal.world-models",
"robotics.robotics-topic"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-25T17:30:13Z | Official code repository for the paper "Hallucination in World Models is Predictable and Preventable". | [
"robotics",
"robotics-and-control"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T14:34:36Z | false | 7 | 1,280,541,353 | http://www.nicklashansen.com/mmbench2/ | Python | MIT | [
"robot-learning"
] | nicklashansen/mmbench2 | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-19T16:36:56Z | [] | 2026-10-02T19:02:22Z | [
"robotics.robotics-topic"
] | 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 | 111 | [
"dreamer4",
"hallucination-detection",
"robotics",
"world-models"
] | 2026-10-16T08:47:33Z | https://github.com/nicklashansen/mmbench2 | null |
[
"generative-ai",
"generative-modeling",
"multimodal",
"video"
] | [
"diffusion",
"text-to-video",
"video-generation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.text-to-video"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-26T05:35:20Z | [ECCV 2026] Official Implementation of EraseSAE: Sparse Autoencoder Based Concept Erasure for Text-to-Video Diffusion Models | [
"generative-ai",
"generative-modeling",
"multimodal",
"video"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 0 | 1,280,972,943 | null | Python | null | [
"diffusion",
"text-to-video",
"video-generation"
] | HiDream-ai/EraseSAE | [
"description",
"query-match",
"repository-metadata"
] | 4 | 2026-10-03T15:45:52Z | [] | 2026-09-04T07:20:22Z | [
"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 | [] | 2026-09-21T10:57:40Z | https://github.com/HiDream-ai/EraseSAE | null |
[
"computer-vision",
"information-retrieval",
"multimodal-learning",
"natural-language-processing"
] | [
"dense-retrieval",
"rag",
"referring-expression-comprehension",
"retrieval-augmented-generation",
"vision-language-learning",
"visual-grounding"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"nlp.dense-retrieval",
"recall26.visual-grounding"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-26T06:35:48Z | [ECCV 2026] Official implementation of MG²-RAG: Multi-Granularity Graph for Multimodal Retrieval-Augmented Generation | [
"information-retrieval",
"multimodal-learning",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-10T16:53:26Z | false | 2 | 1,281,015,568 | https://daboolu.github.io/MG2-RAG/examples/web/ | Python | MIT | [
"dense-retrieval",
"rag",
"retrieval-augmented-generation"
] | Daboolu/MG2-RAG | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-11-01T17:09:41Z | [] | 2026-08-31T16:45:54Z | [
"nlp.dense-retrieval"
] | 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 | [] | 2026-10-14T07:24:21Z | https://github.com/Daboolu/MG2-RAG | null |
[
"embodied-ai",
"generative-modeling",
"reinforcement-learning",
"robotics"
] | [
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"imitation-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"robotics.diffusion-policy"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-26T13:55:21Z | Official implementation of Diffusion Policy Policy Optimization, arxiv 2024 | [
"embodied-ai",
"generative-modeling",
"reinforcement-learning",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 0 | 1,281,381,713 | https://diffusion-ppo.github.io | Python | MIT | [
"diffusion",
"diffusion-policy",
"imitation-learning"
] | V373/dppo_reward | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-09-26T14:50:46Z | [] | 2026-06-27T09:24:45Z | [
"robotics.diffusion-policy"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-06-27T09:24:48Z | https://github.com/V373/dppo_reward | null |
[
"computer-vision",
"language",
"machine-learning-systems",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"vision"
] | [
"contrastive-learning",
"image-text",
"pruning",
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] | [
"description",
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] | [
"multimodal.vision-language",
"multimodal.vlm"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-27T04:51:26Z | Official code for "Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models" (ECCV 2026) | [
"computer-vision",
"language",
"machine-learning-systems",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-17T13:17:44Z | false | 0 | 1,281,919,633 | null | Jupyter Notebook | null | [
"contrastive-learning",
"pruning",
"vision-language-model"
] | cau-hai-lab/PORTA | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-24T14:31:13Z | [] | 2026-09-02T11:18:01Z | [
"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 | 4 | [] | 2026-09-17T07:49:32Z | https://github.com/cau-hai-lab/PORTA | null |
[
"cybersecurity",
"decision-making",
"privacy-and-federated-learning",
"reinforcement-learning"
] | [
"contextual-bandits",
"federated-learning",
"hierarchical-clustering",
"multi-armed-bandits"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.bandits"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-27T07:15:50Z | A robust Federated Learning framework implementing the novel FedCADS-UCB algorithm. Engineered for resilient client selection using CUSUM drift detection, adaptive multi-armed bandits, and hierarchical clustering to maintain high accuracy (>95%) during concept drift and label poisoning attacks. | [
"cybersecurity",
"decision-making",
"privacy-and-federated-learning",
"reinforcement-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-03T15:45:52Z | false | 0 | 1,281,994,999 | null | Python | MIT | [
"contextual-bandits",
"federated-learning",
"hierarchical-clustering",
"multi-armed-bandits"
] | rishidatt2006-gif/FedCADS-UCB-Client-Selection | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-03T15:45:52Z | [] | 2026-06-28T05:55:02Z | [
"rl.bandits"
] | 7fb58e731aa11f67dd61283943fb8343d28f3636 | 2026-10-03T17:01:43Z | gh-ml-readme-evidence-v2 | "7fb58e731aa11f67dd61283943fb8343d28f3636" | [
"installation",
"other",
"references"
] | [
"ml-method-context"
] | ok | 2026-10-03T17:01:43Z | rishidatt2006-gif/FedCADS-UCB-Client-Selection | specific-method-with-novelty-claim | [
"contribution-language",
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue"
] | include | ml-contribution-v5 | 1 | [
"client-selection",
"concept-drift",
"cusum",
"cybersecurity",
"federated-learning",
"multi-armed-bandit",
"pytorch"
] | 2026-06-29T14:32:31Z | https://github.com/rishidatt2006-gif/FedCADS-UCB-Client-Selection | null |
[
"computer-vision",
"embodied-ai",
"language",
"multimodal",
"multimodal-learning",
"robotics",
"vision"
] | [
"contrastive-learning",
"robot-learning",
"vision-language-action",
"vision-language-model"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.vision-language",
"robotics.visual-language-action"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-27T09:41:32Z | [ICML 2026 Oral] Official Implementation for From Abstraction to Instantiation: Learning Behavioral Representation for Vision-Language-Action Model | [
"computer-vision",
"language",
"multimodal",
"multimodal-learning",
"vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T14:50:46Z | false | 0 | 1,282,077,672 | null | Python | Apache-2.0 | [
"contrastive-learning",
"vision-language-model"
] | iLearn-Lab/ICML26-BehaviorVLA | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-24T14:31:13Z | [] | 2026-07-05T05:47:48Z | [
"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"
] | include | ml-contribution-v5 | 11 | [] | 2026-09-21T13:59:49Z | https://github.com/iLearn-Lab/ICML26-BehaviorVLA | null |
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