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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
[
"natural-language-processing",
"reinforcement-learning"
] | [
"machine-translation",
"neural-machine-translation",
"reinforcement-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"nlp.machine-translation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-04-14T15:59:37Z | [EMNLP'25] Code for paper "MT-R1-Zero: Advancing LLM-based Machine Translation via R1-Zero-like Reinforcement Learning" | [
"natural-language-processing",
"reinforcement-learning"
] | [
"large-language-model",
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 11 | 966,236,610 | null | Python | Apache-2.0 | [
"machine-translation",
"neural-machine-translation",
"reinforcement-learning"
] | fzp0424/MT-R1-Zero | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-18T14:44:29Z | [] | 2025-04-15T04:56:00Z | [
"nlp.machine-translation"
] | 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 | 69 | [] | 2026-10-17T07:33:28Z | https://github.com/fzp0424/MT-R1-Zero | null |
[
"artificial-intelligence",
"computer-vision",
"generative-modeling",
"machine-learning"
] | [
"diffusion",
"interpretability",
"mechanistic-interpretability"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"specialized.mechanistic-interpretability"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-04-14T23:20:04Z | [MIV-CVPR 2025] Official implementation of "Language-Guided Trajectory Traversal in Disentangled Stable Diffusion Latent Space for Factorized Medical Image Generation" | [
"artificial-intelligence",
"computer-vision",
"generative-modeling",
"machine-learning"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-28T18:33:08Z | false | 0 | 966,435,287 | https://tehraninasab.github.io/sd-latent-traversal/ | Jupyter Notebook | MIT | [
"diffusion",
"interpretability",
"mechanistic-interpretability"
] | tehraninasab/sd-latent-traversal | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-02T16:59:40Z | [] | 2026-01-27T20:48:10Z | [
"specialized.mechanistic-interpretability"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 4 | [
"controlled-generation",
"diffusion-models",
"disentanglement",
"genai",
"generative-ai",
"stable-diffusion"
] | 2026-04-29T03:29:21Z | https://github.com/tehraninasab/sd-latent-traversal | null |
[
"biology",
"computational-biology",
"health-and-biomedicine",
"structural-biology"
] | [
"protein-folding",
"structure-prediction"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"bio.protein-folding"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-04-15T08:47:19Z | Official implementation for our paper: Repurposing AlphaFold3-like Protein Folding Models for Antibody Sequence and Structure Co-design | [
"biology",
"computational-biology",
"health-and-biomedicine",
"structural-biology"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 8 | 966,651,445 | null | Python | MIT | [
"protein-folding",
"structure-prediction"
] | yangnianzu0515/MFDesign | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 8 | 2026-10-04T15:58:58Z | [] | 2026-01-08T18:22:36Z | [
"bio.protein-folding"
] | 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 | 48 | [] | 2026-07-29T05:52:55Z | https://github.com/yangnianzu0515/MFDesign | null |
[
"forecasting",
"machine-learning",
"time-series",
"time-series-and-forecasting"
] | [
"curriculum-learning",
"forecasting",
"multivariate-forecasting",
"sequence-modeling",
"training-strategy",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"specialized.curriculum-learning",
"timeseries.forecasting",
"timeseries.multivariate"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-04-16T16:48:58Z | The official implementation of the paper: "You Only Adapt Once: An Adaptive Transformer for Dynamic Multivariate Time Series Forecasting across Time-varying Topologies and Multi-patterns" | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 0 | 967,567,771 | https://www.sciencedirect.com/science/article/pii/S0020025525004827 | Python | MIT | [
"forecasting",
"sequence-modeling",
"transformer"
] | JasonStraka/YOAO | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-28T17:27:44Z | [] | 2025-05-29T06:58:06Z | [
"timeseries.forecasting"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [] | 2025-07-09T05:14:38Z | https://github.com/JasonStraka/YOAO | null |
[
"sequence-modeling",
"time-series"
] | [
"sequence-modeling",
"state-space-model"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"timeseries.state-space-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-04-17T04:08:44Z | code for paper: Interpretable Knowledge Tracing with Difficulty-Aware Attention and Selective State Space Model | [
"sequence-modeling",
"time-series"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 0 | 967,824,604 | null | Python | Apache-2.0 | [
"sequence-modeling",
"state-space-model"
] | mrsser/ASIKT | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-01T17:50:46Z | [] | 2026-02-03T08:01:07Z | [
"timeseries.state-space-models"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 10 | [] | 2026-09-03T12:41:57Z | https://github.com/mrsser/ASIKT | null |
[
"generative-ai",
"generative-modeling",
"graph-learning"
] | [
"diffusion",
"graph-diffusion",
"graph-generation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.graph-diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-04-17T13:34:36Z | The official implementation of 'Equivariant Denoisers Cannot Copy Graphs: Aligned your Graph Diffusion Models' (ICLR2025) | [
"generative-ai",
"generative-modeling",
"graph-learning"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 1 | 968,084,706 | https://aalto-quml.github.io/DiffAlign/ | Python | null | [
"diffusion",
"graph-diffusion",
"graph-generation"
] | Aalto-QuML/DiffAlign | [
"description",
"query-match",
"repository-metadata"
] | 5 | 2026-09-27T15:53:49Z | [] | 2026-06-04T12:04:36Z | [
"graph.graph-diffusion"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 8 | [] | 2026-09-21T12:49:12Z | https://github.com/Aalto-QuML/DiffAlign | null |
[
"computer-vision",
"embodied-ai",
"generative-ai",
"language",
"multimodal",
"multimodal-learning",
"robotics",
"vision"
] | [
"contrastive-learning",
"fine-tuning",
"instruction-tuning",
"robot-learning",
"vision-language-action",
"vision-language-model"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.instruction-tuning",
"multimodal.vision-language",
"robotics.visual-language-action"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-04-18T10:15:54Z | Official Code of CVPR 2025 paper "SOLAMI: Social Vision-Language-Action Modeling for Immersive Interaction with 3D Autonomous Characters" | [
"computer-vision",
"generative-ai",
"language",
"multimodal-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 8 | 968,569,419 | https://solami-ai.github.io/ | Python | MIT | [
"fine-tuning",
"instruction-tuning"
] | AlanJiang98/SOLAMI | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-24T14:31:13Z | [] | 2025-07-13T08:06:24Z | [
"llm.instruction-tuning"
] | 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 | 57 | [] | 2026-09-10T21:26:38Z | https://github.com/AlanJiang98/SOLAMI | null |
[
"autonomous-driving",
"computer-vision",
"multimodal",
"multimodal-learning",
"robotics",
"robotics-and-control"
] | [
"multimodal-learning",
"perception",
"planning",
"vision-language"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"robotics.autonomous-driving",
"vision.vision-language"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-04-19T19:47:27Z | This repo contains the code for paper "LightEMMA: Lightweight End-to-End Multimodal Model for Autonomous Driving" | [
"computer-vision",
"multimodal",
"multimodal-learning",
"robotics-and-control"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T16:16:53Z | false | 20 | 969,282,041 | null | Python | MIT | [
"multimodal-learning",
"vision-language"
] | michigan-traffic-lab/LightEMMA | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 8 | 2026-10-22T16:09:07Z | [] | 2025-11-19T18:14:37Z | [
"vision.vision-language"
] | 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 | 145 | [] | 2026-09-22T16:44:27Z | https://github.com/michigan-traffic-lab/LightEMMA | null |
[
"generative-ai",
"generative-modeling",
"vision"
] | [
"diffusion"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"llm.diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-04-20T03:05:12Z | Official code of FDS (CVPR 2025) | [
"generative-ai",
"generative-modeling",
"vision"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T16:52:35Z | false | 3 | 969,386,884 | https://ivrl.github.io/fds-webpage/ | Python | null | [
"diffusion"
] | IVRL/FDS | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 5 | 2026-09-27T15:53:49Z | [] | 2025-04-20T03:08:53Z | [
"llm.diffusion"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 11 | [
"diffusion-model",
"editing-image"
] | 2026-05-01T01:32:26Z | https://github.com/IVRL/FDS | null |
[
"distributed-ml",
"health-and-biomedicine",
"privacy-and-federated-learning"
] | [
"collaborative-learning",
"federated-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-04-20T17:55:05Z | This project implements a novel architecture combining Federated Learning (FL), Fog Computing, and Private Blockchain technology to deliver secure, scalable, and privacy-preserving predictive healthcare services using wearable IoT devices. | [
"distributed-ml",
"health-and-biomedicine",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-20T15:59:06Z | false | 1 | 969,685,561 | null | Python | null | [
"collaborative-learning",
"federated-learning"
] | Rach1810/federated-learning-blockchain-fog-healthcare | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-20T15:59:06Z | [] | 2025-04-20T18:39:56Z | [
"trust.federated-learning"
] | 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 | 1 | [] | 2025-07-31T04:55:34Z | https://github.com/Rach1810/federated-learning-blockchain-fog-healthcare | null |
[
"computational-science",
"physics"
] | [
"neural-operators",
"surrogate-modeling"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"science.neural-operators"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-04-21T17:47:35Z | This is the official code for L4DC 2025 paper "Safe PDE Boundary Control with Neural Operators" | [
"computational-science",
"physics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 0 | 970,240,709 | null | Jupyter Notebook | MIT | [
"neural-operators",
"surrogate-modeling"
] | intelligent-control-lab/safe-pde-control | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-09-28T18:33:08Z | [] | 2025-05-27T19:04:16Z | [
"science.neural-operators"
] | 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 | 6 | [] | 2026-02-18T13:43:42Z | https://github.com/intelligent-control-lab/safe-pde-control | null |
[
"information-retrieval",
"natural-language-processing"
] | [
"rag",
"retrieval-augmented-generation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"nlp.rag"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-04-22T14:31:56Z | This repository contains the implementation of HiSem-RAG, a novel hierarchical semantic-driven retrieval-augmented generation method, as proposed in our paper. | [
"information-retrieval",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-26T17:25:59Z | false | 1 | 970,785,381 | null | Python | NOASSERTION | [
"rag",
"retrieval-augmented-generation"
] | CharmingDaiDai/HiSem-RAG | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-26T17:25:59Z | [] | 2025-04-23T01:43:54Z | [
"nlp.rag"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 4 | [] | 2026-05-26T21:32:46Z | https://github.com/CharmingDaiDai/HiSem-RAG | null |
[
"3d",
"computer-vision",
"human-motion"
] | [
"3d-pose-estimation",
"keypoint-detection",
"pose-estimation",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.3d-human-pose",
"vision.pose-estimation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-04-23T11:24:32Z | Official PyTorch implementation of the paper "KASportsFormer: Kinematic Anatomy Enhanced Transformer for 3D Human Pose Estimation on Short Sports Scene Video". | [
"computer-vision"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T16:16:53Z | false | 0 | 971,339,120 | https://jw0r1n.github.io/KASportsFormer/ | Python | Apache-2.0 | [
"keypoint-detection",
"pose-estimation",
"transformer"
] | jw0r1n/KASportsFormer | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 7 | 2026-10-22T16:09:07Z | [] | 2025-07-29T06:04:37Z | [
"vision.pose-estimation"
] | 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 | 13 | [
"arxiv",
"pytorch"
] | 2026-04-23T01:39:25Z | https://github.com/jw0r1n/KASportsFormer | null |
[
"generative-modeling",
"health-and-biomedicine",
"medical-imaging",
"privacy-and-federated-learning",
"radiology"
] | [
"deep-learning",
"federated-learning",
"image-reconstruction",
"inverse-problems",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"medical.medical-image-reconstruction"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-04-23T12:50:59Z | Official implementation of FedGAT: Generative Autoregressive Transformers for Model-Agnostic Federated MRI Reconstruction (https://arxiv.org/abs/2502.04521) | [
"generative-modeling",
"health-and-biomedicine",
"medical-imaging",
"privacy-and-federated-learning",
"radiology"
] | [
"deep-learning",
"generative-model",
"machine-learning",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 1 | 971,385,128 | null | Python | MIT | [
"deep-learning",
"federated-learning",
"image-reconstruction",
"inverse-problems",
"transformer"
] | icon-lab/FedGAT | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 7 | 2026-10-02T16:59:40Z | [] | 2025-05-22T19:24:29Z | [
"medical.medical-image-reconstruction"
] | 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 | 21 | [
"artificial-intelligence",
"autoregressive",
"collaborative-learning",
"deep-learning",
"federated-learning",
"generative-models",
"heterogeneous",
"imaging",
"medical",
"model-agnostic",
"mri",
"privacy-preserving-machine-learning",
"python",
"pytorch",
"reconstruction",
"synthetic-im... | 2026-09-30T08:44:34Z | https://github.com/icon-lab/FedGAT | null |
[
"automl",
"optimization"
] | [
"hyperparameter-optimization",
"rag"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.hyperparameter-optimization"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-04-24T00:29:08Z | A domain-specific evaluation framework for assessing LLMs in tobacco research RAG systems. Implements automated metrics and hyperparameter optimization, demonstrating Mixtral 8x7B's superior performance (p<0.05) in accuracy and domain specificity. Includes reproducible code for the evaluation methodology presented in o... | [
"automl",
"optimization"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-03T15:45:52Z | false | 0 | 971,715,151 | null | Jupyter Notebook | null | [
"hyperparameter-optimization",
"rag"
] | sherifelmitwalli/tobacco-rag-evaluation | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-03T15:45:52Z | [] | 2025-06-08T12:48:42Z | [
"efficiency.hyperparameter-optimization"
] | 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 | [] | 2025-06-08T12:48:46Z | https://github.com/sherifelmitwalli/tobacco-rag-evaluation | null |
[
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"forecasting",
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-04-24T08:50:23Z | This repository contains the official implementation of the paper: Goal-Oriented Time-Series Forecasting: Foundation Framework Design | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 0 | 971,915,192 | https://arxiv.org/abs/2504.17493 | Jupyter Notebook | MIT | [
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] | netop-team/gotsf | [
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"query-match",
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] | 2 | 2026-10-26T17:25:59Z | [] | 2025-06-05T14:18:57Z | [
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] | include | ml-contribution-v5 | 13 | [
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] | 2026-10-08T07:54:20Z | https://github.com/netop-team/gotsf | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-04-24T09:47:14Z | Official Code Release for [SIGGRAPH 2025] RenderFormer: Transformer-based Neural Rendering of Triangle Meshes with Global Illumination | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 62 | 971,944,464 | https://microsoft.github.io/renderformer/ | Python | MIT | [
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] | microsoft/renderformer | [
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] | 10 | 2026-10-24T13:28:22Z | [] | 2026-10-13T16:08:42Z | [
"llm.transformers"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 981 | [
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] | 2026-10-23T07:05:04Z | https://github.com/microsoft/renderformer | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-04-24T11:35:23Z | Source code for paper "ASCE-PPIS:A Protein-Protein Interaction Sites Prediction Method Based on an Equivariant Graph Neural Network with the Fusion of Structure-Aware Pooling and Graph Collapse" | [
"deep-learning",
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 1 | 971,998,269 | null | Python | null | [
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] | nunhehheh/ASCE-PPIS | [
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] | 7 | 2026-10-18T14:44:29Z | [] | 2025-05-14T07:43:53Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 4 | [] | 2026-04-23T10:39:37Z | https://github.com/nunhehheh/ASCE-PPIS | null |
[
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-04-25T06:42:05Z | Code for paper: A Bayesian-Optimized SVM Framework for Encrypted Traffic Attack Detection | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 0 | 972,483,258 | null | Python | MIT | [
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] | ZBoyu/BO-SVM-CyberDefense | [
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] | 1 | 2026-10-05T20:10:04Z | [] | 2025-04-25T10:32:11Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2025-04-25T10:32:15Z | https://github.com/ZBoyu/BO-SVM-CyberDefense | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-04-25T08:28:28Z | A novel during-training pruning algorithm combining the decision metric of Relative-Importance-and-Activations(Zhang et al. 2024) and the scheduling algorithm of To-prune-or-not-to-prune(Zhu & Gupta) | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-28T17:27:44Z | false | 0 | 972,533,446 | null | Python | null | [
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] | WarishAli22/ScheduleRIA | [
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] | 1 | 2026-10-28T17:27:44Z | [] | 2025-04-25T09:51:51Z | [
"efficiency.pruning"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
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] | include | ml-contribution-v5 | 0 | [] | 2025-04-25T09:51:55Z | https://github.com/WarishAli22/ScheduleRIA | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-04-27T02:31:21Z | Official implementation for the paper: Diffusion Augmented Retrieval: A Training-Free Approach to Interactive Text-to-Image Retrieval | [
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] | [
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] | 4 | 2026-10-29T17:49:51Z | [] | 2026-03-20T08:42:36Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 6 | [] | 2026-06-07T06:32:07Z | https://github.com/longkukuhi/Diffusion-Agumented-Retrieval | null |
[
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] | [
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] | [
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-04-27T18:42:13Z | Official implementation of "Robust Deep Reinforcement Learning Using Formal Verification" paper (TASE'25) | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-29T17:49:51Z | false | 0 | 973,792,284 | null | Python | null | [
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] | AvrahamRaviv/Robust-DRL-FV | [
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] | 1 | 2026-10-29T17:49:51Z | [] | 2025-04-27T18:50:05Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2025-04-27T18:50:40Z | https://github.com/AvrahamRaviv/Robust-DRL-FV | null |
[
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] | [
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] | [
"timeseries.state-space-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-04-28T01:38:21Z | Code for paper: "SSM2Mel: State Space Model to Reconstruct Mel Spectrogram from the EEG" | [
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"time-series"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 1 | 973,917,887 | null | Python | null | [
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] | fchest/SSM2Mel | [
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] | 1 | 2026-10-01T17:50:46Z | [] | 2025-04-28T02:19:09Z | [
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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 | 3 | [] | 2026-03-25T08:41:10Z | https://github.com/fchest/SSM2Mel | null |
[
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"repository-metadata"
] | [
"medical.medical-image-segmentation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-04-28T13:05:57Z | code for paper "Synchronous Image-label Diffusion with Anisotropic Noise for Medical Image Segmentation" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 974,231,301 | null | Python | MIT | [
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] | Zzz0251/SADPM | [
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"query-match",
"repository-metadata"
] | 1 | 2026-10-04T15:58:58Z | [] | 2025-06-04T04:48:36Z | [
"medical.medical-image-segmentation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 1 | [] | 2026-01-04T06:16:13Z | https://github.com/Zzz0251/SADPM | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-04-29T19:03:40Z | PyTorch implementation of the Recurrent Sigmoid Piecewise Linear (RSPL) neuron, a novel recurrent neural network component designed for time series forecasting | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-28T17:27:44Z | false | 0 | 975,087,575 | null | Python | null | [
"forecasting",
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] | rfdickerson/rspl_neuron | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 1 | 2026-10-28T17:27:44Z | [] | 2025-04-29T21:49:35Z | [
"timeseries.forecasting"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
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"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [
"forecasting",
"pytorch",
"rnn"
] | 2025-04-29T21:56:04Z | https://github.com/rfdickerson/rspl_neuron | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-04-30T01:12:50Z | [TMLR 2025] Source code for paper "Reasoning Under 1 Billion: Memory-Augmented Reinforcement Learning for Large Language Models" | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-19T16:36:56Z | false | 2 | 975,219,164 | null | Python | Apache-2.0 | [
"artificial-intelligence",
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] | thaihungle/Memory-R | [
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] | 7 | 2026-10-29T16:41:07Z | [] | 2026-10-06T03:12:04Z | [
"probable.adaptation-finetuning-readme",
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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 | 9 | [] | 2026-10-11T08:33:10Z | https://github.com/thaihungle/Memory-R | null |
[
"information-retrieval",
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] | [
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] | [
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"query-match",
"repository-metadata"
] | [
"nlp.dense-retrieval"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-04-30T12:51:44Z | This is the official code for the paper: ALDEN: Reinforcement Learning for Active Navigation and Evidence Gathering in Long Documents | [
"information-retrieval",
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"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 0 | 975,516,980 | null | Python | Apache-2.0 | [
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] | gipplab/ALDEN | [
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] | 1 | 2026-10-05T20:10:04Z | [] | 2026-09-02T19:36:18Z | [
"nlp.dense-retrieval"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 6 | [] | 2026-09-02T19:36:38Z | https://github.com/gipplab/ALDEN | null |
[
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"repository-metadata"
] | [
"timeseries.state-space-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-04-30T15:12:49Z | Official implementation of IEEE T-CSVT paper "Shuffle Mamba: State Space Models with Random Shuffle for Multi-Modal Image Fusion" | [
"multimodal-learning",
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"time-series"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 1 | 975,594,403 | null | Python | null | [
"mamba",
"sequence-modeling",
"state-space-model"
] | caoke-963/Shuffle-Mamba | [
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] | 1 | 2026-10-01T17:50:46Z | [] | 2025-04-30T15:30:49Z | [
"timeseries.state-space-models"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 6 | [] | 2026-09-21T01:11:51Z | https://github.com/caoke-963/Shuffle-Mamba | null |
[
"generative-modeling",
"geospatial",
"hydrology",
"meteorology",
"time-series-and-forecasting"
] | [
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] | [
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] | [
"geo.precipitation-forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-01T04:04:01Z | Code for paper "Post-processing for 24-hour advanced forecasting of extreme precipitation using deep learning generative models" | [
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] | [
"deep-learning",
"generative-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 0 | 975,872,991 | null | Python | null | [
"forecasting",
"scientific-machine-learning",
"spatiotemporal-modeling"
] | NMC-DAVE/CGAN_extreme_precipitation | [
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"repository-metadata"
] | 1 | 2026-09-30T16:47:43Z | [] | 2025-05-01T04:34:45Z | [
"geo.precipitation-forecasting"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-context-only",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [] | 2026-04-08T08:25:43Z | https://github.com/NMC-DAVE/CGAN_extreme_precipitation | null |
[
"reinforcement-learning"
] | [
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] | [
"description",
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"repository-metadata"
] | [
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-02T11:31:27Z | Official implementation of "Robust Deep Reinforcement Learning Using Formal Verification" paper (TASE'25) | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-29T17:49:51Z | false | 0 | 976,588,053 | null | Python | null | [
"deep-reinforcement-learning",
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"reinforcement-learning"
] | ShaielVistuch/Robust_Deep_RL_Agents_Using_Formal_Verification | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-29T17:49:51Z | [] | 2025-05-02T11:46:45Z | [
"rl.deep"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2025-05-02T11:46:49Z | https://github.com/ShaielVistuch/Robust_Deep_RL_Agents_Using_Formal_Verification | null |
[
"cardiology",
"health-and-biomedicine",
"neuroscience",
"signal-processing"
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"transformer"
] | [
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"repository-metadata"
] | [
"medical.ecg",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-02T11:31:40Z | This study introduces EEGTransformer, a novel transformer-based model designed for coma prognosis using EEG signals, addressing a critical gap in applying transformers to clinical EEG analysis. The central hypothesis of this research is that the self-attention mechanism can capture complex spatiotemporal dependencies i... | [
"health-and-biomedicine",
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] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-14T16:10:46Z | false | 1 | 976,588,136 | null | Python | null | [
"neural-decoding",
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"self-attention",
"signal-classification",
"transformer"
] | ludovicoMaxi/EEGTransformer | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-18T14:44:29Z | [] | 2025-05-02T12:54:42Z | [
"medical.eeg"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-01-17T10:38:35Z | https://github.com/ludovicoMaxi/EEGTransformer | null |
[
"physics",
"scientific-computing"
] | [
"neural-operator",
"partial-differential-equations",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"gap2026.physics-neural-pde-solver"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-02T21:44:42Z | [IJCAI 2025] Code for paper 'MMET: A Multi-Input and Multi-Scale Transformer for Efficient PDEs Solving' | [
"physics",
"scientific-computing"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-25T16:01:25Z | false | 1 | 976,851,002 | null | Python | MIT | [
"neural-operator",
"partial-differential-equations",
"transformer"
] | YichenLuo-0/MMET | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-09-29T17:12:21Z | [] | 2026-07-26T13:02:29Z | [
"gap2026.physics-neural-pde-solver"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 7 | [] | 2026-08-05T05:56:02Z | https://github.com/YichenLuo-0/MMET | null |
[
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"forecasting",
"sequence-modeling"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"timeseries.forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-04T06:18:18Z | Official implementation for "CASA: CNN Autoencoder-based Score Attention for Efficient Multivariate Long-term Time-series Forecasting" (IJCAI 2025) | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 2 | 977,408,795 | null | Python | null | [
"forecasting",
"sequence-modeling"
] | lmh9507/CASA | [
"description",
"query-match",
"repository-metadata"
] | 4 | 2026-10-26T17:25:59Z | [] | 2025-05-06T05:38:39Z | [
"timeseries.forecasting"
] | 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 | [] | 2026-10-23T15:41:42Z | https://github.com/lmh9507/CASA | null |
[
"deep-learning",
"efficient-ml",
"language"
] | [
"conditional-computation",
"mixture-of-experts",
"sparse-activation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.mixture-of-experts"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-04T14:34:02Z | Code for paper: STAMImputer: Spatio-Temporal Attention Mixture of Experts Imputer for Sparse Traffic Data. | [
"deep-learning",
"efficient-ml",
"language"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 1 | 977,580,739 | null | Python | null | [
"conditional-computation",
"mixture-of-experts",
"sparse-activation"
] | RingBDStack/STAMImupter | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-09-29T17:30:08Z | [] | 2025-06-27T02:53:43Z | [
"efficiency.mixture-of-experts"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 9 | [] | 2026-03-10T13:10:07Z | https://github.com/RingBDStack/STAMImupter | null |
[
"information-retrieval",
"natural-language-processing"
] | [
"rag",
"retrieval-augmented-generation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"nlp.rag"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-05T12:32:07Z | Leverage Retrieval-Augmented Generation (RAG) to analyze new government policies and assess their impact on the organization. | [
"information-retrieval",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-28T17:27:44Z | false | 0 | 978,071,827 | null | Jupyter Notebook | null | [
"rag",
"retrieval-augmented-generation"
] | vanessabuilds/LeanAI-Policy-Reader | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-28T17:27:44Z | [] | 2025-05-05T14:17:38Z | [
"nlp.rag"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [] | 2025-05-05T14:17:42Z | https://github.com/vanessabuilds/LeanAI-Policy-Reader | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"natural-language-processing"
] | [
"diffusion",
"generative-modeling"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.image-diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-06T10:13:08Z | Official implementation of FidelGen: Visual Amharic Text Generation Using a Multi-Conditional Diffusion Model Paper. | [
"computer-vision",
"generative-ai",
"generative-modeling",
"natural-language-processing"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-13T18:05:26Z | false | 0 | 978,655,415 | https://wendirad.com | Python | Apache-2.0 | [
"diffusion",
"generative-modeling"
] | wendirad/FidelGenV3 | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-13T18:05:26Z | [] | 2026-08-12T09:08:39Z | [
"vision.image-diffusion"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [
"amharic",
"computer-vision",
"diffusion-models",
"generative-ai",
"pytorch",
"text2image"
] | 2026-08-12T09:08:55Z | https://github.com/wendirad/FidelGenV3 | null |
[
"deep-learning",
"efficient-ml",
"language"
] | [
"conditional-computation",
"mixture-of-experts",
"sparse-activation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.mixture-of-experts"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-06T14:41:11Z | Code for paper " HERMES: Heterogeneous Mixture of Experts Based on Segments for Auditory Attention Decoding" | [
"deep-learning",
"efficient-ml",
"language"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 0 | 978,810,302 | null | null | null | [
"conditional-computation",
"mixture-of-experts",
"sparse-activation"
] | Collin8829/HERMES | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-09-30T16:47:43Z | [] | 2025-05-10T08:01:23Z | [
"efficiency.mixture-of-experts"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2025-05-10T08:01:26Z | https://github.com/Collin8829/HERMES | null |
[
"multi-agent-systems",
"reinforcement-learning"
] | [
"centralized-training",
"multi-agent-reinforcement-learning",
"reinforcement-learning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.multiagent"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-06T16:40:32Z | [ICML 2025] Official Code of SMPE: "Enhancing Cooperative Multi-Agent Reinforcement Learning with State Modelling and Adversarial Exploration" | [
"multi-agent-systems",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 3 | 978,878,931 | null | Python | MIT | [
"centralized-training",
"multi-agent-reinforcement-learning",
"reinforcement-learning"
] | andreaskontogiannis/smpe | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-02T16:59:40Z | [] | 2026-02-09T20:28:07Z | [
"rl.multiagent"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 35 | [
"cooperative-ai",
"marl",
"rl"
] | 2026-06-09T02:28:20Z | https://github.com/andreaskontogiannis/smpe | null |
[
"biology",
"computational-biology",
"transcriptomics"
] | [
"gene-expression-modeling",
"representation-learning",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"bio.transcriptomics"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-07T02:32:33Z | A Novel Hybrid Architecture Integrating KAN and Sequential-Transformer for Robust Cell Type Annotation in Cross-Species Transcriptomics | [
"biology",
"computational-biology",
"transcriptomics"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-11T16:48:52Z | false | 0 | 979,109,649 | null | Python | BSD-3-Clause | [
"gene-expression-modeling",
"representation-learning",
"transformer"
] | gladex/Kalstra | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-11T16:48:52Z | [] | 2026-07-06T15:59:11Z | [
"bio.transcriptomics"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-07-06T15:59:15Z | https://github.com/gladex/Kalstra | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"language",
"multimodal",
"safety"
] | [
"alignment",
"diffusion",
"generative-modeling",
"motion-generation",
"preference-optimization"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.preference-optimization",
"multimodal.motion-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-08T00:06:29Z | [ICML 2025] Official Code for "ADHMR: Aligning Diffusion-based Human Mesh Recovery via Direct Preference Optimization" | [
"generative-ai",
"generative-modeling",
"language",
"safety"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 0 | 979,705,040 | null | Python | NOASSERTION | [
"alignment",
"diffusion",
"preference-optimization"
] | MotrixLab/ADHMR | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-10-07T18:07:52Z | [] | 2026-06-28T08:14:52Z | [
"llm.preference-optimization"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 47 | [] | 2026-08-10T08:04:46Z | https://github.com/MotrixLab/ADHMR | null |
[
"efficient-ml",
"machine-learning-systems",
"model-compression",
"natural-language-processing"
] | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-08T06:35:47Z | Official PyTorch implementation of DLP: Dynamic Layerwise Pruning in Large Language Models(ICML'25) | [
"efficient-ml",
"machine-learning-systems",
"model-compression",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 3 | 979,839,027 | https://arxiv.org/abs/2505.23807 | Python | Apache-2.0 | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | ironartisan/DLP | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 5 | 2026-10-26T17:25:59Z | [] | 2025-06-04T06:45:35Z | [
"efficiency.pruning"
] | 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 | 9 | [] | 2026-03-30T11:31:41Z | https://github.com/ironartisan/DLP | null |
[
"computational-neuroscience",
"machine-learning",
"sequence-modeling",
"time-series"
] | [
"neural-network",
"neuromorphic-computing",
"sequence-modeling",
"spiking-neural-network",
"state-space-model"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"specialized.spiking-neural-network",
"timeseries.state-space-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-08T13:18:55Z | Official implementation of the SiLIF and C-SiLIF models from the paper "Structured State Space Model Dynamics and Parametrization for Spiking Neural Networks" | [
"computational-neuroscience",
"machine-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 1 | 980,040,207 | null | Python | null | [
"neural-network",
"neuromorphic-computing",
"spiking-neural-network",
"state-space-model"
] | Maxtimer97/SSM-inspired-LIF | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-16T15:36:56Z | [] | 2026-01-29T11:25:58Z | [
"specialized.spiking-neural-network"
] | 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 | 10 | [] | 2026-10-15T07:32:12Z | https://github.com/Maxtimer97/SSM-inspired-LIF | null |
[
"computer-vision",
"language",
"machine-learning",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"vision"
] | [
"adaptation",
"contrastive-learning",
"image-text",
"test-time-adaptation",
"vision-language-model"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.vision-language",
"multimodal.vlm",
"specialized.test-time-adaptation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-09T05:49:33Z | Official code implementation for the paper "Efficient Open Set Single Image Test Time Adaptation of Vision Language Models" | [
"computer-vision",
"language",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 0 | 980,430,876 | null | Python | MIT | [
"contrastive-learning",
"test-time-adaptation",
"vision-language-model"
] | manogna-s/ROSITA | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-09T17:31:36Z | [] | 2025-05-09T05:52:21Z | [
"multimodal.vision-language"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 2 | [] | 2025-09-03T07:22:30Z | https://github.com/manogna-s/ROSITA | null |
[
"computer-vision",
"efficient-ml",
"image-processing",
"low-level-vision",
"model-compression"
] | [
"distillation",
"image-enhancement",
"image-restoration",
"knowledge-distillation",
"low-light-enhancement"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation",
"recall26.low-light-enhancement"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-09T06:56:19Z | official implementation of paper "DLIENet: A Lightweight Low-light Image Enhancement Network via Knowledge Distillation" | [
"computer-vision",
"efficient-ml",
"image-processing",
"low-level-vision",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 980,459,178 | null | Python | null | [
"distillation",
"image-enhancement",
"image-restoration",
"knowledge-distillation",
"low-light-enhancement"
] | OK932001/DLIENet | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-04T15:58:58Z | [] | 2025-07-31T10:42:07Z | [
"efficiency.knowledge-distillation",
"recall26.low-light-enhancement"
] | 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 | [] | 2026-01-22T02:33:28Z | https://github.com/OK932001/DLIENet | null |
[
"multimodal",
"reinforcement-learning",
"robotics"
] | [
"behavior-cloning",
"environment-modeling",
"imitation-learning",
"world-model"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.world-models",
"rl.imitation-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-10T12:15:26Z | Official code implementation of ICML 2025 paper: Reward-free World Models for Online Imitation Learning | [
"reinforcement-learning",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 0 | 981,132,814 | null | Python | null | [
"behavior-cloning",
"imitation-learning"
] | TobyLeelsz/iqmpc | [
"description",
"query-match",
"repository-metadata"
] | 4 | 2026-10-01T17:50:46Z | [] | 2025-06-21T15:31:25Z | [
"rl.imitation-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 | 12 | [] | 2026-07-15T16:22:18Z | https://github.com/TobyLeelsz/iqmpc | null |
[
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"forecasting",
"sequence-modeling"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"timeseries.forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-11T08:39:08Z | [IJCAI 2025] Official implementation of "FreqLLM: Bridging Frequency and Language for Enhanced Time Series Forecasting" | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 0 | 981,495,955 | null | Python | Apache-2.0 | [
"forecasting",
"sequence-modeling"
] | biya0105/FreqLLM | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-26T17:25:59Z | [] | 2026-08-06T13:29:41Z | [
"timeseries.forecasting"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 13 | [] | 2026-08-06T13:30:22Z | https://github.com/biya0105/FreqLLM | null |
[
"computer-vision",
"efficient-ml",
"model-compression"
] | [
"distillation",
"knowledge-distillation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-12T09:10:46Z | Code for paper 'Semantic Web-based Knowledge Distillation and Distribution Calibration for Few-Shot Object Detection' | [
"computer-vision",
"efficient-ml",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 0 | 982,027,654 | null | Python | null | [
"distillation",
"knowledge-distillation"
] | Erica-Yang/T-CLD | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2025-05-12T09:28:27Z | [
"efficiency.knowledge-distillation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2025-05-12T09:28:31Z | https://github.com/Erica-Yang/T-CLD | null |
[
"distributed-ml",
"privacy-and-federated-learning"
] | [
"collaborative-learning",
"federated-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-13T14:30:50Z | [NeurIPS 2025] Official implementation of PHP-FL: A Fair Federated Learning Method for Handling Client Participation Probability Inconsistencies in Heterogeneous Environments | [
"distributed-ml",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-19T17:46:36Z | false | 1 | 982,874,034 | null | Python | Apache-2.0 | [
"collaborative-learning",
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] | Siyuan01/PHP-FL-main | [
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] | 1 | 2026-10-19T17:46:36Z | [] | 2025-10-14T08:50:13Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 5 | [] | 2026-10-14T11:15:01Z | https://github.com/Siyuan01/PHP-FL-main | null |
[
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] | [
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] | [
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"repository-metadata"
] | [
"general.neural-network",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-14T13:27:24Z | [AAAI Oral 2026] This is the official code of our Paper "Sonnet: Spectral Operator Neural Network for Multivariable Time Series Forecasting" | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 9 | 983,501,450 | null | Jupyter Notebook | null | [
"forecasting",
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] | ClaudiaShu/Sonnet | [
"description",
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] | 8 | 2026-10-26T17:25:59Z | [] | 2026-03-20T10:32:42Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 59 | [] | 2026-10-21T03:00:30Z | https://github.com/ClaudiaShu/Sonnet | null |
[
"computer-vision",
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] | [
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"repository-metadata"
] | [
"multimodal.text-to-image"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-15T19:30:50Z | Official Code for the paper Cross-modal RAG: Sub-dimensional Retrieval-Augmented Text-to-Image Generation | [
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] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-10T16:53:26Z | false | 0 | 984,346,843 | null | Python | Apache-2.0 | [
"image-generation",
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] | mengdanzhu/Cross-modal-RAG | [
"description",
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"repository-metadata"
] | 1 | 2026-10-10T16:53:26Z | [] | 2025-05-29T04:31:14Z | [
"multimodal.text-to-image"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 3 | [] | 2026-03-25T21:00:38Z | https://github.com/mengdanzhu/Cross-modal-RAG | null |
[
"missing-data",
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] | [
"description",
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"repository-metadata"
] | [
"timeseries.imputation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-16T02:40:17Z | [NeurIPS 2025] Official Implementation for "Glocal Information Bottleneck for Time Series Imputation" | [
"missing-data",
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T15:29:21Z | false | 3 | 984,487,855 | https://arxiv.org/abs/2510.04910 | Python | null | [
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] | Muyiiiii/NeurIPS-25-Glocal-IB | [
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] | 4 | 2026-09-27T15:53:49Z | [] | 2025-11-04T17:45:13Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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] | include | ml-contribution-v5 | 19 | [] | 2026-09-17T23:39:35Z | https://github.com/Muyiiiii/NeurIPS-25-Glocal-IB | null |
[
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"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-16T15:19:54Z | This is the official implementation of "Multi-modal Anchor Gated Transformer with Knowledge Distillation for Emotion Recognition in Conversation" (IJCAI 2025) | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 2 | 984,829,528 | null | Python | GPL-3.0 | [
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] | JieLi-dd/MAGTKD | [
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] | 3 | 2026-10-03T15:45:52Z | [] | 2025-12-15T13:12:27Z | [
"efficiency.knowledge-distillation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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] | include | ml-contribution-v5 | 12 | [] | 2026-08-30T14:32:44Z | https://github.com/JieLi-dd/MAGTKD | null |
[
"audio",
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] | [
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] | [
"description",
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] | [
"audio-speech-synthesis"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-16T15:56:36Z | Official implementation of paper: Shallow Flow Matching for Coarse-to-Fine Text-to-Speech Synthesis | [
"audio",
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"speech",
"speech-and-audio"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T16:16:53Z | false | 8 | 984,848,091 | null | Python | null | [
"architecture",
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] | ydqmkkx/ShallowFlowMatching-TTS | [
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] | 2 | 2026-09-26T20:57:15Z | [] | 2025-09-20T01:43:47Z | [
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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 | 56 | [] | 2026-09-22T12:10:46Z | https://github.com/ydqmkkx/ShallowFlowMatching-TTS | null |
[
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] | [
"timeseries.forecasting",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-17T12:05:25Z | [ACM MM 2025] Official implementation of "DualSG: A Dual-Stream Explicit Semantic-Guided Multivariate Time Series Forecasting Framework" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 11 | 985,266,942 | https://arxiv.org/abs/2507.21830 | Python | null | [
"forecasting",
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] | BenchCouncil/DualSG | [
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] | 13 | 2026-10-26T17:25:59Z | [] | 2026-01-03T01:33:23Z | [
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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 | 103 | [
"time-series-forecasting"
] | 2026-10-26T12:58:21Z | https://github.com/BenchCouncil/DualSG | null |
[
"data-centric-ai",
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] | [
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] | [
"llm.synthetic-data",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-17T13:26:12Z | Code for paper: "LLM-based Automated Theorem Proving Hinges on Scalable Synthetic Data Generation" | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 0 | 985,299,808 | null | Python | null | [
"language-model",
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] | NJUDeepEngine/llm_based_atp | [
"description",
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] | 6 | 2026-10-13T18:05:26Z | [] | 2026-07-28T07:08:27Z | [
"llm.synthetic-data"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 4 | [] | 2025-08-15T08:53:25Z | https://github.com/NJUDeepEngine/llm_based_atp | null |
[
"deep-learning",
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] | [
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"description",
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] | [
"general.continual-learning",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-18T13:04:14Z | [ACL'25 Main] Official Implementation of HiDe-LLaVA: Hierarchical Decoupling for Continual Instruction Tuning of Multimodal Large Language Model | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 10 | 985,751,931 | null | Python | null | [
"fine-tuning",
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] | Ghy0501/HiDe-LLaVA | [
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] | 8 | 2026-10-24T14:31:13Z | [] | 2026-06-01T05:20:21Z | [
"llm.instruction-tuning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 57 | [] | 2026-10-22T07:54:45Z | https://github.com/Ghy0501/HiDe-LLaVA | null |
[
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"multimodal.vlm",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-19T02:13:44Z | Official Code Repository for the paper "Simple yet Effective Semi-supervised Knowledge Distillation from Vision-Language Models via Dual-Head Optimization" | [
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"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 986,030,464 | https://dual-head-optimization.github.io/ | Python | null | [
"distillation",
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] | erjui/DHO | [
"description",
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] | 5 | 2026-10-20T14:47:52Z | [] | 2026-10-05T03:39:28Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 8 | [] | 2026-10-05T03:39:32Z | https://github.com/erjui/DHO | null |
[
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] | [
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] | [
"description",
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"query-match",
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] | [
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-19T04:16:25Z | Code for paper Energy Management in Networked Multi-Energy Microgrids A Safety-Guaranteed Multi-Actor Single-Critic Deep Reinforcement Learning Approach | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 4 | 986,070,380 | null | Python | GPL-3.0 | [
"deep-reinforcement-learning",
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] | NextEasywood/MASC_MSL | [
"description",
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] | 4 | 2026-10-28T17:27:44Z | [] | 2026-01-09T03:31:28Z | [
"rl.deep"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 3 | [] | 2026-04-15T05:08:21Z | https://github.com/NextEasywood/MASC_MSL | null |
[
"embodied-ai",
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] | [
"description",
"query-match",
"repository-metadata"
] | [
"robotics.diffusion-policy"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-19T06:24:21Z | Official implementation of the paper "Conditioning Matters: Training Diffusion Policies is Faster Than You Think". | [
"embodied-ai",
"generative-modeling",
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 0 | 986,120,106 | null | Python | null | [
"diffusion",
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] | ZibinDong/cocos | [
"description",
"query-match",
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] | 5 | 2026-09-27T15:53:49Z | [] | 2025-05-19T08:46:10Z | [
"robotics.diffusion-policy"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
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] | include | ml-contribution-v5 | 18 | [] | 2026-07-07T22:12:09Z | https://github.com/ZibinDong/cocos | null |
[
"mathematics",
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] | [
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] | [
"description",
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"query-match",
"repository-metadata"
] | [
"gap2026.math-neural-combinatorial-optimization"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-19T14:14:37Z | code for paper "BOPO: Neural Combinatorial Optimization via Best-anchored and Objective-guided Preference Optimization" | [
"mathematics",
"operations-research"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-25T16:01:25Z | false | 4 | 986,377,774 | null | Java | MIT | [
"combinatorial-optimization",
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] | L-Z-7/BOPO | [
"description",
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"query-match",
"repository-metadata"
] | 2 | 2026-09-29T17:12:21Z | [] | 2026-03-01T05:54:37Z | [
"gap2026.math-neural-combinatorial-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 | 16 | [] | 2026-09-28T15:14:27Z | https://github.com/L-Z-7/BOPO | null |
[
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] | [
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] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-19T15:26:00Z | Official Code for Paper《Towards More Faithful Natural Language Explanation Using Multi-Level Contrastive Learning in VQA》 | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 5 | 986,421,181 | null | Python | null | [
"contrastive-learning",
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] | laichengen/MCLE | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-09-28T18:33:08Z | [] | 2025-05-20T02:31:26Z | [
"general.contrastive-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 54 | [] | 2025-07-03T08:05:11Z | https://github.com/laichengen/MCLE | null |
[
"natural-language-processing",
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] | [
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] | [
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"query-match",
"repository-metadata"
] | [
"nlp.machine-translation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-21T09:17:48Z | Code for paper "SSR-Zero: Simple Self-Rewarding Reinforcement Learning for Machine Translation" | [
"natural-language-processing",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 0 | 987,573,631 | https://arxiv.org/abs/2505.16637 | null | null | [
"machine-translation",
"neural-machine-translation",
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] | Kelaxon/SSR-Zero | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | 4 | 2026-10-18T14:44:29Z | [] | 2025-05-28T12:29:27Z | [
"nlp.machine-translation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 9 | [] | 2025-12-15T00:25:58Z | https://github.com/Kelaxon/SSR-Zero | null |
[
"computer-vision",
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"natural-language-processing",
"robust-ml",
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] | [
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] | [
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"query-match",
"repository-metadata"
] | [
"trust.distribution-shift"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-21T15:44:04Z | Official implementation of ICML 2025 paper "Understanding Multimodal LLMs Under Distribution Shifts: An Information-Theoretic Approach" | [
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"multimodal-learning",
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"robust-ml",
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] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 2 | 987,796,762 | null | Python | null | [
"distribution-shift-detection",
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] | deeplearning-wisc/mllmshift-emi | [
"description",
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"query-match",
"repository-metadata"
] | 5 | 2026-09-28T18:33:08Z | [] | 2025-05-27T22:39:54Z | [
"trust.distribution-shift"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-context-only",
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] | include | ml-contribution-v5 | 12 | [
"distribution-shift",
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"multimodal-llm",
"robustness",
"vision-language-model"
] | 2026-04-01T10:20:03Z | https://github.com/deeplearning-wisc/mllmshift-emi | null |
[
"data-centric-ai",
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] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.semi-supervised-learning",
"general.weak-supervision"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-22T02:48:02Z | Official code for the paper "Weakly-Supervised Contrastive Learning for Imprecise Class Labels" | [
"general-ml",
"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 1 | 988,075,934 | null | Python | null | [
"contrastive-learning",
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"semi-supervised-learning"
] | Speechless-10308/WSC | [
"description",
"query-match",
"repository-metadata"
] | 6 | 2026-10-28T17:27:44Z | [] | 2025-11-12T08:26:47Z | [
"general.semi-supervised-learning"
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[
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] | include | ml-contribution-v5 | 272 | [] | 2026-09-30T18:00:34Z | https://github.com/Nardien/agent-distillation | null |
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[
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[
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[
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] | include | ml-contribution-v5 | 19 | [] | 2025-10-25T09:30:58Z | https://github.com/yannqi/RCTS-RAG | null |
[
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[
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[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-24T06:47:00Z | The official code implementation for paper "PM-KVQ: Progressive Mixed-precision KV Cache Quantization for Long-CoT LLMs" | [
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[
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] | 1 | 2026-10-04T15:58:58Z | [] | 2025-08-18T13:01:43Z | [
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] | include | ml-contribution-v5 | 2 | [] | 2025-09-28T09:36:56Z | https://github.com/HuSTeP-Human-Speech-Text-Processing-Lab/XLSR-GRKAN-Conformer | null |
[
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] | [
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] | [
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] | [
"specialized.test-time-adaptation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-25T13:14:18Z | Code for ICML 2025 paper — Beyond Entropy: Region Confidence Proxy for Wild Test-Time Adaptation | [
"machine-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 3 | 990,080,468 | null | Python | BSD-3-Clause | [
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] | hzcar/ReCAP | [
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] | 6 | 2026-10-02T16:59:40Z | [] | 2025-07-02T14:40:01Z | [
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] | include | ml-contribution-v5 | 61 | [] | 2026-08-15T08:13:22Z | https://github.com/hzcar/ReCAP | null |
[
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-25T20:19:33Z | Official implementation of ACL25 paper "GraphNarrator: Textual Explanations for Graph Neural Networks" | [
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] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 0 | 990,258,481 | null | Jupyter Notebook | null | [
"graph-neural-network",
"message-passing",
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] | pb0316/GraphNarrator | [
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"query-match",
"repository-metadata"
] | 3 | 2026-10-17T13:17:44Z | [] | 2025-09-11T15:06:50Z | [
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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 | 13 | [] | 2026-06-04T05:04:14Z | https://github.com/pb0316/GraphNarrator | null |
[
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] | [
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] | [
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] | [
"audio-music-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-25T23:16:01Z | [NeurIPS 2025] The official code of "Training-Free Guidance Beyond Differentiability: Scalable Path Steering with Tree Search in Diffusion and Flow Models". | [
"audio",
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-26T17:25:59Z | false | 0 | 990,309,990 | null | Python | null | [
"architecture",
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] | yukang123/UniTreeG | [
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] | 2 | 2026-10-28T17:27:44Z | [] | 2025-12-03T18:40:24Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 5 | [] | 2026-09-15T08:59:35Z | https://github.com/yukang123/UniTreeG | null |
[
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] | [
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] | [
"science.physics"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-26T01:07:56Z | Official implementation of "Force Prompting: Video Generation Models Can Learn and Generalize Physics-based Control Signals" (NeurIPS 2025) | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 7 | 990,340,169 | https://force-prompting.github.io/ | Python | MIT | [
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] | brown-palm/force-prompting | [
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] | 2 | 2026-09-28T18:33:08Z | [] | 2026-04-29T23:35:42Z | [
"science.physics"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 162 | [] | 2026-09-25T18:56:16Z | https://github.com/brown-palm/force-prompting | null |
[
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] | [
"multimodal.text-to-image"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-26T07:21:10Z | Official PyTorch implementation of the paper: A DEBIASING FRAMEWORK FOR ATTRIBUTE BINDING IN DIFFUSION-BASED TEXT-TO-IMAGE. | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-12T20:31:19Z | false | 0 | 990,481,196 | null | Python | null | [
"diffusion",
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] | PRIS-CV/A-Debiasing-Framework-For-Attribute-Binding-In-Diffusion-Based-Text-To-Image | [
"description",
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] | 1 | 2026-10-12T20:31:19Z | [] | 2025-05-26T09:23:23Z | [
"multimodal.text-to-image"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2025-05-26T09:23:27Z | https://github.com/PRIS-CV/A-Debiasing-Framework-For-Attribute-Binding-In-Diffusion-Based-Text-To-Image | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-26T12:01:49Z | Official code for "From Seeing to Doing: Bridging Reasoning and Decision for Robotic Manipulation" (ICLR2026) | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 1 | 990,632,101 | null | Python | Apache-2.0 | [
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] | pickxiguapi/Embodied-FSD | [
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] | 4 | 2026-10-21T15:48:06Z | [] | 2026-03-01T16:01:00Z | [
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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 | 39 | [] | 2026-09-15T12:02:53Z | https://github.com/pickxiguapi/Embodied-FSD | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-26T22:29:12Z | [TMLR2026] The official code for the paper "Incorporating Flexible Image Conditioning into Text-to-Video Diffusion Models without Training"" | [
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"video"
] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 0 | 990,946,127 | https://bolinlai.github.io/projects/FlexTI2V/ | Python | Apache-2.0 | [
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] | BolinLai/FlexTI2V | [
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] | 2 | 2026-10-09T17:31:36Z | [] | 2026-03-14T22:13:28Z | [
"multimodal.image-to-video"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 3 | [] | 2026-10-03T19:14:05Z | https://github.com/BolinLai/FlexTI2V | null |
[
"embodied-ai",
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] | [
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] | [
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"repository-metadata"
] | [
"robotics.diffusion-policy"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-27T01:40:41Z | [ICML2025] Official implementation of Efficient Online Reinforcement Learning for Diffusion Policies appearing in ICML 2025. | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 11 | 991,005,427 | null | Python | null | [
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] | mahaitongdae/diffusion_policy_online_rl | [
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] | 5 | 2026-09-27T15:53:49Z | [] | 2026-04-25T13:50:18Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 62 | [] | 2026-09-27T01:04:40Z | https://github.com/mahaitongdae/diffusion_policy_online_rl | null |
[
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] | [
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] | [
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] | [
"efficiency.pruning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-28T02:20:15Z | [ACL25] Code for paper "ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models" | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-25T15:11:36Z | false | 0 | 991,685,598 | null | Python | Apache-2.0 | [
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] | VoiceBeer/ALPS | [
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] | 2 | 2026-10-26T17:25:59Z | [] | 2025-07-18T05:11:07Z | [
"efficiency.pruning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 5 | [] | 2026-05-11T15:43:07Z | https://github.com/VoiceBeer/ALPS | null |
[
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] | [
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] | [
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] | [
"specialized.test-time-adaptation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-28T06:04:13Z | Code for ICML 2025 paper — Self-Bootstrapping for Versatile Test-Time Adaptation | [
"machine-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-28T18:33:08Z | false | 1 | 991,770,069 | null | Python | NOASSERTION | [
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] | mr-eggplant/SPA | [
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] | 2 | 2026-10-02T16:59:40Z | [] | 2025-09-30T08:06:15Z | [
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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 | 17 | [] | 2026-05-18T01:17:09Z | https://github.com/mr-eggplant/SPA | null |
[
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] | [
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] | [
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"query-match",
"repository-metadata"
] | [
"graph.signal-processing"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-28T13:16:32Z | [IJCAI'25] Official PyTorch Implementation of "Learning Advanced Self-Attention for Linear Transformers in the Singular Value Domain" (AGF) | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T15:29:21Z | false | 7 | 991,999,596 | https://arxiv.org/abs/2505.08516 | TypeScript | MIT | [
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] | hyowonwi/AGF | [
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] | 4 | 2026-09-29T17:12:21Z | [] | 2025-12-01T05:13:29Z | [
"graph.signal-processing"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 13 | [] | 2026-04-20T21:50:47Z | https://github.com/hyowonwi/AGF | null |
[
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] | [
"llm.diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-29T07:20:00Z | [ICML 2025] Official implementation of "Efficiently Access Diffusion Fisher: Within the Outer Product Span Space". | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T16:52:35Z | false | 0 | 992,454,611 | null | Python | MIT | [
"diffusion"
] | zituitui/DiffusionFisher | [
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"github-topics",
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] | 5 | 2026-09-27T15:53:49Z | [] | 2025-06-02T13:36:37Z | [
"llm.diffusion"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 9 | [
"aigc",
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"diffusion-models",
"fisher-information",
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] | 2026-01-19T05:32:57Z | https://github.com/zituitui/DiffusionFisher | null |
[
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] | [
"science.neural-operators"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-29T17:31:52Z | This repository is the official implementation of the paper "RONOM: Reduced-Order Neural Operator Modeling" published in the SIAM Journal on Scientific Computing | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 0 | 992,773,475 | https://epubs.siam.org/doi/full/10.1137/25M1777700?af=R | Python | null | [
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] | SCdummer/RONOM | [
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] | 1 | 2026-09-29T17:30:08Z | [] | 2026-07-23T10:31:06Z | [
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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 | [
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] | 2026-07-23T10:32:26Z | https://github.com/SCdummer/RONOM | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-29T20:54:21Z | Code for paper: "Learning Diffusion Models with Flexible Representation Guidance" | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 3 | 992,867,583 | https://chenyuwang-monica.github.io/REED/ | Jupyter Notebook | null | [
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] | ChenyuWang-Monica/REED | [
"description",
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"repository-metadata"
] | 7 | 2026-10-22T16:09:07Z | [] | 2026-03-18T20:24:11Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 16 | [] | 2026-10-19T06:42:42Z | https://github.com/ChenyuWang-Monica/REED | null |
[
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] | [
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"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.promptable-segmentation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-30T08:54:54Z | [NeurIPS 2025] The official code for "IllumiCraft: Unified Geometry and Illumination Diffusion for Controllable Video Generation" | [
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"generative-modeling"
] | [
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"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 1 | 993,122,654 | https://yuanze-lin.me/IllumiCraft_page/ | Python | Apache-2.0 | [
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] | yuanze-lin/IllumiCraft | [
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] | 8 | 2026-09-29T17:30:08Z | [] | 2026-07-22T00:44:37Z | [
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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 | 111 | [
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] | 2026-08-24T02:10:41Z | https://github.com/yuanze-lin/IllumiCraft | null |
[
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] | [
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"repository-metadata"
] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-31T04:06:50Z | This repository contains the code for our ACL 2025 paper: "HOH: A Dynamic Benchmark for Evaluating the Impact of Outdated Information on Retrieval-Augmented Generation". | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-29T17:49:51Z | false | 2 | 993,578,712 | null | Python | Apache-2.0 | [
"rag",
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] | 0russwest0/HoH | [
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"repository-metadata"
] | 1 | 2026-10-29T17:49:51Z | [] | 2025-05-31T04:12:07Z | [
"nlp.rag"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 7 | [] | 2026-08-17T09:21:02Z | https://github.com/0russwest0/HoH | null |
[
"audio",
"generative-modeling"
] | [
"architecture",
"diffusion",
"generative-modeling",
"training"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"audio-music-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-31T05:29:41Z | Official implementation of the paper MGE-LDM: Joint Latent Diffusion for Simultaneous Music Generation and Source Extraction | [
"audio",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-26T17:25:59Z | false | 1 | 993,601,934 | null | Python | MIT | [
"architecture",
"diffusion",
"generative-modeling",
"training"
] | yoongi43/MGE-LDM | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-28T17:27:44Z | [] | 2026-02-19T15:13:13Z | [
"audio-music-generation"
] | 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 | 22 | [] | 2026-09-14T15:04:48Z | https://github.com/yoongi43/MGE-LDM | null |
[
"automl",
"general-ml"
] | [
"few-shot-learning",
"meta-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.meta-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-31T09:07:15Z | [KDD'25] Code for paper "Spatial Hierarchical Meta-Learning for Single-Point Map Matching". | [
"automl",
"general-ml"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-25T15:11:36Z | false | 0 | 993,671,431 | null | Python | null | [
"few-shot-learning",
"meta-learning"
] | zouyiqing-221/SHSMM | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-25T15:11:36Z | [] | 2026-01-15T07:22:40Z | [
"general.meta-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 | 1 | [] | 2026-01-15T07:26:34Z | https://github.com/zouyiqing-221/SHSMM | null |
[
"deep-learning",
"efficient-ml",
"language",
"machine-learning-systems",
"model-compression"
] | [
"conditional-computation",
"mixture-of-experts",
"quantization",
"sparse-activation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.mixture-of-experts",
"efficiency.quantization"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-31T10:51:18Z | official implementation of the ACL25(findings) paper Automated Fine-Grained Mixture-of-Experts Quantization | [
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 0 | 993,707,456 | null | null | Apache-2.0 | [
"mixture-of-experts",
"quantization"
] | cei1ing4/Fine-Grained-MoE-Quantization | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-21T15:48:06Z | [] | 2025-05-31T10:51:18Z | [
"efficiency.quantization"
] | 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 | [] | 2025-06-19T12:03:12Z | https://github.com/cei1ing4/Fine-Grained-MoE-Quantization | null |
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