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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
[
"multimodal",
"reinforcement-learning",
"robotics"
] | [
"behavior-cloning",
"environment-modeling",
"imitation-learning",
"world-model"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.world-models",
"rl.imitation-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-10T12:15:26Z | Official code implementation of ICML 2025 paper: Reward-free World Models for Online Imitation Learning | [
"reinforcement-learning",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 0 | 981,132,814 | null | Python | null | [
"behavior-cloning",
"imitation-learning"
] | TobyLeelsz/iqmpc | [
"description",
"query-match",
"repository-metadata"
] | 4 | 2026-10-01T17:50:46Z | [] | 2025-06-21T15:31:25Z | [
"rl.imitation-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 12 | [] | 2026-07-15T16:22:18Z | https://github.com/TobyLeelsz/iqmpc | null |
[
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"forecasting",
"sequence-modeling"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"timeseries.forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-11T08:39:08Z | [IJCAI 2025] Official implementation of "FreqLLM: Bridging Frequency and Language for Enhanced Time Series Forecasting" | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 0 | 981,495,955 | null | Python | Apache-2.0 | [
"forecasting",
"sequence-modeling"
] | biya0105/FreqLLM | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-26T17:25:59Z | [] | 2026-08-06T13:29:41Z | [
"timeseries.forecasting"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 13 | [] | 2026-08-06T13:30:22Z | https://github.com/biya0105/FreqLLM | null |
[
"computer-vision",
"efficient-ml",
"model-compression"
] | [
"distillation",
"knowledge-distillation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-12T09:10:46Z | Code for paper 'Semantic Web-based Knowledge Distillation and Distribution Calibration for Few-Shot Object Detection' | [
"computer-vision",
"efficient-ml",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 0 | 982,027,654 | null | Python | null | [
"distillation",
"knowledge-distillation"
] | Erica-Yang/T-CLD | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2025-05-12T09:28:27Z | [
"efficiency.knowledge-distillation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2025-05-12T09:28:31Z | https://github.com/Erica-Yang/T-CLD | null |
[
"graph-learning",
"information-retrieval",
"natural-language-processing"
] | [
"rag",
"retrieval-augmented-generation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"nlp.rag"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-12T15:29:00Z | This is the official implementation of the paper RAGTKGC: Undertaking Temporal Knowledge Graph Completion with Retrieval Augmented Generation. | [
"graph-learning",
"information-retrieval",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-30T17:16:29Z | false | 0 | 982,243,887 | null | Jupyter Notebook | null | [
"rag",
"retrieval-augmented-generation"
] | AnonOctopus/RAGTKGC | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-30T17:16:29Z | [] | 2026-09-15T11:52:07Z | [
"nlp.rag"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-09-15T11:52:12Z | https://github.com/AnonOctopus/RAGTKGC | null |
[
"distributed-ml",
"privacy-and-federated-learning"
] | [
"collaborative-learning",
"federated-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-13T14:30:50Z | [NeurIPS 2025] Official implementation of PHP-FL: A Fair Federated Learning Method for Handling Client Participation Probability Inconsistencies in Heterogeneous Environments | [
"distributed-ml",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-19T17:46:36Z | false | 1 | 982,874,034 | null | Python | Apache-2.0 | [
"collaborative-learning",
"federated-learning"
] | Siyuan01/PHP-FL-main | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-19T17:46:36Z | [] | 2025-10-14T08:50:13Z | [
"trust.federated-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 5 | [] | 2026-10-14T11:15:01Z | https://github.com/Siyuan01/PHP-FL-main | null |
[
"forecasting",
"general-ml",
"time-series",
"time-series-and-forecasting"
] | [
"forecasting",
"neural-network",
"sequence-modeling"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.neural-network",
"timeseries.forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-14T13:27:24Z | [AAAI Oral 2026] This is the official code of our Paper "Sonnet: Spectral Operator Neural Network for Multivariable Time Series Forecasting" | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 9 | 983,501,450 | null | Jupyter Notebook | null | [
"forecasting",
"neural-network",
"sequence-modeling"
] | ClaudiaShu/Sonnet | [
"description",
"query-match",
"repository-metadata"
] | 8 | 2026-10-26T17:25:59Z | [] | 2026-03-20T10:32:42Z | [
"timeseries.forecasting"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 59 | [] | 2026-10-21T03:00:30Z | https://github.com/ClaudiaShu/Sonnet | null |
[
"computer-vision",
"generative-ai",
"multimodal",
"vision"
] | [
"image-generation",
"rag",
"text-to-image"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.text-to-image"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-15T19:30:50Z | Official Code for the paper Cross-modal RAG: Sub-dimensional Retrieval-Augmented Text-to-Image Generation | [
"computer-vision",
"generative-ai",
"multimodal",
"vision"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-10T16:53:26Z | false | 0 | 984,346,843 | null | Python | Apache-2.0 | [
"image-generation",
"rag",
"text-to-image"
] | mengdanzhu/Cross-modal-RAG | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-10T16:53:26Z | [] | 2025-05-29T04:31:14Z | [
"multimodal.text-to-image"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 3 | [] | 2026-03-25T21:00:38Z | https://github.com/mengdanzhu/Cross-modal-RAG | null |
[
"missing-data",
"time-series",
"time-series-and-forecasting"
] | [
"imputation",
"time-series-modeling"
] | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"timeseries.imputation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-16T02:40:17Z | [NeurIPS 2025] Official Implementation for "Glocal Information Bottleneck for Time Series Imputation" | [
"missing-data",
"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T15:29:21Z | false | 3 | 984,487,855 | https://arxiv.org/abs/2510.04910 | Python | null | [
"imputation",
"time-series-modeling"
] | Muyiiiii/NeurIPS-25-Glocal-IB | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | 4 | 2026-09-27T15:53:49Z | [] | 2025-11-04T17:45:13Z | [
"timeseries.imputation"
] | 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 | 19 | [] | 2026-09-17T23:39:35Z | https://github.com/Muyiiiii/NeurIPS-25-Glocal-IB | null |
[
"efficient-ml",
"model-compression",
"multimodal-learning"
] | [
"distillation",
"knowledge-distillation",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-16T15:19:54Z | This is the official implementation of "Multi-modal Anchor Gated Transformer with Knowledge Distillation for Emotion Recognition in Conversation" (IJCAI 2025) | [
"efficient-ml",
"model-compression",
"multimodal-learning"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 2 | 984,829,528 | null | Python | GPL-3.0 | [
"distillation",
"knowledge-distillation",
"transformer"
] | JieLi-dd/MAGTKD | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-03T15:45:52Z | [] | 2025-12-15T13:12:27Z | [
"efficiency.knowledge-distillation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 12 | [] | 2026-08-30T14:32:44Z | https://github.com/JieLi-dd/MAGTKD | null |
[
"audio",
"generative-modeling",
"speech",
"speech-and-audio"
] | [
"architecture",
"flow-matching",
"generative-modeling",
"training"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"audio-speech-synthesis"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-16T15:56:36Z | Official implementation of paper: Shallow Flow Matching for Coarse-to-Fine Text-to-Speech Synthesis | [
"audio",
"generative-modeling",
"speech",
"speech-and-audio"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T16:16:53Z | false | 8 | 984,848,091 | null | Python | null | [
"architecture",
"flow-matching",
"generative-modeling",
"training"
] | ydqmkkx/ShallowFlowMatching-TTS | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-09-26T20:57:15Z | [] | 2025-09-20T01:43:47Z | [
"audio-speech-synthesis"
] | 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 | 56 | [] | 2026-09-22T12:10:46Z | https://github.com/ydqmkkx/ShallowFlowMatching-TTS | null |
[
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"forecasting",
"multivariate-forecasting",
"sequence-modeling"
] | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"timeseries.forecasting",
"timeseries.multivariate"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-17T12:05:25Z | [ACM MM 2025] Official implementation of "DualSG: A Dual-Stream Explicit Semantic-Guided Multivariate Time Series Forecasting Framework" | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 11 | 985,266,942 | https://arxiv.org/abs/2507.21830 | Python | null | [
"forecasting",
"sequence-modeling"
] | BenchCouncil/DualSG | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | 13 | 2026-10-26T17:25:59Z | [] | 2026-01-03T01:33:23Z | [
"timeseries.forecasting"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 103 | [
"time-series-forecasting"
] | 2026-10-26T12:58:21Z | https://github.com/BenchCouncil/DualSG | null |
[
"data-centric-ai",
"language",
"natural-language-processing"
] | [
"language-model",
"synthetic-data"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.synthetic-data",
"llm.synthetic-data-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-17T13:26:12Z | Code for paper: "LLM-based Automated Theorem Proving Hinges on Scalable Synthetic Data Generation" | [
"data-centric-ai",
"language",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 0 | 985,299,808 | null | Python | null | [
"language-model",
"synthetic-data"
] | NJUDeepEngine/llm_based_atp | [
"description",
"query-match",
"repository-metadata"
] | 6 | 2026-10-13T18:05:26Z | [] | 2026-07-28T07:08:27Z | [
"llm.synthetic-data"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-context-only",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 4 | [] | 2025-08-15T08:53:25Z | https://github.com/NJUDeepEngine/llm_based_atp | null |
[
"deep-learning",
"generative-ai",
"language",
"multimodal-learning",
"natural-language-processing",
"representation-learning"
] | [
"continual-learning",
"fine-tuning",
"instruction-tuning",
"lifelong-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.continual-learning",
"llm.instruction-tuning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-18T13:04:14Z | [ACL'25 Main] Official Implementation of HiDe-LLaVA: Hierarchical Decoupling for Continual Instruction Tuning of Multimodal Large Language Model | [
"generative-ai",
"language",
"multimodal-learning",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 10 | 985,751,931 | null | Python | null | [
"fine-tuning",
"instruction-tuning"
] | Ghy0501/HiDe-LLaVA | [
"description",
"query-match",
"repository-metadata"
] | 8 | 2026-10-24T14:31:13Z | [] | 2026-06-01T05:20:21Z | [
"llm.instruction-tuning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 57 | [] | 2026-10-22T07:54:45Z | https://github.com/Ghy0501/HiDe-LLaVA | null |
[
"computer-vision",
"efficient-ml",
"general-ml",
"language",
"model-compression",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"representation-learning",
"vision"
] | [
"contrastive-learning",
"distillation",
"image-text",
"knowledge-distillation",
"language-model",
"language-modeling",
"multimodal-learning",
"representation-learning",
"semi-supervised-learning",
"vision-language",
"vision-language-model"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation",
"general.semi-supervised-learning",
"llm.language-model",
"multimodal.vision-language",
"multimodal.vlm",
"nlp.language-modeling",
"vision.vision-language"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-19T02:13:44Z | Official Code Repository for the paper "Simple yet Effective Semi-supervised Knowledge Distillation from Vision-Language Models via Dual-Head Optimization" | [
"computer-vision",
"language",
"multimodal-learning",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 986,030,464 | https://dual-head-optimization.github.io/ | Python | null | [
"distillation",
"knowledge-distillation",
"language-model",
"language-modeling"
] | erjui/DHO | [
"description",
"query-match",
"repository-metadata"
] | 5 | 2026-10-20T14:47:52Z | [] | 2026-10-05T03:39:28Z | [
"llm.language-model",
"nlp.language-modeling"
] | 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 | 8 | [] | 2026-10-05T03:39:32Z | https://github.com/erjui/DHO | null |
[
"reinforcement-learning"
] | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-19T04:16:25Z | Code for paper Energy Management in Networked Multi-Energy Microgrids A Safety-Guaranteed Multi-Actor Single-Critic Deep Reinforcement Learning Approach | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 4 | 986,070,380 | null | Python | GPL-3.0 | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | NextEasywood/MASC_MSL | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-28T17:27:44Z | [] | 2026-01-09T03:31:28Z | [
"rl.deep"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 3 | [] | 2026-04-15T05:08:21Z | https://github.com/NextEasywood/MASC_MSL | null |
[
"embodied-ai",
"generative-modeling",
"robotics"
] | [
"diffusion",
"diffusion-policy",
"imitation-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"robotics.diffusion-policy"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-19T06:24:21Z | Official implementation of the paper "Conditioning Matters: Training Diffusion Policies is Faster Than You Think". | [
"embodied-ai",
"generative-modeling",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 0 | 986,120,106 | null | Python | null | [
"diffusion",
"diffusion-policy",
"imitation-learning"
] | ZibinDong/cocos | [
"description",
"query-match",
"repository-metadata"
] | 5 | 2026-09-27T15:53:49Z | [] | 2025-05-19T08:46:10Z | [
"robotics.diffusion-policy"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 18 | [] | 2026-07-07T22:12:09Z | https://github.com/ZibinDong/cocos | null |
[
"mathematics",
"operations-research"
] | [
"combinatorial-optimization",
"neural-network"
] | [
"description",
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-19T14:14:37Z | code for paper "BOPO: Neural Combinatorial Optimization via Best-anchored and Objective-guided Preference Optimization" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-25T16:01:25Z | false | 4 | 986,377,774 | null | Java | MIT | [
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] | L-Z-7/BOPO | [
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] | 2 | 2026-09-29T17:12:21Z | [] | 2026-03-01T05:54:37Z | [
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] | include | ml-contribution-v5 | 16 | [] | 2026-09-28T15:14:27Z | https://github.com/L-Z-7/BOPO | null |
[
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"description",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-19T15:26:00Z | Official Code for Paper《Towards More Faithful Natural Language Explanation Using Multi-Level Contrastive Learning in VQA》 | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 5 | 986,421,181 | null | Python | null | [
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] | laichengen/MCLE | [
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] | 3 | 2026-11-01T17:09:41Z | [] | 2025-05-20T02:31:26Z | [
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] | include | ml-contribution-v5 | 54 | [] | 2025-07-03T08:05:11Z | https://github.com/laichengen/MCLE | null |
[
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] | [
"nlp.machine-translation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-21T09:17:48Z | Code for paper "SSR-Zero: Simple Self-Rewarding Reinforcement Learning for Machine Translation" | [
"natural-language-processing",
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 0 | 987,573,631 | https://arxiv.org/abs/2505.16637 | null | null | [
"machine-translation",
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] | Kelaxon/SSR-Zero | [
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] | 4 | 2026-10-18T14:44:29Z | [] | 2025-05-28T12:29:27Z | [
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] | include | ml-contribution-v5 | 9 | [] | 2025-12-15T00:25:58Z | https://github.com/Kelaxon/SSR-Zero | null |
[
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] | [
"trust.distribution-shift"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-21T15:44:04Z | Official implementation of ICML 2025 paper "Understanding Multimodal LLMs Under Distribution Shifts: An Information-Theoretic Approach" | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 2 | 987,796,762 | null | Python | null | [
"distribution-shift-detection",
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] | deeplearning-wisc/mllmshift-emi | [
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] | 5 | 2026-09-28T18:33:08Z | [] | 2025-05-27T22:39:54Z | [
"trust.distribution-shift"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 12 | [
"distribution-shift",
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"multimodal-llm",
"robustness",
"vision-language-model"
] | 2026-04-01T10:20:03Z | https://github.com/deeplearning-wisc/mllmshift-emi | null |
[
"data-centric-ai",
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"description",
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] | [
"general.semi-supervised-learning",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-22T02:48:02Z | Official code for the paper "Weakly-Supervised Contrastive Learning for Imprecise Class Labels" | [
"general-ml",
"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 1 | 988,075,934 | null | Python | null | [
"contrastive-learning",
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] | Speechless-10308/WSC | [
"description",
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"repository-metadata"
] | 8 | 2026-11-01T17:09:41Z | [] | 2025-11-12T08:26:47Z | [
"general.semi-supervised-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 46 | [] | 2026-09-02T02:36:35Z | https://github.com/Speechless-10308/WSC | null |
[
"general-ml"
] | [
"neural-network"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.neural-network"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-22T08:21:18Z | Code for paper: Deep Operator Neural Network Model Predictive Control | [
"general-ml"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 6 | 988,218,729 | null | Jupyter Notebook | null | [
"neural-network"
] | todejong/Deep-Operator-Neural-Network-Model-Predictive-Control | [
"description",
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"repository-metadata"
] | 2 | 2026-09-29T17:12:21Z | [] | 2025-09-23T11:05:00Z | [
"general.neural-network"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-context-only",
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] | include | ml-contribution-v5 | 20 | [] | 2026-08-07T13:30:21Z | https://github.com/todejong/Deep-Operator-Neural-Network-Model-Predictive-Control | null |
[
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"repository-metadata"
] | [
"audio-speech-emotion-recognition",
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-22T09:27:26Z | [INTERSPEECH 2025] Official code for "SEED: Speaker Embedding Enhancement Diffusion Model" | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T16:16:53Z | false | 3 | 988,255,075 | null | Python | null | [
"diffusion"
] | kaistmm/seed-pytorch | [
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] | 6 | 2026-10-22T16:09:07Z | [] | 2025-11-03T11:02:09Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 61 | [
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"representation-learning",
"speaker-recognition",
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] | 2026-08-13T07:39:58Z | https://github.com/kaistmm/seed-pytorch | null |
[
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] | [
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] | [
"description",
"query-match",
"repository-metadata"
] | [
"timeseries.forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-22T09:58:46Z | Example code for paper Comparison of Loss Functions for Time Series Forecasting in NSSA field | [
"forecasting",
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-28T17:27:44Z | false | 0 | 988,272,565 | null | Jupyter Notebook | null | [
"forecasting",
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] | Kr1zA/loss_comparison | [
"description",
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] | 1 | 2026-10-28T17:27:44Z | [] | 2026-04-14T22:19:45Z | [
"timeseries.forecasting"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-04-14T22:19:48Z | https://github.com/Kr1zA/loss_comparison | null |
[
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] | [
"chem-drug-discovery",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-22T11:00:17Z | Official implementation of "Robust Molecular Property Prediction via Densifying Scarce Labeled Data" (GenBio Workship @ ICML 2025). | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T16:16:53Z | false | 0 | 988,305,592 | null | Jupyter Notebook | null | [
"ranking",
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] | JinA0218/drugood-densify | [
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] | 5 | 2026-10-31T16:49:26Z | [] | 2025-08-23T16:05:05Z | [
"chem-drug-discovery"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 3 | [] | 2025-08-23T16:05:09Z | https://github.com/JinA0218/drugood-densify | null |
[
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"repository-metadata"
] | [
"efficiency.pruning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-22T14:05:31Z | Official implementation of "Structured Pruning for Diverse Best-of-N Reasoning Optimization" (ACL'25 - Short Findings) | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 3 | 988,412,154 | null | Python | MIT | [
"neural-network-pruning",
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] | HieuNT91/attention_pruning | [
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] | 4 | 2026-10-26T17:25:59Z | [] | 2025-05-22T14:56:45Z | [
"efficiency.pruning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 1 | [] | 2025-09-24T10:01:21Z | https://github.com/HieuNT91/attention_pruning | null |
[
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] | [
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] | [
"description",
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"repository-metadata"
] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-23T04:29:55Z | Code for paper: Performance Guaranteed Poisoning Attacks in Federated Learning: A Sliding Mode Approach | [
"distributed-ml",
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-19T17:46:36Z | false | 0 | 988,784,959 | null | null | null | [
"collaborative-learning",
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] | Halsey777/FedSA | [
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"query-match",
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] | 1 | 2026-10-19T17:46:36Z | [] | 2025-05-23T04:29:55Z | [
"trust.federated-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2025-05-23T04:29:58Z | https://github.com/Halsey777/FedSA | null |
[
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] | [
"multimodal.text-to-video",
"recall.name.artificial-intelligence",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-23T04:36:24Z | [NeurIPS 2025] The official implementation of paper "Safe-Sora: Safe Text-to-Video Generation via Graphical Watermarking" | [
"general-ml"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 9 | 988,787,409 | null | Python | null | [
"artificial-intelligence"
] | Zihan-Su/Safe-Sora | [
"description",
"query-match",
"repository-metadata"
] | 9 | 2026-10-29T16:41:07Z | [] | 2026-10-06T05:41:11Z | [
"recall.name.artificial-intelligence"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 26 | [] | 2026-10-11T02:34:05Z | https://github.com/Zihan-Su/Safe-Sora | null |
[
"multi-agent-systems",
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] | [
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] | [
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"query-match",
"repository-metadata"
] | [
"rl.multiagent"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-23T06:11:46Z | Official implementation of "Agent-Centric Actor-Critic for Asynchronous Multi-Agent Reinforcement Learning" (ICML 2025) by LG AI Research. | [
"multi-agent-systems",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 3 | 988,824,631 | null | Jupyter Notebook | BSD-3-Clause | [
"centralized-training",
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] | LGAI-Research/acac | [
"description",
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"query-match",
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] | 4 | 2026-10-02T16:59:40Z | [] | 2025-09-22T10:12:28Z | [
"rl.multiagent"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 12 | [] | 2026-09-24T13:07:47Z | https://github.com/LGAI-Research/acac | null |
[
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"repository-metadata"
] | [
"nlp.dense-retrieval"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-23T07:35:33Z | Official Code Repository for the paper "Distilling LLM Agent into Small Models with Retrieval and Code Tools" | [
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] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 38 | 988,865,138 | https://arxiv.org/abs/2505.17612 | Python | Apache-2.0 | [
"dense-retrieval",
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] | Nardien/agent-distillation | [
"description",
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"query-match",
"repository-metadata"
] | 10 | 2026-10-04T15:58:58Z | [] | 2025-10-22T07:53:51Z | [
"nlp.dense-retrieval"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 272 | [] | 2026-09-30T18:00:34Z | https://github.com/Nardien/agent-distillation | null |
[
"generative-ai",
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] | [
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] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-23T08:46:40Z | Official Implementation of Paper [NeurIPS 2024] DiffPO: A causal diffusion model for predicting potential outcomes of treatments | [
"generative-ai",
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] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-22T16:09:07Z | false | 6 | 988,900,798 | null | Python | Apache-2.0 | [
"diffusion"
] | yccm/DiffPO | [
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"query-match",
"repository-metadata"
] | 1 | 2026-10-22T16:09:07Z | [] | 2025-05-23T09:25:41Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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] | include | ml-contribution-v5 | 14 | [] | 2026-09-24T19:58:41Z | https://github.com/yccm/DiffPO | null |
[
"forecasting",
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] | [
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"paper-reference",
"query-match",
"repository-metadata"
] | [
"timeseries.forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-23T10:27:48Z | Official Code for the NeurIPS'25 paper: Selective Learning for Deep Time Series Forecasting | [
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"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 4 | 988,953,810 | https://arxiv.org/abs/2510.25207 | Python | null | [
"forecasting",
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] | GestaltCogTeam/selective-learning | [
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"query-match",
"repository-metadata"
] | 6 | 2026-10-26T17:25:59Z | [] | 2025-11-07T03:28:44Z | [
"timeseries.forecasting"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 42 | [] | 2026-10-26T12:35:09Z | https://github.com/GestaltCogTeam/selective-learning | null |
[
"generative-ai",
"generative-modeling",
"multimodal",
"vision"
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] | [
"description",
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"repository-metadata"
] | [
"llm.diffusion-phrase",
"multimodal.text-to-image"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-23T11:44:23Z | [ICLR 2026] Official Implementation of Muddit [Meissonic II]: Liberating Generation Beyond Text-to-Image with a Unified Discrete Diffusion Model. | [
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"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 2 | 988,992,040 | null | Python | null | [
"diffusion"
] | M-E-AGI-Lab/Muddit | [
"description",
"query-match",
"repository-metadata"
] | 5 | 2026-10-22T16:09:07Z | [] | 2026-04-13T07:48:25Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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] | include | ml-contribution-v5 | 123 | [] | 2026-10-22T03:32:09Z | https://github.com/M-E-AGI-Lab/Muddit | null |
[
"computer-vision",
"language",
"multimodal",
"multimodal-learning",
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-23T12:27:17Z | [ICML 2025 Spotlight] Official implementation of the paper: Re-ranking Reasoning Context with Tree Search Makes Large Vision-Language Models Stronger | [
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"language",
"multimodal",
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"natural-language-processing",
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 989,014,199 | null | Python | Apache-2.0 | [
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] | yannqi/RCTS-RAG | [
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] | 2 | 2026-10-08T18:15:14Z | [] | 2025-09-01T10:39:59Z | [
"multimodal.vision-language"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 19 | [] | 2025-10-25T09:30:58Z | https://github.com/yannqi/RCTS-RAG | null |
[
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] | [
"general.self-supervised-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-23T18:28:42Z | This repo contains the official implementation of ICCV 2025 paper "MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning | [
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"deep-learning",
"representation-learning"
] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 2 | 989,244,339 | null | Python | NOASSERTION | [
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] | SMSD75/MoSiC | [
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"query-match",
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] | 3 | 2026-10-03T15:45:52Z | [] | 2025-09-12T12:04:40Z | [
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] | include | ml-contribution-v5 | 23 | [
"dense-features",
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"in-context-learning",
"object-detection",
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"temporal-clustering",
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] | 2026-09-12T08:38:48Z | https://github.com/SMSD75/MoSiC | null |
[
"generative-ai",
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] | [
"diffusion"
] | [
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"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-24T01:33:23Z | Official Implementation of DiffPuter: Empowering Diffusion Models for Missing Data Imputation, (ICLR 2025 Spotlight) | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-22T16:09:07Z | false | 6 | 989,379,742 | null | Python | MIT | [
"diffusion"
] | hengruizhang98/DiffPuter | [
"description",
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"query-match",
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] | 1 | 2026-10-22T16:09:07Z | [] | 2025-08-21T06:15:59Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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] | include | ml-contribution-v5 | 28 | [] | 2026-10-06T09:04:51Z | https://github.com/hengruizhang98/DiffPuter | null |
[
"efficient-ml",
"generative-ai",
"inference-systems",
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] | [
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] | [
"description",
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] | [
"efficiency.kv-cache",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-24T06:47:00Z | The official code implementation for paper "PM-KVQ: Progressive Mixed-precision KV Cache Quantization for Long-CoT LLMs" | [
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"model-compression"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 8 | 989,468,777 | null | Python | null | [
"quantization"
] | thu-nics/PM-KVQ | [
"description",
"query-match",
"repository-metadata"
] | 9 | 2026-10-20T15:59:06Z | [] | 2025-05-24T07:17:12Z | [
"efficiency.quantization"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
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] | include | ml-contribution-v5 | 31 | [] | 2026-10-12T10:14:31Z | https://github.com/thu-nics/PM-KVQ | null |
[
"deep-learning",
"representation-learning"
] | [
"representation-learning",
"self-supervised-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.self-supervised-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-24T10:42:23Z | Official implementation of the paper "Pushing the Performance of Synthetic Speech Detection with Kolmogorov-Arnold Networks and Self-Supervised Learning Models", accepted at INTERSPEECH 2025. | [
"deep-learning",
"representation-learning"
] | [
"self-supervised-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 989,556,243 | null | Python | MIT | [
"representation-learning",
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] | HuSTeP-Human-Speech-Text-Processing-Lab/XLSR-GRKAN-Conformer | [
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"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-04T15:58:58Z | [] | 2025-08-18T13:01:43Z | [
"general.self-supervised-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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] | include | ml-contribution-v5 | 2 | [] | 2025-09-28T09:36:56Z | https://github.com/HuSTeP-Human-Speech-Text-Processing-Lab/XLSR-GRKAN-Conformer | null |
[
"machine-learning"
] | [
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] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"specialized.test-time-adaptation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-25T13:14:18Z | Code for ICML 2025 paper — Beyond Entropy: Region Confidence Proxy for Wild Test-Time Adaptation | [
"machine-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 3 | 990,080,468 | null | Python | BSD-3-Clause | [
"adaptation",
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] | hzcar/ReCAP | [
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"query-match",
"repository-metadata"
] | 6 | 2026-10-02T16:59:40Z | [] | 2025-07-02T14:40:01Z | [
"specialized.test-time-adaptation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 61 | [] | 2026-08-15T08:13:22Z | https://github.com/hzcar/ReCAP | null |
[
"deep-learning",
"graph-learning"
] | [
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] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-25T20:19:33Z | Official implementation of ACL25 paper "GraphNarrator: Textual Explanations for Graph Neural Networks" | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 0 | 990,258,481 | null | Jupyter Notebook | null | [
"graph-neural-network",
"message-passing",
"neural-network"
] | pb0316/GraphNarrator | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-10-17T13:17:44Z | [] | 2025-09-11T15:06:50Z | [
"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 | 13 | [] | 2026-06-04T05:04:14Z | https://github.com/pb0316/GraphNarrator | null |
[
"audio",
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] | [
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"training"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"audio-music-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-25T23:16:01Z | [NeurIPS 2025] The official code of "Training-Free Guidance Beyond Differentiability: Scalable Path Steering with Tree Search in Diffusion and Flow Models". | [
"audio",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-26T17:25:59Z | false | 0 | 990,309,990 | null | Python | null | [
"architecture",
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] | yukang123/UniTreeG | [
"description",
"query-match",
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] | 2 | 2026-10-28T17:27:44Z | [] | 2025-12-03T18:40:24Z | [
"audio-music-generation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 5 | [] | 2026-09-15T08:59:35Z | https://github.com/yukang123/UniTreeG | null |
[
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] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"science.physics"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-26T01:07:56Z | Official implementation of "Force Prompting: Video Generation Models Can Learn and Generalize Physics-based Control Signals" (NeurIPS 2025) | [
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"physics",
"scientific-computing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 7 | 990,340,169 | https://force-prompting.github.io/ | Python | MIT | [
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] | brown-palm/force-prompting | [
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"query-match",
"repository-metadata"
] | 2 | 2026-09-28T18:33:08Z | [] | 2026-04-29T23:35:42Z | [
"science.physics"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 162 | [] | 2026-09-25T18:56:16Z | https://github.com/brown-palm/force-prompting | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
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] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.text-to-image"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-26T07:21:10Z | Official PyTorch implementation of the paper: A DEBIASING FRAMEWORK FOR ATTRIBUTE BINDING IN DIFFUSION-BASED TEXT-TO-IMAGE. | [
"computer-vision",
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"generative-modeling",
"multimodal",
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] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-12T20:31:19Z | false | 0 | 990,481,196 | null | Python | null | [
"diffusion",
"image-generation",
"text-to-image"
] | PRIS-CV/A-Debiasing-Framework-For-Attribute-Binding-In-Diffusion-Based-Text-To-Image | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-12T20:31:19Z | [] | 2025-05-26T09:23:23Z | [
"multimodal.text-to-image"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2025-05-26T09:23:27Z | https://github.com/PRIS-CV/A-Debiasing-Framework-For-Attribute-Binding-In-Diffusion-Based-Text-To-Image | null |
[
"embodied-ai",
"generative-ai",
"language",
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] | [
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] | [
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"repository-metadata"
] | [
"llm.evaluation",
"robotics.manipulation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-26T12:01:49Z | Official code for "From Seeing to Doing: Bridging Reasoning and Decision for Robotic Manipulation" (ICLR2026) | [
"generative-ai",
"language",
"robotics-and-control"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 1 | 990,632,101 | null | Python | Apache-2.0 | [
"benchmarking",
"evaluation"
] | pickxiguapi/Embodied-FSD | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-21T15:48:06Z | [] | 2026-03-01T16:01:00Z | [
"llm.evaluation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 39 | [] | 2026-09-15T12:02:53Z | https://github.com/pickxiguapi/Embodied-FSD | null |
[
"generative-ai",
"generative-modeling",
"multimodal",
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] | [
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] | [
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"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.image-to-video",
"multimodal.text-to-video"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-26T22:29:12Z | [TMLR2026] The official code for the paper "Incorporating Flexible Image Conditioning into Text-to-Video Diffusion Models without Training"" | [
"generative-ai",
"generative-modeling",
"multimodal",
"video"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 0 | 990,946,127 | https://bolinlai.github.io/projects/FlexTI2V/ | Python | Apache-2.0 | [
"diffusion",
"image-to-video",
"video-generation"
] | BolinLai/FlexTI2V | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-09T17:31:36Z | [] | 2026-03-14T22:13:28Z | [
"multimodal.image-to-video"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 3 | [] | 2026-10-03T19:14:05Z | https://github.com/BolinLai/FlexTI2V | null |
[
"embodied-ai",
"generative-modeling",
"reinforcement-learning",
"robotics"
] | [
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] | [
"description",
"query-match",
"repository-metadata"
] | [
"robotics.diffusion-policy"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-27T01:40:41Z | [ICML2025] Official implementation of Efficient Online Reinforcement Learning for Diffusion Policies appearing in ICML 2025. | [
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"reinforcement-learning",
"robotics"
] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 11 | 991,005,427 | null | Python | null | [
"diffusion",
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"reinforcement-learning"
] | mahaitongdae/diffusion_policy_online_rl | [
"description",
"query-match",
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] | 5 | 2026-09-27T15:53:49Z | [] | 2026-04-25T13:50:18Z | [
"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"
] | include | ml-contribution-v5 | 62 | [] | 2026-09-27T01:04:40Z | https://github.com/mahaitongdae/diffusion_policy_online_rl | null |
[
"efficient-ml",
"interpretability-and-safety",
"machine-learning-systems",
"model-compression",
"natural-language-processing"
] | [
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] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-28T02:20:15Z | [ACL25] Code for paper "ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models" | [
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"model-compression",
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-25T15:11:36Z | false | 0 | 991,685,598 | null | Python | Apache-2.0 | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | VoiceBeer/ALPS | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-26T17:25:59Z | [] | 2025-07-18T05:11:07Z | [
"efficiency.pruning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 5 | [] | 2026-05-11T15:43:07Z | https://github.com/VoiceBeer/ALPS | null |
[
"machine-learning"
] | [
"adaptation",
"test-time-adaptation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"specialized.test-time-adaptation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-28T06:04:13Z | Code for ICML 2025 paper — Self-Bootstrapping for Versatile Test-Time Adaptation | [
"machine-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-28T18:33:08Z | false | 1 | 991,770,069 | null | Python | NOASSERTION | [
"adaptation",
"test-time-adaptation"
] | mr-eggplant/SPA | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-02T16:59:40Z | [] | 2025-09-30T08:06:15Z | [
"specialized.test-time-adaptation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 17 | [] | 2026-05-18T01:17:09Z | https://github.com/mr-eggplant/SPA | null |
[
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] | [
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"self-attention",
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] | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"graph.signal-processing"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-28T13:16:32Z | [IJCAI'25] Official PyTorch Implementation of "Learning Advanced Self-Attention for Linear Transformers in the Singular Value Domain" (AGF) | [
"graph-learning",
"signal-processing"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T15:29:21Z | false | 7 | 991,999,596 | https://arxiv.org/abs/2505.08516 | TypeScript | MIT | [
"graph-signal-processing",
"self-attention",
"spectral-graph-learning"
] | hyowonwi/AGF | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 4 | 2026-09-29T17:12:21Z | [] | 2025-12-01T05:13:29Z | [
"graph.signal-processing"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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] | include | ml-contribution-v5 | 13 | [] | 2026-04-20T21:50:47Z | https://github.com/hyowonwi/AGF | null |
[
"generative-ai",
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"diffusion"
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"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-29T07:20:00Z | [ICML 2025] Official implementation of "Efficiently Access Diffusion Fisher: Within the Outer Product Span Space". | [
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"generative-modeling",
"vision"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T16:52:35Z | false | 0 | 992,454,611 | null | Python | MIT | [
"diffusion"
] | zituitui/DiffusionFisher | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-09-27T15:53:49Z | [] | 2025-06-02T13:36:37Z | [
"llm.diffusion"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 9 | [
"aigc",
"diffusion-model",
"diffusion-models",
"fisher-information",
"icml",
"icml-2025"
] | 2026-01-19T05:32:57Z | https://github.com/zituitui/DiffusionFisher | null |
[
"computational-science",
"physics"
] | [
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"neural-operator",
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] | [
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"query-match",
"repository-metadata"
] | [
"science.neural-operators"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-29T17:31:52Z | This repository is the official implementation of the paper "RONOM: Reduced-Order Neural Operator Modeling" published in the SIAM Journal on Scientific Computing | [
"computational-science",
"physics"
] | [
"deep-learning",
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 0 | 992,773,475 | https://epubs.siam.org/doi/full/10.1137/25M1777700?af=R | Python | null | [
"neural-network",
"neural-operator",
"neural-operators",
"surrogate-modeling"
] | SCdummer/RONOM | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-09-29T17:30:08Z | [] | 2026-07-23T10:31:06Z | [
"science.neural-operators"
] | 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 | [
"deep-learning",
"ml4physics",
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"neural-operators",
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"pytorch",
"reduced-order-modeling",
"reduced-order-models",
"scientific-computing"
] | 2026-07-23T10:32:26Z | https://github.com/SCdummer/RONOM | null |
[
"biology",
"computational-biology",
"generative-ai",
"generative-modeling",
"protein-engineering"
] | [
"diffusion",
"inverse-folding",
"protein-design"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"bio.inverse-folding",
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-29T20:54:21Z | Code for paper: "Learning Diffusion Models with Flexible Representation Guidance" | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 3 | 992,867,583 | https://chenyuwang-monica.github.io/REED/ | Jupyter Notebook | null | [
"diffusion"
] | ChenyuWang-Monica/REED | [
"description",
"query-match",
"repository-metadata"
] | 7 | 2026-10-22T16:09:07Z | [] | 2026-03-18T20:24:11Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 16 | [] | 2026-10-19T06:42:42Z | https://github.com/ChenyuWang-Monica/REED | null |
[
"computer-vision",
"generative-modeling"
] | [
"diffusion",
"promptable-segmentation",
"segmentation",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.promptable-segmentation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-30T08:54:54Z | [NeurIPS 2025] The official code for "IllumiCraft: Unified Geometry and Illumination Diffusion for Controllable Video Generation" | [
"computer-vision",
"generative-modeling"
] | [
"diffusion-model",
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 1 | 993,122,654 | https://yuanze-lin.me/IllumiCraft_page/ | Python | Apache-2.0 | [
"diffusion",
"promptable-segmentation",
"segmentation",
"transformer"
] | yuanze-lin/IllumiCraft | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 8 | 2026-09-29T17:30:08Z | [] | 2026-07-22T00:44:37Z | [
"vision.promptable-segmentation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 111 | [
"aigc",
"controllable-ai",
"controllable-generation",
"diffusion-models",
"diffusion-transformer",
"video-generation"
] | 2026-08-24T02:10:41Z | https://github.com/yuanze-lin/IllumiCraft | null |
[
"information-retrieval",
"natural-language-processing"
] | [
"rag",
"retrieval-augmented-generation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"nlp.rag"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-31T04:06:50Z | This repository contains the code for our ACL 2025 paper: "HOH: A Dynamic Benchmark for Evaluating the Impact of Outdated Information on Retrieval-Augmented Generation". | [
"information-retrieval",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-29T17:49:51Z | false | 2 | 993,578,712 | null | Python | Apache-2.0 | [
"rag",
"retrieval-augmented-generation"
] | 0russwest0/HoH | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-29T17:49:51Z | [] | 2025-05-31T04:12:07Z | [
"nlp.rag"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 7 | [] | 2026-08-17T09:21:02Z | https://github.com/0russwest0/HoH | null |
[
"audio",
"generative-modeling"
] | [
"architecture",
"diffusion",
"generative-modeling",
"training"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"audio-music-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-31T05:29:41Z | Official implementation of the paper MGE-LDM: Joint Latent Diffusion for Simultaneous Music Generation and Source Extraction | [
"audio",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-26T17:25:59Z | false | 1 | 993,601,934 | null | Python | MIT | [
"architecture",
"diffusion",
"generative-modeling",
"training"
] | yoongi43/MGE-LDM | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-28T17:27:44Z | [] | 2026-02-19T15:13:13Z | [
"audio-music-generation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 22 | [] | 2026-09-14T15:04:48Z | https://github.com/yoongi43/MGE-LDM | null |
[
"automl",
"general-ml"
] | [
"few-shot-learning",
"meta-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.meta-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-31T09:07:15Z | [KDD'25] Code for paper "Spatial Hierarchical Meta-Learning for Single-Point Map Matching". | [
"automl",
"general-ml"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-25T15:11:36Z | false | 0 | 993,671,431 | null | Python | null | [
"few-shot-learning",
"meta-learning"
] | zouyiqing-221/SHSMM | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-25T15:11:36Z | [] | 2026-01-15T07:22:40Z | [
"general.meta-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [] | 2026-01-15T07:26:34Z | https://github.com/zouyiqing-221/SHSMM | null |
[
"deep-learning",
"efficient-ml",
"language",
"machine-learning-systems",
"model-compression"
] | [
"conditional-computation",
"mixture-of-experts",
"quantization",
"sparse-activation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.mixture-of-experts",
"efficiency.quantization"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-31T10:51:18Z | official implementation of the ACL25(findings) paper Automated Fine-Grained Mixture-of-Experts Quantization | [
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 0 | 993,707,456 | null | null | Apache-2.0 | [
"mixture-of-experts",
"quantization"
] | cei1ing4/Fine-Grained-MoE-Quantization | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-21T15:48:06Z | [] | 2025-05-31T10:51:18Z | [
"efficiency.quantization"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [] | 2025-06-19T12:03:12Z | https://github.com/cei1ing4/Fine-Grained-MoE-Quantization | null |
[
"efficient-ml",
"generative-modeling",
"machine-learning-systems",
"model-compression"
] | [
"diffusion",
"post-training-quantization",
"quantization"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.post-training-quantization",
"efficiency.quantization"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-05-31T14:58:39Z | PyTorch code for our paper "QuantFace: Low-Bit Post-Training Quantization for One-Step Diffusion Face Restoration" | [
"efficient-ml",
"generative-modeling",
"machine-learning-systems",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 0 | 993,802,690 | null | null | null | [
"diffusion",
"quantization"
] | jiatongli2024/QuantFace | [
"description",
"query-match",
"repository-metadata"
] | 7 | 2026-10-20T15:59:06Z | [] | 2025-11-21T10:17:01Z | [
"efficiency.quantization"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 10 | [] | 2025-11-21T10:17:05Z | https://github.com/jiatongli2024/QuantFace | null |
[
"computer-vision",
"generative-ai",
"language",
"multimodal-learning",
"natural-language-processing"
] | [
"fine-tuning",
"parameter-efficient-fine-tuning",
"self-supervised-learning"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"llm.parameter-efficient-finetuning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-01T10:20:59Z | [ICLR 2026] - Official implementation of "Attention, Please! Revisiting Attentive Probing Through the Lens of Efficiency" | [
"computer-vision",
"generative-ai",
"language",
"multimodal-learning",
"natural-language-processing"
] | [
"deep-learning",
"self-supervised-learning",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-20T15:59:06Z | false | 0 | 994,149,892 | https://arxiv.org/abs/2506.10178 | Python | Apache-2.0 | [
"fine-tuning",
"parameter-efficient-fine-tuning",
"self-supervised-learning"
] | billpsomas/efficient-probing | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-10-20T15:59:06Z | [] | 2026-09-22T21:04:51Z | [
"llm.parameter-efficient-finetuning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 41 | [
"attention-mechanism",
"attentive-pooling",
"computer-vision",
"deep-learning",
"probing",
"self-supervised-learning",
"vision-language-models",
"vision-transformers"
] | 2026-10-16T02:16:17Z | https://github.com/billpsomas/efficient-probing | 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 | [] | 2025-06-02T06:59:23Z | [ICML 2025] The official implementation of "Origin Identification for Text-Guided Image-to-Image Diffusion Models" | [
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-12T20:31:19Z | false | 0 | 994,584,607 | null | Python | NOASSERTION | [
"diffusion",
"generative-modeling"
] | WangWenhao0716/ID2 | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-12T20:31:19Z | [] | 2025-06-03T03:46:54Z | [
"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 | 0 | [] | 2025-06-03T03:46:56Z | https://github.com/WangWenhao0716/ID2 | null |
[
"generative-ai",
"language",
"reinforcement-learning"
] | [
"language-model",
"reasoning",
"reinforcement-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm-reasoning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-03T06:46:45Z | The official implementations of the Paper "G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning" | [
"generative-ai",
"language",
"reinforcement-learning"
] | [
"large-language-model",
"machine-learning",
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-17T13:17:44Z | false | 3 | 995,224,699 | null | Python | Apache-2.0 | [
"language-model",
"reasoning",
"reinforcement-learning"
] | PKU-ML/G1 | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-18T14:44:29Z | [] | 2025-06-14T08:20:17Z | [
"llm-reasoning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 45 | [] | 2026-09-17T12:35:28Z | https://github.com/PKU-ML/G1 | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-03T07:17:59Z | This repository contains the code for our paper "Graph Neural Networks for Predicting Hydration Free Energies with Physics-based Descriptors." | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-18T14:44:29Z | false | 0 | 995,239,742 | null | Jupyter Notebook | null | [
"graph-neural-network",
"message-passing",
"neural-network"
] | anujksirohi/GNN_FreeSolve | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-18T14:44:29Z | [] | 2025-09-05T10:29:33Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 2 | [] | 2026-09-12T09:28:29Z | https://github.com/anujksirohi/GNN_FreeSolve | null |
[
"generative-ai",
"language",
"reinforcement-learning"
] | [
"language-model",
"reasoning",
"reinforcement-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm-reasoning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-03T08:30:46Z | Official code for our paper "Reasoning Models Hallucinate More: Factuality-Aware Reinforcement Learning for Large Reasoning Models" | [
"generative-ai",
"language",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-17T13:17:44Z | false | 0 | 995,276,017 | null | Python | Apache-2.0 | [
"language-model",
"reasoning",
"reinforcement-learning"
] | nusnlp/FSPO | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-18T14:44:29Z | [] | 2025-10-31T05:53:30Z | [
"llm-reasoning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 26 | [] | 2026-10-08T06:20:42Z | https://github.com/nusnlp/FSPO | null |
[
"computer-vision",
"data-centric-ai",
"efficient-ml",
"language",
"machine-learning",
"model-compression",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"vision"
] | [
"active-learning",
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"distillation",
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"image-text",
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"language-model",
"language-modeling",
"model-adaptation",
"multimodal-learning",
"sample-selection",
"vision-language",
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] | [
"description",
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] | [
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"multimodal.vlm",
"nlp.language-modeling",
"probable.adaptation-finetuning-readme",
"recall.name.language-model",
"vision.vision-language"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-03T09:31:19Z | Official Code Repository for the paper "PCoreSet: Effective Active Learning through Knowledge Distillation from Vision-Language Models" | [
"computer-vision",
"multimodal-learning",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 995,307,840 | https://pcoreset.github.io/ | Python | null | [
"distillation",
"knowledge-distillation",
"language-model"
] | erjui/PCoreSet | [
"description",
"query-match",
"repository-metadata"
] | 7 | 2026-11-01T16:11:12Z | [] | 2026-10-05T03:57:37Z | [
"recall.name.language-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 | 3 | [] | 2026-10-05T03:57:53Z | https://github.com/erjui/PCoreSet | null |
[
"generative-ai",
"language",
"natural-language-processing"
] | [
"fine-tuning",
"instruction-tuning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.instruction-tuning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-03T13:52:05Z | Code to our ICML 2025 Paper "Calibrated Language Models and How to Find Them with Label Smoothing" | [
"generative-ai",
"language",
"natural-language-processing"
] | [
"deep-learning",
"large-language-model",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-28T17:27:44Z | false | 0 | 995,449,208 | https://proceedings.mlr.press/v267/huang25w.html | Python | MIT | [
"fine-tuning",
"instruction-tuning"
] | jhuang265/Calibrating-LLMs-with-Label-Smoothing | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-28T17:27:44Z | [] | 2026-02-07T07:37:44Z | [
"llm.instruction-tuning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 3 | [
"deep-learning",
"huggingface-transformers",
"natural-language-processing",
"triton"
] | 2026-01-20T07:58:31Z | https://github.com/jhuang265/Calibrating-LLMs-with-Label-Smoothing | null |
[
"chemistry",
"generative-modeling",
"organic-chemistry"
] | [
"flow-matching",
"generative-modeling",
"planning",
"reaction-prediction"
] | [
"description",
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] | [
"chem-retrosynthesis-planning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-03T16:05:32Z | The official implementation of NeurIPS 2025 paper named "RetroSynFlow: Discrete Flow Matching for Accurate and Diverse Single-Step Retrosynthesis". | [
"chemistry",
"generative-modeling",
"organic-chemistry"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T16:16:53Z | false | 1 | 995,528,880 | null | Python | null | [
"flow-matching",
"generative-modeling",
"planning",
"reaction-prediction"
] | DSL-Lab/RetroSynFlow | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-09-29T17:30:08Z | [] | 2025-12-25T01:25:39Z | [
"chem-retrosynthesis-planning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 9 | [] | 2026-07-25T04:55:05Z | https://github.com/DSL-Lab/RetroSynFlow | null |
[
"generative-ai",
"interpretability-and-safety",
"language"
] | [
"language-model",
"long-context-modeling"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.long-context"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-04T04:51:08Z | The official implementation for ICML 2025 paper Long-Short Alignment for Effective Long-Context Modeling in LLMs | [
"generative-ai",
"interpretability-and-safety",
"language"
] | [
"large-language-model",
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-10T16:53:26Z | false | 0 | 995,839,001 | null | Python | null | [
"language-model",
"long-context-modeling"
] | PKU-ML/LongShortAlignment | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-10T16:53:26Z | [] | 2026-07-17T12:57:17Z | [
"llm.long-context"
] | 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 | 1 | [] | 2026-07-19T14:25:49Z | https://github.com/PKU-ML/LongShortAlignment | null |
[
"efficient-ml",
"machine-learning-systems",
"model-compression",
"natural-language-processing"
] | [
"neural-network-pruning",
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"structured-pruning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-04T06:39:02Z | [ICML 2025] Official implementation of the paper "SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling". (By Anhao Zhao) | [
"efficient-ml",
"machine-learning-systems",
"model-compression",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 2 | 995,886,387 | null | Python | Apache-2.0 | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | EIT-NLP/SkipGPT | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-26T17:25:59Z | [] | 2025-11-17T15:54:41Z | [
"efficiency.pruning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 21 | [] | 2026-07-14T07:20:00Z | https://github.com/EIT-NLP/SkipGPT | null |
[
"embodied-ai",
"robotics",
"robotics-and-control"
] | [
"manipulation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"robotics.manipulation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-04T07:07:14Z | Official code for "Embodied-R1: Reinforced Embodied Reasoning for General Robotic Manipulation" (ICLR2026) | [
"embodied-ai",
"robotics",
"robotics-and-control"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 6 | 995,901,329 | null | Python | Apache-2.0 | [
"manipulation"
] | pickxiguapi/Embodied-R1 | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 7 | 2026-09-30T16:47:43Z | [] | 2026-03-03T08:48:17Z | [
"robotics.manipulation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 156 | [] | 2026-09-14T17:16:13Z | https://github.com/pickxiguapi/Embodied-R1 | null |
[
"chemistry",
"computer-vision",
"drug-discovery",
"language",
"materials-science",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"science-and-engineering",
"vision"
] | [
"contrastive-learning",
"image-text",
"multimodal-learning",
"property-prediction",
"qsar",
"ranking",
"representation-learning",
"supervised-learning",
"virtual-screening",
"vision-language",
"vision-language-model"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
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] | [
"chem-drug-discovery",
"chem-property-prediction",
"multimodal.vision-language",
"multimodal.vlm",
"vision.vision-language"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-04T12:22:03Z | About [NeurIPS 2025 D&B] Official implementation and benchmark evaluation repository of <MolVision: Molecular Property Prediction with Vision Language Models> | [
"chemistry",
"computer-vision",
"drug-discovery",
"materials-science",
"multimodal-learning",
"natural-language-processing",
"science-and-engineering"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 5 | 996,074,612 | https://molvision.github.io/MolVision/ | Jupyter Notebook | Apache-2.0 | [
"ranking",
"representation-learning",
"virtual-screening"
] | molvision/MolVision | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 15 | 2026-11-01T17:09:41Z | [] | 2025-10-23T21:29:25Z | [
"chem-drug-discovery"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 7 | [
"computation-chemistry",
"molecular-property-prediction",
"vision-language-models"
] | 2026-08-24T00:58:33Z | https://github.com/molvision/MolVision | null |
[
"reinforcement-learning"
] | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-04T14:07:06Z | Official code for the paper entitled "From Static to Adaptive Defense: Federated Multi-Agent Deep Reinforcement Learning-Driven Moving Target Defense Against DoS Attacks in UAV Swarm Networks" | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-28T17:27:44Z | false | 0 | 996,138,425 | null | Python | GPL-2.0 | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | SEU-ProactiveSecurity-Group/PG-FMADRL | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-28T17:27:44Z | [] | 2025-06-12T14:12:45Z | [
"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 | 10 | [] | 2026-09-29T02:10:35Z | https://github.com/SEU-ProactiveSecurity-Group/PG-FMADRL | null |
[
"generative-ai",
"language",
"natural-language-processing",
"software-engineering"
] | [
"code-generation",
"fine-tuning",
"instruction-tuning",
"language-model"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm-code-generation",
"llm.instruction-tuning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-05T00:52:37Z | The official code of "VisCoder: Fine-Tuning LLMs for Executable Python Visualization Code Generation" [EMNLP25] | [
"generative-ai",
"language",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-21T15:48:06Z | false | 2 | 996,444,372 | https://tiger-ai-lab.github.io/VisCoder | Python | Apache-2.0 | [
"fine-tuning",
"instruction-tuning"
] | TIGER-AI-Lab/VisCoder | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-28T17:27:44Z | [] | 2025-09-21T23:07:15Z | [
"llm.instruction-tuning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-context-only",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 19 | [
"coder",
"llm",
"visualization"
] | 2026-06-30T16:51:01Z | https://github.com/TIGER-AI-Lab/VisCoder | null |
[
"health-and-biomedicine",
"neuroscience",
"signal-processing"
] | [
"neural-decoding",
"representation-learning",
"signal-classification"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"medical.eeg"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-06T05:32:36Z | Official code for the paper "Neuro-Biomarker Driven Hybrid Neural Architecture for Parkinson’s Disease Detection" This repository contains the full implementation of the BioNFM model, along with preprocessing pipelines, training scripts, and evaluation tools. | [
"health-and-biomedicine",
"neuroscience",
"signal-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-17T13:17:44Z | false | 0 | 997,190,165 | null | Python | null | [
"neural-decoding",
"representation-learning",
"signal-classification"
] | Abiqb93/BioNFM | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-17T13:17:44Z | [] | 2025-06-06T05:35:57Z | [
"medical.eeg"
] | 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 | 0 | [] | 2025-10-08T21:07:43Z | https://github.com/Abiqb93/BioNFM | null |
[
"deep-learning",
"representation-learning"
] | [
"representation-learning",
"self-supervised-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.self-supervised-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-06T06:31:02Z | Official Pytorch implementation of MRM 2024 paper "Physics-Guided Self-Supervised Learning: Demonstration for Generalized RF Pulse Design" | [
"deep-learning",
"representation-learning"
] | [
"self-supervised-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 997,213,261 | null | null | BSD-2-Clause | [
"representation-learning",
"self-supervised-learning"
] | GradLoader/GPS_RF | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-04T15:58:58Z | [] | 2025-03-22T19:11:32Z | [
"general.self-supervised-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2025-06-13T08:19:31Z | https://github.com/GradLoader/GPS_RF | 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 | [] | 2025-06-06T08:19:45Z | Official PyTorch implementation of the paper "FlowDirector: Training-Free Flow Steering for Precise Text-to-Video Editing" | [
"generative-ai",
"multimodal",
"video"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 5 | 997,261,510 | null | Python | Apache-2.0 | [
"text-to-video",
"video-generation"
] | Westlake-AGI-Lab/FlowDirector | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 9 | 2026-09-29T17:30:08Z | [] | 2025-12-12T14:17:44Z | [
"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 | 88 | [] | 2026-09-01T10:20:16Z | https://github.com/Westlake-AGI-Lab/FlowDirector | null |
[
"information-retrieval",
"natural-language-processing"
] | [
"rag",
"retrieval-augmented-generation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"nlp.rag"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-06T09:13:48Z | [AAAI 2026] Official implementation of "Joint-GCG: Unified Gradient-Based Poisoning Attacks on Retrieval-Augmented Generation Systems". | [
"information-retrieval",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-26T17:25:59Z | false | 0 | 997,286,643 | https://nicerwang.github.io/Joint-GCG/ | Python | null | [
"rag",
"retrieval-augmented-generation"
] | NicerWang/Joint-GCG | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-11-01T17:09:41Z | [] | 2026-03-23T05:07:31Z | [
"nlp.rag"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 21 | [] | 2026-10-16T02:49:07Z | https://github.com/NicerWang/Joint-GCG | null |
[
"deep-learning",
"graph-learning",
"health-and-biomedicine"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-06T09:18:46Z | Official implementation of paper "Predicting Acute-Phase Antidepressant Response with a Local-Global Fusion Graph Neural Network Based on Clinical and Functional Imaging Features Short title: Prediction of Acute-Phase Antidepressant Response" | [
"deep-learning",
"graph-learning",
"health-and-biomedicine"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-18T14:44:29Z | false | 0 | 997,289,115 | null | Python | null | [
"graph-neural-network",
"message-passing",
"neural-network"
] | ATP-BME/LGCIF-GNN | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-18T14:44:29Z | [] | 2025-09-18T05:06:59Z | [
"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 | 2 | [] | 2025-09-28T02:19:52Z | https://github.com/ATP-BME/LGCIF-GNN | null |
[
"computer-vision",
"generative-ai",
"language"
] | [
"language-model",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.transformers"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-08T12:45:49Z | [CVPR 2026] The official PyTorch implementation of the "Vision Transformer Needs More Than Registers". | [
"computer-vision",
"generative-ai",
"language"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 26 | 998,356,210 | null | Python | MIT | [
"language-model",
"transformer"
] | ChengShiest/LAST-ViT | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-09-29T17:12:21Z | [] | 2026-05-18T04:28:55Z | [
"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 | 471 | [] | 2026-09-27T10:08:42Z | https://github.com/ChengShiest/LAST-ViT | null |
[
"3d-vision",
"computer-vision",
"generative-modeling"
] | [
"3d-perception",
"depth-estimation",
"diffusion"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"vision.depth-estimation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-09T03:20:40Z | [ICCV 2025] Official implementation of the paper "DreamCube: 3D Panorama Generation via Multi-plane Synchronization". | [
"3d-vision",
"computer-vision",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 12 | 998,654,702 | https://yukun-huang.github.io/DreamCube/ | Python | null | [
"3d-perception",
"depth-estimation",
"diffusion"
] | Yukun-Huang/DreamCube | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 3 | 2026-10-29T17:49:51Z | [] | 2026-02-04T07:49:59Z | [
"vision.depth-estimation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 182 | [
"3d",
"cubemap",
"diffusion-models",
"generation",
"panorama"
] | 2026-09-07T12:47:50Z | https://github.com/Yukun-Huang/DreamCube | null |
[
"computer-vision",
"generative-ai",
"multimodal",
"video"
] | [
"diffusion",
"generative-modeling",
"image-to-video",
"video-generation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.image-to-video",
"vision.image-diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-09T12:44:56Z | The official code for NeurIPS 2025 "MagCache: Fast Video Generation with Magnitude-Aware Cache" | [
"computer-vision",
"generative-ai"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 7 | 998,904,521 | https://zehong-ma.github.io/MagCache/ | Python | Apache-2.0 | [
"diffusion",
"generative-modeling"
] | Zehong-Ma/MagCache | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-10T16:53:26Z | [] | 2025-11-17T03:42:43Z | [
"vision.image-diffusion"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 279 | [] | 2026-09-23T12:31:16Z | https://github.com/Zehong-Ma/MagCache | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-09T18:49:51Z | [CVPR'25 Oral] Official implementation for "DiffusionRenderer: Neural Inverse and Forward Rendering with Video Diffusion Models" | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 23 | 999,109,080 | https://research.nvidia.com/labs/toronto-ai/DiffusionRenderer/ | Python | NOASSERTION | [
"diffusion"
] | nv-tlabs/diffusion-renderer | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-22T16:09:07Z | [] | 2025-06-13T03:02:58Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 434 | [
"cvpr",
"delighting",
"genai",
"relighting"
] | 2026-10-21T06:47:27Z | https://github.com/nv-tlabs/diffusion-renderer | null |
[
"generative-ai",
"generative-modeling",
"vision"
] | [
"diffusion"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"llm.diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-10T12:32:10Z | Official implementation of T-PAMI25 paper "M²Diffuser: Diffusion-based Trajectory Optimization for Mobile Manipulation in 3D Scenes" | [
"generative-ai",
"generative-modeling",
"vision"
] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 7 | 999,553,758 | https://m2diffuser.github.io/ | Python | null | [
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] | m2diffuser/M2Diffuser | [
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] | 7 | 2026-09-27T15:53:49Z | [] | 2025-06-17T11:44:01Z | [
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] | include | ml-contribution-v5 | 124 | [
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] | 2026-09-20T03:13:36Z | https://github.com/m2diffuser/M2Diffuser | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-12T09:11:22Z | Official code for the paper "Graph Neural Networks for Automatic Addition of Optimizing Components in Printed Circuit Board Schematics" published at ECML 2025. | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-17T13:17:44Z | false | 1 | 1,000,751,841 | null | Python | MIT | [
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] | pasplett/pcb-node-pair-gnn | [
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] | 1 | 2026-10-17T13:17:44Z | [] | 2025-06-12T09:29:00Z | [
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] | include | ml-contribution-v5 | 4 | [] | 2026-10-08T19:25:32Z | https://github.com/pasplett/pcb-node-pair-gnn | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-12T20:18:35Z | [NeurIPS 2025] Official code for paper: Beyond Attention or Similarity: Maximizing Conditional Diversity for Token Pruning in MLLMs. | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 5 | 1,001,096,598 | http://theia4869.com/CDPruner/ | Python | Apache-2.0 | [
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] | 5 | 2026-10-26T17:25:59Z | [] | 2025-09-20T02:23:31Z | [
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] | include | ml-contribution-v5 | 112 | [
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"visual-token-pruning"
] | 2026-10-23T10:31:58Z | https://github.com/Theia-4869/CDPruner | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-13T06:27:41Z | Official code for "DiffLens: Dissecting and Mitigating Diffusion Bias via Mechanistic Interpretability" (CVPR 2025) | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-28T18:33:08Z | false | 2 | 1,001,294,678 | null | Python | null | [
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] | foundation-model-research/DiffLens | [
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] | 3 | 2026-10-02T16:59:40Z | [] | 2025-06-13T08:03:15Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 17 | [] | 2026-04-09T10:10:56Z | https://github.com/foundation-model-research/DiffLens | null |
[
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] | [
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] | [
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"query-match",
"repository-metadata"
] | [
"general.neural-processes"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-13T09:05:22Z | Official implementation of Renyi Neural Processes (ICML 2025) | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 0 | 1,001,369,631 | null | Python | NOASSERTION | [
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] | csiro-funml/renyineuralprocesses | [
"description",
"github-topics",
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"query-match",
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] | 4 | 2026-10-03T15:45:52Z | [] | 2025-06-23T01:02:42Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 9 | [
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] | 2026-05-20T02:20:42Z | https://github.com/csiro-funml/renyineuralprocesses | null |
[
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] | [
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] | [
"nlp.dense-retrieval"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-16T03:11:01Z | Introducing a novel and improved RAG architecture by replicating and combining approaches outlined in HyDE and SelfRAG papers | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-06T17:10:23Z | false | 0 | 1,002,717,335 | null | Python | null | [
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] | tanya-bas/self-hyde-rag | [
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] | 1 | 2026-10-06T17:10:23Z | [] | 2025-06-16T03:17:00Z | [
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] | af8ca423424e88dfbd1696118b3df989dbd1f7ab | 2026-10-06T18:32:27Z | gh-ml-readme-evidence-v2 | "af8ca423424e88dfbd1696118b3df989dbd1f7ab" | [
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] | [
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] | ok | 2026-10-06T18:32:27Z | tanya-bas/self-hyde-rag | specific-method-with-novelty-claim | [
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] | include | ml-contribution-v5 | 0 | [] | 2025-06-16T03:22:35Z | https://github.com/tanya-bas/self-hyde-rag | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-16T07:26:35Z | Official code for paper "TimeMaster: Training Time-Series Multimodal LLMs to Reason via Reinforcement Learning" | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 8 | 1,002,814,146 | https://arxiv.org/abs/2506.13705 | Python | Apache-2.0 | [
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] | langfengQ/TimeMaster | [
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] | 4 | 2026-10-24T14:31:13Z | [] | 2025-10-22T17:39:33Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 69 | [
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] | 2026-10-05T23:55:16Z | https://github.com/langfengQ/TimeMaster | null |
[
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] | [
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] | [
"graph.graph-transformer"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-17T02:40:06Z | Official PyTorch implementation of DUALFormer in 'DUALFORMER: DUAL GRAPH TRANSFORMER' (ICLR 2025) | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 1 | 1,003,348,704 | null | Python | MIT | [
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] | JiamingZhuo/DUALFormer | [
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] | 3 | 2026-09-28T18:33:08Z | [] | 2025-06-17T03:23:46Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 9 | [] | 2026-09-01T08:34:52Z | https://github.com/JiamingZhuo/DUALFormer | null |
[
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-17T03:48:40Z | BC-Mamba: A Novel Hybird CNN-Mamba Network For Medical Ultrasound Image Segmentation | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-31T16:49:26Z | false | 0 | 1,003,371,647 | https://github.com/ScholarChen20/HCMNet | Python | null | [
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] | ScholarChen20/HCMNet | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 1 | 2026-10-31T16:49:26Z | [] | 2026-06-05T12:44:13Z | [
"vision.segmentation"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
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] | include | ml-contribution-v5 | 4 | [
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"medical-image-segmentation",
"pytorch",
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] | 2026-01-29T13:56:36Z | https://github.com/ScholarChen20/HCMNet | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-17T04:46:05Z | [ECCV 2026] Official implementation of CEED-VLA: Consistency Vision-Language-Action Model with Early-Exit Decoding. | [
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"vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 0 | 1,003,391,985 | https://irpn-eai.github.io/CEED-VLA/ | Python | MIT | [
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] | OpenHelix-Team/CEED-VLA | [
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] | 2 | 2026-10-08T18:15:14Z | [] | 2025-09-15T12:20:40Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 53 | [
"robotics",
"speculative-decoding",
"vision-language-action-model",
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] | 2026-09-30T04:34:16Z | https://github.com/OpenHelix-Team/CEED-VLA | null |
[
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] | [
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] | [
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"query-match",
"repository-metadata"
] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-17T09:19:47Z | This repository contains the official implementation of the paper 'Glioblastoma Overall Survival Prediction with Vision Transformers'. | [
"clinical-informatics",
"computer-vision",
"health-and-biomedicine"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-13T18:05:26Z | false | 0 | 1,003,521,992 | null | null | null | [
"outcome-prediction",
"risk-prediction",
"survival-analysis"
] | Lyn-sudo/Glioblastoma-Overall-Survival-Prediction-with-VisionTransformers | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-13T18:05:26Z | [] | 2025-06-17T09:23:37Z | [
"medical.survival-analysis"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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"ml-method-cue",
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] | include | ml-contribution-v5 | 0 | [] | 2025-06-17T09:23:40Z | https://github.com/Lyn-sudo/Glioblastoma-Overall-Survival-Prediction-with-VisionTransformers | null |
[
"general-ml"
] | [
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] | [
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"license-metadata",
"query-match",
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] | [
"general.neural-network"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-17T09:44:09Z | Code for paper: Mambe Neural Networks meets Model Predictive Control | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T15:56:46Z | false | 0 | 1,003,535,581 | null | Jupyter Notebook | NOASSERTION | [
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] | cmichiel/Mamba-MPC | [
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"github-topics",
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] | 2 | 2026-10-25T14:09:27Z | [] | 2026-10-15T13:52:23Z | [
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] | include | ml-contribution-v5 | 0 | [
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] | 2026-10-15T13:52:29Z | https://github.com/cmichiel/Mamba-MPC | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-17T16:17:17Z | [NeurIPS'25] The official code of "PeRL: Permutation-Enhanced Reinforcement Learning for Interleaved Vision-Language Reasoning" | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 3 | 1,003,761,912 | null | Python | null | [
"contrastive-learning",
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] | alchemistyzz/PeRL | [
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] | 1 | 2026-10-08T18:15:14Z | [] | 2026-03-30T06:34:25Z | [
"multimodal.vision-language"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 30 | [
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"reasoning",
"vlm"
] | 2026-04-28T21:55:02Z | https://github.com/alchemistyzz/PeRL | null |
[
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] | [
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] | [
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"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-18T01:39:43Z | [NeurIPS'25] Official implementation of "Emergent Temporal Correspondences from Video Diffusion Models" | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 5 | 1,004,010,894 | null | Python | Apache-2.0 | [
"diffusion"
] | cvlab-kaist/DiffTrack | [
"description",
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"query-match",
"repository-metadata"
] | 4 | 2026-10-22T16:09:07Z | [] | 2025-12-03T15:59:18Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 104 | [] | 2026-10-02T14:14:02Z | https://github.com/cvlab-kaist/DiffTrack | null |
[
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] | [
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"query-match",
"repository-metadata"
] | [
"audio-text-to-audio"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-18T13:18:39Z | Official implementation of the paper - GD-Retriever: Controllable generative text-music retrieval with diffusion models (Accepted at ISMIR25!) | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-13T18:05:26Z | false | 0 | 1,004,335,283 | null | Python | GPL-3.0 | [
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] | Pliploop/GDRetriever | [
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"repository-metadata"
] | 1 | 2026-10-13T18:05:26Z | [] | 2025-09-25T04:04:18Z | [
"audio-text-to-audio"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 20 | [] | 2026-10-04T08:01:49Z | https://github.com/Pliploop/GDRetriever | null |
[
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"description",
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"query-match",
"repository-metadata"
] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-18T13:52:30Z | Code for our paper titled "Boosting Resource-Constrained Federated Learning Systems With Guessed Updates", published in IEEE Transactions on Parallel and Distributed Systems. | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-20T15:59:06Z | false | 0 | 1,004,355,449 | null | null | MIT | [
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] | sacs-epfl/gel | [
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"query-match",
"repository-metadata"
] | 1 | 2026-10-20T15:59:06Z | [] | 2025-06-18T13:53:40Z | [
"trust.federated-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2025-06-18T13:53:43Z | https://github.com/sacs-epfl/gel | null |
[
"general-ml",
"generative-modeling"
] | [
"diffusion",
"distillation",
"novel-method"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.novel-method"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-18T20:00:56Z | This repo introduces a Novel method for distillation of diffusion model leveraging the teacher's intermediate steps for supervision while distillation training. | [
"general-ml",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 1 | 1,004,549,833 | null | Jupyter Notebook | Apache-2.0 | [
"diffusion",
"distillation",
"novel-method"
] | dsgiitr/SADD | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-09-29T17:12:21Z | [] | 2026-02-08T13:22:44Z | [
"general.novel-method"
] | null | 2026-09-26T15:53:33Z | gh-ml-readme-evidence-v2 | null | [] | [] | missing | 2026-09-26T15:53:33Z | dsgiitr/SADD | specific-method-with-novelty-claim | [
"contribution-language",
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 6 | [] | 2026-02-08T13:22:48Z | https://github.com/dsgiitr/SADD | null |
[
"forecasting",
"generative-modeling",
"time-series",
"time-series-and-forecasting"
] | [
"diffusion",
"probabilistic-forecasting",
"uncertainty-estimation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"timeseries.probabilistic-forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-18T23:54:45Z | Official implementation of DIFF-SPARSE, a conditional diffusion model for probabilistic coastal inundation forecasting from sparse sensor observations (AAAI 2026). | [
"forecasting",
"generative-modeling",
"time-series",
"time-series-and-forecasting"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-03T15:45:52Z | false | 0 | 1,004,630,038 | null | Python | null | [
"diffusion",
"probabilistic-forecasting",
"uncertainty-estimation"
] | KAI10/Diff-Sparse | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-03T15:45:52Z | [] | 2026-09-20T01:05:13Z | [
"timeseries.probabilistic-forecasting"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 3 | [] | 2026-09-22T08:27:47Z | https://github.com/KAI10/Diff-Sparse | null |
[
"reinforcement-learning"
] | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-19T03:29:57Z | Official implementation of the paper "Action-Curiosity-Based Deep Reinforcement Learning Algorithm for Path Planning in a Nondeterministic Environment" (Intelligent Computing, Vol. 4, 2025). This repository includes training code and benchmark environments for adaptive path planning in uncertain settings. | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-29T17:49:51Z | false | 0 | 1,004,695,698 | null | Makefile | null | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | shadowtowind/action-curiosity-drl | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-29T17:49:51Z | [] | 2025-06-20T07:06:04Z | [
"rl.deep"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2025-08-13T01:58:01Z | https://github.com/shadowtowind/action-curiosity-drl | null |
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