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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
[
"computer-vision",
"efficient-ml",
"language",
"machine-learning-systems",
"model-compression",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"vision"
] | [
"contrastive-learning",
"image-text",
"language-model",
"multimodal-learning",
"neural-network-pruning",
"pruning",
"structured-pruning",
"vision-language",
"vision-language-model"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning",
"llm.language-model",
"multimodal.vision-language",
"multimodal.vlm",
"recall.name.multimodal-model",
"vision.vision-language"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-09T08:18:04Z | Official implementation of MVP: Multi-View-Guided Token Pruning for Vision-Language Models (EMNLP 2026 Main Conference). | [
"computer-vision",
"machine-learning-systems",
"multimodal",
"multimodal-learning",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-10T15:26:41Z | false | 0 | 1,411,510,010 | null | Python | Apache-2.0 | [
"multimodal-learning",
"pruning"
] | Chenxi-CCW/MVP | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-10-30T16:05:33Z | [] | 2026-10-09T08:27:30Z | [
"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"
] | include | ml-contribution-v5 | 0 | [] | 2026-10-11T10:00:48Z | https://github.com/Chenxi-CCW/MVP | null |
[
"generative-ai",
"language"
] | [
"fine-tuning",
"language-model",
"parameter-efficient-fine-tuning",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.finetuning",
"llm.transformers"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-09T12:29:26Z | Official implementation of "Mixture-of-Control: State-Aware Fine-Tuning for Transformer-based Models" (NeurIPS 2026). | [
"generative-ai",
"language"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-10T15:26:41Z | false | 0 | 1,411,814,617 | null | null | MIT | [
"fine-tuning",
"language-model",
"parameter-efficient-fine-tuning",
"transformer"
] | duc-anh-2002/Mixture-of-Control | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-22T14:58:59Z | [] | 2026-10-09T12:29:28Z | [
"llm.finetuning",
"llm.transformers"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-10-09T12:29:32Z | https://github.com/duc-anh-2002/Mixture-of-Control | null |
[
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion",
"flow-matching"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"recall.name.diffusion-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-09T14:09:18Z | Official implementation of the paper: RECON: Representation Conditioning for Generation | [
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-31T15:48:22Z | false | 0 | 1,411,951,296 | https://arxiv.org/pdf/2610.20608 | Python | MIT | [
"diffusion",
"flow-matching"
] | saeidrazavi/RECON | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-10-31T15:48:22Z | [] | 2026-10-16T07:40:09Z | [
"recall.name.diffusion-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 11 | [
"diffusion",
"diffusion-models",
"flow-matching",
"generative-ai",
"image-generation"
] | 2026-10-27T12:44:03Z | https://github.com/saeidrazavi/RECON | null |
[
"multi-agent-systems",
"reinforcement-learning"
] | [
"centralized-training",
"multi-agent-reinforcement-learning",
"reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"rl.multiagent"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-09T15:46:49Z | Code for paper "Multi-Agent Reinforcement Learning for Cross-Region Placement of Branched Service Function Chains" | [
"multi-agent-systems",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-10T15:26:41Z | false | 0 | 1,412,091,542 | null | Python | null | [
"centralized-training",
"multi-agent-reinforcement-learning",
"reinforcement-learning"
] | Shuxin-Wang/MAPPO-SFC | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-18T13:47:34Z | [] | 2026-10-09T15:49:52Z | [
"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 | [] | 2026-10-09T15:49:57Z | https://github.com/Shuxin-Wang/MAPPO-SFC | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion",
"flow-matching"
] | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"recall.name.diffusion-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-09T17:43:51Z | [coming soon] The official implementation of the paper titled "Controllable Exaggeration for Generative Motion Models via Training-Time Adaptation and Inference-Time Guidance"" | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-28T16:14:12Z | false | 0 | 1,412,236,437 | https://arxiv.org/abs/2610.12316 | null | null | [
"diffusion",
"flow-matching"
] | AHHHZ975/Controllable-Motion-Exaggeration | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-10-28T16:14:12Z | [] | 2026-10-09T18:11:37Z | [
"recall.name.diffusion-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 2 | [
"animation-principle",
"character-animation",
"character-motion-guide",
"diffusion-models",
"flow-matching",
"generative-ai",
"motion-generation",
"supervised-learning",
"training-free-method"
] | 2026-10-25T20:36:38Z | https://github.com/AHHHZ975/Controllable-Motion-Exaggeration | null |
[
"generative-ai",
"language",
"reinforcement-learning"
] | [
"language-model",
"reinforcement-learning",
"transformer"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.transformers"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-10T07:44:39Z | Official implementations of experiments in paper "Transformers Provably Implement In-Context Reinforcement Learning with Policy Improvement". | [
"generative-ai",
"language",
"reinforcement-learning"
] | [
"reinforcement-learning",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-24T13:28:22Z | false | 0 | 1,412,915,713 | null | Jupyter Notebook | null | [
"language-model",
"reinforcement-learning",
"transformer"
] | harris-liang/Transformer-in-context-RL | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-24T13:28:22Z | [] | 2026-10-10T08:00:01Z | [
"llm.transformers"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [] | 2026-10-10T08:00:05Z | https://github.com/harris-liang/Transformer-in-context-RL | null |
[
"automl",
"data-centric-ai",
"general-ml",
"language"
] | [
"few-shot-learning",
"language-model",
"meta-learning",
"synthetic-data"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.meta-learning",
"llm.synthetic-data"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-10T14:08:34Z | Code for paper: MotherTree: Meta-learning on synthetic data improves decision tree training | [
"automl",
"data-centric-ai",
"general-ml",
"language"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-10T15:26:41Z | false | 0 | 1,413,265,398 | null | Python | null | [
"few-shot-learning",
"language-model",
"meta-learning",
"synthetic-data"
] | Stanwang218/Mother-Tree | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-10-18T13:47:34Z | [] | 2026-10-14T08:15:05Z | [
"general.meta-learning",
"llm.synthetic-data"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 4 | [] | 2026-10-14T10:26:20Z | https://github.com/Stanwang218/Mother-Tree | 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 | [] | 2026-10-10T14:16:17Z | Official implementation for the ICCA 2026 paper: Federated Learning for Privacy-Preserving Adult Content Detection under Non-IID Data Distributions. | [
"distributed-ml",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-11T15:47:04Z | false | 0 | 1,413,273,543 | null | Jupyter Notebook | null | [
"collaborative-learning",
"federated-learning"
] | Sagor000000/icca-2026-privacy-preserving-fl | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-13T16:59:50Z | [] | 2026-10-10T14:17:07Z | [
"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 | 0 | [] | 2026-10-10T14:17:10Z | https://github.com/Sagor000000/icca-2026-privacy-preserving-fl | null |
[
"computer-vision",
"generative-ai",
"language"
] | [
"language-model",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.transformers"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-11T03:41:16Z | Official implementation of Patch Rebirth: Fast and Transferable Model Inversion of Vision Transformers (NeurIPS 2026). | [
"computer-vision",
"generative-ai",
"language"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-24T13:28:22Z | false | 0 | 1,413,894,549 | null | Python | MIT | [
"language-model",
"transformer"
] | bigdata-inha/PRI | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-24T13:28:22Z | [] | 2026-10-11T03:44:06Z | [
"llm.transformers"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-10-21T16:32:22Z | https://github.com/bigdata-inha/PRI | null |
[
"computer-vision",
"efficient-ml",
"image-processing",
"low-level-vision",
"machine-learning-systems",
"model-compression"
] | [
"image-denoising",
"image-restoration",
"mamba",
"quantization",
"restoration",
"self-supervised-learning",
"super-resolution"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.quantization",
"recall26.image-denoising",
"vision.image-restoration",
"vision.image-super-resolution"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-11T06:46:29Z | [NeurIPS 2026] Official PyTorch implementation of "Hardware-Friendly Token-Group Activation Quantization for Low-Bit Mamba Super-Resolution" | [
"computer-vision",
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-12T19:07:30Z | false | 0 | 1,414,024,388 | null | Python | Apache-2.0 | [
"mamba",
"quantization",
"restoration"
] | imaboybut/TGQ-MambaSR | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-21T14:37:12Z | [] | 2026-10-14T03:11:38Z | [
"efficiency.quantization",
"vision.image-restoration"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 2 | [] | 2026-10-14T04:18:28Z | https://github.com/imaboybut/TGQ-MambaSR | null |
[
"efficient-ml",
"machine-learning-systems",
"model-compression",
"natural-language-processing"
] | [
"quantization"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.quantization"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-12T22:20:49Z | Official implementation of paper "MoBiQuant: Mixture-of-Bits Quantization for Token-Adaptive Any-Precision LLM" | [
"efficient-ml",
"machine-learning-systems",
"model-compression",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-14T14:33:31Z | false | 0 | 1,416,187,325 | null | Python | MIT | [
"quantization"
] | SimWangArizona/MoBiQuant | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-21T14:37:12Z | [] | 2026-10-17T17:57:39Z | [
"efficiency.quantization"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 2 | [] | 2026-10-20T19:50:46Z | https://github.com/SimWangArizona/MoBiQuant | null |
[
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"recall.name.diffusion-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-13T03:15:02Z | Official PyTorch implementation of our paper: "PixelRush: Ultra-Fast, Training-Free High-Resolution Image Generation via One-step Diffusion" | [
"computer-vision",
"generative-ai",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-30T16:05:33Z | false | 0 | 1,416,390,892 | null | Python | NOASSERTION | [
"diffusion"
] | Qualcomm-AI-research/fast-highres-t2i | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-30T16:05:33Z | [] | 2026-10-15T09:06:53Z | [
"recall.name.diffusion-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 3 | [] | 2026-10-26T10:58:21Z | https://github.com/Qualcomm-AI-research/fast-highres-t2i | null |
[
"embodied-ai",
"general-ml",
"multimodal",
"robotics"
] | [
"foundation-model",
"robot-foundation-model",
"robot-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"recall.name.foundation-model",
"robotics.robot-foundation-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-13T13:41:33Z | Official implementation of the paper "Value as Memory: Online Value-Guided Post-training for General Robot Policies" | [
"embodied-ai",
"multimodal",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-14T14:33:31Z | false | 0 | 1,417,143,487 | null | Python | Apache-2.0 | [
"robot-foundation-model",
"robot-learning"
] | Virlus/VaM | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-23T14:45:14Z | [] | 2026-10-16T02:52:52Z | [
"robotics.robot-foundation-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 24 | [] | 2026-10-21T17:21:02Z | https://github.com/Virlus/VaM | null |
[
"data-centric-ai",
"deep-learning",
"efficient-ml",
"language",
"machine-learning"
] | [
"conditional-computation",
"feature-selection",
"machine-learning",
"mixture-of-experts",
"sparse-activation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.mixture-of-experts",
"probable.data-feature-selection"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-13T15:22:54Z | Official code for the paper "RouterInterp: Understanding Superposed Specialisation in Mixture of Experts Routing" | [
"data-centric-ai",
"machine-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-14T14:33:31Z | false | 0 | 1,417,340,009 | null | Python | MIT | [
"feature-selection",
"machine-learning",
"mixture-of-experts"
] | ilyalasy/routerinterp | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-25T14:09:27Z | [] | 2026-10-13T15:23:40Z | [
"probable.data-feature-selection"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-10-21T17:14:14Z | https://github.com/ilyalasy/routerinterp | null |
[
"biology",
"computational-biology",
"generative-modeling",
"protein-engineering"
] | [
"diffusion",
"inverse-folding",
"protein-design"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"bio.inverse-folding"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-13T20:06:00Z | Official implementation of GOLD: Geometric Optimized Latent Diffusion for Structure-Aware RNA Inverse Folding (NeurIPS 2026 Spotlight). | [
"biology",
"computational-biology",
"generative-modeling",
"protein-engineering"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-14T14:33:31Z | false | 0 | 1,417,897,308 | null | Python | Apache-2.0 | [
"diffusion",
"inverse-folding",
"protein-design"
] | darkflash03/GOLD | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-18T13:47:34Z | [] | 2026-10-15T09:13:35Z | [
"bio.inverse-folding"
] | 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 | 2 | [] | 2026-10-15T12:37:15Z | https://github.com/darkflash03/GOLD | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"recall.name.diffusion-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-14T08:33:39Z | Official implementation of "Robust Policy Learning via Offline Skill Diffusion" (AAAI 2024) | [
"generative-ai",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-30T16:05:33Z | false | 0 | 1,418,603,749 | null | Python | NOASSERTION | [
"diffusion"
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] | include | ml-contribution-v5 | 0 | [] | 2026-10-14T08:49:18Z | https://github.com/kwk2696/DuSkill | null |
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-14T08:46:46Z | [NeurIPS 2026] Official implementation of "BoxTuning: Object-Aware Visual Prompting for Multimodal Model Fine-Tuning" | [
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] | 4 | 2026-10-23T14:45:14Z | [] | 2026-10-14T08:54:44Z | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-10-14T08:54:48Z | https://github.com/zekunqian/BoxTuning | null |
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] | [
"recall.name.multimodal-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-15T17:05:44Z | [NeurIPS 2026] Official implementation of Latent Reasoning in Continuous Space for Unified Multimodal Models (LARC) | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-16T14:00:50Z | false | 0 | 1,421,071,945 | null | null | null | [
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] | 1 | 2026-10-16T14:00:50Z | [] | 2026-10-15T17:09:48Z | [
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] | include | ml-contribution-v5 | 1 | [] | 2026-10-16T01:59:54Z | https://github.com/rootyJeon/LARC | null |
[
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] | [
"general.self-supervised-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-16T04:33:57Z | The source code of our TNNLS-2026 paper "Efficient and Scalable Multi-View Masked Autoencoder for Heterogeneous Graph Self-Supervised Learning". | [
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] | include | ml-contribution-v5 | 1 | [] | 2026-10-16T14:58:05Z | https://github.com/hcmdgh/MVMAE | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-16T07:30:25Z | 【ECCV 2026】This repo is the official implementation for "Beyond Alignment: A Generative Matching Paradigm via Flow Matching for Zero-Shot Skeleton-Based Action Recognition", presented as a poster at ECCV 2026. | [
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] | include | ml-contribution-v5 | 1 | [] | 2026-10-21T18:27:48Z | https://github.com/reques/ProtoFM | null |
[
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-16T14:00:50Z | false | 0 | 1,421,847,714 | null | null | null | [
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] | 3 | 2026-10-18T13:47:34Z | [] | 2026-10-16T08:09:46Z | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-10-16T08:09:50Z | https://github.com/DarrenJum/StBR | null |
[
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-18T13:21:37Z | false | 0 | 1,422,351,750 | null | Python | MIT | [
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] | 4 | 2026-10-31T15:48:22Z | [] | 2026-10-16T15:41:51Z | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-10-21T18:32:12Z | https://github.com/Jean-Yifan-Sun/DCT-MoE-Release | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-16T19:30:20Z | [NeurIPS 2026] Official code for "Progress-Aware Distillation for Mitigating Stagnation in Small Language Model Agents" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-20T17:39:40Z | false | 0 | 1,422,592,981 | null | Python | Apache-2.0 | [
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] | CaoYuanpu/ProD | [
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] | 5 | 2026-10-25T14:09:27Z | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-10-16T19:54:01Z | https://github.com/CaoYuanpu/ProD | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-17T01:56:49Z | Official PyTorch implementation of our paper : HFCNet: A hybrid CNN-transformer network with frequency-aware gating and cross-stream interaction for low-light image enhancement | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-18T13:21:37Z | false | 0 | 1,422,851,801 | null | Python | MIT | [
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] | 3 | 2026-10-25T14:09:27Z | [] | 2026-10-17T03:22:30Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-10-21T18:31:06Z | https://github.com/clearlove7780/HFCNet | null |
[
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] | [
"timeseries.forecasting",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-17T13:09:27Z | Official implementation of the paper "CTLite: Lightweight Irregular Multivariate Time Series Forecasting with Reusable Continuous-Time Summaries" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-18T13:47:34Z | false | 0 | 1,423,341,269 | null | Shell | null | [
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] | SSMa913/NICLab-CTLite | [
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] | 3 | 2026-10-19T16:36:56Z | [] | 2026-10-17T13:30:51Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-10-17T13:30:54Z | https://github.com/SSMa913/NICLab-CTLite | null |
[
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] | [
"llm.transformers",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-18T03:19:12Z | Source code for paper "Breaking the Vicious Spiral: Achieving Stable Transformer-based World Models for Reinforcement Learning". | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-18T13:21:37Z | false | 0 | 1,423,948,698 | null | Python | null | [
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] | lsb829/stw | [
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] | 4 | 2026-10-25T14:09:27Z | [] | 2026-10-18T03:20:21Z | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-10-19T02:38:42Z | https://github.com/lsb829/stw | null |
[
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] | [
"nlp-bm25-retrieval",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-18T14:42:59Z | A new architecture of RAG | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-26T16:16:45Z | false | 0 | 1,424,476,053 | null | Python | null | [
"bm25",
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] | Sanidhya555/Documind | [
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] | 2 | 2026-11-01T16:11:12Z | [] | 2026-10-18T14:57:21Z | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-10-18T14:57:25Z | https://github.com/Sanidhya555/Documind | null |
[
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] | [
"general.contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-20T00:48:44Z | [SIGIR 2023 Oral] This is our Pytorch implementation for the paper: "Meta-optimized Contrastive Learning for Sequential Recommendation". | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-20T14:21:44Z | false | 0 | 1,426,338,283 | https://arxiv.org/abs/2304.07763v1 | null | MIT | [
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] | AliasJeff/MCLRec | [
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] | 2 | 2026-10-23T14:17:19Z | [] | 2026-10-20T05:31:23Z | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-10-20T01:04:47Z | https://github.com/AliasJeff/MCLRec | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-20T01:37:19Z | Official implementation of CCG Diffusion: Combinatory Categorial Grammar-Driven Compositional and Syntactically Disambiguated Text-to-Image Generation (arXiv:2610.29415). | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-21T14:32:05Z | false | 0 | 1,426,371,704 | https://arxiv.org/abs/2610.29415 | Python | MIT | [
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] | include | ml-contribution-v5 | 0 | [
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] | 2026-10-22T00:56:52Z | https://github.com/ryosuke-yamaki/ccg-diffusion | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-20T06:57:21Z | This is the official code repository of the paper "A Byzantine-Robust Federated Learning with Nesterov Momentum". | [
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] | 2 | 2026-10-24T13:23:18Z | [] | 2026-10-20T07:11:23Z | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-10-20T07:19:25Z | https://github.com/IHXU/Byrd-NAFL-IOTJ | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-21T04:50:43Z | Official code for "Let It Be Simple: One-Step Action Generation for Vision-Language-Action Models" — self-contained openpi-based implementation, paper run configs, diagnostics, and LIBERO evaluation. | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-23T14:17:19Z | false | 0 | 1,427,890,827 | https://arxiv.org/abs/2606.05737 | Python | Apache-2.0 | [
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] | 1 | 2026-10-23T14:17:19Z | [] | 2026-10-22T11:04:35Z | [
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] | include | ml-contribution-v5 | 2 | [
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] | 2026-10-23T13:55:04Z | https://github.com/bondtesty/let-it-be-simple | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-22T02:53:56Z | The official implementation of KD paper in IJCV: "Relaxed Knowledge Distillation" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-23T14:17:19Z | false | 0 | 1,429,168,600 | null | Python | MIT | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-23T14:17:19Z | false | 0 | 1,430,581,146 | null | Python | GPL-3.0 | [
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] | cvlab-yonsei/GCG | [
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] | 2 | 2026-10-25T13:45:15Z | [] | 2026-10-23T04:44:56Z | [
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] | include | ml-contribution-v5 | 1 | [] | 2026-10-23T10:38:10Z | https://github.com/cvlab-yonsei/GCG | null |
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-23T05:03:34Z | The official code repository of paper "Time-Lapse Seismic Inversion Using a 3D Neural Operator " . | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-23T14:17:19Z | false | 0 | 1,430,597,582 | null | Jupyter Notebook | MIT | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-10-23T05:10:23Z | https://github.com/maxiao6668/Time_Lapse_inversion | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-23T17:52:40Z | Official PyTorch implementation of The Locality Trade-off in Masked Patch Self-Distillation (NeurIPS 2026) | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-24T13:23:18Z | false | 0 | 1,431,374,793 | null | Python | Apache-2.0 | [
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] | 2 | 2026-10-26T16:11:12Z | [] | 2026-10-25T12:06:11Z | [
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] | include | ml-contribution-v5 | 1 | [] | 2026-10-25T12:06:15Z | https://github.com/winci-ai/jea | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-24T11:30:20Z | Official PyTorch implementation of "GPFA-Q: Post-Training Quantization with Gradient-Projected Fisher Approximation for Vision Transformers", [NeurIPS 2026.] | [
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] | [
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] | Nobody-96/GPFA-Q | [
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] | 1 | 2026-10-25T13:45:15Z | [] | 2026-10-25T11:10:16Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 7 | [] | 2026-10-25T12:37:17Z | https://github.com/Nobody-96/GPFA-Q | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-24T15:30:53Z | Official PyTorch implementation of "Revisiting Diffusion Fine-Tuning for Unsupervised Domain Adaptation" (NeurIPS 2026). | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-25T13:45:15Z | false | 0 | 1,432,322,267 | https://xuanqi99.github.io/MUSE/ | Python | MIT | [
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] | xuanqi99/MUSE-Code | [
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] | 1 | 2026-10-25T13:45:15Z | [] | 2026-10-24T16:04:22Z | [
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] | include | ml-contribution-v5 | 0 | [
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] | 2026-10-24T19:25:15Z | https://github.com/xuanqi99/MUSE-Code | null |
[
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] | [
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] | [
"description",
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"repository-metadata"
] | [
"science.neural-operators"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-25T05:05:59Z | Official implementation of "CoupledFlow: One-Step Neural Operators for Coupled Multi-Physics PDEs", which is accpeted at NeurIPS 2026 | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-25T13:45:15Z | false | 0 | 1,432,848,066 | null | null | null | [
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] | khiembk/CoupledFlow | [
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] | 2 | 2026-10-27T15:25:06Z | [] | 2026-10-25T05:06:46Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-10-25T05:06:51Z | https://github.com/khiembk/CoupledFlow | null |
[
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] | [
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] | [
"timeseries.state-space-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-25T09:19:18Z | Official PyTorch implementation of the ECCV 2026 paper "S2Gest: Split-Scan State Space Models for Dynamic Hand Gesture Recognition". | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-26T16:11:12Z | false | 0 | 1,433,020,480 | null | Python | MIT | [
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] | Chen-Ke-Fan/S2Gest | [
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] | 2 | 2026-10-28T16:08:23Z | [] | 2026-10-25T10:27:39Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-10-25T15:23:33Z | https://github.com/Chen-Ke-Fan/S2Gest | null |
[
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-25T10:20:44Z | [NeurIPS 2026] Official implementation of FreeSpec: Training-Free Long Video Generation via Singular-Spectrum Reconstruction | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-26T16:11:12Z | false | 0 | 1,433,069,030 | https://fdchen24.github.io/FreeSpec-Website/ | null | Apache-2.0 | [
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] | 2 | 2026-10-28T16:08:23Z | [] | 2026-10-26T04:39:22Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-10-26T04:39:26Z | https://github.com/fdchen24/FreeSpec | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-26T03:15:55Z | [NeurIPS 2026] Official implementation of paper "SwiftVLM: Efficient Vision-Language Model Inference via Cross-Layer Token Bypass" | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-27T15:25:06Z | false | 0 | 1,433,849,114 | null | null | MIT | [
"multimodal-learning",
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] | qianchen214/SwiftVLM | [
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"query-match",
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] | 2 | 2026-10-28T16:08:23Z | [] | 2026-10-26T03:15:57Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 2 | [] | 2026-10-28T07:19:18Z | https://github.com/qianchen214/SwiftVLM | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-26T14:06:26Z | In this code repo we present a new representation learning method for time series forecasting, specifically applied to electricity load forecasting. This new representation learning method is called similarity preserving, based on similarity metric learning like contrastive learning but much more efficient and with oth... | [
"forecasting",
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-27T15:25:06Z | false | 0 | 1,434,468,308 | null | Python | MIT | [
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] | lear-ner97/Similarity-preserving-representation-learning-for-weekly-electricity-load-forecasting | [
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] | 2 | 2026-10-28T16:08:23Z | [] | 2026-10-26T14:11:48Z | [
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] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-10-26T17:46:32Z | https://github.com/lear-ner97/Similarity-preserving-representation-learning-for-weekly-electricity-load-forecasting | null |
[
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] | [
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-27T08:27:48Z | An official implementation for NeurIPS 2026 paper "AdderDiff: All-Addition Spiking Diffusion Models with Attention Enhancement" | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-27T15:25:06Z | false | 0 | 1,435,451,886 | null | Python | null | [
"diffusion"
] | hanbaobao950123/AdderDiff | [
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] | 2 | 2026-10-29T16:14:17Z | [] | 2026-10-27T09:28:26Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-10-27T12:00:12Z | https://github.com/hanbaobao950123/AdderDiff | null |
[
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] | [
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] | [
"efficiency.knowledge-distillation",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-28T00:56:54Z | A novel knowledge distillation technique beyond Teacher-Student. FFTD trains a new model using a peer (Friend) and an expert (Teacher's Friend), enforcing independence via a negative similarity penalty. Proof of Concept on CPU. | [
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"model-compression"
] | [
"deep-learning",
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-29T16:14:17Z | false | 0 | 1,436,445,742 | null | Python | null | [
"deep-learning",
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] | mouad-tarif/fftd-distillation | [
"description",
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"query-match",
"repository-metadata"
] | 1 | 2026-10-29T16:14:17Z | [] | 2026-10-28T03:12:03Z | [
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] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
"contribution-language",
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] | include | ml-contribution-v5 | 0 | [
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] | 2026-10-28T03:12:07Z | https://github.com/mouad-tarif/fftd-distillation | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-28T08:39:37Z | The core code for our paper "DADBA: Data Augmentation-based Distributed Backdoor Attack in Federated Learning". | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-30T15:59:57Z | false | 0 | 1,436,815,137 | null | Python | null | [
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] | 18380120207/DADBA | [
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] | 1 | 2026-10-30T15:59:57Z | [] | 2026-10-28T14:32:26Z | [
"trust.federated-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-10-28T14:32:30Z | https://github.com/18380120207/DADBA | null |
[
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"generative-ai",
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-29T09:43:40Z | Official PyTorch implementation of the paper "PixelMoE: Scaling Pixel-Space Diffusion with LLM-Style Mixture-of-Experts" | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-29T16:14:17Z | false | 1 | 1,438,219,171 | null | Python | MIT | [
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"sparse-activation"
] | KlingAIResearch/PixelMoE | [
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] | 2 | 2026-10-31T15:23:19Z | [] | 2026-10-29T09:57:28Z | [
"efficiency.mixture-of-experts",
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] | 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 | [] | 2026-10-29T13:03:46Z | https://github.com/KlingAIResearch/PixelMoE | null |
[
"3d",
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] | [
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] | [
"multimodal.3d-object-detection",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-29T15:47:24Z | Official implementation of PointLAM: Local Attentive Mamba for Efficient Point-based 3D Object Detection (ECCV 2026) | [
"computer-vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-29T16:14:17Z | false | 0 | 1,438,630,366 | https://pointlam.github.io/ | Python | Apache-2.0 | [
"detection",
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] | VISION-SJTU/PointLAM | [
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"query-match",
"repository-metadata"
] | 4 | 2026-11-01T16:06:29Z | [] | 2026-10-29T15:47:38Z | [
"vision.object-detection"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 1 | [] | 2026-11-01T00:39:54Z | https://github.com/VISION-SJTU/PointLAM | null |
[
"generative-ai",
"generative-modeling",
"reinforcement-learning"
] | [
"diffusion",
"reinforcement-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"recall.name.diffusion-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-30T08:55:43Z | Official code for Coupled Flow (NeurIPS 2026): What Kind of Diffusion Models Do We Need in Online Reinforcement Learning? | [
"generative-ai",
"generative-modeling",
"reinforcement-learning"
] | [
"reinforcement-learning",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-31T15:23:19Z | false | 0 | 1,439,490,623 | null | Python | MIT | [
"diffusion",
"reinforcement-learning"
] | wzhhasadream/diffusionrl | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-31T15:23:19Z | [] | 2026-10-30T08:55:51Z | [
"llm.diffusion-phrase",
"recall.name.diffusion-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-10-30T09:07:56Z | https://github.com/wzhhasadream/diffusionrl | null |
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