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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-12-17T08:54:56Z | Official reference implementation of our paper "On Oversquashing in Graph Neural Networks Through The Lens of Dynamical Systems" accepted at AAAI 2025 | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-15T15:20:52Z | false | 1 | 904,625,932 | null | Python | null | [
"graph-neural-network",
"message-passing",
"neural-network"
] | gravins/SWAN | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-15T15:20:52Z | [] | 2025-05-01T17:35:27Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 10 | [] | 2025-08-08T08:05:24Z | https://github.com/gravins/SWAN | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-12-18T08:47:05Z | [AAAI'25] Official implementation of "Pixel Is Not A Barrier: An Effective Evasion Attack for Pixel-Domain Diffusion Models" https://alexpeng517.github.io/AtkPDM/ | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-18T14:44:29Z | false | 2 | 905,142,954 | null | Python | null | [
"diffusion"
] | AlexPeng517/AtkPDM | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-18T14:44:29Z | [] | 2025-02-22T17:18:04Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 3 | [] | 2026-05-05T16:04:29Z | https://github.com/AlexPeng517/AtkPDM | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"image-processing",
"low-level-vision"
] | [
"diffusion",
"image-enhancement",
"image-restoration",
"low-light-enhancement"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"recall26.low-light-enhancement"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-12-18T09:12:32Z | [AAAI 2025] Official implementation of the paper "Zero-Shot Low-Light Image Enhancement via Latent Diffusion Models" | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-30T16:30:43Z | false | 1 | 905,153,753 | null | Python | null | [
"diffusion"
] | Eileen000/LLIEDiff | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-10-18T14:44:29Z | [] | 2025-06-14T03:49:40Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 18 | [] | 2026-08-19T08:21:44Z | https://github.com/Eileen000/LLIEDiff | null |
[
"forecasting",
"interpretability-and-safety",
"language",
"natural-language-processing",
"safety",
"time-series",
"time-series-and-forecasting"
] | [
"alignment",
"forecasting",
"multivariate-forecasting",
"reinforcement-learning",
"sequence-modeling"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.alignment",
"timeseries.forecasting",
"timeseries.multivariate"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-12-18T12:34:36Z | [AAAI 2025] Official implementation of "TimeCMA: Towards LLM-Empowered Multivariate Time Series Forecasting via Cross-Modality Alignment" | [
"forecasting",
"interpretability-and-safety",
"natural-language-processing",
"time-series",
"time-series-and-forecasting"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 21 | 905,238,519 | null | Python | NOASSERTION | [
"forecasting",
"sequence-modeling"
] | ChenxiLiu-HNU/TimeCMA | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 12 | 2026-10-22T16:09:07Z | [] | 2025-12-08T11:40:16Z | [
"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 | 171 | [] | 2026-10-13T03:39:21Z | https://github.com/ChenxiLiu-HNU/TimeCMA | null |
[
"computer-vision",
"machine-learning"
] | [
"adaptation",
"test-time-adaptation"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"specialized.test-time-adaptation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-12-19T05:19:20Z | [AAAI 2025] Official code of paper "PALM: Pushing Adaptive Learning Rate Mechanisms for Continual Test-Time Adaptation". | [
"computer-vision",
"machine-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 0 | 905,571,471 | null | Python | null | [
"adaptation",
"test-time-adaptation"
] | sarthaxxxxx/PALM | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 1 | 2026-10-02T16:59:40Z | [] | 2025-06-29T01:22:17Z | [
"specialized.test-time-adaptation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 5 | [
"computer-vision",
"test-time-adaptation"
] | 2026-04-22T11:18:11Z | https://github.com/sarthaxxxxx/PALM | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-12-19T09:02:30Z | The appendix and code of GRANCE in our paper: Refine then Classify: Robust Graph Neural Networks with Reliable Neighborhood Contrastive Refinement, AAAI 2025. | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-15T15:20:52Z | false | 1 | 905,652,552 | null | Python | null | [
"graph-neural-network",
"message-passing",
"neural-network"
] | shumanzhuang/GRANCE | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-15T15:20:52Z | [] | 2025-08-05T04:34:11Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 5 | [] | 2026-06-03T07:50:39Z | https://github.com/shumanzhuang/GRANCE | null |
[
"generative-ai",
"generative-modeling",
"multimodal",
"video"
] | [
"diffusion",
"image-to-video",
"video-generation"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"multimodal.image-to-video"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-12-19T12:15:48Z | [CVPR'25 Highlight] Official implementation for paper - LeviTor: 3D Trajectory Oriented Image-to-Video Synthesis | [
"generative-ai",
"generative-modeling",
"multimodal",
"video"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-01T17:35:19Z | false | 9 | 905,732,861 | https://ppetrichor.github.io/levitor.github.io/ | Python | null | [
"diffusion",
"image-to-video",
"video-generation"
] | robbyant-research/LeviTor | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 2 | 2026-10-04T15:58:58Z | [] | 2025-04-15T06:21:00Z | [
"multimodal.image-to-video"
] | 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 | 163 | [
"image-to-video-generation",
"trajectory-control",
"video-diffusion-model"
] | 2026-08-29T07:18:16Z | https://github.com/robbyant-research/LeviTor | null |
[
"generative-ai",
"language"
] | [
"benchmarking",
"evaluation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.evaluation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-12-19T15:19:50Z | Code for paper "A novel Machine Learning-based Method for Identifying Industrial Symbiosis Opportunities" | [
"generative-ai",
"language"
] | [
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-13T18:05:26Z | false | 0 | 905,815,198 | null | Python | null | [
"benchmarking",
"evaluation"
] | nancycyzl/ML-based-method-for-identifying-Industrial-Symbiosis-opportunties | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-13T18:05:26Z | [] | 2024-12-22T10:23:47Z | [
"llm.evaluation"
] | 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 | 0 | [] | 2024-12-22T10:23:50Z | https://github.com/nancycyzl/ML-based-method-for-identifying-Industrial-Symbiosis-opportunties | null |
[
"embodied-ai",
"robotics",
"robotics-and-control"
] | [
"manipulation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"robotics.manipulation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-12-19T16:02:32Z | Official implementation of paper on IROS 2026 "Long-Horizon Robotic Manipulation with Progressive In-Context Code Generation and Episodic Feedback" | [
"embodied-ai",
"robotics",
"robotics-and-control"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-30T16:30:43Z | false | 1 | 905,833,907 | https://ghiara.github.io/DAHLIA/ | Python | MIT | [
"manipulation"
] | Ghiara/DAHLIA | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-09-30T16:47:43Z | [] | 2026-06-17T14:16:29Z | [
"robotics.manipulation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 21 | [] | 2026-09-28T15:46:02Z | https://github.com/Ghiara/DAHLIA | null |
[
"general-ml",
"reinforcement-learning"
] | [
"novel-method",
"reinforcement-learning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.novel-method"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-12-19T17:55:54Z | RL-FRB/US - The novel method which enhance the FRB/US model by Reinforcement Learning and Active Relocation algorithms | [
"general-ml",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 0 | 905,881,198 | null | Python | NOASSERTION | [
"novel-method",
"reinforcement-learning"
] | matyascorvinus/RL_FRBUS | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-02T16:44:32Z | [] | 2026-03-11T17:20:42Z | [
"general.novel-method"
] | 41328a2721b92a84b0473b43bfc272aaa0b5ade8 | 2026-09-28T20:05:39Z | gh-ml-readme-evidence-v2 | "41328a2721b92a84b0473b43bfc272aaa0b5ade8" | [
"installation",
"other"
] | [
"ml-method-context",
"paper-reference"
] | ok | 2026-09-28T20:05:39Z | matyascorvinus/RL_FRBUS | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue",
"paper-reference"
] | include | ml-contribution-v5 | 1 | [
"active-learning",
"fiscal-policy",
"frbus",
"ppo",
"reinforcement-learning"
] | 2025-10-02T15:39:12Z | https://github.com/matyascorvinus/RL_FRBUS | null |
[
"reinforcement-learning",
"safety"
] | [
"constrained-reinforcement-learning",
"reinforcement-learning",
"safe-reinforcement-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.safe"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-12-19T21:05:25Z | Official implementation of the paper "Offline Safe Reinforcement Learning Using Trajectory Classification" in AAAI 2025. | [
"reinforcement-learning",
"safety"
] | [
"reinforcement-learning",
"classifier"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 0 | 905,953,519 | null | Python | MIT | [
"constrained-reinforcement-learning",
"reinforcement-learning",
"safe-reinforcement-learning"
] | zgong11/TraC | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-02T16:44:32Z | [] | 2025-04-19T01:28:47Z | [
"rl.safe"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [] | 2026-05-10T06:38:54Z | https://github.com/zgong11/TraC | null |
[
"efficient-ml",
"generative-ai",
"language"
] | [
"fine-tuning",
"lora",
"low-rank-adaptation",
"parameter-efficient-fine-tuning",
"transformer"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.lora",
"llm.parameter-efficient-finetuning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-12-21T09:29:11Z | Official implementation for our paper: "GinGer at SemEval-2025 Task 11: Leveraging Fine-Tuned Transformer Models and LoRA for Sentiment Analysis in Low-Resource Languages" , Proceedings of SemEval-2025, ACL 2025 | [
"efficient-ml",
"language"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-11T16:48:52Z | false | 0 | 906,574,218 | null | Jupyter Notebook | null | [
"lora",
"low-rank-adaptation",
"parameter-efficient-fine-tuning",
"transformer"
] | AylinNaebzadeh/Text-Based-Emotion-Detection-SemEval-2025 | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-28T17:27:44Z | [] | 2026-03-15T09:37:20Z | [
"efficiency.lora"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [] | 2026-03-15T09:37:23Z | https://github.com/AylinNaebzadeh/Text-Based-Emotion-Detection-SemEval-2025 | null |
[] | [] | [
"description",
"repository-metadata"
] | [] | false | unknown | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-12-22T08:51:02Z | This repository hosts the code for the FKAN model, proposed in our IEEE ICASSP paper "Implicit Neural Representations with Fourier Kolmogorov-Arnold Networks". | [] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-31T18:35:38Z | false | 3 | 906,901,634 | null | Jupyter Notebook | null | [] | Ali-Meh619/FKAN | [
"description",
"repository-metadata"
] | 1 | 2026-10-31T18:35:38Z | [
"2409.09323"
] | 2024-12-22T21:36:49Z | [] | 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 | 21 | [] | 2026-05-09T14:49:09Z | https://github.com/Ali-Meh619/FKAN | {"discovery_source":"hf_daily_papers","paper_evidence":"unverified","queryless":true} |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-12-22T16:32:52Z | Official Implementation for the paper titled: "Counterfactual Disease Removal and Generation in Chest X-Rays Using Diffusion Models" | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-18T14:44:29Z | false | 0 | 907,038,499 | https://berkegokmen1.github.io/counterfactual-disease-removal-and-generation-chest-xray/ | Python | null | [
"diffusion"
] | berkegokmen1/counterfactual-chexray-disease-editing | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 1 | 2026-10-18T14:44:29Z | [] | 2025-12-08T05:47:52Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 15 | [
"attention",
"chest",
"chexray",
"counterfactual",
"diffusion",
"disease",
"disease-control",
"fine-tune",
"x-ray",
"xray"
] | 2026-07-20T14:50:15Z | https://github.com/berkegokmen1/counterfactual-chexray-disease-editing | null |
[
"earth-science",
"generative-ai",
"generative-modeling",
"geospatial-science",
"remote-sensing",
"science-and-engineering"
] | [
"diffusion",
"foundation-model",
"geospatial-learning",
"remote-sensing"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"science.remote-sensing"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-12-23T00:49:05Z | [ICLR ML4RS 2025] Official implementation for the paper "Tackling Few-Shot Segmentation in Remote Sensing via Inpainting Diffusion Model" | [
"generative-ai",
"generative-modeling",
"science-and-engineering"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-20T15:59:06Z | false | 0 | 907,157,971 | https://steveimm.id/rs-paint | Jupyter Notebook | Apache-2.0 | [
"diffusion"
] | steveimm/rs-paint | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-21T15:48:06Z | [] | 2026-02-02T01:51:50Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 17 | [] | 2026-09-08T08:38:21Z | https://github.com/steveimm/rs-paint | null |
[
"3d",
"computer-vision",
"efficient-ml",
"model-compression",
"robotics"
] | [
"3d-object-detection",
"3d-perception",
"distillation",
"knowledge-distillation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation",
"multimodal.3d-object-detection"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-12-23T02:56:08Z | [CVPR2024] Official Implementation of "RadarDistill: Boosting Radar-based Object Detection Performance via Knowledge Distillation from LiDAR Features" | [
"3d",
"computer-vision",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 7 | 907,184,617 | null | Python | Apache-2.0 | [
"3d-object-detection",
"3d-perception",
"distillation",
"knowledge-distillation"
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] | 4 | 2026-10-28T17:27:44Z | [] | 2025-03-30T07:22:03Z | [
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] | include | ml-contribution-v5 | 326 | [] | 2026-09-29T19:54:09Z | https://github.com/TencentARC/DiTCtrl | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-12-25T09:13:06Z | Code repo for our paper of "Audio-Visual Representation Learning via Knowledge Distillation from Speech Foundation Models" | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-01T17:35:19Z | false | 1 | 908,148,435 | null | Python | null | [
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] | jxzhanggg/DistillAV | [
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] | 3 | 2026-10-07T18:07:52Z | [] | 2025-02-14T08:37:43Z | [
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] | include | ml-contribution-v5 | 12 | [] | 2026-03-20T01:24:13Z | https://github.com/jxzhanggg/DistillAV | null |
[
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 2 | 908,284,251 | https://sankalpsinha-cmos.github.io/MARVEL/ | Python | MIT | [
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] | 1 | 2026-10-01T17:50:46Z | [] | 2026-03-23T15:05:52Z | [
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] | include | ml-contribution-v5 | 3 | [
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] | 2026-03-25T02:00:13Z | https://github.com/SadilKhan/MARVEL-FX3D | null |
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"multimodal.text-to-3d",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-12-29T14:41:34Z | [CVPR 2025] Official implementation of "Kiss3DGen: Repurposing Image Diffusion Models for 3D Asset Generation" | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 26 | 909,705,234 | https://ltt-o.github.io/Kiss3dgen.github.io | Python | MIT | [
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] | EnVision-Research/Kiss3DGen | [
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] | 17 | 2026-10-20T15:59:06Z | [] | 2025-05-24T04:57:40Z | [
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] | 2026-10-13T01:57:00Z | https://github.com/EnVision-Research/Kiss3DGen | null |
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-12-30T06:34:39Z | Official implementation of Iterative Redundancy-Based Head Pruning for Efficient Self-Supervised Speech Recognition Models (ICASSP 2026). | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-25T15:11:36Z | false | 0 | 909,957,129 | null | Python | Apache-2.0 | [
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] | qwer55252/IRHP | [
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] | 1 | 2026-10-25T15:11:36Z | [] | 2026-10-21T07:54:59Z | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-10-21T20:03:58Z | https://github.com/qwer55252/IRHP | null |
[
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] | [
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"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-12-30T12:55:12Z | An official pytorch implementation for the paper: SDR-GNN: Spectral Domain Reconstruction Graph Neural Network for incomplete multimodal learning in conversational emotion recognition | [
"deep-learning",
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"multimodal-learning"
] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-14T16:10:46Z | false | 2 | 910,081,477 | null | Python | null | [
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] | fufangze/SDR-GNN | [
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] | 2 | 2026-10-17T13:17:44Z | [] | 2024-12-30T13:04:38Z | [
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] | include | ml-contribution-v5 | 18 | [] | 2026-07-07T14:24:21Z | https://github.com/fufangze/SDR-GNN | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-12-31T04:26:20Z | This is the source code for AAAI 2025 Paper: Reducing AUV Energy Consumption Through Dynamic Sensor Directions Switching via Deep Reinforcement Learning. | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-29T17:49:51Z | false | 0 | 910,359,237 | null | null | null | [
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] | MICLab-Rec/RECS | [
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] | 1 | 2026-10-29T17:49:51Z | [] | 2024-12-31T04:26:36Z | [
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] | include | ml-contribution-v5 | 1 | [] | 2025-04-20T07:42:11Z | https://github.com/MICLab-Rec/RECS | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-12-31T11:17:20Z | Official PyTorch implementation of paper: "Diffusion-based Synthetic Data Generation for Visible-Infrared Person Re-Identification". | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 2 | 910,472,858 | null | Python | null | [
"diffusion",
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] | BorgDiven/DiVE | [
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] | 12 | 2026-10-11T16:48:52Z | [] | 2025-05-27T05:10:41Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 20 | [] | 2026-09-08T08:39:52Z | https://github.com/BorgDiven/DiVE | null |
[
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] | [
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] | [
"description",
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"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-01T14:06:33Z | Official implementation of CA-ToMe for stable diffusion models paper | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-21T15:48:06Z | false | 0 | 910,812,958 | null | Python | MIT | [
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] | 1 | 2026-10-21T15:48:06Z | [] | 2025-02-21T17:16:42Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 5 | [] | 2026-03-19T09:19:31Z | https://github.com/omidiu/ca_tome | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-02T12:13:10Z | Code for paper Confidence-Guided Human-AI Collaboration: Reinforcement Learning with Distributed Proxy Value Propagation for Autonomous Driving | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T15:29:21Z | false | 3 | 911,176,005 | null | Python | null | [
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] | lzqw/C-HAC | [
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] | 5 | 2026-10-13T18:05:26Z | [] | 2025-06-05T06:39:42Z | [
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] | include | ml-contribution-v5 | 5 | [] | 2026-02-17T12:34:04Z | https://github.com/lzqw/C-HAC | null |
[
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] | [
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] | [
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"license-metadata",
"query-match",
"repository-metadata"
] | [
"applied.collaborative-filtering",
"graph.graph-contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-03T06:32:22Z | The code implementation of our paper Hyperbolic Graph Contrastive Learning for Collaborative Filtering (TKDE 2025) | [
"recommender-systems"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T16:52:35Z | false | 1 | 911,493,456 | null | Python | MIT | [
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] | Qin-lab-code/Hyperbolic-Graph-Contrastive-Learning-for-Collaborative-Filtering | [
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] | 6 | 2026-10-26T17:25:59Z | [] | 2025-04-07T05:01:02Z | [
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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 | [] | 2026-08-03T07:06:59Z | https://github.com/Qin-lab-code/Hyperbolic-Graph-Contrastive-Learning-for-Collaborative-Filtering | null |
[
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] | [
"general.self-supervised-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-03T13:48:13Z | [ICASSP 2025] Official Pytorch Implementation of Self-Supervised Learning for Detecting AI-Generated Faces as Anomalies | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 0 | 911,639,100 | null | Python | NOASSERTION | [
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] | include | ml-contribution-v5 | 4 | [] | 2026-07-23T06:21:15Z | https://github.com/MZMMSEC/AIGFD_EXIF | null |
[
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] | [
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] | [
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] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-05T08:02:54Z | The source code of our paper "Spatio-Temporal Knowledge driven Diffusion Model for Mobile Traffic Generation" | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-21T15:48:06Z | false | 1 | 912,314,023 | null | Python | MIT | [
"diffusion"
] | tsinghua-fib-lab/STK-Diff | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-21T15:48:06Z | [] | 2025-10-21T09:41:42Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 9 | [] | 2026-04-21T11:15:29Z | https://github.com/tsinghua-fib-lab/STK-Diff | null |
[
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] | [
"description",
"query-match",
"repository-metadata"
] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-05T13:15:10Z | Detail code implementation and experimental setting for our paper: Federated Learning on Multilabel Evolving Data Streams | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-18T14:44:29Z | false | 0 | 912,396,675 | https://ieeexplore.ieee.org/document/11098479 | Python | null | [
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] | kholam/FedMuL | [
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"query-match",
"repository-metadata"
] | 1 | 2026-10-18T14:44:29Z | [] | 2025-10-22T13:14:34Z | [
"trust.federated-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 1 | [] | 2025-10-22T13:18:44Z | https://github.com/kholam/FedMuL | null |
[
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] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-05T19:47:39Z | A novel architecture that integrates blockchain technology with federated learning to decentralize machine learning. | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-18T14:44:29Z | false | 0 | 912,518,765 | null | TypeScript | null | [
"collaborative-learning",
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] | anupa-perera/DML-Chain | [
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] | 1 | 2026-10-18T14:44:29Z | [] | 2026-06-08T04:20:58Z | [
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] | include | ml-contribution-v5 | 1 | [] | 2026-06-08T04:21:03Z | https://github.com/anupa-perera/DML-Chain | null |
[
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"video-generation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.video-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-05T23:59:55Z | Source code for paper GS-DiT: Advancing Video Generation with Pseudo 4D Gaussian Fields through Efficient Dense 3D Point Tracking | [
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"generative-ai"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-28T17:27:44Z | false | 4 | 912,572,693 | null | null | Apache-2.0 | [
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] | 1 | 2026-10-28T17:27:44Z | [] | 2025-01-05T23:59:55Z | [
"vision.video-generation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 55 | [] | 2026-03-30T08:10:38Z | https://github.com/wkbian/GS-DiT | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-06T04:06:23Z | An open-source implementation of Regional Adaptive Sampling (RAS), a novel diffusion model sampling strategy that introduces regional variability in sampling steps | [
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"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-20T15:59:06Z | false | 10 | 912,626,359 | https://aka.ms/ras-dit | Python | MIT | [
"diffusion"
] | microsoft/RAS | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-20T15:59:06Z | [] | 2026-04-10T02:59:11Z | [
"llm.diffusion-phrase"
] | 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 | 157 | [
"diffusion"
] | 2026-10-09T17:15:08Z | https://github.com/microsoft/RAS | null |
[
"computer-vision",
"language",
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"multimodal",
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"vision"
] | [
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] | [
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] | [
"multimodal.vision-language",
"multimodal.vlm",
"specialized.test-time-adaptation",
"vision.vision-language"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-06T14:25:52Z | [CVPR 2025 - Highlight] Official implementation of the paper "Realistic Test-Time Adaptation of Vision-Language Models" (StatA). | [
"computer-vision",
"multimodal",
"multimodal-learning",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-28T18:33:08Z | false | 1 | 912,852,331 | https://arxiv.org/abs/2501.03729 | Python | AGPL-3.0 | [
"multimodal-learning",
"test-time-adaptation",
"vision-language"
] | MaxZanella/StatA | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 6 | 2026-10-23T15:59:01Z | [] | 2025-10-31T15:07:49Z | [
"vision.vision-language"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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] | include | ml-contribution-v5 | 66 | [] | 2026-09-17T11:28:35Z | https://github.com/MaxZanella/StatA | null |
[
"forecasting",
"time-series",
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] | [
"forecasting",
"sequence-modeling"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"timeseries.forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-06T18:22:41Z | The official implementation for the paper "ST-MTM: Masked Time Series Modeling with Seasonal-Trend Decomposition for Time Series Forecasting" | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 1 | 912,953,173 | null | Python | MIT | [
"forecasting",
"sequence-modeling"
] | hwseo95/st-mtm | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-24T14:31:13Z | [] | 2025-05-22T19:03:37Z | [
"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 | 12 | [] | 2026-04-06T15:41:25Z | https://github.com/hwseo95/st-mtm | null |
[
"efficient-ml",
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] | [
"distillation",
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] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-08T06:43:36Z | Official implementation of the paper "Generalizing teacher networks for effective knowledge distillation across student architectures" (BMVC'24) | [
"efficient-ml",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 0 | 913,682,622 | null | Python | null | [
"distillation",
"knowledge-distillation"
] | kuluhan/GTN | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2025-01-08T07:07:18Z | [
"efficiency.knowledge-distillation"
] | 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 | [] | 2025-05-15T07:52:07Z | https://github.com/kuluhan/GTN | null |
[
"efficient-ml",
"model-compression",
"natural-language-processing"
] | [
"distillation",
"knowledge-distillation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-08T23:29:36Z | Official Code Repository for the paper, Multi-aspect knowledge distillation with Large Language Model, CVPRW 2025 | [
"efficient-ml",
"model-compression",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 914,087,124 | null | Python | NOASSERTION | [
"distillation",
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] | taegyeong-lee/MaKD-Multi-aspect-knowledge-distillation-with-Large-Language-Model | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-04T15:58:58Z | [] | 2025-08-30T05:26:23Z | [
"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 | 5 | [] | 2026-07-11T08:27:51Z | https://github.com/taegyeong-lee/MaKD-Multi-aspect-knowledge-distillation-with-Large-Language-Model | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-09T01:21:38Z | A novel Graph Neural Network called PCE-GNN | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-17T13:17:44Z | false | 0 | 914,113,107 | null | null | null | [
"graph-neural-network",
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] | SanJinCabbage/Classfier-GNN | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-17T13:17:44Z | [] | 2025-01-09T01:21:39Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [] | 2025-01-09T01:21:42Z | https://github.com/SanJinCabbage/Classfier-GNN | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"recall.name.diffusion-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-09T05:26:11Z | The official implementation of CVPR'25 Oral paper "Go-with-the-Flow: Motion-Controllable Video Diffusion Models Using Real-Time Warped Noise" | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 50 | 914,184,308 | https://eyeline-labs.github.io/Go-with-the-Flow/ | Python | NOASSERTION | [
"diffusion"
] | Eyeline-Labs/Go-with-the-Flow | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-20T15:59:06Z | [] | 2025-10-13T17:04:31Z | [
"llm.diffusion-phrase"
] | 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 | 1,096 | [] | 2026-10-13T06:31:01Z | https://github.com/Eyeline-Labs/Go-with-the-Flow | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
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"message-passing",
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] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description",
"graph.representation-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-10T07:20:51Z | The code for paper "Heterophilous distribution propagation for Graph Neural Networks". | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 0 | 914,726,535 | null | Python | null | [
"graph-neural-network",
"message-passing",
"neural-network"
] | zzn-promax/HDP | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-18T14:44:29Z | [] | 2025-01-10T07:55:37Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 1 | [] | 2025-05-14T07:40:37Z | https://github.com/zzn-promax/HDP | null |
[
"computational-science",
"physics",
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] | [
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] | [
"description",
"query-match",
"repository-metadata"
] | [
"science.neural-operators"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-11T07:05:25Z | Code for paper "Beyond Closure Models: Learning Chaotic Systems via Physics-Informed Neural Operators". | [
"computational-science",
"physics",
"science-and-engineering"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 1 | 915,180,189 | null | Python | null | [
"neural-operators",
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] | neuraloperator/pino-closure-models | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-09-28T18:33:08Z | [] | 2025-12-24T03:09:33Z | [
"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 | 16 | [] | 2026-03-12T16:31:44Z | https://github.com/neuraloperator/pino-closure-models | null |
[
"efficient-ml",
"model-compression"
] | [
"distillation",
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] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-11T11:39:57Z | This repository includes official implementation for the paper: Gradient Scaling Knowledge Distillation: Alleviating Gradient Insensitivity to Enhance Non-target Class Learning | [
"efficient-ml",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 0 | 915,259,348 | null | Python | NOASSERTION | [
"distillation",
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] | snw2021/GKD | [
"description",
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"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2025-02-03T16:00:01Z | [
"efficiency.knowledge-distillation"
] | 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 | 2 | [] | 2026-08-21T05:58:40Z | https://github.com/snw2021/GKD | null |
[
"distributed-ml",
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] | [
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] | [
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"license-metadata",
"query-match",
"repository-metadata"
] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-12T09:34:26Z | Official Code Repository for our paper - Homophily Heterogeneity Matters in Graph Federated Learning: A Spectrum Sharing and Complementing | [
"distributed-ml",
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-18T14:44:29Z | false | 0 | 915,594,821 | null | Python | GPL-3.0 | [
"collaborative-learning",
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] | blgpb/FedGSP | [
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"query-match",
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] | 1 | 2026-10-18T14:44:29Z | [] | 2026-03-18T12:35:20Z | [
"trust.federated-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 | 1 | [] | 2026-03-18T12:35:24Z | https://github.com/blgpb/FedGSP | null |
[
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] | [
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] | [
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"query-match",
"repository-metadata"
] | [
"vision.vision-language"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-12T09:50:30Z | Official Implementation (Pytorch) of "Super-class guided Transformer for Zero-Shot Attribute Classification", AAAI 2025 | [
"computer-vision",
"multimodal"
] | [
"classifier",
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 1 | 915,598,954 | null | Python | null | [
"multimodal-learning",
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] | mlvlab/SugaFormer | [
"description",
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"query-match",
"repository-metadata"
] | 1 | 2026-10-23T15:59:01Z | [] | 2025-01-15T14:38:03Z | [
"vision.vision-language"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 15 | [
"aaai2025",
"zero-shot-learning"
] | 2026-02-02T18:36:45Z | https://github.com/mlvlab/SugaFormer | null |
[
"distributed-ml",
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] | [
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"federated-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-13T13:42:18Z | Official code for the paper "Efficient Privacy Auditing in Federated Learning", published at the 33rd USENIX Security Symposium (USENIX Security 2024). | [
"distributed-ml",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-17T13:17:44Z | false | 0 | 916,116,241 | null | Python | null | [
"collaborative-learning",
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] | changhongyan123/privacy_auditing_in_FL | [
"description",
"query-match",
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] | 1 | 2026-10-17T13:17:44Z | [] | 2025-01-13T14:39:48Z | [
"trust.federated-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 5 | [] | 2026-08-18T06:35:53Z | https://github.com/changhongyan123/privacy_auditing_in_FL | null |
[
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] | [
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] | [
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] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-14T03:11:34Z | Official Implementation of the paper "Variational Control for Guidance in Diffusion Models" accepted at ICML'25 | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-21T15:48:06Z | false | 1 | 916,408,613 | null | Python | MIT | [
"diffusion"
] | czi-ai/oc-guidance | [
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] | 1 | 2026-10-21T15:48:06Z | [] | 2025-12-05T21:40:14Z | [
"llm.diffusion-phrase"
] | 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 | 20 | [] | 2026-04-30T00:49:37Z | https://github.com/czi-ai/oc-guidance | null |
[
"deep-learning",
"efficient-ml",
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] | [
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] | [
"description",
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"query-match",
"repository-metadata"
] | [
"efficiency.mixture-of-experts"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-14T07:56:19Z | A state-of-the-art text-to-motion generation model combining Mixture of Experts (MoE) with diffusion models for high-quality human motion synthesis. This model advances the field of motion generation by introducing a novel architecture that combines the strengths of MoE and diffusion models. | [
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"language"
] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 1 | 916,495,387 | null | Python | Apache-2.0 | [
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"sparse-activation"
] | ltdoanh2004/MotionDiffusion-MoE | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-09-30T16:47:43Z | [] | 2025-03-04T07:12:55Z | [
"efficiency.mixture-of-experts"
] | 767bf806a31761e2f2af28d3725622e37b28c5db | 2026-09-30T18:16:34Z | gh-ml-readme-evidence-v2 | "767bf806a31761e2f2af28d3725622e37b28c5db" | [
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] | [
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"ml-method-context",
"paper-reference"
] | ok | 2026-09-30T18:16:34Z | ltdoanh2004/MotionDiffusion-MoE | specific-method-with-novelty-claim | [
"method-contribution",
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"ml-method-context",
"ml-method-cue",
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] | include | ml-contribution-v5 | 1 | [] | 2025-06-06T09:10:24Z | https://github.com/ltdoanh2004/MotionDiffusion-MoE | null |
[
"forecasting",
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] | [
"timeseries.forecasting",
"timeseries.state-space-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-14T20:46:00Z | Official Code for "SDE: A Simplified and Disentangled Dependency Encoding Framework for State Space Models in Time Series Forecasting" (KDD 2025） | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 0 | 916,828,528 | https://arxiv.org/abs/2408.12068 | Python | MIT | [
"forecasting",
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] | YukinoAsuna/SAMBA | [
"description",
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"paper-reference",
"query-match",
"repository-metadata"
] | 4 | 2026-10-24T14:31:13Z | [] | 2026-04-07T09:02:33Z | [
"timeseries.forecasting"
] | 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 | [] | 2026-05-27T13:06:15Z | https://github.com/YukinoAsuna/SAMBA | null |
[
"deep-learning",
"graph-learning",
"time-series-and-forecasting"
] | [
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] | [
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"github-topics",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-15T02:14:05Z | [AAAI 2025] Official Code for "Dynamics-based Feature Augmentation of Graph Neural Networks for Variant Emergence Prediction" | [
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] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-18T14:44:29Z | false | 0 | 916,919,128 | https://ojs.aaai.org/index.php/AAAI/article/view/34995 | Python | null | [
"graph-neural-network",
"message-passing",
"neural-network"
] | MansoorehMontazerin/gnnvariants | [
"description",
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"query-match",
"repository-metadata"
] | 1 | 2026-10-18T14:44:29Z | [] | 2025-06-10T21:46:32Z | [
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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 | [
"covid19",
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"graph-neural-networks",
"time-series-analysis",
"time-series-prediction"
] | 2025-06-10T21:46:35Z | https://github.com/MansoorehMontazerin/gnnvariants | null |
[
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"time-series-and-forecasting"
] | [
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] | [
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] | [
"timeseries.forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-15T21:03:58Z | The repo is the official implementation for the paper: Intriguing Properties of Positional Encoding in Time Series Forecasting. | [
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"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-24T14:31:13Z | false | 0 | 917,366,299 | null | Python | null | [
"forecasting",
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] | bochen0909/T2B-PE | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-24T14:31:13Z | [] | 2025-01-15T21:52:40Z | [
"timeseries.forecasting"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2025-01-15T21:52:42Z | https://github.com/bochen0909/T2B-PE | null |
[
"information-retrieval",
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] | [
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] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"nlp.rag"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-16T02:38:20Z | The code for paper: Hierarchical Document Refinement for Long-context Retrieval-augmented Generation [ACL2025 Oral] | [
"information-retrieval",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 6 | 917,454,885 | null | Python | MIT | [
"rag",
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] | ignorejjj/LongRefiner | [
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"query-match",
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] | 3 | 2026-10-19T17:46:36Z | [] | 2025-08-25T16:07:18Z | [
"nlp.rag"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 48 | [] | 2026-09-07T08:01:49Z | https://github.com/ignorejjj/LongRefiner | null |
[
"reinforcement-learning"
] | [
"deep-reinforcement-learning",
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] | [
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] | [
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-16T09:24:29Z | Code for paper " Asynchronous Fractional Multi-Agent Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing". | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-28T17:27:44Z | false | 1 | 917,589,846 | null | Python | Apache-2.0 | [
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] | Lendle-King/AF-MADRL | [
"description",
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] | 1 | 2026-10-28T17:27:44Z | [] | 2025-01-16T10:42:59Z | [
"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 | [] | 2026-10-08T10:25:47Z | https://github.com/Lendle-King/AF-MADRL | 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 | [] | 2025-01-19T08:15:28Z | Official implementation of the paper "Amplifier: Bringing Attention to Neglected Low-Energy Components in Time Series Forecasting". | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 5 | 918,920,164 | null | Python | Apache-2.0 | [
"forecasting",
"sequence-modeling"
] | aikunyi/Amplifier | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-10-24T14:31:13Z | [] | 2026-03-11T01:31:11Z | [
"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 | 34 | [] | 2026-09-22T06:14:18Z | https://github.com/aikunyi/Amplifier | null |
[
"distributed-ml",
"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 | [] | 2025-01-20T13:35:42Z | Code for paper “Communication-Efficient Federated Learning by Exploiting Spatio-Temporal Correlations”. | [
"distributed-ml",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-18T14:44:29Z | false | 0 | 919,487,697 | null | Python | null | [
"collaborative-learning",
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] | zsl503/GradESTC | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-18T14:44:29Z | [] | 2025-04-10T11:23:24Z | [
"trust.federated-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 | 0 | [] | 2025-04-10T11:23:28Z | https://github.com/zsl503/GradESTC | null |
[
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] | [
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"query-match",
"repository-metadata"
] | [
"timeseries.forecasting",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-21T18:18:36Z | [KDD 2026] Official implementation of "TimeDistill: Efficient Long-Term Time Series Forecasting with MLPs via Cross-Architecture Distillation" | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"deep-learning",
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 5 | 920,195,406 | https://arxiv.org/abs/2502.15016 | Python | Apache-2.0 | [
"distillation",
"forecasting",
"sequence-modeling"
] | LingFengGold/TimeDistill | [
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"github-topics",
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"repository-metadata"
] | 9 | 2026-10-24T14:31:13Z | [] | 2026-03-22T17:52:49Z | [
"timeseries.forecasting",
"timeseries.topic"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 37 | [
"deep-learning",
"machine-learning",
"time-series",
"time-series-analysis",
"time-series-forecast",
"time-series-forecasting"
] | 2026-09-17T17:30:46Z | https://github.com/LingFengGold/TimeDistill | null |
[
"generative-modeling",
"mathematics",
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] | [
"combinatorial-optimization",
"diffusion",
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] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"gap2026.math-neural-combinatorial-optimization"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-22T11:11:58Z | Official implementation of ICLR 2025 paper: "Unify ML4TSP: Drawing Methodological Principles for TSP and Beyond from Streamlined Design Space of Learning and Search". | [
"generative-modeling",
"mathematics",
"operations-research"
] | [
"machine-learning",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-25T16:01:25Z | false | 0 | 920,557,135 | null | C | null | [
"combinatorial-optimization",
"diffusion",
"neural-network"
] | Thinklab-SJTU/ML4TSPBench | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 2 | 2026-09-29T17:12:21Z | [] | 2025-05-20T07:30:42Z | [
"gap2026.math-neural-combinatorial-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 | 46 | [
"combinatorial-optimization",
"diffusion-models",
"machine-learning",
"mcts-algorithm",
"tsp-problem"
] | 2026-06-19T13:24:29Z | https://github.com/Thinklab-SJTU/ML4TSPBench | null |
[
"reinforcement-learning"
] | [
"policy-gradient",
"policy-learning",
"policy-optimization",
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] | [
"description",
"github-topics",
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"query-match",
"repository-metadata"
] | [
"rl.core",
"rl.ppo"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-22T17:29:39Z | Official Implementation of ICLR2025 Paper: Songyuan Zhang, Oswin So, Mitchell Black, Chuchu Fan: "Discrete GCBF Proximal Policy Optimization for Multi-agent Safe Optimal Control". | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 10 | 920,737,695 | https://mit-realm.github.io/dgppo/ | Python | MIT | [
"policy-learning",
"reinforcement-learning"
] | MIT-REALM/dgppo | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 9 | 2026-10-19T16:36:56Z | [] | 2026-10-07T13:30:41Z | [
"rl.core"
] | 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 | 35 | [
"control-barrier-functions",
"multi-agent-systems",
"reinforcement-learning",
"safety"
] | 2026-10-16T17:04:47Z | https://github.com/MIT-REALM/dgppo | null |
[
"general-ml",
"generative-modeling"
] | [
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"diffusion",
"machine-learning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.topic-deep-learning",
"general.topic-machine-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-22T21:15:04Z | Official implementation for "Stable Flow: Vital Layers for Training-Free Image Editing" [CVPR 2025] | [
"general-ml",
"generative-modeling"
] | [
"deep-learning",
"machine-learning",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 25 | 920,835,737 | https://omriavrahami.com/stable-flow/ | Python | NOASSERTION | [
"deep-learning",
"diffusion",
"machine-learning"
] | snap-research/stable-flow | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-09-29T17:12:21Z | [] | 2025-06-08T11:15:13Z | [
"general.topic-deep-learning",
"general.topic-machine-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 411 | [
"deep-learning",
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"flow-models",
"generative-ai",
"image-editing",
"machine-learning",
"text-to-image"
] | 2026-09-27T06:17:57Z | https://github.com/snap-research/stable-flow | null |
[
"deep-learning",
"graph-learning",
"recommender-systems"
] | [
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"message-passing",
"transformer"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.graph-transformer"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-23T03:11:39Z | The official PyTorch implementation for WWW 2025 paper "Rankformer: A Graph Transformer for Recommendation based on Ranking Objective". | [
"deep-learning",
"graph-learning",
"recommender-systems"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 1 | 920,935,530 | null | Python | null | [
"graph-transformer",
"message-passing",
"transformer"
] | StupidThree/Rankformer | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-09-28T18:33:08Z | [] | 2026-01-26T12:55:17Z | [
"graph.graph-transformer"
] | 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 | 15 | [] | 2026-09-17T14:01:40Z | https://github.com/StupidThree/Rankformer | null |
[
"generative-ai",
"generative-modeling",
"linguistics",
"multimodal-learning",
"natural-language-processing",
"synthetic-data",
"time-series",
"time-series-and-forecasting"
] | [
"diffusion",
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] | [
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] | [
"llm.diffusion-phrase",
"recall.computational-linguistics",
"timeseries.synthetic-generation",
"timeseries.topic"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-23T17:14:27Z | [IJCAI 2025] Official implementation of "T2S: High-resolution Time Series Generation with Text-to-Series Diffusion Models" | [
"generative-modeling",
"multimodal-learning",
"natural-language-processing",
"time-series",
"time-series-and-forecasting"
] | [
"deep-learning",
"machine-learning",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T15:29:21Z | false | 11 | 921,284,134 | https://arxiv.org/abs/2505.02417 | Python | Apache-2.0 | [
"diffusion",
"sequence-modeling"
] | WinfredGe/T2S | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 14 | 2026-10-24T14:31:13Z | [] | 2026-05-01T14:34:57Z | [
"timeseries.topic"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 95 | [
"cross-modal-learning",
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"language-model",
"machine-learning",
"multimodal-deep-learning",
"multimodal-time-series",
"time-series",
"time-series-analysis",
"time-series-generation"
] | 2026-10-22T11:27:45Z | https://github.com/WinfredGe/T2S | null |
[
"computer-vision"
] | [
"mamba",
"restoration",
"super-resolution"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.image-super-resolution"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-24T17:59:48Z | Official implementation of paper 'A Collaborative Network of Mamba and CNN for Lightweight Image Super-Resolution'. | [
"computer-vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-28T17:27:44Z | false | 0 | 921,838,838 | null | Python | Apache-2.0 | [
"mamba",
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] | HITXinWang/CNMC | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-28T17:27:44Z | [] | 2025-05-31T08:54:31Z | [
"vision.image-super-resolution"
] | 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 | 9 | [] | 2026-03-20T06:35:18Z | https://github.com/HITXinWang/CNMC | null |
[
"generative-ai",
"generative-modeling",
"interpretability-and-safety"
] | [
"diffusion"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-25T12:54:36Z | [ICLR'25] Official implementation of "Training-free Diffusion Model Alignment with Sampling Demons" | [
"generative-ai",
"generative-modeling",
"interpretability-and-safety"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-20T15:59:06Z | false | 2 | 922,147,429 | null | Python | Apache-2.0 | [
"diffusion"
] | aiiu-lab/DemonSampling | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-20T15:59:06Z | [] | 2025-10-27T13:21:15Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 24 | [] | 2026-10-12T07:39:44Z | https://github.com/aiiu-lab/DemonSampling | null |
[
"general-ml",
"health-and-biomedicine",
"natural-language-processing"
] | [
"novel-method"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.novel-method"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-27T04:33:09Z | ESM4SL is a novel method using the pre-trained protein language model ESM-2 for the prediction of cancer cell line-specific synthetic lethality. | [
"general-ml",
"health-and-biomedicine",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 0 | 922,792,923 | null | Python | MIT | [
"novel-method"
] | JieZheng-ShanghaiTech/ESM4SL | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-09-29T17:12:21Z | [] | 2026-05-15T08:25:40Z | [
"general.novel-method"
] | 683be9d723d1d6aa290ebbfe09a27a7a7de06e98 | 2026-09-26T15:53:33Z | gh-ml-readme-evidence-v2 | "683be9d723d1d6aa290ebbfe09a27a7a7de06e98" | [
"other",
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"usage"
] | [
"method-contribution",
"ml-method-context"
] | ok | 2026-09-26T15:53:33Z | JieZheng-ShanghaiTech/ESM4SL | specific-method-with-novelty-claim | [
"contribution-language",
"method-contribution",
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-05-15T08:25:45Z | https://github.com/JieZheng-ShanghaiTech/ESM4SL | null |
[
"chemistry",
"drug-discovery",
"materials-science",
"multimodal-learning"
] | [
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] | [
"description",
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"repository-metadata"
] | [
"chem-property-prediction"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-28T03:08:04Z | This repository is the official implementation of MHGCL, which is model proposed in a paper: MHGCL: A multi-modal hypergraph contrastive learning framework for molecular property prediction. | [
"chemistry",
"drug-discovery",
"materials-science",
"multimodal-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-03T15:45:52Z | false | 0 | 923,327,529 | null | null | null | [
"contrastive-learning",
"property-prediction",
"qsar",
"supervised-learning"
] | GuanRuiYun/MHGCL | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-03T15:45:52Z | [] | 2025-02-06T05:19:17Z | [
"chem-property-prediction"
] | 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 | [] | 2025-08-12T14:28:12Z | https://github.com/GuanRuiYun/MHGCL | null |
[
"computer-vision",
"generative-modeling"
] | [
"diffusion",
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] | [
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"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.image-restoration"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-28T04:45:29Z | Official Pytorch Code Implementation for "UniDB: A Unified Diffusion Bridge Framework via Stochastic Optimal Control", accepted by ICML 2025. | [
"computer-vision",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 6 | 923,354,668 | null | Python | MIT | [
"diffusion",
"restoration"
] | UniDB-SOC/UniDB | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-09-28T18:33:08Z | [] | 2026-08-14T02:27:23Z | [
"vision.image-restoration"
] | 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 | 41 | [] | 2026-09-11T11:41:55Z | https://github.com/UniDB-SOC/UniDB | null |
[
"3d",
"computer-vision",
"generative-ai",
"generative-modeling",
"graphics"
] | [
"diffusion",
"gaussian-splatting",
"generative-modeling",
"neural-rendering"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"multimodal.gaussian-splatting",
"vision.image-diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-28T09:01:29Z | [ICLR 2025] Official implementation of "DiffSplat: Repurposing Image Diffusion Models for Scalable 3D Gaussian Splat Generation". | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 35 | 923,445,230 | https://chenguolin.github.io/projects/DiffSplat | Python | MIT | [
"diffusion"
] | chenguolin/DiffSplat | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 8 | 2026-10-20T15:59:06Z | [] | 2026-03-19T07:14:00Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 548 | [] | 2026-10-13T06:23:57Z | https://github.com/chenguolin/DiffSplat | null |
[
"distributed-ml",
"privacy-and-federated-learning"
] | [
"collaborative-learning",
"federated-learning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-28T15:54:13Z | Official code of the paper: "Federated Learning with Multiple Fairness Constraints" | [
"distributed-ml",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-18T14:44:29Z | false | 0 | 923,638,100 | null | Python | Apache-2.0 | [
"collaborative-learning",
"federated-learning"
] | michelefontana92/GLOFAIR | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-18T14:44:29Z | [] | 2026-04-10T13:20:09Z | [
"trust.federated-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 | [
"ethical-ai",
"fairness",
"federated-learning",
"glofair"
] | 2026-04-10T13:21:08Z | https://github.com/michelefontana92/GLOFAIR | null |
[
"3d-vision",
"computer-vision"
] | [
"3d-perception",
"contrastive-learning",
"depth-estimation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.depth-estimation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-29T05:11:29Z | Official implementation ICRA 2025 paper "Brdige Spectral-wise and Multi-spectral Depth Estimation via Geometry-guided Contrastive Learning" | [
"3d-vision",
"computer-vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-30T17:16:29Z | false | 1 | 923,935,292 | null | Python | MIT | [
"3d-perception",
"contrastive-learning",
"depth-estimation"
] | UkcheolShin/BridgeMultiSpectralDepth | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-30T17:16:29Z | [] | 2025-01-29T05:21:58Z | [
"vision.depth-estimation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 15 | [] | 2026-10-23T17:24:08Z | https://github.com/UkcheolShin/BridgeMultiSpectralDepth | null |
[
"deep-learning",
"efficient-ml",
"language",
"sequence-modeling",
"time-series"
] | [
"conditional-computation",
"mamba",
"mixture-of-experts",
"sequence-modeling",
"sparse-activation",
"state-space-model"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.mixture-of-experts",
"timeseries.state-space-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-29T06:19:13Z | Mamba R1 represents a novel architecture that combines the efficiency of Mamba's state space models with the scalability of Mixture of Experts (MoE). | [
"sequence-modeling",
"time-series"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 2 | 923,956,748 | https://swarms.ai | Python | MIT | [
"mamba",
"mixture-of-experts",
"sequence-modeling",
"state-space-model"
] | The-Swarm-Corporation/Mamba-R1 | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-01T17:50:46Z | [] | 2025-10-13T02:21:50Z | [
"timeseries.state-space-models"
] | a0f8916da753bfe58de3ae5277099bd2bc6834e0 | 2026-09-26T15:53:33Z | gh-ml-readme-evidence-v2 | "a0f8916da753bfe58de3ae5277099bd2bc6834e0" | [
"citation",
"installation",
"method",
"other",
"overview",
"results",
"usage"
] | [
"method-contribution",
"ml-method-context"
] | ok | 2026-09-26T15:53:33Z | The-Swarm-Corporation/Mamba-R1 | specific-method-with-novelty-claim | [
"method-contribution",
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue"
] | include | ml-contribution-v5 | 26 | [
"agents",
"ai",
"alibaba",
"chat",
"deepseek",
"qwen",
"swarm-agents",
"swarms"
] | 2026-09-04T21:57:16Z | https://github.com/The-Swarm-Corporation/Mamba-R1 | null |
[
"generative-ai",
"generative-modeling",
"graph-learning"
] | [
"generative-modeling",
"graph-generation"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"graph.graph-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-29T13:39:41Z | Official code for "Flatten Graphs as Sequences: Transformers are scalable graph generators" (NeurIPS 2025) | [
"generative-ai",
"generative-modeling",
"graph-learning"
] | [
"generative-model",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 1 | 924,138,062 | https://arxiv.org/abs/2502.02216 | Python | BSD-3-Clause | [
"generative-modeling",
"graph-generation"
] | BorgwardtLab/AutoGraph | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 3 | 2026-10-22T16:09:07Z | [] | 2025-10-17T07:38:48Z | [
"graph.graph-generation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 18 | [
"autoregressive-models",
"graph-generative-models",
"molecular-generation",
"neurips-2025"
] | 2026-08-13T09:33:08Z | https://github.com/BorgwardtLab/AutoGraph | 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 | [] | 2025-01-29T14:22:59Z | The official implementation of the paper "Is Attention Mechanism Effective? The Degeneration of Transformers into MLPs in Time Series Forecasting" | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 1 | 924,160,618 | null | Python | null | [
"forecasting",
"sequence-modeling"
] | Greek-Guardian/TST-Degeneration | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-24T14:31:13Z | [] | 2025-10-21T05:18:43Z | [
"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 | 35 | [] | 2026-06-21T05:25:49Z | https://github.com/Greek-Guardian/TST-Degeneration | null |
[
"deep-learning",
"graph-learning",
"materials-science"
] | [
"graph-neural-network",
"message-passing",
"neural-network",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.topic-gnn"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-30T06:14:55Z | The official code respository for "Rethinking the role of frames for SE(3)-invariant crystal structure modeling" (ICLR 2025) | [
"deep-learning",
"graph-learning",
"materials-science"
] | [
"machine-learning",
"neural-network",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T16:52:35Z | false | 3 | 924,509,386 | https://omron-sinicx.github.io/crystalframer/ | Python | MIT | [
"graph-neural-network",
"message-passing",
"neural-network",
"transformer"
] | omron-sinicx/crystalframer | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-09-29T17:12:21Z | [] | 2025-10-16T12:56:33Z | [
"graph.topic-gnn"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 21 | [
"graph-neural-network",
"graph-neural-networks",
"iclr",
"iclr2025",
"machine-learning",
"materials",
"materials-informatics",
"materials-science",
"transformer",
"transformer-architecture",
"transformers"
] | 2026-07-05T05:13:09Z | https://github.com/omron-sinicx/crystalframer | null |
[
"generative-ai",
"generative-modeling",
"optimization"
] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-01-31T07:06:52Z | Official implementation of "Half-order Fine-Tuning for Diffusion Model: A Recursive Likelihood Ratio Optimizer", accepted by ICLR 2026 (oral presentation) | [
"generative-ai",
"generative-modeling",
"optimization"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-21T15:48:06Z | false | 1 | 925,073,273 | null | Python | null | [
"diffusion"
] | RTkenny/RLR-Optimizer | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-21T15:48:06Z | [] | 2026-04-05T07:22:48Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 14 | [] | 2026-04-28T12:33:11Z | https://github.com/RTkenny/RLR-Optimizer | null |
[
"applied-mathematics",
"computational-science",
"scientific-computing"
] | [
"differentiable-programming",
"learned-solvers",
"neural-network",
"surrogate-modeling"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"science.scientific-machine-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-02-01T04:58:08Z | Code for Paper "Exploring Kolmogorov-Arnold Neural Networks for Hybrid and Transparent Hydrological Modeling" | [
"applied-mathematics",
"computational-science",
"scientific-computing"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-03T15:45:52Z | false | 0 | 925,533,792 | null | Julia | null | [
"differentiable-programming",
"learned-solvers",
"neural-network",
"surrogate-modeling"
] | chooron/HydroPaperCodes | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-03T15:45:52Z | [] | 2025-06-21T08:16:44Z | [
"science.scientific-machine-learning"
] | 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 | 0 | [] | 2025-06-21T08:16:48Z | https://github.com/chooron/HydroPaperCodes | null |
[
"information-retrieval",
"natural-language-processing"
] | [
"bm25",
"lexical-retrieval",
"rag",
"retrieval-augmented-generation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"nlp-bm25-retrieval",
"nlp.rag"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-02-02T02:43:03Z | [NAACL 2025] Large-Scale Corpus Construction and Retrieval-Augmented Generation for Ancient Chinese Poetry: New Method and Data Insights (ACP-Corpus; ACP-QA; ACP-RAG) | [
"information-retrieval"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-19T17:46:36Z | false | 0 | 925,902,286 | null | Python | null | [
"bm25",
"lexical-retrieval",
"rag",
"retrieval-augmented-generation"
] | SCUT-DLVCLab/ACP-RAG | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-21T15:48:06Z | [] | 2026-07-17T03:47:16Z | [
"nlp-bm25-retrieval"
] | 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 | 13 | [] | 2026-09-18T02:21:12Z | https://github.com/SCUT-DLVCLab/ACP-RAG | null |
[
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-02-02T15:38:34Z | Official implementation of SFPK-pruner in ICLR 2025 "Probabilistic Neural Pruning via Sparsity Evolutionary Fokker-Planck-Kolmogorov Equation" | [
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-25T15:11:36Z | false | 0 | 926,115,100 | null | Python | Apache-2.0 | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | mzf666/SFPK-main | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-25T15:11:36Z | [] | 2025-02-02T15:45:24Z | [
"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 | 4 | [] | 2026-09-12T18:31:22Z | https://github.com/mzf666/SFPK-main | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-02-03T00:59:22Z | This repository contains the implementation for our work "TopoDiffusionNet: A Topology-aware Diffusion Model", accepted to ICLR 2025. | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-21T15:48:06Z | false | 2 | 926,288,673 | null | Python | MIT | [
"diffusion"
] | Saumya-Gupta-26/TopoDiffusionNet | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-21T15:48:06Z | [] | 2025-04-17T20:58:26Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 29 | [] | 2026-09-23T06:15:11Z | https://github.com/Saumya-Gupta-26/TopoDiffusionNet | null |
[
"computer-vision",
"generative-modeling"
] | [
"diffusion",
"restoration",
"super-resolution"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.image-super-resolution"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-02-03T07:11:38Z | [ICML 2025] Official implementation of the paper "Compressed Image Generation with Denoising Diffusion Codebook Models" | [
"computer-vision",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-28T17:27:44Z | false | 1 | 926,397,086 | null | Python | MIT | [
"diffusion",
"restoration",
"super-resolution"
] | DDCM-2025/ddcm-compressed-image-generation | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-28T17:27:44Z | [] | 2025-08-10T14:57:23Z | [
"vision.image-super-resolution"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 91 | [] | 2026-10-20T22:11:31Z | https://github.com/DDCM-2025/ddcm-compressed-image-generation | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-02-03T09:03:55Z | [ICLR'25] The official implementation of the paper titled "Jump Your Steps: Optimizing Sampling Schedule of Discrete Diffusion Models" | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-21T15:48:06Z | false | 1 | 926,441,018 | null | Python | null | [
"diffusion"
] | enkeejunior1/jump-your-steps | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-21T15:48:06Z | [] | 2025-02-25T04:20:09Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 7 | [] | 2026-02-23T11:17:11Z | https://github.com/enkeejunior1/jump-your-steps | null |
[
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-02-03T10:52:08Z | PyTorch code for our paper "Progressive Binarization with Semi-Structured Pruning for LLMs" | [
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 0 | 926,489,562 | null | Python | Apache-2.0 | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | XIANGLONGYAN/PBS2P | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-25T15:11:36Z | [] | 2026-07-11T12:52:02Z | [
"efficiency.pruning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 13 | [] | 2026-07-11T12:52:11Z | https://github.com/XIANGLONGYAN/PBS2P | 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 | [] | 2025-02-03T17:07:28Z | Official code repository for the paper "On the Regularization of Learnable Embeddings for Time Series Forecasting" | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"embedding"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-24T14:31:13Z | false | 0 | 926,673,384 | null | Python | Apache-2.0 | [
"forecasting",
"sequence-modeling"
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[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-02-04T01:24:35Z | Official code for the AAAI2024 "Generating diagnostic and actionable explanations for fair graph neural networks" | [
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] | include | ml-contribution-v5 | 0 | [] | 2025-02-27T14:51:55Z | https://github.com/WanyuGroup/AAAI24-Geb | null |
[
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] | [
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] | [] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-02-04T04:16:21Z | [ICML 2025 Poster] Official PyTorch Implementation of "Habitizing Diffusion Planning for Efficient and Effective Decision Making" | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T20:57:28.489717Z | false | 1 | 926,910,248 | https://bayesbrain.github.io/ | Python | MIT | [
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] | Josh00-Lu/Habi | [
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] | 4 | 2026-10-02T18:08:49.004055Z | [] | 2025-05-26T09:17:42Z | [] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 36 | [
"offline-reinforcement-learning"
] | 2026-02-26T05:38:53Z | https://github.com/Josh00-Lu/Habi | {"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["offline-reinforcement-learning"]} |
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-02-05T07:27:06Z | An official implementation for the KDD 2025 paper 'Unlocking the Power of Diffusion Models in Sequential Recommendation: A Simple and Effective Approach'. | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-21T15:48:06Z | false | 5 | 927,576,335 | null | Jupyter Notebook | MIT | [
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] | Nemo-1024/ADRec | [
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] | 1 | 2026-10-21T15:48:06Z | [] | 2025-06-04T06:56:27Z | [
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] | include | ml-contribution-v5 | 29 | [] | 2026-10-14T06:34:16Z | https://github.com/Nemo-1024/ADRec | null |
[
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] | [
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] | [
"multimodal.3d-generation",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-02-06T12:21:56Z | The official Pytorch implementation of paper RecDreamer: Consistent Text-to-3D Generation via Uniform Score Distillation | [
"3d",
"computer-vision",
"generative-ai",
"graphics",
"multimodal"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 0 | 928,306,969 | https://chansey0529.github.io/RecDreamer_proj/ | Python | NOASSERTION | [
"3d-generation",
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"generative-modeling",
"text-to-3d"
] | chansey0529/RecDreamer | [
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] | 3 | 2026-10-01T17:50:46Z | [] | 2025-04-23T17:42:20Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 7 | [] | 2025-08-07T08:38:52Z | https://github.com/chansey0529/RecDreamer | null |
[
"classical-ml",
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] | [
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] | [
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] | [
"general.gradient-boosting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-02-07T10:13:42Z | This is the official clone for the implementation of the NIPS18 paper Multi-Layered Gradient Boosting Decision Trees (mGBDT) . | [
"classical-ml",
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-18T14:44:29Z | false | 0 | 928,831,258 | null | Jupyter Notebook | null | [
"ensemble-learning",
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] | silverwolf-x/mGBDT | [
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] | 1 | 2026-10-18T14:44:29Z | [] | 2025-02-09T19:58:05Z | [
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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 | [] | 2025-04-22T09:52:13Z | https://github.com/silverwolf-x/mGBDT | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-02-07T10:34:40Z | This repository contains the code and experiments related to our paper: "Mitigating Unintended Memorization with LoRA in Federated Learning for LLMs" | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 2 | 928,840,591 | null | Python | Apache-2.0 | [
"federated-learning",
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] | tuneinsight/federated-llms | [
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] | 5 | 2026-10-29T17:49:51Z | [] | 2025-04-01T10:00:03Z | [
"efficiency.lora"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 14 | [] | 2026-10-03T15:39:17Z | https://github.com/tuneinsight/federated-llms | null |
[
"computer-vision",
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] | [
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] | [
"description",
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] | [
"vision.video-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-02-08T04:23:00Z | [Siggraph Asia 2025] Official code release of our paper "Shape-for-Motion: Precise and Consistent Video Editing with 3D Proxy" | [
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"generative-ai"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-28T17:27:44Z | false | 4 | 929,234,510 | null | Jupyter Notebook | null | [
"generative-modeling",
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] | yuhaoliu7456/Shape-for-Motion | [
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] | 1 | 2026-10-28T17:27:44Z | [] | 2025-09-26T08:01:48Z | [
"vision.video-generation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 59 | [] | 2026-06-02T17:20:01Z | https://github.com/yuhaoliu7456/Shape-for-Motion | null |
[
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"repository-metadata"
] | [
"multimodal.world-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-02-09T15:00:51Z | Implementation of the new SOTA for model based RL, from the paper "Improving Transformer World Models for Data-Efficient RL", in Pytorch | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T20:57:28.489717Z | false | 5 | 929,851,616 | null | Python | MIT | [
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] | lucidrains/improving-transformers-world-model-for-rl | [
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] | 2 | 2026-09-29T17:30:08Z | [] | 2025-05-02T16:18:51Z | [
"multimodal.world-models"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
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] | include | ml-contribution-v5 | 156 | [
"artificial-intelligence",
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"world-models"
] | 2026-09-02T08:28:25Z | https://github.com/lucidrains/improving-transformers-world-model-for-rl | null |
[
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] | [
"timeseries.forecasting",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-02-09T19:26:10Z | [ICLR 2025] Official implementation of "A Simple Baseline for Multivariate Time Series Forecasting" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-03T15:45:52Z | false | 0 | 929,955,075 | https://openreview.net/pdf?id=oANkBaVci5 | Python | null | [
"forecasting",
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] | vietluong2110/simplebaseline_efficiency | [
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] | 2 | 2026-10-24T14:31:13Z | [] | 2025-04-05T00:19:48Z | [
"timeseries.forecasting"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 1 | [] | 2025-04-02T03:30:37Z | https://github.com/vietluong2110/simplebaseline_efficiency | null |
[
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] | [
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] | [
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] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-02-10T01:55:17Z | [ICML 25] The official implementation of the paper: CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models. | [
"generative-ai",
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-21T15:48:06Z | false | 0 | 930,059,884 | null | Python | MIT | [
"diffusion"
] | senp98/cat | [
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] | 1 | 2026-10-21T15:48:06Z | [] | 2025-06-09T02:46:35Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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"ml-method-cue",
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] | include | ml-contribution-v5 | 9 | [] | 2026-04-28T13:10:56Z | https://github.com/senp98/cat | null |
[
"asset-allocation",
"finance",
"investment",
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] | [] | [
"description",
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"repository-metadata"
] | [
"applied.portfolio-optimization"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-02-10T05:44:32Z | [ICLR 2025 workshop] Official implementation of "Integrating LLM-Generated Views into Mean-Variance Optimization Using the Black-Litterman Model" | [
"asset-allocation",
"finance",
"investment",
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 11 | 930,128,069 | null | Jupyter Notebook | null | [] | youngandbin/LLM-BLM | [
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] | 4 | 2026-10-21T15:48:06Z | [] | 2025-10-21T12:41:42Z | [
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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 | 42 | [] | 2026-10-12T07:44:02Z | https://github.com/youngandbin/LLM-BLM | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-02-10T14:30:36Z | Official implementation code for the paper "Unleashing the Potential of Pre-Trained Diffusion Models for Generalizable Person Re-Identification". | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-21T15:48:06Z | false | 0 | 930,372,327 | null | Python | null | [
"diffusion"
] | RikoLi/DCAC | [
"description",
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"repository-metadata"
] | 1 | 2026-10-21T15:48:06Z | [] | 2025-10-17T10:06:09Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 7 | [] | 2026-08-20T02:05:16Z | https://github.com/RikoLi/DCAC | null |
[
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"query-match",
"repository-metadata"
] | [
"nlp.rag"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-02-10T15:05:16Z | Implementation of our paper "SearchRAG: Aligning Search Engines with Retrieval Augmented Generation for Medical Question Answering" | [
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"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-20T15:59:06Z | false | 0 | 930,392,330 | null | Python | null | [
"rag",
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] | sycny/SearchRAG | [
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] | 1 | 2026-10-20T15:59:06Z | [] | 2025-11-04T01:07:21Z | [
"nlp.rag"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2025-11-04T01:07:24Z | https://github.com/sycny/SearchRAG | null |
[
"deep-learning",
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] | [
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] | [
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"query-match",
"repository-metadata"
] | [
"graph.topic-gnn"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-02-10T15:35:36Z | This is the official implementation of the ICML 2026 paper "Beyond Model Base Selection: Weaving Knowledge to Master Fine-grained Neural Network Design" | [
"deep-learning",
"graph-learning"
] | [
"machine-learning",
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T16:52:35Z | false | 0 | 930,409,801 | null | Python | Apache-2.0 | [
"graph-neural-network",
"message-passing",
"neural-network"
] | jilwang84/M-DESIGN | [
"description",
"github-topics",
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"query-match",
"repository-metadata"
] | 5 | 2026-09-29T17:12:21Z | [] | 2026-05-07T16:58:41Z | [
"graph.topic-gnn"
] | 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 | [
"graph-neural-network",
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"machine-learning",
"model-selection",
"neural-architecture-search",
"python"
] | 2026-05-07T19:32:09Z | https://github.com/jilwang84/M-DESIGN | null |
[
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] | [
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] | [
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"paper-reference",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-02-11T03:48:55Z | Official Code for Wind Farm Layout Optimization with Diffusion Models (KDD 2025) | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-21T15:48:06Z | false | 0 | 930,694,396 | https://dl.acm.org/doi/10.1145/3711896.3737181 | Python | null | [
"diffusion"
] | dbsxodud-11/layopt | [
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] | 1 | 2026-10-21T15:48:06Z | [] | 2025-02-21T03:36:57Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 4 | [] | 2026-08-16T08:57:20Z | https://github.com/dbsxodud-11/layopt | null |
[
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"query-match",
"repository-metadata"
] | [
"nlp.rag"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-02-11T04:12:04Z | [ICML2025] The official implementation of "C-3PO: Compact Plug-and-Play Proxy Optimization to Achieve Human-like Retrieval-Augmented Generation" | [
"information-retrieval",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-19T17:46:36Z | false | 2 | 930,701,579 | null | Python | Apache-2.0 | [
"rag",
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] | Chen-GX/C-3PO | [
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] | 1 | 2026-10-19T17:46:36Z | [] | 2025-05-03T07:39:31Z | [
"nlp.rag"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 44 | [] | 2026-07-06T13:25:02Z | https://github.com/Chen-GX/C-3PO | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-02-11T11:25:43Z | [ICML 2025] Official PyTorch Implementation of "History-Guided Video Diffusion" | [
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"generative-modeling"
] | [
"diffusion-model",
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 41 | 930,886,482 | https://boyuan.space/history-guidance | Python | NOASSERTION | [
"diffusion",
"generative-modeling",
"transformer",
"video-generation"
] | kwsong0113/diffusion-forcing-transformer | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 12 | 2026-10-28T17:27:44Z | [] | 2025-07-01T19:19:54Z | [
"vision.video-generation"
] | 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 | 711 | [
"diffusion-models",
"generative-ai",
"image-to-video",
"transformer",
"video-generation"
] | 2026-10-22T19:05:51Z | https://github.com/kwsong0113/diffusion-forcing-transformer | null |
[
"deep-learning",
"efficient-ml",
"forecasting",
"graph-learning",
"language",
"time-series",
"time-series-and-forecasting"
] | [
"conditional-computation",
"dynamic-graph-learning",
"forecasting",
"long-horizon-forecasting",
"mixture-of-experts",
"sparse-activation",
"temporal-graph-learning"
] | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"efficiency.mixture-of-experts",
"graph.temporal-learning",
"timeseries.long-horizon"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-02-11T11:50:40Z | [KDD 2026] Official implementation of "FaST: Efficient and Effective Long-Horizon Forecasting for Large-Scale Spatial-Temporal Graphs via Mixture-of-Experts" | [
"graph-learning",
"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 1 | 930,898,212 | https://arxiv.org/abs/2601.05174 | Python | null | [
"dynamic-graph-learning",
"mixture-of-experts",
"temporal-graph-learning"
] | yijizhao/FaST | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | 9 | 2026-10-10T16:53:26Z | [] | 2026-06-01T09:40:26Z | [
"graph.temporal-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 21 | [] | 2026-10-04T12:23:50Z | https://github.com/yijizhao/FaST | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"language",
"safety"
] | [
"alignment",
"diffusion",
"generative-modeling",
"preference-optimization",
"video-generation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"llm.preference-optimization",
"vision.video-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-02-11T22:03:46Z | [ICLR 2025, AAAI 2026] official implementation of "Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models" | [
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 2 | 931,201,622 | null | Jupyter Notebook | Apache-2.0 | [
"diffusion",
"generative-modeling",
"video-generation"
] | G-U-N/Diffusion-NPO | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-28T17:27:44Z | [] | 2026-01-26T14:35:16Z | [
"vision.video-generation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 40 | [] | 2026-10-23T03:45:49Z | https://github.com/G-U-N/Diffusion-NPO | null |
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