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",
"model-compression",
"multimodal-learning",
"natural-language-processing"
] | [
"distillation",
"knowledge-distillation",
"referring-expression-comprehension",
"vision-language-learning",
"visual-grounding"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation",
"recall26.visual-grounding"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-23T14:43:51Z | [ECCV 2026] Official code for "Token-level Response-visual Attention Guidance for Multimodal LLM Knowledge Distillation" | [
"computer-vision",
"multimodal-learning",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 1 | 1,278,185,167 | null | null | null | [
"distillation",
"knowledge-distillation",
"referring-expression-comprehension",
"vision-language-learning",
"visual-grounding"
] | jhjangjh/TRAG | [
"description",
"query-match",
"repository-metadata"
] | 6 | 2026-10-15T15:20:52Z | [] | 2026-07-07T08:48:20Z | [
"recall26.visual-grounding"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 7 | [] | 2026-07-22T06:20:04Z | https://github.com/jhjangjh/TRAG | null |
[
"reinforcement-learning"
] | [
"policy-gradient",
"policy-optimization",
"self-attention"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.policy-gradient"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-23T16:32:04Z | This repository contains the code for our paper "Policy Gradient with Self-Attention for Model-Free Distributed Nonlinear Multi-Agent Games", published and presented at IEEE/RSJ IROS, 2026 | [
"reinforcement-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 0 | 1,278,280,013 | null | Python | NOASSERTION | [
"policy-gradient",
"policy-optimization",
"self-attention"
] | EduardoSebastianRodriguez/Self-Attention-for-Multi-agent-Games | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2026-07-07T09:34:25Z | [
"rl.policy-gradient"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-07-07T09:34:39Z | https://github.com/EduardoSebastianRodriguez/Self-Attention-for-Multi-agent-Games | null |
[
"computer-vision",
"generative-modeling"
] | [
"diffusion",
"mixture-of-experts",
"restoration",
"super-resolution"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.image-super-resolution"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-24T07:08:21Z | [ECCV 2026] Official code repository for "FreqOrtho-SR: Frequency-Guided Orthogonal Expert Learning for Real-World Image Super-Resolution" | [
"computer-vision",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 2 | 1,278,874,815 | null | Python | Apache-2.0 | [
"diffusion",
"mixture-of-experts",
"restoration",
"super-resolution"
] | sonhm3029/FreqOrtho-SR | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-09-26T10:12:50Z | [] | 2026-08-27T08:09:00Z | [
"vision.image-super-resolution"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 15 | [
"diffusion",
"mixture-of-experts",
"orthogonal-projection",
"super-resolution"
] | 2026-09-15T12:39:04Z | https://github.com/sonhm3029/FreqOrtho-SR | null |
[
"computer-vision",
"multi-agent-systems",
"reinforcement-learning"
] | [
"action-recognition",
"centralized-training",
"multi-agent-reinforcement-learning",
"reinforcement-learning",
"video-understanding"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.multiagent",
"vision.action-recognition"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-25T07:09:09Z | Code for paper "Potentially Optimal Joint Actions Recognition for Cooperative Multi-Agent Reinforcement Learning" | [
"computer-vision",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 1,279,988,119 | null | Python | MIT | [
"action-recognition",
"reinforcement-learning",
"video-understanding"
] | tiev-tongji/potentially-optimal-joint-actions-recognition-marl | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-08T18:15:14Z | [] | 2026-06-25T07:23:30Z | [
"vision.action-recognition"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-06-25T07:23:36Z | https://github.com/tiev-tongji/potentially-optimal-joint-actions-recognition-marl | null |
[
"multimodal",
"reinforcement-learning",
"robotics",
"robotics-and-control"
] | [
"environment-modeling",
"robot-learning",
"world-model"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.world-models",
"robotics.robotics-topic"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-25T17:30:13Z | Official code repository for the paper "Hallucination in World Models is Predictable and Preventable". | [
"robotics",
"robotics-and-control"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T14:34:36Z | false | 7 | 1,280,541,353 | http://www.nicklashansen.com/mmbench2/ | Python | MIT | [
"robot-learning"
] | nicklashansen/mmbench2 | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-19T16:36:56Z | [] | 2026-10-02T19:02:22Z | [
"robotics.robotics-topic"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 111 | [
"dreamer4",
"hallucination-detection",
"robotics",
"world-models"
] | 2026-10-16T08:47:33Z | https://github.com/nicklashansen/mmbench2 | null |
[
"generative-ai",
"generative-modeling",
"multimodal",
"video"
] | [
"diffusion",
"text-to-video",
"video-generation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.text-to-video"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-26T05:35:20Z | [ECCV 2026] Official Implementation of EraseSAE: Sparse Autoencoder Based Concept Erasure for Text-to-Video Diffusion Models | [
"generative-ai",
"generative-modeling",
"multimodal",
"video"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 0 | 1,280,972,943 | null | Python | null | [
"diffusion",
"text-to-video",
"video-generation"
] | HiDream-ai/EraseSAE | [
"description",
"query-match",
"repository-metadata"
] | 4 | 2026-10-03T15:45:52Z | [] | 2026-09-04T07:20:22Z | [
"multimodal.text-to-video"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 6 | [] | 2026-09-21T10:57:40Z | https://github.com/HiDream-ai/EraseSAE | null |
[
"computer-vision",
"information-retrieval",
"multimodal-learning",
"natural-language-processing"
] | [
"dense-retrieval",
"rag",
"referring-expression-comprehension",
"retrieval-augmented-generation",
"vision-language-learning",
"visual-grounding"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"nlp.dense-retrieval",
"recall26.visual-grounding"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-26T06:35:48Z | [ECCV 2026] Official implementation of MG²-RAG: Multi-Granularity Graph for Multimodal Retrieval-Augmented Generation | [
"information-retrieval",
"multimodal-learning",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-10T16:53:26Z | false | 1 | 1,281,015,568 | https://daboolu.github.io/MG2-RAG/examples/web/ | Python | MIT | [
"dense-retrieval",
"rag",
"retrieval-augmented-generation"
] | Daboolu/MG2-RAG | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-28T17:27:44Z | [] | 2026-08-31T16:45:54Z | [
"nlp.dense-retrieval"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 8 | [] | 2026-10-14T07:24:21Z | https://github.com/Daboolu/MG2-RAG | null |
[
"embodied-ai",
"generative-modeling",
"reinforcement-learning",
"robotics"
] | [
"diffusion",
"diffusion-policy",
"imitation-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"robotics.diffusion-policy"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-26T13:55:21Z | Official implementation of Diffusion Policy Policy Optimization, arxiv 2024 | [
"embodied-ai",
"generative-modeling",
"reinforcement-learning",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 0 | 1,281,381,713 | https://diffusion-ppo.github.io | Python | MIT | [
"diffusion",
"diffusion-policy",
"imitation-learning"
] | V373/dppo_reward | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-09-26T14:50:46Z | [] | 2026-06-27T09:24:45Z | [
"robotics.diffusion-policy"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-06-27T09:24:48Z | https://github.com/V373/dppo_reward | null |
[
"computer-vision",
"language",
"machine-learning-systems",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"vision"
] | [
"contrastive-learning",
"image-text",
"pruning",
"vision-language-model"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.vision-language",
"multimodal.vlm"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-27T04:51:26Z | Official code for "Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models" (ECCV 2026) | [
"computer-vision",
"language",
"machine-learning-systems",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-17T13:17:44Z | false | 0 | 1,281,919,633 | null | Jupyter Notebook | null | [
"contrastive-learning",
"pruning",
"vision-language-model"
] | cau-hai-lab/PORTA | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-24T14:31:13Z | [] | 2026-09-02T11:18:01Z | [
"multimodal.vision-language"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 4 | [] | 2026-09-17T07:49:32Z | https://github.com/cau-hai-lab/PORTA | null |
[
"cybersecurity",
"decision-making",
"privacy-and-federated-learning",
"reinforcement-learning"
] | [
"contextual-bandits",
"federated-learning",
"hierarchical-clustering",
"multi-armed-bandits"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.bandits"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-27T07:15:50Z | A robust Federated Learning framework implementing the novel FedCADS-UCB algorithm. Engineered for resilient client selection using CUSUM drift detection, adaptive multi-armed bandits, and hierarchical clustering to maintain high accuracy (>95%) during concept drift and label poisoning attacks. | [
"cybersecurity",
"decision-making",
"privacy-and-federated-learning",
"reinforcement-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-03T15:45:52Z | false | 0 | 1,281,994,999 | null | Python | MIT | [
"contextual-bandits",
"federated-learning",
"hierarchical-clustering",
"multi-armed-bandits"
] | rishidatt2006-gif/FedCADS-UCB-Client-Selection | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-03T15:45:52Z | [] | 2026-06-28T05:55:02Z | [
"rl.bandits"
] | 7fb58e731aa11f67dd61283943fb8343d28f3636 | 2026-10-03T17:01:43Z | gh-ml-readme-evidence-v2 | "7fb58e731aa11f67dd61283943fb8343d28f3636" | [
"installation",
"other",
"references"
] | [
"ml-method-context"
] | ok | 2026-10-03T17:01:43Z | rishidatt2006-gif/FedCADS-UCB-Client-Selection | specific-method-with-novelty-claim | [
"contribution-language",
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue"
] | include | ml-contribution-v5 | 1 | [
"client-selection",
"concept-drift",
"cusum",
"cybersecurity",
"federated-learning",
"multi-armed-bandit",
"pytorch"
] | 2026-06-29T14:32:31Z | https://github.com/rishidatt2006-gif/FedCADS-UCB-Client-Selection | null |
[
"computer-vision",
"embodied-ai",
"language",
"multimodal",
"multimodal-learning",
"robotics",
"vision"
] | [
"contrastive-learning",
"robot-learning",
"vision-language-action",
"vision-language-model"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.vision-language",
"robotics.visual-language-action"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-27T09:41:32Z | [ICML 2026 Oral] Official Implementation for From Abstraction to Instantiation: Learning Behavioral Representation for Vision-Language-Action Model | [
"computer-vision",
"language",
"multimodal",
"multimodal-learning",
"vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T14:50:46Z | false | 0 | 1,282,077,672 | null | Python | Apache-2.0 | [
"contrastive-learning",
"vision-language-model"
] | iLearn-Lab/ICML26-BehaviorVLA | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-24T14:31:13Z | [] | 2026-07-05T05:47:48Z | [
"multimodal.vision-language"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 11 | [] | 2026-09-21T13:59:49Z | https://github.com/iLearn-Lab/ICML26-BehaviorVLA | null |
[
"deep-learning",
"efficient-ml",
"language",
"multimodal-learning",
"natural-language-processing"
] | [
"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 | [] | 2026-06-28T13:03:34Z | [ECCV2026] The Official Implementation for ''CL-Anomaly: Layer-Adaptive Mixture-of-Experts with Multimodal Large Language Model for Continual Learning in Anomaly Detection'' | [
"deep-learning",
"efficient-ml",
"language",
"multimodal-learning",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 0 | 1,283,014,702 | null | Python | Apache-2.0 | [
"conditional-computation",
"mixture-of-experts",
"sparse-activation"
] | WenDongyp/CL-Anomaly | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-04T15:58:58Z | [] | 2026-07-03T03:35:35Z | [
"efficiency.mixture-of-experts"
] | 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 | [] | 2026-09-30T09:21:28Z | https://github.com/WenDongyp/CL-Anomaly | 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 | [] | 2026-06-28T15:50:22Z | [ECCV 2026] Official code for DILLO, "Describe-Then-Act: Proactive Agent Steering via Distilled Language-Action World Models". | [
"multimodal",
"reinforcement-learning",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-12T20:31:19Z | false | 0 | 1,283,151,213 | null | Python | null | [
"environment-modeling",
"world-model"
] | MaxPappa/DILLO | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-12T20:31:19Z | [] | 2026-08-28T11:32:53Z | [
"multimodal.world-models"
] | 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 | [] | 2026-09-12T13:27:04Z | https://github.com/MaxPappa/DILLO | 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 | [] | 2026-06-30T05:17:06Z | Official code for paper "CLIMB: Centroid-Based Hierarchical Memory for Online Continual Self-Supervised Learning" [Accepted CoLLAs 2026] | [
"deep-learning",
"representation-learning"
] | [
"self-supervised-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-06T17:10:23Z | false | 0 | 1,284,679,920 | null | Python | null | [
"representation-learning",
"self-supervised-learning"
] | lefebvju/climb | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-06T17:10:23Z | [] | 2026-07-16T11:51:36Z | [
"general.self-supervised-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [] | 2026-09-09T08:59:56Z | https://github.com/lefebvju/climb | null |
[
"efficient-ml",
"model-compression"
] | [
"distillation",
"knowledge-distillation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-30T13:34:42Z | Official PyTorch implementation of the ECCV 2026 paper "SFKD: Spatial--Frequency Joint-Aware Heterogeneous Knowledge Distillation via Multi-Level Wavelet Spectral Interaction". | [
"efficient-ml",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 0 | 1,285,101,847 | null | null | MIT | [
"distillation",
"knowledge-distillation"
] | cpcpWang/SFKD | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-09T17:31:36Z | [] | 2026-06-30T13:39:54Z | [
"efficiency.knowledge-distillation"
] | 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 | 6 | [] | 2026-09-18T13:52:50Z | https://github.com/cpcpWang/SFKD | null |
[
"efficient-ml",
"model-compression"
] | [
"distillation",
"knowledge-distillation",
"mamba"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-06-30T19:27:00Z | Official PyTorch implementation of the paper "Hardware-Efficient Satellite Change Detection via Hybrid Mamba Decoder with Differentiable LUT Classifier and Progressive Knowledge Distillation" | [
"efficient-ml",
"model-compression"
] | [
"classifier"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-11T16:48:52Z | false | 0 | 1,285,412,984 | null | Python | null | [
"distillation",
"knowledge-distillation",
"mamba"
] | etii75/MambaLUT-CD | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-11T16:48:52Z | [] | 2026-07-03T08:38:23Z | [
"efficiency.knowledge-distillation"
] | 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 | [] | 2026-07-21T05:32:15Z | https://github.com/etii75/MambaLUT-CD | null |
[
"computer-vision",
"general-ml",
"multimodal-learning",
"natural-language-processing",
"reinforcement-learning"
] | [
"paper-implementation",
"policy-learning",
"referring-expression-comprehension",
"reinforcement-learning",
"vision-language-learning",
"visual-grounding"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.arxiv",
"recall26.visual-grounding",
"rl.core"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-07-01T10:49:30Z | Official code for OP-HRG (ECCV 2026): reasoning-guided part-level visual grounding via reinforcement learning. | [
"computer-vision",
"general-ml",
"multimodal-learning",
"natural-language-processing",
"reinforcement-learning"
] | [
"large-language-model",
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T10:12:50Z | false | 1 | 1,286,000,019 | null | Python | MIT | [
"paper-implementation",
"reinforcement-learning"
] | sajeedmehrab/op-hrg | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-10-21T14:37:12Z | [] | 2026-10-06T16:22:09Z | [
"general.arxiv"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 7 | [
"eccv2026",
"fine-grained-visual-grounding",
"grpo",
"instructpart",
"multimodal-large-language-models",
"part-grounding",
"part-level-visual-grounding",
"part-localization",
"part-segmentation",
"partimagenet",
"pascalpart",
"qwen3-vl",
"reasoning-segmentation",
"referring-expression-comp... | 2026-10-11T08:30:47Z | https://github.com/sajeedmehrab/op-hrg | null |
[
"3d",
"computer-vision",
"multimodal"
] | [
"3d-reconstruction",
"distillation",
"neural-reconstruction"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.3d-reconstruction"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-07-01T13:50:32Z | Official implementation of "Geometric Foundation Model Distillation for Efficient Lunar 3D Reconstruction" — ECCV 2026 | [
"3d",
"computer-vision",
"multimodal"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 1 | 1,286,154,359 | null | Python | null | [
"3d-reconstruction",
"distillation",
"neural-reconstruction"
] | clementinegrethen/mast3r-distillation | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-08T18:15:14Z | [] | 2026-08-23T14:47:07Z | [
"multimodal.3d-reconstruction"
] | 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 | [] | 2026-09-27T13:04:45Z | https://github.com/clementinegrethen/mast3r-distillation | null |
[
"efficient-ml",
"model-compression",
"privacy-and-federated-learning"
] | [
"distillation",
"federated-learning",
"knowledge-distillation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-07-01T16:02:02Z | Stores here are the source codes for the official implementation of "Benchmarking Federated Learning and Knowledge Distillation for Point Cloud Classification" accepted by ECCV 2026. | [
"efficient-ml",
"model-compression",
"privacy-and-federated-learning"
] | [
"classifier"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-11T16:48:52Z | false | 0 | 1,286,276,310 | http://ezharjan.github.io/FLKD3DBenchmark/ | Python | MIT | [
"distillation",
"federated-learning",
"knowledge-distillation"
] | Ezharjan/FLKD3DBenchmark | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-11T16:48:52Z | [] | 2026-08-29T00:03:49Z | [
"efficiency.knowledge-distillation"
] | 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 | [] | 2026-07-01T16:20:02Z | https://github.com/Ezharjan/FLKD3DBenchmark | null |
[
"deep-learning",
"graph-learning",
"privacy-and-federated-learning"
] | [
"federated-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 | [] | 2026-07-01T22:07:41Z | A scalable and efficient framework for federated learning of Graph Neural Networks on resource-constrained edge devices. This repository contains the official implementation of the paper "Scalable Federated Graph Neural Networks for Edge". | [
"deep-learning",
"graph-learning",
"privacy-and-federated-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-26T17:25:59Z | false | 0 | 1,286,536,663 | null | Python | null | [
"federated-learning",
"graph-neural-network",
"message-passing",
"neural-network"
] | aynazsalamati/scalabale-federated-gnn-edge | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-26T17:25:59Z | [] | 2026-07-06T06:57:55Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-07-06T06:57:58Z | https://github.com/aynazsalamati/scalabale-federated-gnn-edge | null |
[
"deep-learning",
"graph-learning",
"time-series-and-forecasting"
] | [
"graph-transformer",
"message-passing",
"transformer"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.graph-transformer"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-07-02T13:51:32Z | A novel graph-transformer architecture that fuses a temporal Transformer encoder with a time-varying, multi-relational market graph to forecast cross-asset stock returns | [
"deep-learning",
"graph-learning",
"time-series-and-forecasting"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-26T10:12:50Z | false | 0 | 1,287,230,794 | null | null | null | [
"graph-transformer",
"message-passing",
"transformer"
] | Harish-idilingotter/DMRGT | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-09-27T16:09:59Z | [] | 2026-08-14T14:53:34Z | [
"graph.graph-transformer"
] | e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 | 2026-09-26T20:25:07Z | gh-ml-readme-evidence-v2 | "e69de29bb2d1d6434b8b29ae775ad8c2e48c5391" | [] | [] | ok | 2026-09-26T20:25:07Z | Harish-idilingotter/DMRGT | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 2 | [] | 2026-07-02T14:26:37Z | https://github.com/Harish-idilingotter/DMRGT | null |
[
"deep-learning",
"efficient-ml",
"language"
] | [
"conditional-computation",
"mixture-of-experts",
"sparse-activation",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.mixture-of-experts"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-07-07T11:16:13Z | Deep MoE is an all-new mixture-of-experts architecture a transformer layer that does its thinking in a compressed thought space rather than on the wide residual stream. | [
"deep-learning",
"efficient-ml",
"language"
] | [
"machine-learning",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 1 | 1,292,215,837 | https://swarms.ai/hiring | Python | Apache-2.0 | [
"conditional-computation",
"mixture-of-experts",
"sparse-activation",
"transformer"
] | kyegomez/DeepMoE | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-04T15:58:58Z | [] | 2026-07-07T12:58:27Z | [
"efficiency.mixture-of-experts"
] | ca8a9f2a741e40ef4c70fd6244a6a8e0877ebca8 | 2026-10-04T17:18:56Z | gh-ml-readme-evidence-v2 | "ca8a9f2a741e40ef4c70fd6244a6a8e0877ebca8" | [
"dataset",
"installation",
"other",
"usage"
] | [
"ml-method-context",
"paper-reference"
] | ok | 2026-10-04T17:18:56Z | kyegomez/DeepMoE | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue",
"paper-reference"
] | include | ml-contribution-v5 | 8 | [
"ai",
"attention",
"deepseek",
"deepseek-r1",
"ml",
"moe",
"moe-research",
"research",
"tensorflow",
"torch"
] | 2026-07-14T12:04:02Z | https://github.com/kyegomez/DeepMoE | null |
[
"general-ml",
"generative-ai",
"generative-modeling",
"recommender-systems"
] | [
"diffusion",
"paper-implementation"
] | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"applied.recommender-systems",
"general.arxiv",
"llm.diffusion-phrase",
"recall.name.diffusion-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-07-07T21:43:33Z | Official implementation of Generative Diffusion Models of Stochastic Graph Signals (arXiv:2607.06833; under review at IEEE Transactions on Signal Processing) | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-19T16:36:56Z | false | 0 | 1,292,809,627 | https://arxiv.org/abs/2607.06833 | Python | MIT | [
"diffusion"
] | yigit-uslu/graph-signal-diffusion | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 4 | 2026-10-26T16:16:45Z | [] | 2026-10-08T15:51:03Z | [
"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 | [] | 2026-10-16T12:03:08Z | https://github.com/yigit-uslu/graph-signal-diffusion | null |
[
"generative-ai",
"generative-modeling",
"language"
] | [
"diffusion",
"inference",
"scaling"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"growth26.llm-inference-time-scaling",
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-07-07T21:44:20Z | (ECCV 2026): Official code for Flash-BoN: Instant Drafts for Inference-Time Scaling in Diffusion Models | [
"generative-ai",
"generative-modeling",
"language"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-25T15:43:40Z | false | 1 | 1,292,810,049 | https://flash-bon.github.io/ | Python | null | [
"diffusion",
"inference",
"scaling"
] | flash-bon/flash-bon | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 2 | 2026-09-26T22:51:24Z | [] | 2026-07-09T14:05:34Z | [
"growth26.llm-inference-time-scaling"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 23 | [
"diffusion-models",
"test-time-scaling"
] | 2026-09-10T17:58:35Z | https://github.com/flash-bon/flash-bon | 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 | [] | 2026-07-09T08:49:53Z | Novel VLA Architectures for Robotic Manipulation - Paper | [
"embodied-ai",
"robotics",
"robotics-and-control"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 0 | 1,294,927,235 | null | TeX | null | [
"manipulation"
] | lexus-x/vla-paper | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-02T16:59:40Z | [] | 2026-07-09T08:50:03Z | [
"robotics.manipulation"
] | fa5b1a9ba584b712c133bc635ae91a2e009c914c | 2026-10-02T18:21:54Z | gh-ml-readme-evidence-v2 | "fa5b1a9ba584b712c133bc635ae91a2e009c914c" | [
"other"
] | [
"ml-method-context",
"paper-reference"
] | ok | 2026-10-02T18:21:54Z | lexus-x/vla-paper | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue",
"paper-reference"
] | include | ml-contribution-v5 | 0 | [] | 2026-07-09T08:50:08Z | https://github.com/lexus-x/vla-paper | null |
[
"computational-science",
"physics"
] | [
"neural-operator",
"neural-operators",
"surrogate-modeling"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"science.neural-operators"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-07-09T09:02:54Z | Resolvent Neural Operator is a new transform-free neural operator framework for solving PDEs. | [
"computational-science",
"physics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 0 | 1,294,950,319 | null | Python | null | [
"neural-operator",
"neural-operators",
"surrogate-modeling"
] | JohnJoeZhu/Resolvent-Neural-Operator-RNO | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-09-26T14:50:46Z | [] | 2026-07-17T02:31:56Z | [
"science.neural-operators"
] | 7bab2f0bca8e2f64645ec3da4c6ef456a0b25610 | 2026-09-26T15:53:33Z | gh-ml-readme-evidence-v2 | "7bab2f0bca8e2f64645ec3da4c6ef456a0b25610" | [
"citation",
"installation",
"other"
] | [
"ml-method-context"
] | ok | 2026-09-26T15:53:33Z | JohnJoeZhu/Resolvent-Neural-Operator-RNO | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-07-12T02:35:03Z | https://github.com/JohnJoeZhu/Resolvent-Neural-Operator-RNO | null |
[
"computer-vision",
"embodied-ai",
"language",
"multimodal",
"multimodal-learning",
"robotics",
"vision"
] | [
"contrastive-learning",
"robot-learning",
"vision-language-action",
"vision-language-model"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.vision-language",
"robotics.visual-language-action"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-07-10T05:35:58Z | [CoRL 2026] The official implementation of the paper titled “DriveMA: Driving Vision-Language-Action Models with verifiable Meta-Actions". | [
"computer-vision",
"language",
"multimodal",
"multimodal-learning",
"vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T14:50:46Z | false | 0 | 1,296,091,723 | null | Python | Apache-2.0 | [
"contrastive-learning",
"vision-language-model"
] | Tsinghua-MARS-Lab/DriveMA | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-24T14:31:13Z | [] | 2026-09-04T16:27:58Z | [
"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 | 19 | [] | 2026-09-30T09:29:01Z | https://github.com/Tsinghua-MARS-Lab/DriveMA | null |
[
"computer-vision",
"language",
"machine-learning",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"vision"
] | [
"adaptation",
"contrastive-learning",
"image-text",
"test-time-adaptation",
"vision-language-model"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.vision-language",
"multimodal.vlm",
"specialized.test-time-adaptation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-07-12T16:17:29Z | [ACM MM 2026] Official implementation of “Local Margin Restoration for Test-Time Adaptation of Vision-Language Models”. | [
"computer-vision",
"language",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T10:12:50Z | false | 1 | 1,298,432,920 | null | Python | MIT | [
"contrastive-learning",
"test-time-adaptation",
"vision-language-model"
] | DennisHuangYan/LMR | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-24T14:31:13Z | [] | 2026-08-04T07:52:39Z | [
"multimodal.vision-language"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 3 | [] | 2026-09-14T08:21:55Z | https://github.com/DennisHuangYan/LMR | null |
[
"computer-vision",
"generative-modeling",
"reinforcement-learning"
] | [
"diffusion",
"policy-learning",
"reinforcement-learning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.core"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-07-14T09:33:23Z | [arXiv 2026] This is the official PyTorch implementation of "MeanFlowNFT: Bringing Forward-Process RL to Average-Velocity Generators". | [
"computer-vision",
"generative-modeling",
"reinforcement-learning"
] | [
"reinforcement-learning",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 0 | 1,300,282,659 | https://harahan.github.io/meanflownft-project-page/ | Python | Apache-2.0 | [
"diffusion",
"policy-learning",
"reinforcement-learning"
] | Harahan/MeanFlowNFT | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-09-25T15:43:40Z | [] | 2026-08-29T22:20:37Z | [
"rl.core"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 95 | [
"diffusion-models",
"few-step-generation",
"flow-map",
"grpo",
"image-generation",
"meanflow",
"nft",
"reinforcement-learning",
"rl",
"sd35",
"video-generation",
"wan"
] | 2026-09-09T06:36:52Z | https://github.com/Harahan/MeanFlowNFT | null |
[
"computer-vision",
"earth-observation",
"geospatial",
"remote-sensing",
"science-and-engineering"
] | [
"computer-vision",
"deep-learning",
"machine-learning",
"neural-network",
"transformer"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"geo.topic-remote-sensing",
"vision.topic-computer-vision"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-07-14T11:47:45Z | Official code for the UAVid++ paper | [
"computer-vision",
"science-and-engineering"
] | [
"deep-learning",
"neural-network",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T16:16:53Z | false | 2 | 1,300,425,138 | https://vivichiciudean.github.io/uavidplusplus/ | Python | null | [
"computer-vision",
"neural-network",
"transformer"
] | vivichiciudean/uavidplusplus-code | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 8 | 2026-10-23T14:45:14Z | [] | 2026-10-09T07:26:37Z | [
"vision.topic-computer-vision"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 13 | [
"aerial",
"aerial-imagery",
"aerial-images",
"computer",
"computer-vision",
"dataset",
"datasets",
"deep-learning",
"deep-neural-networks",
"dino",
"dinov3",
"remote-sensing",
"segmentation",
"semantic-segmentation",
"uav",
"uav-imagery",
"vision-transformer"
] | 2026-10-11T09:59:20Z | https://github.com/vivichiciudean/uavidplusplus-code | 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 | [] | 2026-07-20T12:51:32Z | Evaluation code of our paper submitted to IEEE INFOCOM'27: "AdapS: Test-Time Adaptation for Semantic Communications" | [
"machine-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-10T16:53:26Z | false | 0 | 1,306,604,064 | null | Python | MIT | [
"adaptation",
"test-time-adaptation"
] | GhostRepo-0315/AdapS | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-10T16:53:26Z | [] | 2026-07-20T12:57:50Z | [
"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 | 0 | [] | 2026-07-20T14:06:16Z | https://github.com/GhostRepo-0315/AdapS | null |
[
"generative-ai",
"multimodal",
"video"
] | [
"text-to-video",
"video-generation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.text-to-video"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-07-20T17:15:04Z | [ECCV 2026] Accelerating Text-to-Video Generation with Calibrated Sparse Attention -- Official Code Repository | [
"generative-ai",
"multimodal",
"video"
] | [
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 1,306,843,126 | null | Python | NOASSERTION | [
"text-to-video",
"video-generation"
] | apple-aiml-research/ml-calibatt | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-04T15:58:58Z | [] | 2026-09-11T15:50:56Z | [
"multimodal.text-to-video"
] | 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 | [] | 2026-09-21T13:55:20Z | https://github.com/apple-aiml-research/ml-calibatt | 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 | [] | 2026-07-21T18:32:20Z | Code for paper "Field-Layer World Model: Efficient OFDM Receiver by Bridging Channel Prediction and Estimation," submitted to JSAC | [
"multimodal",
"reinforcement-learning",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-12T20:31:19Z | false | 0 | 1,308,012,522 | null | Python | null | [
"environment-modeling",
"world-model"
] | yuzhiyang123/FWM_JSAC | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-12T20:31:19Z | [] | 2026-07-23T19:23:29Z | [
"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 | 0 | [] | 2026-10-08T12:22:18Z | https://github.com/yuzhiyang123/FWM_JSAC | null |
[
"computer-vision",
"interpretability-and-safety",
"language",
"machine-learning",
"multimodal",
"multimodal-learning",
"vision"
] | [
"adaptation",
"contrastive-learning",
"test-time-adaptation",
"vision-language-model"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.vision-language",
"specialized.test-time-adaptation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-07-22T05:38:38Z | Code accompanying our CVIP 2026 paper "MemFlow-TTA: Continual Test-Time Adaptation of Vision-Language Object Detectors via Memory-Anchored Feature Alignment and Temporal Consistency" | [
"computer-vision",
"interpretability-and-safety",
"language",
"multimodal",
"multimodal-learning",
"vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-10T16:53:26Z | false | 0 | 1,308,425,101 | null | Jupyter Notebook | BSD-3-Clause | [
"contrastive-learning",
"test-time-adaptation",
"vision-language-model"
] | CandleLabAI/TestTimeAdaptation_VisionLanguageObjectDetectors | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-25T15:11:36Z | [] | 2026-07-25T07:05:21Z | [
"multimodal.vision-language"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-07-25T07:05:25Z | https://github.com/CandleLabAI/TestTimeAdaptation_VisionLanguageObjectDetectors | 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 | [] | 2026-07-22T20:51:09Z | Official implementation of Language-conditioned world model improves policy generalization by reading environmental descriptions (NeurIPS 2025 LAW), with LED-WM training code for Messenger S1-S3 and LWM tasks. | [
"multimodal",
"reinforcement-learning",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-12T20:31:19Z | false | 0 | 1,309,245,149 | null | Python | null | [
"environment-modeling",
"world-model"
] | realjoenguyen/ledwm_neurips2025_law | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-12T20:31:19Z | [] | 2026-07-22T20:58:11Z | [
"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"
] | include | ml-contribution-v5 | 1 | [] | 2026-07-22T20:58:20Z | https://github.com/realjoenguyen/ledwm_neurips2025_law | null |
[
"audio",
"deep-learning",
"efficient-ml",
"language"
] | [
"conditional-computation",
"evaluation",
"mixture-of-experts",
"representation",
"sparse-activation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"audio-music-information-retrieval",
"efficiency.mixture-of-experts"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-07-23T10:18:00Z | Stem-Specialized Multi-gate Mixture-of-Experts for Joint Music Structure Analysis — code and models for our ISMIR 2026 paper. | [
"audio"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 0 | 1,309,807,523 | null | Python | MIT | [
"evaluation",
"mixture-of-experts",
"representation"
] | aneeka657/MMOE_MSA | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-12T20:31:19Z | [] | 2026-08-03T20:35:51Z | [
"audio-music-information-retrieval"
] | 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 | [] | 2026-08-25T20:23:33Z | https://github.com/aneeka657/MMOE_MSA | null |
[
"machine-learning",
"multimodal-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 | [] | 2026-07-26T06:58:16Z | Official PyTorch implementation of "Beyond Unimodal Reliance: Multimodal Synergy for Training-Free Test-Time Adaptation" (ACM MM 2026). | [
"machine-learning",
"multimodal-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-10T16:53:26Z | false | 0 | 1,312,572,580 | null | null | MIT | [
"adaptation",
"test-time-adaptation"
] | Alian2014/MM26-MMS-TTA | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-10T16:53:26Z | [] | 2026-07-26T07:37:34Z | [
"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"
] | include | ml-contribution-v5 | 1 | [] | 2026-08-25T20:27:04Z | https://github.com/Alian2014/MM26-MMS-TTA | null |
[
"sequence-modeling",
"time-series"
] | [
"mamba",
"neural-network",
"recurrent-neural-network",
"sequence-modeling",
"state-space-model"
] | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"timeseries.state-space-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-07-27T15:33:43Z | Naju: Native discrete state-space model (SSM) with independent retain/write gates — official implementation of Naju (arXiv:2607.21000) | [
"sequence-modeling",
"time-series"
] | [
"deep-learning",
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 1,314,002,346 | https://arxiv.org/abs/2607.21000 | Python | null | [
"mamba",
"neural-network",
"recurrent-neural-network",
"sequence-modeling",
"state-space-model"
] | naju-ssm/naju | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-10-04T15:58:58Z | [] | 2026-08-09T11:25:32Z | [
"timeseries.state-space-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 | [
"cuda",
"deep-learning",
"discrete-ssm",
"long-context",
"long-sequence",
"mamba",
"naju",
"naju-discrete-ssm",
"naju-dss",
"naju-state-space-model",
"pytorch",
"recurrent-neural-networks",
"sequence-modeling",
"ssm",
"state-space-models",
"triton"
] | 2026-08-09T11:25:46Z | https://github.com/naju-ssm/naju | null |
[
"computer-vision",
"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 | [] | 2026-07-28T10:46:36Z | Official PyTorch implementation of the paper "MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification" @ EMA4MICCAI 2026 | [
"computer-vision",
"deep-learning",
"efficient-ml",
"language"
] | [
"classifier"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 1,314,860,519 | null | Python | MIT | [
"conditional-computation",
"mixture-of-experts",
"sparse-activation"
] | sdoerrich97/mopet | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-04T15:58:58Z | [] | 2026-08-17T09:01:36Z | [
"efficiency.mixture-of-experts"
] | 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 | [] | 2026-08-25T15:35:39Z | https://github.com/sdoerrich97/mopet | null |
[
"generative-ai",
"multimodal",
"vision"
] | [
"image-generation",
"text-to-image"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.text-to-image"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-07-28T15:58:29Z | Official implementation of Traj-MCPO(Accepted by ACM MM 2026), a trajectory-wise policy optimization framework for stable RL post-training of rectified-flow text-to-image models. | [
"generative-ai",
"multimodal",
"vision"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-22T16:09:07Z | false | 0 | 1,315,168,723 | null | null | null | [
"image-generation",
"text-to-image"
] | Kamisato520/Traj-MCPO-Trajectory-wise-Marginal-Consistent-Policy-Optimization | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-22T16:09:07Z | [] | 2026-07-28T16:33:55Z | [
"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 | 0 | [] | 2026-07-29T04:58:12Z | https://github.com/Kamisato520/Traj-MCPO-Trajectory-wise-Marginal-Consistent-Policy-Optimization | null |
[
"efficient-ml",
"model-compression"
] | [
"distillation",
"knowledge-distillation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-07-30T12:17:07Z | Official code for the ECCV 2026 paper ”Denoising-Enhanced Coarse-to-Fine Infrared Small Target Detection with Attention Prior-Guided Knowledge Distillation“ | [
"efficient-ml",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-01T16:56:07Z | false | 0 | 1,317,235,260 | null | Python | GPL-3.0 | [
"distillation",
"knowledge-distillation"
] | IVPLabs/ECFNet | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-18T13:47:34Z | [] | 2026-10-06T12:22:11Z | [
"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 | 1 | [] | 2026-10-06T12:22:15Z | https://github.com/IVPLabs/ECFNet | null |
[
"computer-vision",
"interpretability-and-safety",
"robotics"
] | [
"transformer",
"visual-localization",
"visual-place-recognition"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"vision.visual-localization"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-07-31T05:44:37Z | Official implementation and architecture figures for our paper on multi-class brain tumor classification using dual-stream CNN-Transformers, CBAM, and Grad-CAM++ | [
"computer-vision",
"interpretability-and-safety",
"robotics"
] | [
"classifier",
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-06T17:10:23Z | false | 0 | 1,317,982,394 | null | Python | null | [
"transformer",
"visual-localization",
"visual-place-recognition"
] | SuperNova-UwU/Brain-Tumor-Classification-CNN-Transformer-CBAM-XAI | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-06T17:10:23Z | [] | 2026-07-31T05:55:48Z | [
"vision.visual-localization"
] | 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 | [] | 2026-07-31T05:56:23Z | https://github.com/SuperNova-UwU/Brain-Tumor-Classification-CNN-Transformer-CBAM-XAI | null |
[
"natural-language-processing"
] | [
"natural-language-inference",
"textual-entailment"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"nlp-natural-language-inference"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-02T10:02:24Z | A new architecture to surpass Transformers | [
"natural-language-processing"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-21T15:48:06Z | false | 0 | 1,320,037,592 | null | Python | null | [
"natural-language-inference",
"textual-entailment"
] | momop1234/NSK | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-21T15:48:06Z | [] | 2026-08-02T10:41:04Z | [
"nlp-natural-language-inference"
] | 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 | [] | 2026-08-02T11:27:13Z | https://github.com/momop1234/NSK | null |
[
"generative-ai",
"multimodal",
"video"
] | [
"text-to-video",
"video-generation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.text-to-video"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-03T08:20:06Z | Official Implementation for the ECCV Paper: "Spatially-Grounded Text-to-Video Generation via Inference-Time Gradient-Free Optimization". | [
"generative-ai",
"multimodal",
"video"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 0 | 1,321,450,722 | null | Python | MIT | [
"text-to-video",
"video-generation"
] | guillaumejs2403/GATO-Vid | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2026-08-17T13:33:39Z | [
"multimodal.text-to-video"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [] | 2026-08-17T13:34:41Z | https://github.com/guillaumejs2403/GATO-Vid | null |
[
"generative-ai",
"multimodal",
"video"
] | [
"text-to-video",
"video-generation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.text-to-video"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-03T12:29:26Z | Official Implementation for the ECCV Paper: "Spatially-Grounded Text-to-Video Generation via Inference-Time Gradient-Free Optimization". | [
"generative-ai",
"multimodal",
"video"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 0 | 1,321,754,024 | null | JavaScript | null | [
"text-to-video",
"video-generation"
] | gato-vid/gato-vid.github.io | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2026-08-17T12:20:36Z | [
"multimodal.text-to-video"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-08-17T12:21:58Z | https://github.com/gato-vid/gato-vid.github.io | null |
[
"computer-vision",
"embodied-ai",
"language",
"multimodal",
"multimodal-learning",
"robotics",
"robotics-and-control",
"vision"
] | [
"contrastive-learning",
"robot-learning",
"vision-language-action",
"vision-language-model"
] | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"multimodal.vision-language",
"robotics.visual-language-action"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-03T13:40:54Z | Official code for "Hermite Curves as Trajectory Priors for Vision-Language-Action Models" (arXiv:2608.01265). | [
"computer-vision",
"language",
"multimodal",
"multimodal-learning",
"robotics-and-control",
"vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T14:50:46Z | false | 0 | 1,321,827,920 | https://aopolin-lv.github.io/Hermite/ | Python | Apache-2.0 | [
"contrastive-learning",
"vision-language-model"
] | aopolin-lv/Hermite-VLA | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 2 | 2026-10-26T17:25:59Z | [] | 2026-08-04T06:10:06Z | [
"multimodal.vision-language"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 3 | [] | 2026-09-20T15:51:26Z | https://github.com/aopolin-lv/Hermite-VLA | null |
[
"generative-modeling"
] | [
"diffusion",
"distillation",
"flow-matching"
] | [
"description",
"github-topics",
"license-metadata",
"repository-metadata"
] | [] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-05T11:55:45Z | Official implementation of the paper "STEP-OPD: Rethinking Output Targets and Internal Dynamics in On-Policy Distillation for Diffusion Models. " | [
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-30T18:03:22.392067Z | false | 0 | 1,323,993,407 | null | null | Apache-2.0 | [
"diffusion",
"distillation",
"flow-matching"
] | LiangrunFlora/STEP-OPD | [
"description",
"github-topics",
"license-metadata",
"repository-metadata"
] | 3 | 2026-10-02T18:08:49.004055Z | [] | 2026-08-09T16:52:29Z | [] | 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 | [
"flow-matching"
] | 2026-09-28T10:41:35Z | https://github.com/LiangrunFlora/STEP-OPD | {"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["flow-matching"]} |
[
"computer-vision",
"embodied-ai",
"language",
"multimodal",
"multimodal-learning",
"robotics",
"robotics-and-control",
"vision"
] | [
"contrastive-learning",
"robot-learning",
"vision-language-action",
"vision-language-model"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.vision-language",
"robotics.visual-language-action"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-06T04:51:14Z | Official implementation of TDSR-VLA: Transition-aware Denoising Sequence Representations for Vision-Language-Action (ECCV 2026) | [
"computer-vision",
"language",
"multimodal",
"multimodal-learning",
"robotics-and-control",
"vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 1 | 1,324,749,283 | null | Python | Apache-2.0 | [
"contrastive-learning",
"vision-language-model"
] | mintlabkorea/TDSR-VLA | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-28T17:27:44Z | [] | 2026-08-06T05:00:07Z | [
"multimodal.vision-language"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-08-06T04:52:39Z | https://github.com/mintlabkorea/TDSR-VLA | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"language"
] | [
"diffusion",
"flow-matching",
"language-model",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.transformers"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-06T17:38:55Z | [ECCV 2026] Official implementation of "HRDiT: Training-Free High-Resolution Image Generation with Off-the-Shelf Diffusion Transformer Models" | [
"computer-vision",
"generative-ai",
"generative-modeling",
"language"
] | [
"diffusion-model",
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T16:16:53Z | false | 6 | 1,325,607,299 | https://zylwithxy.github.io/HRDiT-page/ | Python | MIT | [
"diffusion",
"flow-matching",
"language-model",
"transformer"
] | zylwithxy/HRDiT | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-09-26T14:34:36Z | [] | 2026-09-01T23:02:59Z | [
"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 | 233 | [
"diffusion",
"diffusion-transformers",
"eccv2026",
"flow-matching",
"flux",
"image-generation",
"positional-encoding",
"rope",
"t2i-diffusion-model",
"training-free",
"training-free-acceleration"
] | 2026-09-21T02:16:33Z | https://github.com/zylwithxy/HRDiT | null |
[
"computer-vision",
"deep-learning",
"efficient-ml",
"language"
] | [
"conditional-computation",
"mixture-of-experts",
"sparse-activation",
"video-understanding"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.mixture-of-experts",
"vision.video-understanding"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-09T18:23:38Z | Official code release for "MoE-ViE Mixture of Experts Vision Encoder for Efficient Image and Video Understanding" (ECCV 2026) | [
"computer-vision",
"deep-learning",
"efficient-ml",
"language"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 3 | 1,329,082,227 | null | Python | NOASSERTION | [
"conditional-computation",
"mixture-of-experts",
"sparse-activation"
] | facebookresearch/moe_vie | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 7 | 2026-10-04T15:58:58Z | [] | 2026-08-19T00:51:18Z | [
"efficiency.mixture-of-experts"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 31 | [] | 2026-09-29T17:57:20Z | https://github.com/facebookresearch/moe_vie | null |
[
"generative-ai",
"language",
"natural-language-processing",
"software-engineering"
] | [
"code-generation",
"language-model"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm-code-generation",
"recall.name.language-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-10T10:52:48Z | Supplementary material for our IEEE TENCON 2026 paper: prompts and experimental settings for a plan-and-test LLM code generation pipeline | [
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T15:56:46Z | false | 0 | 1,329,719,039 | null | null | null | [
"language-model"
] | bp22068/tencon2026-plan-and-test | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-08T17:03:06Z | [] | 2026-09-25T15:51:56Z | [
"recall.name.language-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 | 0 | [] | 2026-09-25T15:52:00Z | https://github.com/bp22068/tencon2026-plan-and-test | null |
[
"energy",
"forecasting",
"tabular-and-structured-data",
"time-series",
"time-series-and-forecasting"
] | [
"energy-forecasting",
"load-forecasting",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"timeseries.energy-forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-11T02:00:22Z | Benchmarking ARIMA, XGBoost, LSTM & Transformer for global renewable energy forecasting (World Bank, 1960–2020). Code for our CEIS 2026 paper. | [
"energy",
"forecasting",
"tabular-and-structured-data",
"time-series",
"time-series-and-forecasting"
] | [
"deep-learning",
"machine-learning",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 0 | 1,330,403,484 | https://doi.org/10.7176/CEIS/17-1-05 | Python | NOASSERTION | [
"energy-forecasting",
"load-forecasting",
"transformer"
] | Asadullah-Irshad/Global-Renewable-Energy-Consumption-Forecasting | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-09-27T16:09:59Z | [] | 2026-08-31T16:43:46Z | [
"timeseries.energy-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 | 0 | [
"arima",
"data-science",
"deep-learning",
"energy-transition",
"forecasting",
"lstm",
"machine-learning",
"python",
"pytorch",
"renewable-energy",
"scikit-learn",
"time-series-analysis",
"transformers",
"xgboost"
] | 2026-08-31T16:47:48Z | https://github.com/Asadullah-Irshad/Global-Renewable-Energy-Consumption-Forecasting | null |
[
"multimodal",
"multimodal-learning",
"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 | [] | 2026-08-13T07:02:27Z | Code for paper "HounsWorld: A Multimodal World Model for Hidden Patient-State Readout, Reconstruction, and Simulation" | [
"multimodal",
"multimodal-learning",
"reinforcement-learning",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-13T18:05:26Z | false | 0 | 1,332,808,477 | null | null | null | [
"environment-modeling",
"world-model"
] | byhwhite/HounsWorld | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-13T18:05:26Z | [] | 2026-08-13T07:04:21Z | [
"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 | 3 | [] | 2026-08-17T02:12:47Z | https://github.com/byhwhite/HounsWorld | null |
[
"data-centric-ai",
"machine-learning",
"reinforcement-learning"
] | [
"batch-reinforcement-learning",
"data-selection",
"machine-learning",
"offline-reinforcement-learning",
"reinforcement-learning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"probable.data-training-selection",
"rl.offline"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-13T13:09:45Z | This repository is the official implementation of the paper “Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions". TMLR, 2026. | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T10:12:50Z | false | 0 | 1,333,130,446 | https://openreview.net/forum?id=4vVhm4817Q | Jupyter Notebook | NOASSERTION | [
"batch-reinforcement-learning",
"offline-reinforcement-learning",
"reinforcement-learning"
] | jesserem/CDQAC_scheduling | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 8 | 2026-10-18T13:47:34Z | [] | 2026-10-13T12:48:27Z | [
"rl.offline"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [
"combinatorial-optimization",
"flexible-job-shop",
"flexible-job-shop-scheduling",
"job-shop",
"job-shop-problem",
"job-shop-scheduling",
"job-shop-scheduling-problem",
"job-shop-schedulling",
"offline-reinforcement-learning",
"offline-rl"
] | 2026-10-13T12:48:33Z | https://github.com/jesserem/CDQAC_scheduling | 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 | [] | 2026-08-14T03:26:18Z | Pang-W1 is a self-reinforcing world model in which, within a simulated universe, one discovers physics and mathematics on its own and, through that discovery, enriches the universe once again. I don't give AI work; I give it the universe. | [
"multimodal",
"reinforcement-learning",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-15T15:20:52Z | false | 0 | 1,333,749,912 | null | null | null | [
"environment-modeling",
"world-model"
] | 9xp2m4dwh2-bot/NEW-WORLD-MODEL | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-15T15:20:52Z | [] | 2026-08-14T03:28:47Z | [
"multimodal.world-models"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-08-14T03:29:03Z | https://github.com/9xp2m4dwh2-bot/NEW-WORLD-MODEL | null |
[
"computer-vision",
"efficient-ml",
"language",
"machine-learning-systems",
"model-compression",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"vision"
] | [
"contrastive-learning",
"image-text",
"multimodal-learning",
"neural-network-pruning",
"pruning",
"structured-pruning",
"vision-language",
"vision-language-model"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning",
"multimodal.vision-language",
"multimodal.vlm",
"vision.vision-language"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-14T11:36:35Z | Code for Paper "AdaptInfer: Adaptive Token Pruning for Vision Language Model Inference via Dynamical Text Guidance" | [
"computer-vision",
"language",
"machine-learning-systems",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"vision"
] | [
"model-inference"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-19T16:36:56Z | false | 0 | 1,334,116,740 | null | Python | Apache-2.0 | [
"contrastive-learning",
"pruning",
"vision-language-model"
] | weiczh02/AdaptInfer-base | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-28T17:27:44Z | [] | 2026-10-28T14:42:48Z | [
"multimodal.vision-language"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [] | 2026-10-28T14:42:52Z | https://github.com/weiczh02/AdaptInfer-base | 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 | [] | 2026-08-15T15:06:59Z | Official implementation of SA-optimized knowledge distillation hyperparameters for lightweight breast cancer histopathology classification (AFRICAI @ MICCAI 2026). | [
"efficient-ml",
"model-compression"
] | [
"classifier"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-10T16:53:26Z | false | 0 | 1,335,182,513 | null | Python | null | [
"distillation",
"knowledge-distillation"
] | falmatamkn/SA-KD-BreastCancer | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-10T16:53:26Z | [] | 2026-08-15T21:44:35Z | [
"efficiency.knowledge-distillation"
] | 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 | [] | 2026-08-15T21:44:52Z | https://github.com/falmatamkn/SA-KD-BreastCancer | null |
[
"cybersecurity",
"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 | [] | 2026-08-16T15:22:56Z | Official implementation of HEDM-KD: A Parallel Heterogeneous Knowledge Distillation Framework for Class-Imbalanced Intrusion Detection (Code will be released upon paper acceptance) | [
"cybersecurity",
"efficient-ml",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-11T16:48:52Z | false | 0 | 1,336,070,629 | null | null | null | [
"distillation",
"knowledge-distillation"
] | liys159357/HEDM-KD | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-11T16:48:52Z | [] | 2026-08-16T15:23:51Z | [
"efficiency.knowledge-distillation"
] | 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 | [] | 2026-08-16T15:24:23Z | https://github.com/liys159357/HEDM-KD | null |
[
"generative-ai",
"generative-modeling",
"graph-learning",
"topological-ml"
] | [
"diffusion",
"graph-diffusion",
"graph-generation",
"persistent-homology",
"topological-data-analysis"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.graph-diffusion",
"graph.persistent-homology"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-18T07:32:55Z | Official implementation of "Topology-aware Graph Diffusion Model with Persistent Homology" (NeurIPS 2025). | [
"generative-modeling",
"topological-ml"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 0 | 1,338,111,093 | null | Python | NOASSERTION | [
"diffusion",
"persistent-homology",
"topological-data-analysis"
] | pjh1023/TAGG | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 7 | 2026-09-26T14:50:46Z | [] | 2026-08-18T15:22:40Z | [
"graph.persistent-homology"
] | 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 | [] | 2026-08-18T15:29:22Z | https://github.com/pjh1023/TAGG | null |
[
"earth-observation",
"geospatial",
"machine-learning",
"machine-learning-systems",
"remote-sensing",
"science-and-engineering"
] | [
"change-detection",
"coreset-selection",
"data-selection",
"image-analysis",
"pruning",
"representation-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"geo.change-detection",
"specialized.coreset-selection"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-20T19:18:40Z | Official Implementation for the paper Fidelity-Diversity-Consistency (FDC): Data Pruning for Remote Sensing Change Detection | [
"machine-learning",
"machine-learning-systems",
"science-and-engineering"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 0 | 1,341,069,376 | null | Python | null | [
"coreset-selection",
"data-selection",
"pruning"
] | ddydyd32/fidelity-diversity-consistency | [
"description",
"query-match",
"repository-metadata"
] | 4 | 2026-09-27T16:09:59Z | [] | 2026-08-20T23:52:49Z | [
"specialized.coreset-selection"
] | 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 | [] | 2026-08-20T23:53:02Z | https://github.com/ddydyd32/fidelity-diversity-consistency | null |
[
"sequence-modeling",
"time-series"
] | [
"sequence-modeling",
"state-space-model"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"timeseries.state-space-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-21T05:44:23Z | This is the official code for the paper "Direction-Aware Hybrid Architecture with Cross-Scan State Space Model for Retinal Vessel Segmentation". | [
"sequence-modeling",
"time-series"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 0 | 1,341,444,894 | null | Python | null | [
"sequence-modeling",
"state-space-model"
] | ecustyy/VesselSeg | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2026-08-21T09:44:17Z | [
"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 | [] | 2026-09-16T10:21:20Z | https://github.com/ecustyy/VesselSeg | null |
[
"earth-science",
"geospatial",
"geospatial-science",
"remote-sensing",
"science-and-engineering",
"urban-computing"
] | [
"distillation",
"foundation-model",
"geospatial-learning",
"land-use-classification",
"remote-sensing",
"semantic-segmentation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"geo.land-use",
"science.remote-sensing"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-21T08:07:25Z | Official code for the paper “RS-OPSD: RELIABLE PRIVILEGED ON-POLICY SELF-DISTILLATION FOR ULTRA-HIGH-RESOLUTION REMOTE SENSING VQA”. | [
"geospatial",
"remote-sensing",
"science-and-engineering",
"urban-computing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-02T15:56:46Z | false | 0 | 1,341,558,184 | null | Python | NOASSERTION | [
"distillation",
"land-use-classification",
"semantic-segmentation"
] | Ronniejiang/RS-OPSD | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-21T14:37:12Z | [] | 2026-10-09T06:08:44Z | [
"geo.land-use"
] | 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 | [] | 2026-10-09T06:08:48Z | https://github.com/Ronniejiang/RS-OPSD | null |
[
"information-retrieval",
"natural-language-processing"
] | [
"cross-encoder",
"distillation",
"neural-reranking",
"question-answering",
"reading-comprehension"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"nlp.cross-encoder-reranking",
"nlp.question-answering"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-21T12:57:20Z | [EMNLP 2026] Official implementation of "PROBE: Prospective Retrieval with Oracle-Based End-Goal Distillation for Multi-Hop Question Answering" | [
"information-retrieval",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-22T14:58:59Z | false | 0 | 1,341,813,410 | null | Python | Apache-2.0 | [
"cross-encoder",
"distillation",
"neural-reranking"
] | luxu-zju/PROBE | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-26T16:16:45Z | [] | 2026-10-18T11:01:01Z | [
"nlp.cross-encoder-reranking"
] | 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 | [] | 2026-10-18T11:01:04Z | https://github.com/luxu-zju/PROBE | null |
[
"sequence-modeling",
"time-series"
] | [
"sequence-modeling",
"state-space-model"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"timeseries.state-space-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-24T10:04:13Z | Code for paper: Bounded Reachability & Jailbreak Detection via Contraction-Constrained State Space Models | [
"sequence-modeling",
"time-series"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 1,344,806,712 | null | Python | MIT | [
"sequence-modeling",
"state-space-model"
] | omanshuthapliyal/contraction_constrained-safety-head | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-18T13:47:34Z | [] | 2026-10-08T23:16:32Z | [
"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 | [] | 2026-10-08T23:16:37Z | https://github.com/omanshuthapliyal/contraction_constrained-safety-head | null |
[
"applied-mathematics",
"applied-physics",
"computational-science",
"generative-modeling"
] | [
"data-assimilation",
"diffusion",
"inverse-problems",
"reconstruction"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"science.inverse-problems"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-25T09:07:25Z | This is the official implementation of "Outlier-Robust Diffusion Solvers for Inverse Problems" (CVPR 2026). | [
"applied-mathematics",
"applied-physics",
"computational-science",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 0 | 1,345,910,440 | null | Python | null | [
"data-assimilation",
"diffusion",
"inverse-problems",
"reconstruction"
] | StarNextDay/Robust-DAPS | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-01T17:50:46Z | [] | 2026-08-25T09:47:02Z | [
"science.inverse-problems"
] | 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 | [] | 2026-08-25T09:50:29Z | https://github.com/StarNextDay/Robust-DAPS | null |
[
"computer-vision",
"embodied-ai",
"general-ml",
"machine-learning",
"multimodal",
"natural-language-processing",
"robotics",
"robotics-and-control"
] | [
"fine-tuning",
"foundation-model",
"language-model",
"manipulation",
"model-adaptation",
"multimodal-learning",
"robot-foundation-model",
"robot-learning",
"vision-language"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"probable.adaptation-finetuning-readme",
"recall.name.foundation-model",
"recall.name.language-model",
"robotics.manipulation",
"robotics.robot-foundation-model",
"vision.vision-language"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-26T03:49:26Z | Official implementation of 3DWay: Generalizing Robot Manipulation via 3D Consistent Waypoints, accepted at ECCV 2026. | [
"machine-learning",
"robotics-and-control"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 0 | 1,346,873,459 | null | Python | Apache-2.0 | [
"fine-tuning",
"model-adaptation"
] | ziqin-h/3DWay | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 7 | 2026-10-09T16:23:55Z | [] | 2026-09-25T13:35:50Z | [
"probable.adaptation-finetuning-readme"
] | 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 | 10 | [] | 2026-10-09T02:39:21Z | https://github.com/ziqin-h/3DWay | null |
[
"language",
"multimodal",
"natural-language-processing",
"video"
] | [
"video-language-model",
"video-understanding"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.video-language-model",
"multimodal.video-understanding"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-26T08:55:19Z | [EMNLP 2026] Official implementation of CounterVid, a counterfactual video generation framework for mitigating action and temporal hallucinations in video-language models. | [
"language",
"multimodal",
"natural-language-processing",
"video"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 0 | 1,347,116,087 | https://aimagelab.github.io/CounterVid/ | Python | Apache-2.0 | [
"video-language-model",
"video-understanding"
] | aimagelab/CounterVid | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-09-26T14:34:36Z | [] | 2026-09-14T08:08:00Z | [
"multimodal.video-language-model",
"multimodal.video-understanding"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 4 | [] | 2026-09-25T09:56:05Z | https://github.com/aimagelab/CounterVid | null |
[
"computer-vision",
"embodied-ai",
"generative-ai",
"language",
"machine-learning",
"multimodal",
"multimodal-learning",
"robotics",
"robotics-and-control",
"time-series-and-forecasting",
"vision"
] | [
"contrastive-learning",
"diffusion",
"fine-tuning",
"model-adaptation",
"multimodal-learning",
"robot-learning",
"vision-language-action",
"vision-language-model"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.vision-language",
"probable.adaptation-finetuning-readme",
"recall.name.diffusion-model",
"recall.name.multimodal-model",
"robotics.visual-language-action"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-26T16:13:52Z | [NeurIPS 2026] Official code of "StreamPI: Streaming Multimodal Temporal Modeling for Vision-Language-Action Models" | [
"computer-vision",
"language",
"multimodal",
"multimodal-learning",
"robotics-and-control",
"time-series-and-forecasting",
"vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 9 | 1,347,541,757 | https://happinesslz.github.io/projects/StreamPI/ | Python | Apache-2.0 | [
"contrastive-learning",
"vision-language-model"
] | hku-sail/StreamPI | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 16 | 2026-10-26T17:25:59Z | [] | 2026-09-27T01:03:16Z | [
"multimodal.vision-language"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 184 | [
"vla"
] | 2026-10-26T18:16:50Z | https://github.com/hku-sail/StreamPI | null |
[
"efficient-ml",
"model-compression"
] | [
"distillation",
"knowledge-distillation"
] | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-28T17:20:01Z | Code for our paper: "Knowledge Distillation During Mid-Training Favors Reasoning over Factual Recall" | [
"efficient-ml",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 0 | 1,349,862,287 | https://arxiv.org/abs/2609.01532 | Python | MIT | [
"distillation",
"knowledge-distillation"
] | facebookresearch/midtraining-distillation | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 5 | 2026-10-18T13:47:34Z | [] | 2026-10-10T04:03:18Z | [
"efficiency.knowledge-distillation"
] | 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 | [] | 2026-10-10T19:31:33Z | https://github.com/facebookresearch/midtraining-distillation | null |
[
"computer-vision",
"language",
"machine-learning-systems",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"vision"
] | [
"image-text",
"pruning",
"vision-language-model"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.vlm"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-30T02:58:16Z | [EMNLP 2026 Findings] Official implementation of HACP: Mitigating Object Hallucinations in Vision-Language Models via Head-Level Attribution and Counteractive Pruning | [
"computer-vision",
"language",
"machine-learning-systems",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-20T15:59:06Z | false | 0 | 1,351,060,553 | null | null | Apache-2.0 | [
"image-text",
"pruning",
"vision-language-model"
] | S1yang/HACP | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-20T15:59:06Z | [] | 2026-08-30T03:21:23Z | [
"multimodal.vlm"
] | 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 | [] | 2026-08-30T03:22:19Z | https://github.com/S1yang/HACP | null |
[
"efficient-ml",
"generative-modeling",
"inference-systems",
"language",
"natural-language-processing"
] | [
"diffusion",
"kv-cache-optimization",
"memory-optimization"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.kv-cache"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-30T03:39:24Z | [EMNLP 26 Main] This is the official implementation for the paper "SampKV: Saliency-Aware Mixed-Precision KV Cache for Efficient Diffusion Language Model Inference" in EMNLP 2026 main conference. | [
"efficient-ml",
"generative-modeling",
"inference-systems",
"language",
"natural-language-processing"
] | [
"model-inference"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-14T16:10:46Z | false | 0 | 1,351,078,954 | null | Python | null | [
"diffusion",
"kv-cache-optimization",
"memory-optimization"
] | jerryw95/SampKV | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-14T16:10:46Z | [] | 2026-09-01T19:48:39Z | [
"efficiency.kv-cache"
] | 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 | [] | 2026-09-01T19:49:11Z | https://github.com/jerryw95/SampKV | null |
[
"deep-learning",
"efficient-ml",
"language",
"machine-learning-systems"
] | [
"conditional-computation",
"mixture-of-experts",
"quantization",
"sparse-activation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.mixture-of-experts"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-30T07:00:44Z | Official PyTorch implementation of "Q-Strata: Hierarchical Bit Allocation for Mixed-Precision Quantization of Mixture-of-Experts LLMs" (EMNLP 2026) | [
"deep-learning",
"efficient-ml",
"language",
"machine-learning-systems"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 1,351,177,360 | null | null | null | [
"conditional-computation",
"mixture-of-experts",
"quantization",
"sparse-activation"
] | snu-mllab/Q-Strata | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-04T15:58:58Z | [] | 2026-08-30T07:01:50Z | [
"efficiency.mixture-of-experts"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 4 | [] | 2026-10-02T07:18:48Z | https://github.com/snu-mllab/Q-Strata | null |
[
"3d",
"computer-vision",
"generative-ai",
"generative-modeling",
"graphics"
] | [
"3d-generation",
"diffusion",
"generative-modeling"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"multimodal.3d-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-30T14:35:22Z | Official implementation of TOG26 paper "DecomVoxel: Harnessing 3D-Native Priors with Guided In-situ Denoising Optimization for Decompositional Scene Reconstruction" | [
"3d",
"computer-vision",
"generative-ai",
"generative-modeling",
"graphics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-01T16:56:07Z | false | 0 | 1,351,469,880 | https://decomvoxel.github.io/DecomVoxel-Webpage/ | Python | null | [
"3d-generation",
"diffusion",
"generative-modeling"
] | DecomVoxel/DecomVoxel | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 4 | 2026-10-20T15:59:06Z | [] | 2026-10-02T03:12:48Z | [
"multimodal.3d-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 | 18 | [
"3d-reconstruction",
"diffusion-prior",
"scene-editing"
] | 2026-10-16T08:39:01Z | https://github.com/DecomVoxel/DecomVoxel | null |
[
"efficient-ml",
"generative-modeling",
"inference-systems",
"language",
"natural-language-processing"
] | [
"diffusion",
"kv-cache-optimization",
"memory-optimization"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.kv-cache"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-31T00:46:56Z | Official implementation of “Enabling KV Caching of Shared Prefix for Diffusion Language Models” (EMNLP 2026 Main Conference) | [
"efficient-ml",
"generative-modeling",
"inference-systems",
"language",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 0 | 1,351,851,493 | null | Python | AGPL-3.0 | [
"diffusion",
"kv-cache-optimization",
"memory-optimization"
] | OSSS-KU/BiCache | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-11T16:48:52Z | [] | 2026-08-31T04:41:45Z | [
"efficiency.kv-cache"
] | 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 | [] | 2026-10-02T20:57:11Z | https://github.com/OSSS-KU/BiCache | null |
[
"generative-ai",
"language",
"linguistics",
"natural-language-processing",
"reinforcement-learning"
] | [
"language-model",
"language-modeling",
"pretraining",
"reinforcement-learning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.language-models",
"recall.computational-linguistics"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-31T12:34:25Z | Official implementation of TCS: Two-Stage Reinforcement Learning for Sound and Adversarial Test Generation in Code LLMs (Findings of EMNLP 2026). | [
"generative-ai",
"language",
"natural-language-processing",
"reinforcement-learning"
] | [
"large-language-model",
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T13:50:07Z | false | 0 | 1,352,392,252 | https://xiaobanni.github.io/projects/tcs/ | Python | Apache-2.0 | [
"language-model",
"pretraining",
"reinforcement-learning"
] | xiaobanni/tcs | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-26T17:25:59Z | [] | 2026-09-24T15:57:21Z | [
"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 | [
"automated-testing",
"code-generation",
"emnlp2026",
"large-language-models",
"reinforcement-learning",
"test-generation"
] | 2026-10-16T13:46:42Z | https://github.com/xiaobanni/tcs | 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 | [] | 2026-09-01T04:20:56Z | Official implementation for "InterCMDM: Block-Causal Diffusion for Autoregressive Human Interaction Generation" (ECCV 2026) | [
"generative-ai",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-06T16:04:50Z | false | 1 | 1,353,125,611 | https://yu1ut.com/InterCMDM-HP/ | Python | CC0-1.0 | [
"diffusion"
] | lycorp-jp/InterCMDM | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-06T16:04:50Z | [] | 2026-09-28T07:19:39Z | [
"recall.name.diffusion-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 3 | [] | 2026-10-01T00:47:41Z | https://github.com/lycorp-jp/InterCMDM | null |
[
"efficient-ml",
"linguistics",
"machine-learning-systems",
"model-compression",
"natural-language-processing"
] | [
"language-modeling",
"neural-network-pruning",
"pruning",
"structured-pruning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning",
"recall.computational-linguistics"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-01T06:34:18Z | [EMNLP2026] Official source code for Train Overcomplete, Deploy Compact: Scaling Recovery Capacity for Structured LLM Pruning. | [
"linguistics",
"machine-learning-systems",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-18T13:47:34Z | false | 0 | 1,353,220,058 | https://mmai-laboratory.github.io/OverRep/ | Python | Apache-2.0 | [
"language-modeling",
"pruning"
] | MMAI-Laboratory/OverRep | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-21T14:37:12Z | [] | 2026-10-12T07:30:33Z | [
"recall.computational-linguistics"
] | 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 | [
"llm",
"re-parameterization",
"structured-pruning"
] | 2026-10-12T07:30:50Z | https://github.com/MMAI-Laboratory/OverRep | null |
[
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion",
"generative-modeling"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.image-diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-05T00:26:04Z | Official implementation of Efficient and Training-Free Single-Image Diffusion Models (CVPR 2026). | [
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-16T15:36:56Z | false | 1 | 1,357,699,828 | null | Python | NOASSERTION | [
"diffusion",
"generative-modeling"
] | haojunqiu/efficient-SID-code | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-16T15:36:56Z | [] | 2026-09-05T00:44:51Z | [
"vision.image-diffusion"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 4 | [] | 2026-10-15T17:50:02Z | https://github.com/haojunqiu/efficient-SID-code | 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 | [] | 2026-09-07T14:17:48Z | new AI world model centered on human and humen activity | [
"multimodal",
"reinforcement-learning",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-14T16:10:46Z | false | 0 | 1,360,315,958 | null | null | null | [
"environment-modeling",
"world-model"
] | laozux-cloud/event-world-modek-foe-AI | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-14T16:10:46Z | [] | 2026-09-07T14:17:50Z | [
"multimodal.world-models"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-09-07T14:19:06Z | https://github.com/laozux-cloud/event-world-modek-foe-AI | null |
[
"3d",
"computer-vision",
"robotics"
] | [
"3d-object-detection",
"3d-perception",
"mamba"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.3d-object-detection"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-09T21:22:55Z | Official implementation of PointLAM: Local Attentive Mamba for Efficient Point-based 3D Object Detection (ECCV 2026) | [
"3d",
"computer-vision",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 0 | 1,363,229,222 | https://pointlam.github.io/ | Python | Apache-2.0 | [
"3d-object-detection",
"3d-perception",
"mamba"
] | PointLAM/PointLAM | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-25T15:11:36Z | [] | 2026-09-21T02:47:56Z | [
"multimodal.3d-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 | 5 | [] | 2026-10-09T14:17:10Z | https://github.com/PointLAM/PointLAM | 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 | [] | 2026-09-11T01:54:43Z | Official implementation of the paper “EEMF: ENTROPY--ENERGY-DRIVEN MULTI-CACHE FRAMEWORK WITH DISTRIBUTION CONSTRAINTS FOR TRAINING-FREE POINT CLOUD TEST-TIME ADAPTATION” | [
"machine-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-10T16:53:26Z | false | 0 | 1,365,185,637 | null | Python | null | [
"adaptation",
"test-time-adaptation"
] | TanLinJi/eemf | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-10T16:53:26Z | [] | 2026-09-11T02:20:00Z | [
"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 | 0 | [] | 2026-09-11T02:20:03Z | https://github.com/TanLinJi/eemf | null |
[
"multimodal",
"reinforcement-learning",
"robotics"
] | [
"environment-modeling",
"world-model"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.world-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-11T03:34:57Z | Code for paper: Pelican-Sim 1.0: A General World Model Simulator for Embodied Intelligence | [
"multimodal",
"reinforcement-learning",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-13T18:05:26Z | false | 0 | 1,365,262,937 | https://zoushilong1024.github.io/Pelican-Sim1.0/ | null | Apache-2.0 | [
"environment-modeling",
"world-model"
] | ZouShilong1024/Pelican-Sim1.0 | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-13T18:05:26Z | [] | 2026-09-11T03:34:58Z | [
"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 | 4 | [
"world-model-evaluation",
"world-model-rl",
"world-models"
] | 2026-10-07T13:39:18Z | https://github.com/ZouShilong1024/Pelican-Sim1.0 | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"multimodal",
"multimodal-learning",
"vision"
] | [
"diffusion",
"generative-modeling",
"image-generation",
"text-to-image"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.text-to-image",
"vision.image-diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-12T06:21:38Z | The official implementation repository for our EMNLP 2025 paper, R-Bind: Unified Enhancement of Attribute and Relation Binding in Text-to-Image Diffusion Models. | [
"generative-ai",
"generative-modeling",
"multimodal",
"multimodal-learning",
"vision"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 0 | 1,367,017,408 | null | Python | MIT | [
"diffusion",
"image-generation",
"text-to-image"
] | PKU-ONELab/R-Bind | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-22T16:09:07Z | [] | 2026-09-12T06:23:02Z | [
"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",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [
"attention-mechanism",
"emnlp2025",
"text-to-image",
"text-to-image-diffusion",
"training-free-method"
] | 2026-09-12T07:03:25Z | https://github.com/PKU-ONELab/R-Bind | 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 | [] | 2026-09-13T07:40:43Z | Source code for paper: Influence Maximization in Social Networks via Offline Deep Reinforcement Learning | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-18T13:47:34Z | false | 0 | 1,368,161,005 | null | Python | null | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | harryyan0322/ODRL-IM | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-18T13:47:34Z | [] | 2026-10-17T02:57:38Z | [
"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 | [] | 2026-10-17T02:57:42Z | https://github.com/harryyan0322/ODRL-IM | null |
[
"computer-vision",
"efficient-ml",
"model-compression"
] | [
"distillation",
"knowledge-distillation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-15T06:08:06Z | This repository provides the official and fully reproducible implementation of the paper: Knowledge Distillation via Temporal Frequency for Generative Semantic Image Segmentation | [
"computer-vision",
"efficient-ml",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-11T16:48:52Z | false | 0 | 1,370,983,636 | null | null | null | [
"distillation",
"knowledge-distillation"
] | hoangdv05/TFKD | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-11T16:48:52Z | [] | 2026-09-15T06:08:08Z | [
"efficiency.knowledge-distillation"
] | 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 | [] | 2026-10-08T16:47:37Z | https://github.com/hoangdv05/TFKD | null |
[
"natural-language-processing"
] | [
"named-entity-recognition"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"nlp.named-entity-recognition"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-15T14:09:45Z | Research code for our ICIMCIS 2026 paper: distant supervision with heuristic-augmented denoising for crypto NER on Indonesian Twitter using IndoBERTweet-CRF | [
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-20T14:47:52Z | false | 0 | 1,371,587,400 | null | Jupyter Notebook | MIT | [
"named-entity-recognition"
] | bryandalee/crypto-ner-indobertweet-crf | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-20T14:47:52Z | [] | 2026-10-03T14:06:16Z | [
"nlp.named-entity-recognition"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [
"bert",
"indonesian-nlp",
"named-entity-recognition",
"nlp",
"pytorch"
] | 2026-10-03T14:06:20Z | https://github.com/bryandalee/crypto-ner-indobertweet-crf | 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 | [] | 2026-09-16T00:35:34Z | Official implementation of the paper “EEMC: ENTROPY--ENERGY-DRIVEN MULTI-CACHE FRAMEWORK WITH DISTRIBUTION CONSTRAINTS FOR TRAINING-FREE POINT CLOUD TEST-TIME ADAPTATION” | [
"machine-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-10T16:53:26Z | false | 0 | 1,372,313,683 | null | Python | null | [
"adaptation",
"test-time-adaptation"
] | pla0801/eemc | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-10T16:53:26Z | [] | 2026-09-20T10:13:30Z | [
"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 | 0 | [] | 2026-09-20T10:13:33Z | https://github.com/pla0801/eemc | null |
[
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion",
"distillation",
"generative-modeling",
"video-generation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.video-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-17T11:52:19Z | Official implementation of the paper UnStep Training-Free Acceleration of Causal Video Diffusion with Fewer Steps Than Distillation | [
"computer-vision",
"generative-ai",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-05T18:59:49Z | false | 1 | 1,374,356,103 | null | Python | NOASSERTION | [
"diffusion",
"distillation",
"generative-modeling",
"video-generation"
] | facebookresearch/UnStep | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T18:59:49Z | [] | 2026-09-29T06:41:49Z | [
"vision.video-generation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 12 | [] | 2026-10-04T14:30:14Z | https://github.com/facebookresearch/UnStep | 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 | [] | 2026-09-18T14:12:39Z | new research on world models | [
"multimodal",
"reinforcement-learning",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-13T18:05:26Z | false | 0 | 1,375,968,423 | null | Python | null | [
"environment-modeling",
"world-model"
] | Vixel2006/new-research | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-13T18:05:26Z | [] | 2026-09-27T14:49:23Z | [
"multimodal.world-models"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-09-27T14:50:12Z | https://github.com/Vixel2006/new-research | null |
[
"data-centric-ai",
"embodied-ai",
"machine-learning",
"robotics",
"robotics-and-control"
] | [
"data-selection",
"machine-learning",
"manipulation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"probable.data-training-selection",
"robotics.manipulation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-19T05:45:22Z | Official code implementation of the ICML 2026 paper Move-Then-Operate: Behavioral Phasing for Human-Like Robotic Manipulation | [
"data-centric-ai",
"machine-learning",
"robotics-and-control"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 1,376,779,641 | null | Python | Apache-2.0 | [
"data-selection",
"machine-learning"
] | healenrens/Move-then-Operate | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-20T15:59:06Z | [] | 2026-10-07T02:03:23Z | [
"probable.data-training-selection"
] | 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 | [] | 2026-10-18T10:49:01Z | https://github.com/healenrens/Move-then-Operate | null |
[
"foundation-models",
"generative-ai",
"multimodal-learning",
"natural-language-processing"
] | [
"multimodal",
"pretraining"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"llm.foundation-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-20T03:50:35Z | Official implementation of “Pretrain Once, Route Anywhere: Towards a Foundation Model for LLM Routing” (arXiv:2609.37362). | [
"foundation-models",
"generative-ai",
"multimodal-learning",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T15:56:46Z | false | 0 | 1,377,878,450 | https://arxiv.org/abs/2609.37362 | Python | Apache-2.0 | [
"multimodal",
"pretraining"
] | LAMDA-Model-Reuse/RouteFM | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 7 | 2026-10-22T16:09:07Z | [
"2609.37362"
] | 2026-10-14T04:08:57Z | [
"llm.foundation-models"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-context-only",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 11 | [
"foundation-models",
"in-context-learning",
"llm-routing",
"model-routing",
"multimodal-routing",
"routefm"
] | 2026-10-18T08:42:28Z | https://github.com/LAMDA-Model-Reuse/RouteFM | null |
[
"computer-vision",
"efficient-ml",
"generative-ai",
"language",
"machine-learning-systems",
"model-compression",
"multimodal",
"multimodal-learning",
"natural-language-processing"
] | [
"language-model",
"language-modeling",
"large-language-model",
"multimodal-learning",
"neural-network-pruning",
"pretraining",
"pruning",
"referring-expression-comprehension",
"structured-pruning",
"vision-language-learning",
"visual-grounding"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning",
"llm.language-model",
"llm.large-language-model",
"nlp.language-modeling",
"recall.name.language-model",
"recall.name.large-language-model",
"recall.name.multimodal-model",
"recall26.visual-grounding"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-20T07:26:58Z | Code for Paper "Layer-Aware Position Embeddings for Visual Token Pruning in Multimodal Large Language Model" | [
"computer-vision",
"machine-learning-systems",
"multimodal-learning",
"natural-language-processing"
] | [
"large-language-model",
"embedding"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-25T15:00:52Z | false | 0 | 1,378,060,420 | null | Python | Apache-2.0 | [
"pruning",
"referring-expression-comprehension",
"vision-language-learning",
"visual-grounding"
] | YahongWang1/LayerPos | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-18T14:44:29Z | [] | 2026-09-23T02:00:06Z | [
"recall26.visual-grounding"
] | 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 | [] | 2026-09-23T02:00:10Z | https://github.com/YahongWang1/LayerPos | null |
[
"3d-vision",
"computer-vision",
"efficient-ml",
"model-compression"
] | [
"3d-perception",
"depth-estimation",
"distillation",
"knowledge-distillation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation",
"vision.depth-estimation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-22T09:48:12Z | This repository contains the official implementation for the paper "KD-CAUNet: Geometry-Aware Attentive UNet with Channel-Level Knowledge Distillation for Joint Acoustic Source Range and Depth Estimation in Shallow Water" | [
"efficient-ml",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-25T15:00:52Z | false | 0 | 1,381,225,394 | null | null | MIT | [
"distillation",
"knowledge-distillation"
] | binzhangbin/KD-CAUNet | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-11T16:48:52Z | [] | 2026-09-24T20:51:22Z | [
"efficiency.knowledge-distillation"
] | 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 | [] | 2026-10-02T12:55:28Z | https://github.com/binzhangbin/KD-CAUNet | null |
[
"general-ml",
"generative-ai",
"generative-modeling"
] | [
"diffusion",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"recall.name.diffusion-model",
"recall.name.transformer-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-22T16:11:41Z | [ECCV 2026] Official implementation of LayerVerse: Finding the Sweet Spot for KV-Injection in Training-Free Image Editing. | [
"general-ml",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-27T15:08:59Z | false | 0 | 1,381,903,299 | null | Python | Apache-2.0 | [
"diffusion",
"transformer"
] | v-gen-ai/LayerVerse | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-14T15:02:21Z | [] | 2026-09-30T20:42:49Z | [
"recall.name.transformer-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 | 3 | [
"flux",
"image-editing",
"rectified-flow",
"stable-diffusion-3-5",
"training-free"
] | 2026-10-11T06:47:04Z | https://github.com/v-gen-ai/LayerVerse | null |
[
"general-ml",
"generative-modeling"
] | [
"flow-matching",
"transformer"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"recall.name.transformer-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-22T23:10:22Z | official code repo for paper "SKELETONS IN FLOW: GRAPH STRUCTURED FLOW MATCHING FOR HUMAN MOTION PREDICTION" | [
"general-ml",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-29T16:25:50Z | false | 0 | 1,382,453,181 | null | Python | null | [
"flow-matching",
"transformer"
] | noMushroomw/GSFM | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-06T16:04:50Z | [] | 2026-09-24T17:39:28Z | [
"recall.name.transformer-model"
] | 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 | [] | 2026-09-24T17:40:34Z | https://github.com/noMushroomw/GSFM | null |
[
"computational-physics",
"general-ml",
"physics",
"science-and-engineering",
"scientific-computing"
] | [
"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 | [] | 2026-09-23T08:29:49Z | This code is for our paper:Efficient multiphysics system modeling enabled by data-free physics informed neural networks | [
"computational-physics",
"physics",
"science-and-engineering"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T13:20:52Z | false | 0 | 1,383,058,852 | null | Python | null | [
"neural-network",
"physics-informed-learning"
] | cloud139969/PINN-for-Multiphysics-Modeling | [
"description",
"query-match",
"repository-metadata"
] | 5 | 2026-10-11T16:48:52Z | [] | 2026-09-23T12:05:39Z | [
"science.physics-informed-neural-networks"
] | 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 | [] | 2026-09-30T22:48:45Z | https://github.com/cloud139969/PINN-for-Multiphysics-Modeling | null |
[
"classical-ml",
"computational-physics",
"deep-learning",
"general-ml",
"generative-ai",
"graph-learning",
"language",
"metric-learning",
"physics",
"scientific-computing"
] | [
"classification",
"diffusion",
"graph-transformer",
"language-model",
"machine-learning",
"message-passing",
"nearest-neighbors",
"neural-operators",
"physics-informed-learning",
"surrogate-modeling",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.machine-learning-readme",
"general.nearest-neighbors",
"graph.graph-transformer",
"llm.transformers",
"recall.name.diffusion-model",
"recall.name.machine-learning",
"recall.name.transformer-model",
"science.physics"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-23T09:36:13Z | Official code for the IEEE MLSP 2026 paper: Physics-Augmented Graph Transformers for Patch-Antenna Forward and Inverse Design | [
"deep-learning",
"graph-learning"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T13:07:37Z | false | 0 | 1,383,153,451 | null | Jupyter Notebook | MIT | [
"graph-transformer",
"message-passing"
] | AviEpstein/GNN-for-Antenna-design | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 7 | 2026-10-18T13:47:34Z | [] | 2026-10-07T16:03:33Z | [
"graph.graph-transformer"
] | 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 | [] | 2026-10-07T16:03:37Z | https://github.com/AviEpstein/GNN-for-Antenna-design | null |
[
"general-ml",
"machine-learning"
] | [
"distillation",
"fine-tuning",
"model-adaptation",
"neural-network"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"probable.adaptation-finetuning-readme",
"recall.name.neural-network"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-24T05:51:43Z | Official code for the NeurIPS 2026 paper 'The Shape of Events: Edge-Based Inductive Biases via Cross-Domain Distillation'. | [
"machine-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-08T17:03:06Z | false | 0 | 1,384,785,758 | null | Python | NOASSERTION | [
"distillation",
"fine-tuning",
"model-adaptation"
] | snskysk/event2rgb-distillation | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-10T15:52:45Z | [] | 2026-09-28T10:43:16Z | [
"probable.adaptation-finetuning-readme"
] | 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 | [] | 2026-09-30T23:16:50Z | https://github.com/snskysk/event2rgb-distillation | null |
[
"general-ml",
"generative-ai",
"generative-modeling",
"language"
] | [
"diffusion",
"language-model",
"transformer"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"llm.transformers",
"recall.name.diffusion-model",
"recall.name.transformer-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-24T21:23:59Z | Official AutoTarget implementation: solver-aware cache target selection for few-step diffusion transformers (submitted to ICLR 2027). | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model",
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-05T18:59:49Z | false | 0 | 1,386,382,273 | null | Python | null | [
"diffusion"
] | wali1024-offical/AutoTarget | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 6 | 2026-10-22T14:58:59Z | [] | 2026-10-01T03:17:30Z | [
"recall.name.diffusion-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 2 | [
"diffusion-models",
"diffusion-transformers",
"inference-acceleration"
] | 2026-10-15T04:54:02Z | https://github.com/wali1024-offical/AutoTarget | null |
[
"deep-learning",
"general-ml",
"generative-ai",
"graph-learning",
"language"
] | [
"graph-transformer",
"language-model",
"message-passing",
"neural-network",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.graph-transformer",
"llm.transformers",
"recall.name.neural-network",
"recall.name.transformer-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-25T15:33:23Z | The official GitHub repository of our ACCV 2026 paper "Learned Suppression for 3D Keypoint Detection with a Graph-Transformer Backbone" | [
"general-ml"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-18T13:47:34Z | false | 2 | 1,387,784,568 | null | Python | MIT | [
"transformer"
] | cansdev/learned-suppression-3d | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-28T16:14:12Z | [] | 2026-10-02T06:54:55Z | [
"recall.name.transformer-model"
] | 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 | [] | 2026-10-11T07:17:34Z | https://github.com/cansdev/learned-suppression-3d | null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.