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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
[
"efficient-ml",
"generative-ai",
"language",
"machine-learning-systems",
"model-compression",
"natural-language-processing"
] | [
"efficient-inference",
"quantization"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.quantization",
"llm.quantization"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-01T13:05:42Z | [ICML 2025] Official PyTorch implementation of "FlatQuant: Flatness Matters for LLM Quantization" | [
"efficient-ml",
"machine-learning-systems",
"model-compression",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 0 | 1,030,329,722 | null | Python | MIT | [
"quantization"
] | rdimauro01/FlatQuant | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-23T15:59:01Z | [] | 2025-09-30T12:14:57Z | [
"efficiency.quantization"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 0 | [] | 2025-09-30T12:15:01Z | https://github.com/rdimauro01/FlatQuant | null |
[
"embodied-ai",
"robotics",
"robotics-and-control"
] | [
"manipulation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"robotics.manipulation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-01T17:16:47Z | This repository contains the official implementation for the paper "REMAC: Self-Reflective and Self-Evolving Multi-Agent Collaboration for Long-Horizon Robot Manipulation". | [
"embodied-ai",
"robotics",
"robotics-and-control"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 0 | 1,030,460,009 | null | Python | null | [
"manipulation"
] | reamac-repo/remac-repo | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-02T16:59:40Z | [] | 2026-05-25T12:09:14Z | [
"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 | 4 | [] | 2026-05-25T12:09:17Z | https://github.com/reamac-repo/remac-repo | null |
[
"generative-ai",
"generative-modeling",
"health-and-biomedicine",
"language",
"natural-language-processing"
] | [
"diffusion",
"fine-tuning",
"instruction-tuning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"llm.instruction-tuning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-02T05:30:20Z | Code for paper: LLaDA-MedV: Exploring Large Language Diffusion Models for Biomedical Image Understanding | [
"generative-ai",
"generative-modeling",
"health-and-biomedicine",
"language",
"natural-language-processing"
] | [
"large-language-model",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 2 | 1,030,686,718 | null | Python | null | [
"diffusion",
"fine-tuning",
"instruction-tuning"
] | LLM-VLM-GSL/LLaDA-MedV | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-31T16:49:26Z | [] | 2026-06-06T05:32:48Z | [
"llm.instruction-tuning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 80 | [] | 2026-10-09T17:22:02Z | https://github.com/LLM-VLM-GSL/LLaDA-MedV | null |
[
"multi-agent-systems",
"reinforcement-learning"
] | [
"centralized-training",
"multi-agent-reinforcement-learning",
"reinforcement-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.multiagent"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-02T06:35:08Z | Source code for paper "Improving Cross-Task Applicability of Parameter Sharing in Cooperative Multi-Agent Reinforcement Learning" | [
"multi-agent-systems",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 0 | 1,030,707,531 | null | Python | Apache-2.0 | [
"centralized-training",
"multi-agent-reinforcement-learning",
"reinforcement-learning"
] | Yurui-Li/CSP | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2025-08-02T06:37:45Z | [
"rl.multiagent"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-05-22T14:28:24Z | https://github.com/Yurui-Li/CSP | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-02T10:52:15Z | Simple repository to create a photo editing application (with in painting), to differentiate between subject and background. This application will also have the ability to create new background using diffusion models. | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-25T15:11:36Z | false | 0 | 1,030,798,955 | null | Jupyter Notebook | null | [
"diffusion"
] | AdityaIyengar27/AI-Photo-Editing | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-25T15:11:36Z | [] | 2025-08-02T10:53:28Z | [
"llm.diffusion-phrase"
] | 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 | [] | 2025-08-02T10:53:31Z | https://github.com/AdityaIyengar27/AI-Photo-Editing | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"multimodal",
"privacy-and-federated-learning",
"privacy-ml",
"vision"
] | [
"differential-privacy",
"diffusion",
"generative-modeling",
"image-generation",
"privacy-preserving-ml",
"text-to-image"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"multimodal.text-to-image",
"trust.differential-privacy",
"vision.image-diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-02T19:43:50Z | [AAAI'26 Oral] Official Implementation of Copyright Infringement Detection in Text-to-Image Diffusion Models via Differential Privacy | [
"computer-vision",
"generative-ai",
"generative-modeling",
"privacy-and-federated-learning"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 1,031,001,644 | https://leo-xfm.github.io/pubs/dpm | Python | MIT | [
"diffusion"
] | leo-xfm/DPM-copyright-infringement-detection | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-25T15:11:36Z | [] | 2025-09-30T08:03:01Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 1 | [
"computer-vision",
"copyright-violation-detection"
] | 2025-12-16T11:10:47Z | https://github.com/leo-xfm/DPM-copyright-infringement-detection | null |
[
"audio",
"generative-modeling",
"interpretability-and-safety"
] | [
"architecture",
"diffusion",
"generative-modeling",
"training"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"audio-music-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-03T08:16:35Z | We present SAMUeL, Soft Alignment U-net Latent diffusion - a novel, low parameter model for music generation for vocals. This is the public source code for the model | [
"audio",
"generative-modeling",
"interpretability-and-safety"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-31T16:49:26Z | false | 1 | 1,031,197,568 | null | Python | MIT | [
"architecture",
"diffusion",
"generative-modeling",
"training"
] | HaysonC/SAMUeL-GEN | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-31T16:49:26Z | [] | 2025-08-04T07:15:45Z | [
"audio-music-generation"
] | 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 | 9 | [] | 2026-07-18T17:18:02Z | https://github.com/HaysonC/SAMUeL-GEN | null |
[
"generative-ai",
"generative-modeling",
"multimodal-learning"
] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-04T07:04:05Z | Official implementation of Bifrost-1: Bridging Multimodal LLMs and Diffusion Models with Patch-level CLIP Latents (NeurIPS 2025) | [
"generative-ai",
"generative-modeling",
"multimodal-learning"
] | [
"large-language-model",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 3 | 1,031,678,372 | null | Python | null | [
"diffusion"
] | HL-hanlin/Bifrost-1 | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-23T15:59:01Z | [] | 2025-11-24T22:28:26Z | [
"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 | 47 | [] | 2026-04-18T14:55:07Z | https://github.com/HL-hanlin/Bifrost-1 | null |
[
"computational-science",
"physics",
"science-and-engineering"
] | [
"neural-operator",
"neural-operators",
"surrogate-modeling"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"science.neural-operators"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-04T15:48:17Z | code for paper "Physics-Informed Chebyshev Polynomial Neural Operator for Parametric Partial Differential Equations" | [
"computational-science",
"physics",
"science-and-engineering"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 0 | 1,031,968,183 | null | Python | null | [
"neural-operator",
"neural-operators",
"surrogate-modeling"
] | biao023/CPNO | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-09-27T16:09:59Z | [] | 2025-08-04T15:56:53Z | [
"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 | 0 | [] | 2025-08-04T15:56:57Z | https://github.com/biao023/CPNO | null |
[
"computer-vision",
"control",
"embodied-ai",
"foundation-models",
"generative-ai",
"generative-modeling",
"robotics",
"robotics-and-control"
] | [
"control",
"diffusion",
"diffusion-policy",
"imitation-learning",
"manipulation",
"multimodal",
"pretraining"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"llm.foundation-models",
"robotics.diffusion-policy",
"robotics.manipulation",
"robotics.robot-control"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-05T08:58:03Z | Official implementation of VGGT-DP: Generalizable Robot Control via Vision Foundation Models (arXiv:2509.18778). A diffusion policy that uses the frozen VGGT vision foundation model as a geometry-aware visual encoder for robot manipulation, evaluated on MetaWorld. | [
"computer-vision",
"foundation-models",
"generative-ai",
"generative-modeling",
"robotics-and-control"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T14:50:46Z | false | 0 | 1,032,403,110 | https://tigerdwgth.github.io/vggt-dp/ | Python | MIT | [
"diffusion",
"multimodal",
"pretraining"
] | Tigerdwgth/vggt-dp | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 4 | 2026-10-11T16:48:52Z | [] | 2026-09-06T07:40:45Z | [
"llm.foundation-models"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 4 | [
"3d",
"computer-vision",
"diffusion",
"diffusion-policy",
"foundation-models",
"geometry",
"imitation-learning",
"manipulation",
"metaworld",
"pytorch",
"reconstruction",
"robot-learning",
"robot-manipulation",
"robotics",
"vggt",
"vision-foundation-model",
"visuomotor-policy",
"vl... | 2026-09-28T06:20:31Z | https://github.com/Tigerdwgth/vggt-dp | null |
[
"biology",
"computational-biology",
"structural-biology"
] | [
"mamba",
"protein-folding",
"structure-prediction"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"bio.protein-folding"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-05T09:20:16Z | DNA mutation classification using VIO — a new, Mamba-inspired ML architecture that replaces transformers with gated depthwise convolutions. Lightweight, fast, and Colab-friendly. Achieves ~80% accuracy with just a simple CSV upload. | [
"biology",
"computational-biology",
"structural-biology"
] | [
"machine-learning",
"classifier",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-09T17:31:36Z | false | 0 | 1,032,415,838 | null | Jupyter Notebook | MIT | [
"mamba",
"protein-folding",
"structure-prediction"
] | Shaheer-op9872uw/HeliX | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-09T17:31:36Z | [] | 2025-08-13T07:44:28Z | [
"bio.protein-folding"
] | 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 | [] | 2025-08-13T07:44:32Z | https://github.com/Shaheer-op9872uw/HeliX | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-05T12:51:03Z | Official implementation of ICML2025 paper "ToMA: Token Merge with Attention for Diffusion Models" | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 1 | 1,032,531,367 | null | Python | null | [
"diffusion"
] | wenboluu/ToMA | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-23T15:59:01Z | [] | 2025-08-06T07:33:58Z | [
"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 | 22 | [] | 2026-10-21T12:18:25Z | https://github.com/wenboluu/ToMA | null |
[
"computer-vision",
"language",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"reinforcement-learning",
"robotics-and-control",
"vision"
] | [
"contrastive-learning",
"image-text",
"reinforcement-learning",
"vision-language-model"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.vision-language",
"multimodal.vlm"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-06T07:32:44Z | Official implementation of the paper "Enhancing Vision-Language Model Training with Reinforcement Learning in Synthetic Worlds for Real-World Success" | [
"computer-vision",
"language",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"reinforcement-learning",
"robotics-and-control",
"vision"
] | [
"reinforcement-learning",
"model-training"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 0 | 1,033,026,955 | https://corl-team.github.io/VL-DAC/ | Python | MIT | [
"contrastive-learning",
"reinforcement-learning",
"vision-language-model"
] | corl-team/VL-DAC | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-11T16:48:52Z | [] | 2026-08-24T13:49:25Z | [
"multimodal.vision-language"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 13 | [
"embodied-ai",
"pytorch",
"reinforcement-learning",
"vision-language-model",
"vla"
] | 2026-09-01T01:52:41Z | https://github.com/corl-team/VL-DAC | null |
[
"generative-ai",
"generative-modeling",
"tabular-and-structured-data",
"tabular-ml"
] | [
"diffusion",
"tabular-generation"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"growth26.tabular-diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-06T13:42:58Z | Official Implementation of Harpoon: Generalised Manifold Guidance for Conditional Tabular Diffusion, (ICLR 2026) | [
"generative-ai",
"generative-modeling",
"tabular-and-structured-data",
"tabular-ml"
] | [
"generative-model",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T22:51:24Z | false | 1 | 1,033,226,937 | https://openreview.net/forum?id=G5g6tDg1ZE | Python | MIT | [
"diffusion",
"tabular-generation"
] | adis98/Harpoon | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-09-26T22:51:24Z | [] | 2025-11-26T12:02:56Z | [
"growth26.tabular-diffusion"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 1 | [
"diffusion-models",
"generative-model",
"manifold-learning",
"tabular-data"
] | 2026-07-06T16:59:04Z | https://github.com/adis98/Harpoon | null |
[
"information-retrieval",
"reinforcement-learning",
"search"
] | [
"learning-to-rank",
"reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"applied.learning-to-rank"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-07T06:36:32Z | Code of our paper: Reinforcement Learning to Rank Using Coarse-grained Rewards | [
"information-retrieval",
"reinforcement-learning",
"search"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 0 | 1,033,655,042 | null | Python | null | [
"learning-to-rank",
"reinforcement-learning"
] | StibiumT16/RLTR | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-09-29T17:12:21Z | [] | 2026-06-04T12:09:31Z | [
"applied.learning-to-rank"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-06-04T12:09:37Z | https://github.com/StibiumT16/RLTR | null |
[
"foundation-models",
"generative-ai",
"generative-modeling",
"health-and-biomedicine"
] | [
"diffusion",
"lora",
"multimodal",
"pretraining"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"llm.foundation-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-10T21:49:41Z | [ELAMI-MICCAI 2025] Official implementation of "Pixels Under Pressure: Exploring Fine-Tuning Paradigms for Foundation Models in High-Resolution Medical Imaging" | [
"foundation-models",
"generative-ai",
"generative-modeling",
"health-and-biomedicine"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-12T20:31:19Z | false | 0 | 1,035,662,106 | https://tehraninasab.github.io/PixelUPressure/ | Python | null | [
"diffusion",
"lora",
"multimodal",
"pretraining"
] | tehraninasab/PixelUPressure | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 1 | 2026-10-12T20:31:19Z | [] | 2025-08-15T19:52:47Z | [
"llm.foundation-models"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 0 | [
"bitfit",
"difffit",
"diffusion-models",
"dora",
"fine-tuning",
"generative-ai",
"lora",
"parameter-efficient-finetuning",
"peft"
] | 2026-03-08T17:40:52Z | https://github.com/tehraninasab/PixelUPressure | null |
[
"applied-mathematics",
"applied-physics",
"computational-science",
"generative-ai",
"generative-modeling"
] | [
"data-assimilation",
"diffusion",
"inverse-problems",
"reconstruction"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"science.inverse-problems"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-11T09:34:34Z | This is the official implementation of "Integrating Intermediate Layer Optimization and Projected Gradient Descent for Solving Inverse Problems with Diffusion Models" (ICML 2025). | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 0 | 1,035,916,397 | null | Python | null | [
"diffusion"
] | StarNextDay/DMILO | [
"description",
"query-match",
"repository-metadata"
] | 4 | 2026-10-24T14:31:13Z | [] | 2025-08-15T09:08:15Z | [
"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 | 7 | [] | 2026-05-24T14:30:56Z | https://github.com/StarNextDay/DMILO | null |
[
"deep-learning",
"graph-learning",
"interpretability-and-safety"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-11T11:06:45Z | Benchmarking and generating PE for GNNs via the Graph Alignment task. Code for our paper: Graph Alignment for Benchmarking Graph Neural Networks and Learning Positional Encodings | [
"deep-learning",
"graph-learning",
"interpretability-and-safety"
] | [
"deep-learning",
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-21T15:48:06Z | false | 0 | 1,035,964,894 | null | Python | MIT | [
"graph-neural-network",
"message-passing",
"neural-network"
] | adrien-lagesse/graph-alignment-benchmark | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-21T15:48:06Z | [] | 2025-10-03T10:14:42Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 2 | [
"benchmark",
"combinatorial-optimization",
"geometric-deep-learning",
"gnn",
"graphs",
"pretraining",
"torch"
] | 2026-07-02T20:13:44Z | https://github.com/adrien-lagesse/graph-alignment-benchmark | null |
[
"computational-physics",
"efficient-ml",
"general-ml",
"model-compression",
"physics",
"science-and-engineering",
"scientific-computing"
] | [
"distillation",
"knowledge-distillation",
"neural-network",
"neural-operators",
"physics-informed-learning",
"surrogate-modeling"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation",
"general.neural-network",
"science.physics",
"science.physics-informed-neural-networks"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-12T01:47:45Z | This is the source code for paper “Automatic Network Structure Discovery of Physics Informed Neural Networks via Knowledge Distillation” | [
"efficient-ml",
"model-compression",
"science-and-engineering"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 21 | 1,036,376,820 | null | Jupyter Notebook | Apache-2.0 | [
"distillation",
"knowledge-distillation",
"neural-network"
] | ZitiLiu/Psi-NN | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 16 | 2026-10-07T18:07:52Z | [] | 2025-10-30T01:30:23Z | [
"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 | 91 | [
"neuralnetwork",
"physics"
] | 2026-09-19T10:11:55Z | https://github.com/ZitiLiu/Psi-NN | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-12T04:42:06Z | This is the official implementation of "MaXsive: High-Capacity and Robust Training-Free Generative Image Watermarking in Diffusion Models" ACM MM25 | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-24T14:31:13Z | false | 0 | 1,036,436,546 | null | Python | null | [
"diffusion"
] | Mao718/MaXsive | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-24T14:31:13Z | [] | 2025-08-12T05:47:22Z | [
"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 | 6 | [] | 2026-09-02T03:56:24Z | https://github.com/Mao718/MaXsive | null |
[
"audio",
"generative-modeling",
"natural-language-processing",
"speech"
] | [
"diffusion",
"multimodal-learning",
"speech-language-model"
] | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"nlp-speech-language-modeling"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-12T04:52:19Z | This repository contains a series of works on diffusion-based speech tokenizers, including the official implementation of the paper: "TaDiCodec: Text-aware Diffusion Speech Tokenizer for Speech Language Modeling" https://arxiv.org/pdf/2508.16790 | [
"audio",
"generative-modeling",
"natural-language-processing",
"speech"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 14 | 1,036,440,621 | null | Python | null | [
"diffusion",
"multimodal-learning",
"speech-language-model"
] | HeCheng0625/Diffusion-Speech-Tokenizer | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | 5 | 2026-09-27T15:53:49Z | [] | 2026-01-25T14:41:49Z | [
"nlp-speech-language-modeling"
] | 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 | 196 | [] | 2026-09-23T12:31:22Z | https://github.com/HeCheng0625/Diffusion-Speech-Tokenizer | null |
[
"graph-learning",
"recommender-systems",
"representation-learning"
] | [
"contrastive-learning",
"graph-contrastive-learning",
"self-supervised-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.graph-contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-13T07:42:10Z | EpicCBR: Item-Relation-Enhanced Dual-Scenario Contrastive Learning for Cold-Start Bundle Recommendation. Official implementation of the paper, supporting cold-start and warm-start bundle recommendation with multi-view graph contrastive learning. | [
"graph-learning",
"recommender-systems",
"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T16:52:35Z | false | 1 | 1,037,189,205 | null | Python | MIT | [
"contrastive-learning",
"graph-contrastive-learning",
"self-supervised-learning"
] | TonyLiYH/EpicCBR | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-09-27T15:53:49Z | [] | 2026-04-03T07:35:06Z | [
"graph.graph-contrastive-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 4 | [] | 2026-06-26T01:44:58Z | https://github.com/TonyLiYH/EpicCBR | null |
[
"computer-vision",
"generative-ai"
] | [
"contrastive-learning",
"generative-modeling",
"image-to-image-translation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.image-to-image-translation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-13T10:55:21Z | An Official Implementation of the paper "Contrastive-SDE: Guiding Stochastic Differential Equations with Contrastive Learning for Unpaired Image-to-Image Translation" | [
"computer-vision",
"generative-ai"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 0 | 1,037,290,684 | null | Python | MIT | [
"contrastive-learning",
"generative-modeling",
"image-to-image-translation"
] | kvn-257/Contrastive-SDE | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-29T17:49:51Z | [] | 2025-10-16T04:12:18Z | [
"vision.image-to-image-translation"
] | 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 | [] | 2026-04-04T12:51:55Z | https://github.com/kvn-257/Contrastive-SDE | null |
[
"forecasting",
"generative-ai",
"generative-modeling",
"time-series",
"time-series-and-forecasting"
] | [
"diffusion",
"forecasting",
"sequence-modeling"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"timeseries.forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-14T05:51:35Z | Official code of the ICML 2025 Paper titled "Conditional Diffusion Model with Nonlinear Data Transformation for Time Series Forecasting" https://openreview.net/forum?id=kcUNMKqrCg | [
"forecasting",
"generative-modeling",
"time-series",
"time-series-and-forecasting"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 1 | 1,037,785,462 | null | Python | null | [
"diffusion",
"forecasting",
"sequence-modeling"
] | quest-lab-iisc/CNDiff | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-10-31T16:49:26Z | [] | 2025-08-14T06:16:20Z | [
"timeseries.forecasting"
] | 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 | 32 | [] | 2026-10-07T13:41:31Z | https://github.com/quest-lab-iisc/CNDiff | null |
[
"machine-learning",
"reinforcement-learning"
] | [
"batch-reinforcement-learning",
"curriculum-learning",
"offline-reinforcement-learning",
"reinforcement-learning",
"training-strategy"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.offline",
"specialized.curriculum-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-15T05:32:25Z | Official implementation of the paper "Active Data Curation for Offline Reinforcement Learning: A Curriculum-Based Approach" | [
"machine-learning",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T14:50:46Z | false | 0 | 1,038,389,164 | null | Python | MIT | [
"curriculum-learning",
"reinforcement-learning",
"training-strategy"
] | ajayn1997/adc_offline_RL | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-19T17:46:36Z | [] | 2025-08-15T07:47:31Z | [
"specialized.curriculum-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2025-08-15T07:47:37Z | https://github.com/ajayn1997/adc_offline_RL | null |
[
"efficient-ml",
"machine-learning-systems",
"model-compression",
"natural-language-processing"
] | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-15T18:42:59Z | [AICCSA 2025] Official Implementation of the paper "Z-Pruner: Post-Training Pruning of Large Language Models for Efficiency without Retraining". | [
"efficient-ml",
"machine-learning-systems",
"model-compression",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 3 | 1,038,745,496 | null | Python | MIT | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | sazzadadib/Z-Pruner | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-29T17:49:51Z | [] | 2025-08-26T17:26:30Z | [
"efficiency.pruning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 9 | [
"llm",
"pruning-sparsity"
] | 2026-09-03T22:04:53Z | https://github.com/sazzadadib/Z-Pruner | null |
[
"general-ml"
] | [
"novel-method",
"random-forest"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.novel-method"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-16T01:45:00Z | This repository consists of exploratory data analysis and soil carbon stock modeling at a site in Edgeroi District, Australia using traditional statistical approaches (IWD, Kriging) and novel method of clubbing ML model like Random Forest and residual modeling using kriging. Additionally an in-depth modelling of soil c... | [
"general-ml"
] | [
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 1 | 1,038,875,007 | null | R | null | [
"novel-method",
"random-forest"
] | Archa-Reji/Geospatial-Analysis-and-Predictive-Modelling-of-Soil-Carbon-Stocks | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-09-29T17:30:08Z | [] | 2025-08-16T01:48:51Z | [
"general.novel-method"
] | ec45022faa00b97c31dccb4bb5b5fc12129ac6c3 | 2026-09-29T18:47:14Z | gh-ml-readme-evidence-v2 | "ec45022faa00b97c31dccb4bb5b5fc12129ac6c3" | [
"other"
] | [] | ok | 2026-09-29T18:47:14Z | Archa-Reji/Geospatial-Analysis-and-Predictive-Modelling-of-Soil-Carbon-Stocks | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [] | 2025-08-16T01:48:55Z | https://github.com/Archa-Reji/Geospatial-Analysis-and-Predictive-Modelling-of-Soil-Carbon-Stocks | null |
[
"information-retrieval",
"natural-language-processing"
] | [
"bm25",
"lexical-retrieval",
"rag",
"retrieval-augmented-generation"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"nlp-bm25-retrieval"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-16T21:10:46Z | Official Implementation for the ACL 2026 paper "SARA: Selective and Adaptive Retrieval-augmented Generation with Context Compression" | [
"information-retrieval",
"natural-language-processing"
] | [
"large-language-model",
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-31T16:49:26Z | false | 0 | 1,039,272,201 | https://ahren09.github.io/SARA/ | Python | Apache-2.0 | [
"bm25",
"lexical-retrieval",
"rag",
"retrieval-augmented-generation"
] | Ahren09/SARA | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-11-01T17:09:41Z | [] | 2026-07-23T03:04:29Z | [
"nlp-bm25-retrieval"
] | 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 | 263 | [
"ai",
"context-compression",
"context-engineering",
"efficiency",
"llm",
"ml",
"rag",
"retrieval-augmented-generation"
] | 2026-10-22T02:39:24Z | https://github.com/Ahren09/SARA | 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 | [] | 2025-08-17T06:02:33Z | This is the official PyTorch implementation of the ECAI 2025 paper "HRS: Hybrid Representation Framework with Scheduling Awareness for Time Series Forecasting in Crowdsourced Cloud-Edge Platforms". | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-31T16:49:26Z | false | 0 | 1,039,395,064 | null | Python | MIT | [
"forecasting",
"sequence-modeling"
] | EdgeBigBang/ECAI25_HRS | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-31T16:49:26Z | [] | 2026-02-02T13:14:54Z | [
"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 | 2 | [] | 2026-06-11T08:43:26Z | https://github.com/EdgeBigBang/ECAI25_HRS | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-18T05:12:08Z | [ICLR 2026] Official implementation of DiCache: Let Diffusion Model Determine Its Own Cache | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 3 | 1,039,861,139 | https://bujiazi.github.io/dicache.github.io/ | Python | null | [
"diffusion"
] | Bujiazi/DiCache | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-23T15:59:01Z | [] | 2026-01-26T14:21:54Z | [
"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 | 67 | [] | 2026-10-08T02:37:51Z | https://github.com/Bujiazi/DiCache | null |
[
"generative-ai",
"language",
"reinforcement-learning"
] | [
"language-model",
"reasoning",
"reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm-reasoning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-19T02:20:43Z | [ICASSP '26] This is the code repo for our paper: LegalΔ: Enhancing Legal Reasoning in LLMs via Reinforcement Learning with Chain-of-Thought Guided Information Gain. | [
"generative-ai",
"language",
"reinforcement-learning"
] | [
"large-language-model",
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 3 | 1,040,453,190 | null | Python | null | [
"language-model",
"reasoning",
"reinforcement-learning"
] | NEUIR/LegalDelta | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-23T15:59:01Z | [] | 2026-07-01T11:41:00Z | [
"llm-reasoning"
] | 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 | 31 | [] | 2026-10-17T10:58:42Z | https://github.com/NEUIR/LegalDelta | null |
[
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"neural-network",
"neural-network-pruning",
"pruning",
"structured-pruning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-20T20:43:04Z | The official implementation of synapse pruning in TRIM paper: TRIM: Acceleration of Multiplication-Less Neural Networks via Versatile Sparsities | [
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-30T17:16:29Z | false | 0 | 1,041,667,718 | null | Python | null | [
"neural-network",
"neural-network-pruning",
"pruning",
"structured-pruning"
] | XZH28/TRIM-Sparse-L1-Distance-Net | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-30T17:16:29Z | [] | 2025-12-24T19:15:17Z | [
"efficiency.pruning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 2 | [] | 2026-10-06T13:09:56Z | https://github.com/XZH28/TRIM-Sparse-L1-Distance-Net | null |
[
"sequence-modeling",
"time-series"
] | [
"mamba",
"sequence-modeling",
"state-space-model"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"timeseries.state-space-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-22T07:57:27Z | The repository contains the code for our paper ‘Learning Human-Object Interactions in Videos by State Space Models’. The remaining files will be released available upon acceptance of the manuscript | [
"sequence-modeling",
"time-series"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 0 | 1,042,560,388 | null | Python | null | [
"mamba",
"sequence-modeling",
"state-space-model"
] | labGoku/HOI-Mamba | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2025-08-22T08:13:43Z | [
"timeseries.state-space-models"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2025-08-22T08:13:46Z | https://github.com/labGoku/HOI-Mamba | 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 | [] | 2025-08-22T08:30:27Z | [MICCAI'24] Official Pytorch Code for Multi-order Simplex-based Graph Neural Network for Brain Network Analysis | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-20T15:59:06Z | false | 0 | 1,042,576,473 | null | Python | null | [
"graph-neural-network",
"message-passing",
"neural-network"
] | yechan99/Multi-order-Simplex-based-GNN-for-Brain-Network-Analysis | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-20T15:59:06Z | [] | 2025-08-22T08:48:38Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 3 | [] | 2025-09-15T02:12:33Z | https://github.com/yechan99/Multi-order-Simplex-based-GNN-for-Brain-Network-Analysis | null |
[
"graph-learning",
"information-retrieval",
"linguistics",
"natural-language-processing",
"recommender-systems"
] | [
"language-modeling",
"rag",
"retrieval-augmented-generation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"nlp.rag",
"recall.computational-linguistics"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-25T12:03:38Z | This is the pytorch implementation for our ACL paper Knowledge Graph Retrieval-Augmented Generation for LLM-based Recommendation. | [
"graph-learning",
"information-retrieval",
"natural-language-processing",
"recommender-systems"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 7 | 1,044,250,201 | null | Python | null | [
"rag",
"retrieval-augmented-generation"
] | Sjay-Wang/K-ragrec | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-09-28T18:33:08Z | [] | 2026-06-23T04:24:14Z | [
"nlp.rag"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 37 | [] | 2026-09-19T11:18:45Z | https://github.com/Sjay-Wang/K-ragrec | 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 | [] | 2025-08-25T13:48:46Z | New narrative planner pruning algorithm designed to speed up the planning process | [
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-30T17:16:29Z | false | 0 | 1,044,311,897 | null | Java | NOASSERTION | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | lazzy07/fog-of-war-pruning | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-30T17:16:29Z | [] | 2025-08-25T14:24:33Z | [
"efficiency.pruning"
] | 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 | 1 | [] | 2026-01-26T20:18:26Z | https://github.com/lazzy07/fog-of-war-pruning | null |
[
"efficient-ml",
"interpretability-and-safety",
"machine-learning-systems",
"model-compression",
"natural-language-processing"
] | [
"quantization"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.quantization"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-25T15:45:29Z | This repo contains code for paper: "Interpreting the Effects of Quantization on LLMs" | [
"efficient-ml",
"interpretability-and-safety",
"machine-learning-systems",
"model-compression",
"natural-language-processing"
] | [
"large-language-model",
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 1 | 1,044,383,212 | https://aclanthology.org/2025.ijcnlp-long.123/ | Python | Apache-2.0 | [
"quantization"
] | MSingh-CSE/LLM-Quantization-Effects | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-23T15:59:01Z | [] | 2026-03-09T23:55:32Z | [
"efficiency.quantization"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [
"interpretability",
"machine-learning",
"nlp",
"quantization-interpretability"
] | 2026-06-14T04:00:49Z | https://github.com/MSingh-CSE/LLM-Quantization-Effects | null |
[
"computer-vision",
"machine-learning"
] | [
"adaptation",
"test-time-adaptation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"specialized.test-time-adaptation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-25T21:51:14Z | Code for our paper "GaPaTTA: Gaussian Entropy-Guided Prompt Placement for Test-Time Adaptation in Semantic Segmentation" | [
"computer-vision",
"machine-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 0 | 1,044,559,396 | null | Python | MIT | [
"adaptation",
"test-time-adaptation"
] | ml4papers/GaPaTTA | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2026-03-13T03:13:01Z | [
"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 | 1 | [] | 2026-04-14T11:30:41Z | https://github.com/ml4papers/GaPaTTA | null |
[
"generative-ai",
"generative-modeling",
"language",
"natural-language-processing",
"reinforcement-learning"
] | [
"diffusion",
"language-model",
"pretraining",
"reinforcement-learning",
"reward-modeling"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"growth26.llm-process-reward-model",
"llm.language-models",
"recall.name.diffusion-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-26T00:49:16Z | [ICLR 2026] Official code for TraceRL: Revolutionizing post-training for Diffusion LLMs, powering the SOTA TraDo series. | [
"generative-ai",
"generative-modeling",
"language",
"natural-language-processing",
"reinforcement-learning"
] | [
"large-language-model",
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 45 | 1,044,615,854 | https://arxiv.org/abs/2509.06949 | Python | Apache-2.0 | [
"diffusion",
"language-model",
"pretraining",
"reinforcement-learning"
] | Gen-Verse/dLLM-RL | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 5 | 2026-10-20T15:59:06Z | [] | 2026-01-28T01:58:54Z | [
"llm.language-models"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 523 | [
"code-generation",
"diffusion-language-models",
"large-language-models",
"llm-reasoning",
"mathmatical-reasoning",
"reinforcement-learning-algorithms",
"rlhf"
] | 2026-10-01T08:35:13Z | https://github.com/Gen-Verse/dLLM-RL | null |
[
"deep-learning",
"efficient-ml",
"language"
] | [
"conditional-computation",
"mixture-of-experts",
"sparse-activation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.mixture-of-experts"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-26T05:32:58Z | Source code for paper *MEGO: Learning Mixture-of-Experts for General-Purpose Binary Optimization* | [
"deep-learning",
"efficient-ml",
"language"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 0 | 1,044,715,943 | null | C++ | GPL-3.0 | [
"conditional-computation",
"mixture-of-experts",
"sparse-activation"
] | MetaronWang/MEGO | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-02T16:59:40Z | [] | 2026-05-17T08:34:19Z | [
"efficiency.mixture-of-experts"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 2 | [] | 2026-09-03T19:45:47Z | https://github.com/MetaronWang/MEGO | null |
[
"chemistry",
"drug-discovery",
"graph-learning",
"structural-biology"
] | [
"docking",
"structure-prediction"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"chem-molecular-docking"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-27T00:09:39Z | Official implementation of PRICAI 25 paper - MC-GNNAS-Dock: Multi-criteria GNN-based Algorithm Selection for Molecular Docking | [
"chemistry",
"drug-discovery",
"graph-learning",
"structural-biology"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 0 | 1,045,292,179 | null | Python | MIT | [
"docking",
"structure-prediction"
] | ToothlessOS/MC-GNNAS-Dock | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-08T18:15:14Z | [] | 2025-11-13T21:54:21Z | [
"chem-molecular-docking"
] | 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 | [] | 2026-03-19T11:34:32Z | https://github.com/ToothlessOS/MC-GNNAS-Dock | null |
[
"generative-ai",
"language",
"reinforcement-learning",
"software-engineering"
] | [
"code-generation",
"language-model",
"reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm-code-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-28T11:34:18Z | Official implementation of "Learning to Generate Unit tests via Adversarial Reinforcement Learning", ICLR 2026 | [
"generative-ai",
"language",
"reinforcement-learning",
"software-engineering"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-28T17:27:44Z | false | 0 | 1,046,230,000 | null | Python | null | [
"code-generation",
"language-model",
"reinforcement-learning"
] | dgjun32/UTRL | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-28T17:27:44Z | [] | 2026-03-11T04:01:54Z | [
"llm-code-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 | 5 | [] | 2026-10-12T13:29:11Z | https://github.com/dgjun32/UTRL | null |
[
"efficient-ml",
"model-compression"
] | [
"distillation",
"knowledge-distillation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-29T04:21:05Z | [EMNLP25] Official code for "POSITION BIAS MITIGATES POSITION BIAS: Mitigate Position Bias Through Inter-Position Knowledge Distillation" | [
"efficient-ml",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 0 | 1,046,685,969 | https://aclanthology.org/2025.emnlp-main.78/ | Python | null | [
"distillation",
"knowledge-distillation"
] | wangyifei0047/Pos2Distill- | [
"description",
"query-match",
"repository-metadata"
] | 5 | 2026-10-07T18:07:52Z | [] | 2025-11-11T04:03:07Z | [
"efficiency.knowledge-distillation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 38 | [] | 2026-04-22T12:34:57Z | https://github.com/wangyifei0047/Pos2Distill- | null |
[
"automl",
"general-ml",
"recommender-systems"
] | [
"few-shot-learning",
"meta-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.meta-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-29T11:35:51Z | Source code for paper "Integrating Static and Dynamic Preferences via Meta-Learning for User Cold Start Recommendation". | [
"automl",
"general-ml",
"recommender-systems"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-30T17:16:29Z | false | 0 | 1,046,883,933 | null | Python | MIT | [
"few-shot-learning",
"meta-learning"
] | SodoI66/MetaDSM | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-30T17:16:29Z | [] | 2026-01-30T02:49:15Z | [
"general.meta-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-01-30T02:49:18Z | https://github.com/SodoI66/MetaDSM | null |
[
"computer-vision",
"embodied-ai",
"language",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"robotics",
"robotics-and-control",
"vision"
] | [
"contrastive-learning",
"mamba",
"manipulation",
"robot-learning",
"tactile-sensing",
"vision-language-action",
"vision-language-model"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.vision-language",
"robotics.tactile-learning",
"robotics.visual-language-action"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-29T21:54:39Z | VLAM (Vision-Language-Action Mamba) is a novel multimodal architecture that combines vision perception, natural language understanding, and robotic action prediction in a unified framework. | [
"computer-vision",
"language",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"robotics-and-control",
"vision"
] | [
"large-language-model",
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T14:50:46Z | false | 1 | 1,047,179,803 | https://swarms.xyz/ | Python | Apache-2.0 | [
"contrastive-learning",
"mamba",
"vision-language-model"
] | The-Swarm-Corporation/VLAM | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-12T20:31:19Z | [] | 2025-10-27T11:00:28Z | [
"multimodal.vision-language"
] | 5f27f78d3118a16544e47b5b7009589d4879cba5 | 2026-09-26T15:53:33Z | gh-ml-readme-evidence-v2 | "5f27f78d3118a16544e47b5b7009589d4879cba5" | [
"abstract",
"citation",
"dataset",
"installation",
"method",
"other",
"overview",
"results",
"usage"
] | [
"ml-method-context",
"paper-reference"
] | ok | 2026-09-26T15:53:33Z | The-Swarm-Corporation/VLAM | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue",
"paper-reference"
] | include | ml-contribution-v5 | 7 | [
"ai",
"llms",
"ml",
"rla",
"robotic-modles",
"robotics",
"robots",
"vlams",
"vlms"
] | 2026-09-23T21:23:52Z | https://github.com/The-Swarm-Corporation/VLAM | null |
[
"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 | [] | 2025-08-30T06:24:50Z | [AAAI 2025] Official implementation of the paper: PhysDiff: Physiology-based Dynamicity Disentangled Diffusion Model for Remote Physiological Measurement | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-24T14:31:13Z | false | 0 | 1,047,315,187 | https://ojs.aaai.org/index.php/AAAI/article/view/32704/34859 | Python | null | [
"diffusion"
] | VUT-HFUT/PhysDiff | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 1 | 2026-10-24T14:31:13Z | [] | 2025-08-30T07:08:08Z | [
"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 | 5 | [
"rppg"
] | 2026-05-13T13:05:05Z | https://github.com/VUT-HFUT/PhysDiff | null |
[
"generative-ai",
"generative-modeling",
"language",
"natural-language-processing",
"software-engineering"
] | [
"code-generation",
"diffusion",
"language-model",
"language-modeling",
"large-language-model",
"pretraining"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm-code-generation",
"llm.language-model",
"llm.large-language-model",
"nlp.language-modeling",
"recall.name.diffusion-model",
"recall.name.language-model",
"recall.name.large-language-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-31T07:55:59Z | Offical implementation of our paper "Exploring the Potential of Diffusion Large Language Models in Code Generation". | [
"generative-ai",
"generative-modeling",
"language",
"natural-language-processing",
"software-engineering"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T15:56:46Z | false | 0 | 1,047,779,149 | null | Python | null | [
"code-generation",
"diffusion",
"language-model"
] | zhangyitonggg/dllm4code | [
"description",
"query-match",
"repository-metadata"
] | 8 | 2026-10-28T17:27:44Z | [] | 2026-09-26T06:24:45Z | [
"llm-code-generation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 23 | [] | 2026-09-26T06:24:49Z | https://github.com/zhangyitonggg/dllm4code | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"image-processing",
"low-level-vision"
] | [
"diffusion",
"image-dehazing",
"image-enhancement",
"image-restoration"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"recall26.image-dehazing"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-31T17:00:03Z | Official implementation of ECCV 2024 paper "Unleashing the Potential of the Semantic Latent Space in Diffusion Models for Image Dehazing" | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-27T15:53:49Z | false | 1 | 1,047,983,447 | null | Python | null | [
"diffusion"
] | aaaasan111/difflid | [
"description",
"query-match",
"repository-metadata"
] | 4 | 2026-10-23T15:59:01Z | [] | 2025-09-08T04:35:09Z | [
"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 | 10 | [] | 2026-10-13T08:37:56Z | https://github.com/aaaasan111/difflid | null |
[
"generative-ai",
"language"
] | [
"fine-tuning",
"parameter-efficient-fine-tuning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.parameter-efficient-finetuning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-01T13:31:02Z | Example project to finetune a Llama model and verify the new model in Ollama | [
"generative-ai",
"language"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-25T15:11:36Z | false | 0 | 1,048,461,279 | null | Python | null | [
"fine-tuning",
"parameter-efficient-fine-tuning"
] | dalchr/llama-finetune | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-29T17:49:51Z | [] | 2025-09-16T08:18:45Z | [
"llm.parameter-efficient-finetuning"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
"contribution-language",
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [] | 2025-09-16T08:18:48Z | https://github.com/dalchr/llama-finetune | null |
[
"embodied-ai",
"generative-ai",
"language",
"natural-language-processing",
"robotics",
"robotics-and-control"
] | [
"language-model",
"manipulation",
"pretraining",
"robot-learning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.language-models",
"recall.name.language-model",
"robotics.manipulation",
"robotics.robotics-topic"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-02T13:58:46Z | [CoRL 2026] Official implementation of the paper "Bimanual Robot Manipulation via Multi-Agent In-Context Learning". | [
"generative-ai",
"language",
"natural-language-processing",
"robotics-and-control"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-01T16:56:07Z | false | 0 | 1,049,111,903 | null | Python | MIT | [
"language-model",
"pretraining"
] | alesspalma/icl_bimanual | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 9 | 2026-10-21T15:48:06Z | [] | 2026-10-01T13:32:52Z | [
"llm.language-models"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [
"bimanual-manipulation",
"coppeliasim",
"in-context-learning",
"large-language-models",
"multi-agent-systems",
"pytorch",
"rlbench",
"robot-manipulation",
"robotics"
] | 2026-10-18T12:22:10Z | https://github.com/alesspalma/icl_bimanual | null |
[
"reinforcement-learning"
] | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-02T15:44:14Z | Code for paper 《Structured Sparse Deep Reinforcement Learning for Accelerating Traffic Signal Decision-Making Under Limited Computational Resources》 | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-31T16:49:26Z | false | 0 | 1,049,178,399 | null | Python | null | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | RL-DLMU/SPTS-Light | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-31T16:49:26Z | [] | 2025-09-03T06:06:18Z | [
"rl.deep"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-01-20T06:31:18Z | https://github.com/RL-DLMU/SPTS-Light | null |
[
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion",
"generative-modeling"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"vision.image-diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-03T04:42:24Z | Official implementation for paper RealDeal: Enhancing Realism and Details in Brain Image Generation via Image-to-Image Diffusion Models | [
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-14T16:10:46Z | false | 0 | 1,049,503,890 | null | Jupyter Notebook | null | [
"diffusion"
] | shenzhu128/RealDeal | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-25T15:11:36Z | [] | 2025-10-01T06:04:49Z | [
"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 | 1 | [] | 2025-10-01T17:02:25Z | https://github.com/shenzhu128/RealDeal | null |
[
"computational-science",
"generative-modeling",
"physics",
"trustworthy-ml"
] | [
"calibration",
"diffusion",
"neural-operator",
"neural-operators",
"surrogate-modeling",
"uncertainty-quantification"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"science.neural-operators",
"trust.uncertainty-quantification"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-04T10:09:51Z | Official implementation of the NeurIPS 2025 spotlight paper "Light-Weight Diffusion Multiplier and Uncertainty Quantification for Fourier Neural Operators" | [
"generative-modeling",
"trustworthy-ml"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 2 | 1,050,355,264 | null | Python | GPL-3.0 | [
"calibration",
"diffusion",
"neural-operator",
"uncertainty-quantification"
] | PhysicsXLtd/DINOZAUR | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 8 | 2026-10-04T15:58:58Z | [] | 2025-10-28T16:37:45Z | [
"trust.uncertainty-quantification"
] | 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 | [
"bayesian-inference",
"diffusion-multipler",
"fourier-neural-operator",
"neural-operator",
"uncertainty-quantification",
"variational-inference"
] | 2026-08-19T09:17:09Z | https://github.com/PhysicsXLtd/DINOZAUR | null |
[
"generative-ai"
] | [
"diffusion"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"recall.name.diffusion-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-05T12:51:49Z | Code for paper "Neural racing accumulator model estimation with lightweight monotonic flows". | [
"generative-ai"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-28T16:14:12Z | false | 0 | 1,051,101,023 | null | Python | Apache-2.0 | [
"diffusion"
] | maltelueken/neural-race-model-estimation | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-28T16:14:12Z | [] | 2026-10-09T09:52:17Z | [
"recall.name.diffusion-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-context-only",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [] | 2026-10-09T09:51:18Z | https://github.com/maltelueken/neural-race-model-estimation | null |
[
"deep-learning",
"energy",
"energy-load-forecasting",
"power-systems",
"representation-learning",
"time-series-and-forecasting"
] | [
"representation-learning",
"self-supervised-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"applied.energy-load-forecasting",
"general.self-supervised-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-05T19:29:29Z | This is the official implementation of our submitted paper : "SpanPatchTST: A Self-Supervised Learning Model for Short-term and Mid-term Energy Load Forecasting" | [
"deep-learning",
"energy",
"energy-load-forecasting",
"power-systems",
"representation-learning",
"time-series-and-forecasting"
] | [
"self-supervised-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-07T18:07:52Z | false | 0 | 1,051,316,827 | null | Python | MIT | [
"representation-learning",
"self-supervised-learning"
] | lear-ner97/SpanPatchTST | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-07T18:07:52Z | [] | 2026-05-11T19:52:55Z | [
"applied.energy-load-forecasting",
"general.self-supervised-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-05-11T19:53:00Z | https://github.com/lear-ner97/SpanPatchTST | 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 | [] | 2025-09-08T03:28:01Z | A new graph neural network for Guangdong province | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-20T15:59:06Z | false | 0 | 1,052,428,315 | null | null | null | [
"graph-neural-network",
"message-passing",
"neural-network"
] | czqiang/STGNN-GD | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-20T15:59:06Z | [] | 2025-09-08T03:28:22Z | [
"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 | [] | 2025-09-08T03:28:26Z | https://github.com/czqiang/STGNN-GD | null |
[
"deep-learning",
"representation-learning"
] | [
"representation-learning",
"self-supervised-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.self-supervised-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-08T18:36:37Z | Bootstrap Your Own Latent (BYOL), is a new algorithm for self-supervised learning of image representations. | [
"deep-learning",
"representation-learning"
] | [
"self-supervised-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-06T17:10:23Z | false | 0 | 1,052,927,916 | null | Python | null | [
"representation-learning",
"self-supervised-learning"
] | yrasool/BYOL-self-supervised-learning-on-CIFAR-images | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-06T17:10:23Z | [] | 2025-09-08T18:40:23Z | [
"general.self-supervised-learning"
] | 62aed73396acc5e33b385800ae7458682ee2d03d | 2026-10-06T18:32:27Z | gh-ml-readme-evidence-v2 | "62aed73396acc5e33b385800ae7458682ee2d03d" | [] | [
"ml-method-context"
] | ok | 2026-10-06T18:32:27Z | yrasool/BYOL-self-supervised-learning-on-CIFAR-images | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [] | 2025-09-08T18:40:27Z | https://github.com/yrasool/BYOL-self-supervised-learning-on-CIFAR-images | null |
[
"computational-neuroscience",
"machine-learning"
] | [
"neural-network",
"neuromorphic-computing",
"spiking-neural-network"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"specialized.spiking-neural-network"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-09T01:18:11Z | The source code for paper: Ultra-Low-Latency Spiking Neural Networks with Temporal-Dependent Integrate-and-Fire Neuron Model for Objects Detection | [
"computational-neuroscience",
"machine-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-20T15:59:06Z | false | 0 | 1,053,087,592 | null | Python | MIT | [
"neural-network",
"neuromorphic-computing",
"spiking-neural-network"
] | zhangcj13/tdIF | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-20T15:59:06Z | [] | 2025-09-12T07:10:36Z | [
"specialized.spiking-neural-network"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-context-only",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [] | 2025-09-30T03:20:02Z | https://github.com/zhangcj13/tdIF | null |
[
"deep-learning",
"efficient-ml",
"language",
"multimodal-learning"
] | [
"conditional-computation",
"mixture-of-experts",
"sparse-activation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.mixture-of-experts"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-09T06:34:00Z | Code for paper A Mixture-of-Experts Model for Multimodal Emotion Recognition in Conversations | [
"deep-learning",
"efficient-ml",
"language",
"multimodal-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 1 | 1,053,217,265 | null | Python | null | [
"conditional-computation",
"mixture-of-experts",
"sparse-activation"
] | iiscleap/MiSTER-E | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-10-01T17:50:46Z | [] | 2025-09-09T12:07:32Z | [
"efficiency.mixture-of-experts"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 6 | [] | 2026-09-19T13:04:14Z | https://github.com/iiscleap/MiSTER-E | null |
[
"generative-ai",
"generative-modeling",
"reinforcement-learning"
] | [
"diffusion",
"reinforcement-learning"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-09T07:57:12Z | Official code for the paper "DmC: Nearest Neighbor Guidance Diffusion Model for Offline Cross-domain Reinforcement Learning" | [
"generative-ai",
"generative-modeling",
"reinforcement-learning"
] | [
"reinforcement-learning",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 0 | 1,053,262,821 | null | Python | null | [
"diffusion",
"reinforcement-learning"
] | linhlpv/DmC | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 1 | 2026-10-23T15:59:01Z | [] | 2025-12-10T02:37:29Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 6 | [
"cross-domain-rl",
"diffusion-model-for-cross-domain-rl",
"offline-cross-domain-rl"
] | 2026-02-03T15:45:37Z | https://github.com/linhlpv/DmC | null |
[
"imitation-learning",
"reinforcement-learning"
] | [
"imitation-learning",
"inverse-reinforcement-learning",
"reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"rl.inverse"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-12T05:48:00Z | IRL-CC: A novel Congestion Control algorithm based on inverse reinforcement learning with parallel training | [
"imitation-learning",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 1 | 1,055,332,357 | null | Python | null | [
"imitation-learning",
"inverse-reinforcement-learning",
"reinforcement-learning"
] | luopeng69131/IRL-CC | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-09-28T18:33:08Z | [] | 2025-09-12T05:49:15Z | [
"rl.inverse"
] | 22ee3e80d0e5e7b0ae733f25194fa00208b6fe1a | 2026-09-26T15:53:33Z | gh-ml-readme-evidence-v2 | "22ee3e80d0e5e7b0ae733f25194fa00208b6fe1a" | [
"installation",
"other",
"results",
"usage"
] | [
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-09-26T15:53:33Z | luopeng69131/IRL-CC | specific-method-with-novelty-claim | [
"contribution-language",
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 0 | [] | 2025-09-12T05:49:21Z | https://github.com/luopeng69131/IRL-CC | null |
[
"autonomous-driving",
"computer-vision",
"embodied-ai",
"language",
"multimodal",
"multimodal-learning",
"robotics",
"robotics-and-control",
"vision"
] | [
"contrastive-learning",
"distillation",
"perception",
"planning",
"robot-learning",
"vision-language-action",
"vision-language-model"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.vision-language",
"robotics.autonomous-driving",
"robotics.visual-language-action"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-12T05:51:51Z | Official implementation of paper "EvoDriveVLA: Evolving Autonomous Driving Vision–Language–Action Model via Collaborative Perception-Planning Distillation" | [
"autonomous-driving",
"computer-vision",
"multimodal-learning",
"robotics",
"robotics-and-control"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T16:16:53Z | false | 6 | 1,055,334,193 | null | Python | null | [
"distillation",
"perception",
"planning"
] | hey-cjj/EvoDriveVLA | [
"description",
"query-match",
"repository-metadata"
] | 7 | 2026-10-15T15:20:52Z | [] | 2026-04-02T18:01:40Z | [
"robotics.autonomous-driving"
] | 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 | 50 | [] | 2026-08-17T08:53:19Z | https://github.com/hey-cjj/EvoDriveVLA | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-12T11:15:28Z | [ICLR 2026] Official PyTorch Implementation of "Latent Diffusion Model Without Variational Autoencoder". | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 17 | 1,055,506,975 | https://howlin-wang.github.io/svg/ | Python | null | [
"diffusion"
] | shiml20/SVG | [
"description",
"query-match",
"repository-metadata"
] | 4 | 2026-10-23T15:59:01Z | [] | 2025-12-15T07:01:34Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 469 | [] | 2026-10-21T02:20:53Z | https://github.com/shiml20/SVG | null |
[
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion",
"generative-modeling"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"vision.image-diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-12T19:41:14Z | Official implementation of the paper Locality in Image Diffusion Models Emerges from Data Statistics | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 9 | 1,055,778,558 | null | Jupyter Notebook | NOASSERTION | [
"diffusion"
] | ottogin/locality-in-diffusion-models | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-10-23T15:59:01Z | [] | 2025-12-25T05:01:59Z | [
"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 | 50 | [] | 2026-10-08T12:41:45Z | https://github.com/ottogin/locality-in-diffusion-models | null |
[
"anomaly-detection",
"generative-ai",
"generative-modeling",
"time-series",
"time-series-and-forecasting"
] | [
"anomaly-detection",
"diffusion",
"unsupervised-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"timeseries.anomaly-detection"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-14T07:57:01Z | This is an repository hosting the code of our paper: Iterative Feedback-based Time-Series Anomaly Detection with Adaptive Diffusion Models | [
"generative-ai",
"generative-modeling",
"time-series-and-forecasting"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 0 | 1,056,493,301 | null | Python | null | [
"diffusion"
] | pangddu123/IFAD | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-25T15:11:36Z | [] | 2025-12-21T02:43:36Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2025-12-21T02:43:40Z | https://github.com/pangddu123/IFAD | null |
[
"interpretability-and-safety",
"machine-learning",
"natural-language-processing"
] | [
"machine-unlearning",
"privacy"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"specialized.machine-unlearning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-14T20:04:44Z | ICLR 2026: Implementation of our unlearning method "Partial Model Collapse" introduced in: "Model Collapse Is Not a Bug but a Feature in Machine Unlearning for LLMs". | [
"interpretability-and-safety",
"machine-learning",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 0 | 1,056,790,752 | https://arxiv.org/abs/2507.04219 | Python | MIT | [
"machine-unlearning",
"privacy"
] | partial-model-collapse-unlearning/pmc-unlearning | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 7 | 2026-10-04T15:58:58Z | [] | 2026-01-04T17:19:33Z | [
"specialized.machine-unlearning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-context-only",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 29 | [
"alignment",
"llm-unlearning",
"llms",
"unlearning"
] | 2026-05-09T15:31:12Z | https://github.com/partial-model-collapse-unlearning/pmc-unlearning | null |
[
"artificial-intelligence",
"machine-learning"
] | [
"interpretability",
"mechanistic-interpretability",
"rag"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"specialized.mechanistic-interpretability"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-15T03:24:31Z | Official implementation of ICLR 2026 paper "LUMINA: Detecting Hallucinations in RAG System with Context–Knowledge Signals" | [
"artificial-intelligence",
"machine-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-06T17:10:23Z | false | 3 | 1,056,922,794 | null | Python | MIT | [
"interpretability",
"mechanistic-interpretability",
"rag"
] | deeplearning-wisc/LUMINA | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-06T17:10:23Z | [] | 2026-01-31T16:59:22Z | [
"specialized.mechanistic-interpretability"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 19 | [] | 2026-08-22T20:42:59Z | https://github.com/deeplearning-wisc/LUMINA | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-15T10:05:00Z | [ICLR 2026] Official implementation of "Stage-wise Dynamics of Classifier-Free Guidance in Diffusion Models". | [
"generative-ai",
"generative-modeling"
] | [
"classifier",
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 1 | 1,057,127,368 | https://arxiv.org/abs/2509.22007 | Python | MIT | [
"diffusion"
] | sqt24/tvcfg | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-10-23T15:59:01Z | [] | 2026-03-29T14:54:04Z | [
"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 | 5 | [] | 2026-10-21T02:40:33Z | https://github.com/sqt24/tvcfg | null |
[
"multimodal",
"reinforcement-learning",
"robotics"
] | [
"environment-modeling",
"world-model"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.world-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-15T14:06:05Z | Official implementation of the paper "Learning and Planning Multi-Agent Tasks via a MoE-based World Model" | [
"multimodal",
"reinforcement-learning",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 1 | 1,057,276,472 | null | Python | null | [
"environment-modeling",
"world-model"
] | zhaozijie2022/m3w-marl | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-02T16:59:40Z | [] | 2026-03-01T09:59:35Z | [
"multimodal.world-models"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 34 | [] | 2026-09-26T02:43:06Z | https://github.com/zhaozijie2022/m3w-marl | null |
[
"reinforcement-learning"
] | [
"policy-gradient",
"policy-optimization"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"rl.policy-gradient"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-16T06:14:27Z | code for paper "State Aggregation via Continuous Homomorphic Markov Chains: A Function Space Perspective with Policy Gradient Guarantees" | [
"reinforcement-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 1,057,730,183 | null | Python | null | [
"policy-gradient",
"policy-optimization"
] | student953/HPPO | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-04T15:58:58Z | [] | 2026-01-09T07:22:11Z | [
"rl.policy-gradient"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-01-09T07:22:15Z | https://github.com/student953/HPPO | null |
[
"automl",
"general-ml"
] | [
"few-shot-learning",
"meta-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.meta-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-16T08:08:39Z | This repository contains the official PyTorch implementation and simulation framework for the paper: "Resilient AI for Intermittent Systems: A Hardware-Aware Meta-Learning Approach". | [
"automl",
"general-ml"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-30T17:16:29Z | false | 0 | 1,057,795,275 | null | Jupyter Notebook | null | [
"few-shot-learning",
"meta-learning"
] | binxuwang2002-lang/CG-RL | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-30T17:16:29Z | [] | 2025-09-16T08:11:44Z | [
"general.meta-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2025-09-16T08:11:47Z | https://github.com/binxuwang2002-lang/CG-RL | null |
[
"information-retrieval",
"language",
"time-series-and-forecasting"
] | [
"rag",
"retrieval-augmented-generation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm-retrieval-augmented-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-16T15:49:16Z | [NeurIPS 2025] Official website for code and models of Time Series RAG (TS-RAG) | [
"information-retrieval",
"language",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T16:16:53Z | false | 13 | 1,058,085,988 | null | Python | MIT | [
"rag",
"retrieval-augmented-generation"
] | UConn-DSIS/TS-RAG | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-09-27T15:53:49Z | [] | 2026-04-15T02:21:46Z | [
"llm-retrieval-augmented-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 | 84 | [] | 2026-09-21T10:10:21Z | https://github.com/UConn-DSIS/TS-RAG | null |
[
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"forecasting",
"multivariate-forecasting",
"transformer"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"timeseries.multivariate"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-16T18:45:22Z | Official implementation for paper "Dynamic Relational Learning Improves Transformer in Multivariate Time Series" published at ICML'26. | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-03T15:45:52Z | false | 0 | 1,058,192,758 | null | Python | null | [
"forecasting",
"multivariate-forecasting",
"transformer"
] | timlee0131/Prime-Attention | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-03T15:45:52Z | [] | 2025-09-16T18:51:56Z | [
"timeseries.multivariate"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 2 | [] | 2026-07-15T03:45:25Z | https://github.com/timlee0131/Prime-Attention | null |
[
"3d",
"computer-vision",
"human-motion"
] | [
"3d-pose-estimation",
"keypoint-detection",
"pose-estimation",
"transformer"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.3d-human-pose",
"vision.pose-estimation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-17T06:59:50Z | PICFormer: Perception-Inference-Consistency Loop for Occluded 3D Pose Estimation This repository contains the official PyTorch implementation for PICFormer, a novel closed-loop Transformer architecture for robust 3D human pose estimation from videos, especially under severe occlusion. | [
"computer-vision"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-03T15:45:52Z | false | 0 | 1,058,493,966 | null | Python | null | [
"keypoint-detection",
"pose-estimation",
"transformer"
] | HKCyber20/PICFormer | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-24T14:31:13Z | [] | 2026-09-16T03:18:07Z | [
"vision.pose-estimation"
] | e6c4f5858bde66e76f944144dd74c61709db1f21 | 2026-10-03T17:01:43Z | gh-ml-readme-evidence-v2 | "e6c4f5858bde66e76f944144dd74c61709db1f21" | [
"installation",
"method",
"other",
"overview",
"results",
"usage"
] | [
"ml-method-context"
] | ok | 2026-10-03T17:01:43Z | HKCyber20/PICFormer | specific-method-with-novelty-claim | [
"contribution-language",
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue"
] | include | ml-contribution-v5 | 3 | [] | 2026-10-08T04:28:43Z | https://github.com/HKCyber20/PICFormer | null |
[
"generative-ai",
"language",
"natural-language-processing"
] | [
"language-model",
"pretraining"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"llm.language-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-18T01:19:09Z | Official implementation of "Resolving UnderEdit & OverEdit with Iterative & Neighbor-Assisted Model Editing" (EMNLP 2025 Findings). | [
"generative-ai",
"language",
"natural-language-processing"
] | [
"large-language-model",
"machine-learning",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-21T15:48:06Z | false | 0 | 1,059,083,150 | https://arxiv.org/abs/2503.11895 | Python | MIT | [
"language-model",
"pretraining"
] | bhimanbaghel/ResolveUnderOverEdit | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-10-21T15:48:06Z | [] | 2025-11-14T07:00:53Z | [
"llm.language-models"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 1 | [
"emnlp2025",
"knowledge-editing",
"large-language-models",
"llms",
"machine-learning",
"model-editing",
"natural-language-processing",
"nlp",
"pytorch",
"transformers"
] | 2025-11-14T07:00:56Z | https://github.com/bhimanbaghel/ResolveUnderOverEdit | null |
[
"generative-ai",
"generative-modeling",
"multimodal",
"vision"
] | [
"diffusion",
"image-generation",
"text-to-image"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.text-to-image"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-18T12:33:09Z | Official implementation of "Seg4Diff: Unveiling Open-Vocabulary Segmentation in Text-to-Image Diffusion Transformers" (NeurIPS 2025) | [
"generative-ai",
"generative-modeling",
"multimodal",
"vision"
] | [
"diffusion-model",
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 3 | 1,059,426,089 | https://cvlab-kaist.github.io/Seg4Diff/ | Python | null | [
"diffusion",
"image-generation",
"text-to-image"
] | cvlab-kaist/Seg4Diff | [
"description",
"query-match",
"repository-metadata"
] | 5 | 2026-10-14T16:10:46Z | [] | 2025-09-23T06:18:43Z | [
"multimodal.text-to-image"
] | 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 | 81 | [] | 2026-09-05T13:29:15Z | https://github.com/cvlab-kaist/Seg4Diff | null |
[
"computational-physics",
"general-ml",
"physics",
"science-and-engineering",
"scientific-computing",
"time-series-and-forecasting"
] | [
"neural-network",
"neural-operators",
"physics-informed-learning",
"surrogate-modeling"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.neural-network",
"recall.name.neural-network",
"science.physics",
"science.physics-informed-neural-networks"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-18T14:11:07Z | Official code for the paper "Physics-Informed Neural Network (PINN) for Cyclone Intensity Forecasting in the Chattogram–Cox’s Bazar Region, Bangladesh" | [
"general-ml",
"science-and-engineering",
"time-series-and-forecasting"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-01T16:56:07Z | false | 0 | 1,059,490,404 | null | Jupyter Notebook | null | [
"neural-network"
] | Jawadbro/PINNmodel | [
"description",
"query-match",
"repository-metadata"
] | 6 | 2026-10-07T17:02:17Z | [] | 2026-09-26T03:14:07Z | [
"recall.name.neural-network"
] | 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 | [] | 2026-09-26T03:14:10Z | https://github.com/Jawadbro/PINNmodel | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-transformer",
"message-passing"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.graph-transformer"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-19T08:15:41Z | This is the official implementation of the NeurIPS 2025 paper "Relieving the Over-aggregating Effect in Graph Transformers". | [
"deep-learning",
"graph-learning"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 0 | 1,059,984,463 | null | Python | null | [
"graph-transformer",
"message-passing"
] | sunjss/over-aggregating | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-09-28T18:33:08Z | [] | 2025-10-28T10:54:47Z | [
"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 | 5 | [] | 2026-07-27T10:52:56Z | https://github.com/sunjss/over-aggregating | null |
[
"machine-learning"
] | [
"adaptation",
"test-time-adaptation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"specialized.test-time-adaptation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-19T08:17:35Z | Official implementation of NeurIPS 2025 paper "Test-Time Adaptation by Causal Trimming" | [
"machine-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-28T18:33:08Z | false | 0 | 1,059,985,510 | null | Python | MIT | [
"adaptation",
"test-time-adaptation"
] | NancyQuris/TACT | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-05T20:10:04Z | [] | 2025-12-27T10:00:10Z | [
"specialized.test-time-adaptation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 11 | [] | 2026-05-18T08:36:31Z | https://github.com/NancyQuris/TACT | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-19T08:19:45Z | This repository is the official implementation for the paper "Denoising and Augmentation: A Dual Use of Diffusion Model for Enhanced CSI Recovery". | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-25T15:11:36Z | false | 0 | 1,059,986,713 | null | Python | null | [
"diffusion"
] | fhghwericge/Diffusion-Model-for-Enhanced-CSI-Recovery | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-25T15:11:36Z | [] | 2025-09-19T08:36:40Z | [
"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 | 1 | [] | 2026-02-26T03:17:03Z | https://github.com/fhghwericge/Diffusion-Model-for-Enhanced-CSI-Recovery | 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 | [] | 2025-09-20T04:56:41Z | This is the implementation of our NeurIPS paper "MARS: A Malignity-Aware Backdoor Defense in Federated Learning". | [
"distributed-ml",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 2 | 1,060,532,024 | null | Python | null | [
"collaborative-learning",
"federated-learning"
] | yunming181920/MARS | [
"description",
"query-match",
"repository-metadata"
] | 4 | 2026-10-21T15:48:06Z | [] | 2025-09-29T02:30:53Z | [
"trust.federated-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 9 | [] | 2026-06-18T06:05:21Z | https://github.com/yunming181920/MARS | null |
[
"audio",
"generative-modeling",
"natural-language-processing",
"speech"
] | [
"diffusion",
"multimodal-learning",
"speech-language-model"
] | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"nlp-speech-language-modeling"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-21T07:59:01Z | This repository contains a series of works on diffusion-based speech tokenizers, including the official implementation of the paper: "TaDiCodec: Text-aware Diffusion Speech Tokenizer for Speech Language Modeling [NeurIPS 2025]" https://arxiv.org/pdf/2508.16790 | [
"audio",
"generative-modeling",
"natural-language-processing",
"speech"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 3 | 1,061,086,899 | null | Python | null | [
"diffusion",
"multimodal-learning",
"speech-language-model"
] | AmphionTeam/TaDiCodec | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | 5 | 2026-09-27T15:53:49Z | [] | 2026-01-25T14:44:17Z | [
"nlp-speech-language-modeling"
] | 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 | 79 | [] | 2026-08-05T02:21:23Z | https://github.com/AmphionTeam/TaDiCodec | null |
[
"efficient-ml",
"machine-learning-systems",
"model-compression",
"multimodal-learning"
] | [
"distillation",
"quantization"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.quantization"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-21T15:37:42Z | Official code for the paper "DUAL-PATH COMPRESSION FOR REAL-TIME MULTIMODAL CLICKBAIT DETECTION: QUANTIZATION AND DISTILLATION" . | [
"efficient-ml",
"machine-learning-systems",
"model-compression",
"multimodal-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-22T16:09:07Z | false | 3 | 1,061,273,440 | null | Python | null | [
"distillation",
"quantization"
] | shq3526/DPCMCD | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-22T16:09:07Z | [] | 2026-01-25T04:02:22Z | [
"efficiency.quantization"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 4 | [] | 2026-01-25T04:02:25Z | https://github.com/shq3526/DPCMCD | null |
[
"machine-learning"
] | [
"adaptation",
"test-time-adaptation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"specialized.test-time-adaptation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-22T02:26:44Z | Official Code for ICASSP 2026 [Robust Test-time Adaptation by Unifying Principled Priors and Adaptive Feature Regularization] | [
"machine-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 0 | 1,061,504,642 | null | Python | null | [
"adaptation",
"test-time-adaptation"
] | wangskyone/SynTTA | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2026-01-20T05:51:50Z | [
"specialized.test-time-adaptation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 1 | [] | 2026-01-20T05:54:04Z | https://github.com/wangskyone/SynTTA | null |
[
"generative-modeling",
"machine-learning"
] | [
"curriculum-learning",
"diffusion",
"training-strategy"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"specialized.curriculum-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-22T11:02:27Z | Official implementation of the paper <On the Design of One-Step Diffusion via Shortcutting Flow Paths> | [
"generative-modeling",
"machine-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 31 | 1,061,767,192 | https://edapinenut.github.io/explicitshortcut-project-page/ | Python | MIT | [
"curriculum-learning",
"diffusion",
"training-strategy"
] | EDAPINENUT/ExplicitShortCut | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 8 | 2026-10-20T15:59:06Z | [] | 2026-04-01T10:18:50Z | [
"specialized.curriculum-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 289 | [] | 2026-10-12T15:24:21Z | https://github.com/EDAPINENUT/ExplicitShortCut | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-22T15:18:21Z | Official Code for NeurIPS 2025 Spotlight Paper -- CURE: Concept Unlearning via Orthogonal Representation Editing in Diffusion Models | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 1 | 1,061,930,122 | null | Python | null | [
"diffusion"
] | ShristiDasBiswas/CURE-Concept-Unlearning-via-Orthogonal-Representation-Editing-in-Diffusion-Models | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-23T15:59:01Z | [] | 2025-10-02T19:45:21Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 11 | [] | 2026-05-08T16:53:33Z | https://github.com/ShristiDasBiswas/CURE-Concept-Unlearning-via-Orthogonal-Representation-Editing-in-Diffusion-Models | null |
[
"computational-neuroscience",
"machine-learning"
] | [
"neural-network",
"neuromorphic-computing",
"spiking-neural-network"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"specialized.spiking-neural-network"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-23T04:38:46Z | This is the code for paper: At the edge of chaos: A criticality-based initialization method for deep spiking neural networks. | [
"computational-neuroscience",
"machine-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-20T15:59:06Z | false | 0 | 1,062,297,571 | null | Python | null | [
"neural-network",
"neuromorphic-computing",
"spiking-neural-network"
] | zhewengao/giovanngao_criticality_code | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-20T15:59:06Z | [] | 2026-03-03T05:54:06Z | [
"specialized.spiking-neural-network"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-context-only",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [] | 2026-03-03T05:54:09Z | https://github.com/zhewengao/giovanngao_criticality_code | null |
[
"computer-vision",
"earth-science",
"geospatial-science",
"remote-sensing",
"science-and-engineering"
] | [
"foundation-model",
"geospatial-learning",
"remote-sensing"
] | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"science.remote-sensing"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-23T05:03:51Z | The official implementation of "RSRWKV: A Linear-Complexity 2D Attention Mechanism for Efficient Remote Sensing Vision Task" (https://arxiv.org/abs/2503.20382). | [
"computer-vision",
"earth-science",
"geospatial-science",
"remote-sensing",
"science-and-engineering"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 0 | 1,062,308,251 | null | Python | null | [
"foundation-model",
"geospatial-learning",
"remote-sensing"
] | Ling-yunchi/RSRWKV | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-10-23T15:59:01Z | [] | 2025-09-23T05:20:14Z | [
"science.remote-sensing"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 5 | [] | 2026-03-14T14:06:48Z | https://github.com/Ling-yunchi/RSRWKV | null |
[
"computer-vision",
"generative-ai",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"reinforcement-learning",
"video"
] | [
"image-captioning",
"image-to-video",
"reinforcement-learning",
"video-generation",
"vision-language"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"multimodal.image-to-video",
"vision.image-captioning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-23T09:20:07Z | [ICLR 2026] An official implementation of "CapRL: Stimulating Dense Image Caption Capabilities via Reinforcement Learning" | [
"computer-vision",
"generative-ai",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"reinforcement-learning",
"video"
] | [
"large-language-model",
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 8 | 1,062,451,970 | https://arxiv.org/pdf/2509.22647 | Python | Apache-2.0 | [
"image-to-video",
"reinforcement-learning",
"video-generation"
] | InternLM/CapRL | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 3 | 2026-10-13T18:05:26Z | [] | 2026-06-23T02:40:21Z | [
"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"
] | include | ml-contribution-v5 | 232 | [
"caption-generation",
"grpo",
"image-captioning",
"large-vision-language-models",
"llm",
"multi-modal",
"rlvr",
"vision-language-model"
] | 2026-10-06T08:14:15Z | https://github.com/InternLM/CapRL | null |
[
"general-ml",
"reinforcement-learning"
] | [
"machine-learning",
"policy-gradient",
"policy-learning",
"policy-optimization",
"reinforcement-learning"
] | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"general.machine-learning-readme",
"recall.name.machine-learning",
"rl.core",
"rl.policy-gradient"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-23T10:26:15Z | [ICML2026] Official JAX code for Emergence of Exploration in Policy Gradient Reinforcement Learning via Retrying | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 0 | 1,062,492,406 | https://arxiv.org/abs/2606.00151 | Python | null | [
"policy-gradient",
"policy-optimization",
"reinforcement-learning"
] | nissymori/remax-rl | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | 7 | 2026-10-18T13:47:34Z | [] | 2026-10-17T15:46:41Z | [
"rl.policy-gradient"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 17 | [
"jax",
"reinforcement-learning"
] | 2026-10-17T15:46:44Z | https://github.com/nissymori/remax-rl | null |
[
"education",
"educational-data-mining",
"learning-analytics",
"student-modeling"
] | [
"educational-data-mining",
"student-modeling"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"applied.educational-data-mining"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-23T15:06:09Z | Code for paper "Principled Transformers for Predictive Performance in Knowledge Tracing" (accepted in JEDM) | [
"education",
"educational-data-mining",
"learning-analytics",
"student-modeling"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 1 | 1,062,671,475 | null | Python | MIT | [] | kainbr/kt_set_transformers | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-09-30T16:47:43Z | [] | 2026-05-19T11:44:28Z | [
"applied.educational-data-mining"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 4 | [] | 2026-07-01T09:09:21Z | https://github.com/kainbr/kt_set_transformers | null |
[
"embodied-ai",
"reinforcement-learning",
"robotics",
"robotics-and-control"
] | [
"deep-reinforcement-learning",
"manipulation",
"policy-learning",
"reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"rl.deep",
"robotics.manipulation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-24T05:01:58Z | Official implementation of the paper "Time Reversal Symmetry for Efficient Robotic Manipulations in Deep Reinforcement Learning" at NeurIPS2025. | [
"reinforcement-learning",
"robotics-and-control"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 4 | 1,063,042,459 | https://jyp9961.github.io/TR-DRL_project_page/ | Python | null | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | jyp9961/TR-DRL | [
"description",
"query-match",
"repository-metadata"
] | 5 | 2026-10-30T17:16:29Z | [] | 2026-04-27T06:37:05Z | [
"rl.deep"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 3 | [] | 2026-07-07T05:21:11Z | https://github.com/jyp9961/TR-DRL | null |
[
"generative-ai",
"generative-modeling",
"science-and-engineering"
] | [
"diffusion"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-24T21:03:31Z | Official implementation of "PIRF: Physics-Informed Reward Fine-Tuning for Diffusion Models" (NeurIPS 2025 AI4Science Workshop) | [
"generative-ai",
"generative-modeling",
"science-and-engineering"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-24T14:31:13Z | false | 0 | 1,063,593,422 | null | Python | MIT | [
"diffusion"
] | mingze-yuan/PIRF | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-24T14:31:13Z | [] | 2025-09-24T21:13:29Z | [
"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 | [] | 2026-06-01T04:37:10Z | https://github.com/mingze-yuan/PIRF | null |
[
"multimodal",
"reinforcement-learning",
"robotics",
"robotics-and-control"
] | [
"environment-modeling",
"reinforcement-learning",
"world-model"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"multimodal.world-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-25T06:09:52Z | Official code repository for the paper "Learning Massively Multitask World Models for Continuous Control". | [
"multimodal",
"reinforcement-learning",
"robotics",
"robotics-and-control"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 19 | 1,063,795,966 | https://www.nicklashansen.com/NewtWM | Python | null | [
"environment-modeling",
"reinforcement-learning",
"world-model"
] | nicklashansen/newt | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 5 | 2026-10-02T16:59:40Z | [] | 2026-01-09T22:50:40Z | [
"multimodal.world-models"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 139 | [
"reinforcement-learning",
"robotics",
"world-models"
] | 2026-09-27T00:53:16Z | https://github.com/nicklashansen/newt | null |
[
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"forecasting",
"sequence-modeling"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"timeseries.forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-25T06:16:51Z | [ICLR 2026] Official implementation of the paper "Local Geometry Attention for Time Series Forecasting under Realistic Corruptions" | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"deep-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-30T17:16:29Z | false | 1 | 1,063,799,305 | https://openreview.net/forum?id=NCQPCxN7ds | Python | null | [
"forecasting",
"sequence-modeling"
] | dongbeank/LGA | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 2 | 2026-11-01T17:09:41Z | [] | 2026-02-24T08:26:41Z | [
"timeseries.forecasting"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 15 | [
"attention-mechanism",
"deep-learning",
"robustness",
"time-series-forecasting"
] | 2026-10-13T02:27:49Z | https://github.com/dongbeank/LGA | null |
[
"forecasting",
"general-ml",
"generative-ai",
"language",
"time-series",
"time-series-and-forecasting"
] | [
"forecasting",
"language-model",
"sequence-modeling",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"llm.transformers",
"recall.name.transformer-model",
"timeseries.forecasting",
"timeseries.topic"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-25T07:41:18Z | [NeurIPS 2026] Official implementation of "🎲 EntroPE: Entropy Guided Dynamic Patch Segmentation for Time Series Transformers" | [
"time-series",
"time-series-and-forecasting"
] | [
"representation-learning",
"classifier",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 6 | 1,063,846,732 | https://arxiv.org/pdf/2509.26157 | Python | null | [
"sequence-modeling"
] | Sachithx/EntroPE | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | 9 | 2026-10-28T17:27:44Z | [] | 2026-10-22T06:53:43Z | [
"timeseries.topic"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 50 | [
"anomaly-detection",
"classification",
"entropy",
"forecasting",
"foundation-models",
"information-theory",
"neurips-2026",
"python",
"pytorch",
"representation-learning",
"segmentation",
"time-series",
"transformers",
"unified-model"
] | 2026-10-22T06:53:46Z | https://github.com/Sachithx/EntroPE | null |
[
"generative-ai",
"information-retrieval",
"language",
"reinforcement-learning"
] | [
"bm25",
"language-model",
"lexical-retrieval",
"long-context-modeling",
"reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.long-context",
"nlp-bm25-retrieval"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-25T20:28:15Z | The official implementation of the paper "Mem-α: Learning Memory Construction via Reinforcement Learning" | [
"information-retrieval",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T16:16:53Z | false | 21 | 1,064,293,523 | null | Python | null | [
"bm25",
"lexical-retrieval",
"reinforcement-learning"
] | wangyu-ustc/Mem-alpha | [
"description",
"query-match",
"repository-metadata"
] | 8 | 2026-10-31T16:49:26Z | [] | 2025-12-25T17:50:03Z | [
"nlp-bm25-retrieval"
] | 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 | 228 | [] | 2026-10-15T07:11:07Z | https://github.com/wangyu-ustc/Mem-alpha | null |
[
"computer-vision",
"image-processing",
"low-level-vision"
] | [
"distillation",
"image-denoising",
"image-restoration",
"self-supervised-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"recall26.image-denoising"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-26T04:20:25Z | [submission] Official code of paper "Robust distillation for compute-in-memory: Realizing reliable intelligence using imperfect memristors" | [
"computer-vision",
"image-processing",
"low-level-vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-19T17:46:36Z | false | 0 | 1,064,458,181 | null | Python | MIT | [
"distillation",
"image-denoising",
"image-restoration",
"self-supervised-learning"
] | RL-MIND/MTRD-MNI-CIM | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-23T15:59:01Z | [] | 2026-07-19T16:12:18Z | [
"recall26.image-denoising"
] | 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 | 17 | [] | 2026-10-14T12:35:07Z | https://github.com/RL-MIND/MTRD-MNI-CIM | null |
[
"multimodal",
"reinforcement-learning",
"robotics"
] | [
"environment-modeling",
"multimodal-learning",
"world-model"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.world-models",
"recall.name.multimodal-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-26T06:04:19Z | [ECCV 2026] Official implementation of paper "Towards Unified World Models for Visual Navigation via Memory-Augmented Planning and Foresight" | [
"multimodal"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 34 | 1,064,499,982 | null | Python | null | [
"multimodal-learning"
] | UWMILab/UniWM | [
"description",
"query-match",
"repository-metadata"
] | 10 | 2026-10-24T13:28:22Z | [] | 2026-10-02T04:43:55Z | [
"recall.name.multimodal-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 313 | [] | 2026-10-15T14:42:35Z | https://github.com/UWMILab/UniWM | null |
[
"generative-modeling",
"natural-language-processing",
"reinforcement-learning"
] | [
"diffusion",
"policy-gradient",
"policy-optimization"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.policy-gradient"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-09-26T20:55:20Z | Code for paper "SPG Sandwiched Policy Gradient for Masked Diffusion Language Models" | [
"generative-modeling",
"natural-language-processing",
"reinforcement-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 6 | 1,064,967,646 | https://chenyuwang-monica.github.io/spg/ | Python | MIT | [
"diffusion",
"policy-gradient",
"policy-optimization"
] | facebookresearch/SPG | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 7 | 2026-10-02T16:59:40Z | [] | 2025-10-29T00:44:42Z | [
"rl.policy-gradient"
] | 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 | 66 | [] | 2026-09-06T14:37:40Z | https://github.com/facebookresearch/SPG | null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.