all_domains list | all_methods list | all_novelty_signals list | all_query_ids list | archived bool | candidate_status string | candidate_rule_version string | candidate_eligible bool | candidate_reason string | candidate_evidence list | created_at string | description string | domains list | evidence_signals list | evidence_tier string | evidence_version string | first_observed_at string | fork bool | forks int64 | github_id int64 | homepage string | language string | license string | methods list | name string | novelty_signals list | observation_count int64 | observed_at string | paper_ids list | pushed_at string | query_ids list | readme_blob_sha string | readme_checked_at string | readme_evidence_version string | readme_etag string | readme_sections list | readme_signals list | readme_status string | readme_observed_at string | readme_repository_name_at_fetch string | selection_reason string | selection_signals list | selection_status string | selection_version string | stars int64 | topics list | updated_at string | url string | extra_json string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
[
"computer-vision",
"health-and-biomedicine",
"medical-imaging"
] | [
"contrastive-learning",
"segmentation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"medical.medical-image-segmentation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-07T12:07:15Z | Official Pytorch implementation of the paper MAMIS: Modality-adaptive medical image segmentation via ensemble supervised contrastive learning published in Biomedical Signal Processing and Control. | [
"computer-vision",
"health-and-biomedicine",
"medical-imaging"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-09T17:31:36Z | false | 0 | 1,203,813,947 | null | Python | null | [
"contrastive-learning",
"segmentation"
] | ZXS-Labs/MAMIS | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-09T17:31:36Z | [] | 2026-06-01T02:46:10Z | [
"medical.medical-image-segmentation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-06-01T02:46:13Z | https://github.com/ZXS-Labs/MAMIS | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-07T12:44:55Z | Official code repository of ICML paper "Mitigating Noise-Induced Layout Priors for Object Counting in Diffusion Models" | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-31T16:49:26Z | false | 0 | 1,203,844,591 | null | Python | null | [
"diffusion"
] | lxlong1201/Mitigate_Noise_Prior | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-31T16:49:26Z | [] | 2026-04-08T09:59:14Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-09-14T02:39:13Z | https://github.com/lxlong1201/Mitigate_Noise_Prior | 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 | [] | 2026-04-08T00:05:44Z | [ICML 2026] Source code for paper Reviving Error Correction in Modern Deep Time-Series Forecasting | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 0 | 1,204,360,971 | null | Shell | MIT | [
"forecasting",
"sequence-modeling"
] | DA2I2-SLM/UEC-STD | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-09-25T15:43:40Z | [] | 2026-05-04T06:20:47Z | [
"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 | 6 | [] | 2026-09-11T06:39:43Z | https://github.com/DA2I2-SLM/UEC-STD | null |
[
"agriculture",
"agriculture-and-food",
"computer-vision",
"image-classification",
"plant-disease-detection",
"plant-science"
] | [
"transformer"
] | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"applied.plant-disease-detection"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-08T06:49:45Z | Triplet-MHA ViT: A novel vision transformer network for plant disease detection | [
"agriculture",
"agriculture-and-food",
"computer-vision",
"image-classification",
"plant-disease-detection",
"plant-science"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-24T14:31:13Z | false | 0 | 1,204,605,462 | https://doi.org/10.1016/j.eswa.2025.127743 | Python | NOASSERTION | [
"transformer"
] | mah-iasri/triplet-mha-vit | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-10-24T14:31:13Z | [] | 2026-04-14T14:53:51Z | [
"applied.plant-disease-detection"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 1 | [] | 2026-04-19T15:24:51Z | https://github.com/mah-iasri/triplet-mha-vit | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-08T07:47:58Z | Official code for the paper "Self-Regulating Annealing in Heavy-Tailed Diffusion Models" | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-31T16:49:26Z | false | 0 | 1,204,652,106 | null | null | MIT | [
"diffusion"
] | keito93/self-regulating-annealing-in-heavy-tailed-diffusion-models | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-31T16:49:26Z | [] | 2026-04-08T07:47:59Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-04-08T07:48:03Z | https://github.com/keito93/self-regulating-annealing-in-heavy-tailed-diffusion-models | null |
[
"clinical-informatics",
"deep-learning",
"graph-learning",
"health-and-biomedicine",
"time-series"
] | [
"diagnosis",
"dynamic-graph-learning",
"graph-transformer",
"message-passing",
"risk-prediction",
"temporal-graph-learning",
"transformer"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.graph-transformer",
"graph.temporal-learning",
"medical.diagnosis-prediction"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-08T14:34:40Z | Official implementation for paper "EHRFormer: A Next-Visit Diagnosis Prediction Model Using Electronic Health Records via Temporal Graph Transformer" | [
"graph-learning",
"time-series"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 0 | 1,204,998,610 | null | Python | null | [
"dynamic-graph-learning",
"temporal-graph-learning",
"transformer"
] | shiyuxing1995422-dev/EHRFormer | [
"description",
"query-match",
"repository-metadata"
] | 6 | 2026-10-20T15:59:06Z | [] | 2026-04-08T14:54:28Z | [
"graph.temporal-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-09-21T12:49:11Z | https://github.com/shiyuxing1995422-dev/EHRFormer | null |
[
"multimodal",
"reinforcement-learning",
"robotics"
] | [
"environment-modeling",
"world-model"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.world-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-09T06:29:40Z | Official implementation of the paper: Sub-JEPA: Subspace Gaussian Regularization for Stable End-to-End World Models. | [
"multimodal",
"reinforcement-learning",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 2 | 1,205,638,229 | https://kaizhao.net/sub-jepa | Python | Apache-2.0 | [
"environment-modeling",
"world-model"
] | intcomp/Sub-JEPA | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-06T17:10:23Z | [] | 2026-07-27T12:43:36Z | [
"multimodal.world-models"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 67 | [] | 2026-10-01T14:00:46Z | https://github.com/intcomp/Sub-JEPA | null |
[
"computer-vision",
"information-retrieval"
] | [
"distillation",
"image-retrieval",
"knowledge-distillation",
"metric-learning"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"vision.image-retrieval"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-09T08:36:06Z | The official implementation of [CVPR-2026 Findings] Asymmetric Collaborative Distillation for Asymmetric Image Retrieval. | [
"computer-vision",
"information-retrieval"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 0 | 1,205,740,874 | null | Python | null | [
"distillation",
"image-retrieval",
"knowledge-distillation",
"metric-learning"
] | SCY-X/Asymmetric_Collaborative_Distillation | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 2 | 2026-09-26T10:12:50Z | [] | 2026-06-04T06:44:02Z | [
"vision.image-retrieval"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 8 | [
"asymmetric-image-retrieval",
"image-retrieval",
"knowledge-distillation"
] | 2026-07-21T13:55:08Z | https://github.com/SCY-X/Asymmetric_Collaborative_Distillation | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"multimodal-learning"
] | [
"diffusion",
"generative-modeling",
"image-to-image-translation",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.image-to-image-translation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-09T11:38:23Z | [ICLR 2026] Official Implementation of "ColorCtrl: Training-Free Text-Guided Color Editing with Multi-Modal Diffusion Transformer" | [
"computer-vision",
"generative-ai",
"generative-modeling",
"multimodal-learning"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-11-01T17:09:41Z | false | 1 | 1,205,894,131 | https://zxyin.github.io/ColorCtrl/ | Python | Apache-2.0 | [
"diffusion",
"generative-modeling",
"image-to-image-translation",
"transformer"
] | zxYin/ColorCtrl_Code | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-11-01T17:09:41Z | [] | 2026-04-09T11:44:17Z | [
"vision.image-to-image-translation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 28 | [] | 2026-10-27T04:50:20Z | https://github.com/zxYin/ColorCtrl_Code | null |
[
"artificial-intelligence",
"machine-learning"
] | [
"interpretability",
"mechanistic-interpretability",
"rag"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"specialized.mechanistic-interpretability"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-10T08:49:52Z | Official code for "The Mechanics of Interference: Defusing Distractors in RAG via Sparse Autoencoder Interventions" — Findings of ACL 2026 | [
"artificial-intelligence",
"machine-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-14T16:10:46Z | false | 0 | 1,206,784,089 | null | Python | MIT | [
"interpretability",
"mechanistic-interpretability",
"rag"
] | christian-giannetti/mechanics-of-interference | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-14T16:10:46Z | [] | 2026-07-02T17:06:31Z | [
"specialized.mechanistic-interpretability"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 2 | [] | 2026-08-11T08:27:51Z | https://github.com/christian-giannetti/mechanics-of-interference | null |
[
"machine-learning",
"multimodal-learning",
"natural-language-processing"
] | [
"adaptation",
"test-time-adaptation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"specialized.test-time-adaptation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-11T14:51:22Z | [ACL 2026] Official code for "Generative Giants, Retrieval Weaklings: Why do Multimodal Large Language Models Fail at Multimodal Retrieval?" | [
"machine-learning",
"multimodal-learning",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 0 | 1,207,896,006 | null | Python | NOASSERTION | [
"adaptation",
"test-time-adaptation"
] | Heinz217/mllm-retrieval-analysis | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-08T18:15:14Z | [] | 2026-05-12T07:45:00Z | [
"specialized.test-time-adaptation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 10 | [] | 2026-09-03T05:34:55Z | https://github.com/Heinz217/mllm-retrieval-analysis | null |
[
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion",
"generative-modeling"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"vision.image-diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-13T08:47:22Z | [ICML2026] Official Implementation of "Balancing Fidelity and Diversity in Diffusion Models via Symmetric Attention Decomposition: Hopfield Perspective" | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-16T15:36:56Z | false | 0 | 1,209,259,705 | https://hyeon-cho.github.io/Balancing/ | Python | null | [
"diffusion"
] | hyeon-cho/Attention-Symmetric-Decomposition | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 2 | 2026-10-30T17:16:29Z | [] | 2026-07-06T08:38:48Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 7 | [
"attention-mechanism",
"diffusion-models",
"hopfield-network"
] | 2026-09-29T14:11:30Z | https://github.com/hyeon-cho/Attention-Symmetric-Decomposition | null |
[
"efficient-ml",
"inference-systems",
"language",
"machine-learning-systems",
"model-compression",
"natural-language-processing"
] | [
"kv-cache-optimization",
"memory-optimization",
"quantization"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.kv-cache",
"efficiency.quantization"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-13T20:42:59Z | Official implementation of Paper "System-Aware 4-Bit KV-Cache Quantization for Real-World LLM Serving" | [
"efficient-ml",
"machine-learning-systems",
"model-compression",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 3 | 1,209,838,820 | null | Shell | MIT | [
"quantization"
] | togethercomputer/saw-int4 | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 10 | 2026-11-01T17:09:41Z | [] | 2026-04-17T23:05:36Z | [
"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 | 31 | [] | 2026-10-29T04:09:06Z | https://github.com/togethercomputer/saw-int4 | null |
[
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-14T06:18:39Z | Official pytorch implementation of the paper: " StructDiff: A Structure-Preserving and Spatially Controllable Diffusion Model for Single-Image Generation" | [
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-31T16:49:26Z | false | 0 | 1,210,153,245 | null | Python | null | [
"diffusion"
] | Butter-Crab/StructDiff | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-31T16:49:26Z | [] | 2026-05-11T12:32:28Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 5 | [] | 2026-05-11T12:32:40Z | https://github.com/Butter-Crab/StructDiff | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"recall.name.diffusion-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-14T07:09:55Z | Official implementation for "Causal Motion Diffusion Models for Autoregressive Motion Generation" (CVPR 2026) | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-01T16:56:07Z | false | 1 | 1,210,192,147 | https://yu1ut.com/CMDM-HP/ | Python | CC0-1.0 | [
"diffusion"
] | lycorp-jp/CMDM | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-30T17:16:29Z | [] | 2026-10-12T02:16:20Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 7 | [] | 2026-10-21T13:59:35Z | https://github.com/lycorp-jp/CMDM | null |
[
"computer-vision",
"efficient-ml",
"health-and-biomedicine",
"medical-imaging",
"model-compression"
] | [
"distillation",
"knowledge-distillation",
"segmentation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation",
"medical.medical-image-segmentation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-14T13:58:27Z | Official code for MICCAI2025 paper: "UM-SAM: Unsupervised Medical Image Segmentation using Knowledge Distillation from Segment Anything Model" | [
"computer-vision",
"efficient-ml",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 0 | 1,210,526,165 | null | Python | null | [
"distillation",
"knowledge-distillation"
] | Fujia1998/UM-SAM | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-10T16:53:26Z | [] | 2026-04-14T14:23:42Z | [
"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 | [] | 2026-04-14T14:23:46Z | https://github.com/Fujia1998/UM-SAM | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-14T14:00:14Z | Official code for the paper "Self-Regulating Annealing in Heavy-Tailed Diffusion Models" | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-31T16:49:26Z | false | 0 | 1,210,527,902 | null | Python | MIT | [
"diffusion"
] | keito93/self-regulating-annealing-in-htdms | [
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"query-match",
"repository-metadata"
] | 1 | 2026-10-31T16:49:26Z | [] | 2026-10-23T08:50:42Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 0 | [] | 2026-10-23T08:50:46Z | https://github.com/keito93/self-regulating-annealing-in-htdms | null |
[
"deep-learning",
"graph-learning"
] | [
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] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.graph-transformer"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-15T12:21:15Z | SARE-GT: Structure-Aware Robust Enhancement Graph Transformer for Corporate Credit Rating Official implementation of the paper | [
"deep-learning",
"graph-learning"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-26T10:12:50Z | false | 0 | 1,211,462,925 | null | Python | null | [
"graph-transformer",
"message-passing",
"transformer"
] | yumingjs/SARE-GT-IJCNN2026 | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-09-27T16:09:59Z | [] | 2026-04-15T12:24:51Z | [
"graph.graph-transformer"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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] | include | ml-contribution-v5 | 1 | [] | 2026-04-15T13:13:01Z | https://github.com/yumingjs/SARE-GT-IJCNN2026 | null |
[
"generative-ai",
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] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-15T13:05:16Z | [CVPR'25] The official implementation of paper "Blind Bitstream-corrupted Video Recovery via Metadata-guided Diffusion Model" | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-31T16:49:26Z | false | 0 | 1,211,500,722 | null | Python | null | [
"diffusion"
] | Shuyun-Wang/M-GDM | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-31T16:49:26Z | [] | 2026-04-16T08:14:09Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 4 | [] | 2026-06-25T13:10:11Z | https://github.com/Shuyun-Wang/M-GDM | null |
[
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-15T15:16:27Z | Official implementation of the paper: "An Analysis of Regularization and Fokker–Planck Residuals in Diffusion Models for Image Generation" (IJCNN 2026) | [
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-31T16:49:26Z | false | 0 | 1,211,618,997 | null | null | Apache-2.0 | [
"diffusion"
] | OnnoNiemann/fp_diffusion_analysis | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-31T16:49:26Z | [] | 2026-04-15T15:16:27Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-04-15T15:16:32Z | https://github.com/OnnoNiemann/fp_diffusion_analysis | null |
[
"generative-ai"
] | [
"diffusion",
"distillation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"recall.name.diffusion-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-17T14:38:07Z | This repository contains the official implementation of the paper Wavelet-Constrained Trajectory Distillation for Efficient Transferable Targeted Attacks | [
"generative-ai"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-26T16:16:45Z | false | 0 | 1,213,586,232 | null | Python | null | [
"diffusion",
"distillation"
] | xuejianhuang/WCTD | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-26T16:16:45Z | [] | 2026-10-08T13:57:40Z | [
"recall.name.diffusion-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 2 | [] | 2026-10-11T09:31:34Z | https://github.com/xuejianhuang/WCTD | null |
[
"computer-vision",
"generative-modeling",
"image-processing",
"low-level-vision"
] | [
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"low-light-enhancement"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"recall26.low-light-enhancement"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-17T16:50:12Z | Official implementation of the CVPR 2026 paper "Bi-Bridge: Bidirectional Diffusion Bridges for Low-Light Image Enhancement". | [
"computer-vision",
"generative-modeling",
"image-processing",
"low-level-vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T10:12:50Z | false | 1 | 1,213,690,092 | null | Python | null | [
"diffusion",
"image-enhancement",
"image-restoration",
"low-light-enhancement"
] | qq1332427275/Bi-Bridge | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-10-09T17:31:36Z | [] | 2026-06-07T08:53:00Z | [
"recall26.low-light-enhancement"
] | 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 | 8 | [] | 2026-08-22T02:59:14Z | https://github.com/qq1332427275/Bi-Bridge | null |
[
"science-and-engineering",
"sequence-modeling",
"time-series",
"time-series-and-forecasting"
] | [
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] | [
"description",
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"query-match",
"repository-metadata"
] | [
"timeseries.state-space-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-17T18:24:13Z | Official code for arXiv:2604.11807 - Physics-Informed State Space Models for Off-Grid Solar Forecasting | [
"science-and-engineering",
"sequence-modeling",
"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 0 | 1,213,760,105 | null | Python | null | [
"sequence-modeling",
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] | Moh97746/PISSM-Solar-Forecasting | [
"description",
"github-topics",
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"repository-metadata"
] | 3 | 2026-10-03T15:45:52Z | [] | 2026-04-21T04:44:53Z | [
"timeseries.state-space-models"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 5 | [
"arxiv",
"edge-computing",
"nasa-power",
"physics-informed",
"solar-forecasting",
"state-space-model",
"ultra-lightweight"
] | 2026-06-02T08:08:38Z | https://github.com/Moh97746/PISSM-Solar-Forecasting | null |
[
"computer-vision",
"efficient-ml",
"generative-modeling",
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] | [
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] | [
"description",
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"repository-metadata"
] | [
"efficiency.pruning",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-19T04:17:19Z | [CVPR 2026 Findings] Official implementation of the paper "TinySR: Pruning Diffusion for Real-World Image Super-Resolution". | [
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"generative-modeling",
"machine-learning-systems"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 4 | 1,214,802,301 | null | Python | Apache-2.0 | [
"diffusion",
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] | Microtreei/TinySR | [
"description",
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"query-match",
"repository-metadata"
] | 8 | 2026-10-31T16:49:26Z | [] | 2026-05-25T09:55:14Z | [
"vision.image-super-resolution"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 46 | [] | 2026-10-23T16:28:09Z | https://github.com/Microtreei/TinySR | null |
[
"efficient-ml",
"general-ml",
"generative-ai",
"language",
"linguistics",
"machine-learning-systems",
"model-compression",
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] | [
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"neural-network-pruning",
"pretraining",
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"structured-pruning",
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] | [
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] | [
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"recall.name.language-model",
"recall.name.large-language-model",
"recall.name.transformer-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-19T14:17:22Z | [ACL 2026 Outstanding Paper Award] Official implementation of the paper "From Local to Global: Revisiting Structured Pruning Paradigms for Large Language Models" | [
"general-ml",
"machine-learning-systems",
"natural-language-processing"
] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-18T13:47:34Z | false | 0 | 1,215,128,316 | null | Python | Apache-2.0 | [
"pruning",
"transformer"
] | uncc-efficient-ai/GISP | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-10-29T16:41:07Z | [] | 2026-10-02T20:05:46Z | [
"recall.name.transformer-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 4 | [] | 2026-10-11T07:29:00Z | https://github.com/uncc-efficient-ai/GISP | null |
[
"anomaly-detection",
"generative-modeling",
"graph-learning"
] | [
"anomaly-detection",
"diffusion",
"graph-anomaly-detection"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.graph-anomaly-detection"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-20T05:52:58Z | Official PyTorch implementation for the paper ARLDM: Autoencoder Reconstruction-based Latent1 Diffusion Model for Graph Anomaly Detection. | [
"anomaly-detection",
"generative-modeling",
"graph-learning"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 0 | 1,215,647,499 | null | Python | null | [
"anomaly-detection",
"diffusion",
"graph-anomaly-detection"
] | weihus/ARLDM | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-09-27T16:09:59Z | [] | 2026-04-20T06:23:26Z | [
"graph.graph-anomaly-detection"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [] | 2026-05-18T14:56:47Z | https://github.com/weihus/ARLDM | null |
[
"natural-language-processing",
"representation-learning",
"time-series",
"time-series-and-forecasting"
] | [
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"self-supervised-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"timeseries.temporal-representation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-20T15:47:56Z | Code for our Informer-LLM paper on time series forecasting with Informer and large language models. | [
"natural-language-processing",
"representation-learning",
"time-series",
"time-series-and-forecasting"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T16:16:53Z | false | 0 | 1,216,133,708 | null | Python | MIT | [
"representation-learning",
"self-supervised-learning"
] | YaruShan/Informer-LLM | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-09-26T14:34:36Z | [] | 2026-04-20T16:02:22Z | [
"timeseries.temporal-representation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [] | 2026-04-20T16:02:26Z | https://github.com/YaruShan/Informer-LLM | null |
[
"deep-learning",
"efficient-ml",
"language",
"natural-language-processing"
] | [
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"distillation",
"mamba",
"mixture-of-experts",
"sparse-activation"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"efficiency.mixture-of-experts"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-20T22:25:19Z | Novel architecture integrating Mamba SSM, Mixture-of-Experts, and Chain-of-Thought distillation for text classification | [
"deep-learning",
"efficient-ml",
"language",
"natural-language-processing"
] | [
"deep-learning",
"large-language-model",
"classifier"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 0 | 1,216,424,230 | null | Jupyter Notebook | null | [
"conditional-computation",
"distillation",
"mamba",
"mixture-of-experts",
"sparse-activation"
] | Zara-iqbal-01/Mamba-MoE-CoT | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2026-04-20T22:33:03Z | [
"efficiency.mixture-of-experts"
] | db8df1373b6e14e9654ec5e0a7ccf6dff67dcade | 2026-10-05T21:36:53Z | gh-ml-readme-evidence-v2 | "db8df1373b6e14e9654ec5e0a7ccf6dff67dcade" | [
"method",
"other",
"overview"
] | [
"method-contribution",
"ml-method-context"
] | ok | 2026-10-05T21:36:53Z | Zara-iqbal-01/Mamba-MoE-CoT | specific-method-with-novelty-claim | [
"method-contribution",
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [
"chain-of-thought",
"deep-learning",
"llm",
"mamba",
"mixture-of-experts",
"nlp",
"pytorch",
"sentiment-analysis",
"state-space-models"
] | 2026-04-20T22:33:06Z | https://github.com/Zara-iqbal-01/Mamba-MoE-CoT | null |
[
"health-and-biomedicine",
"information-retrieval",
"natural-language-processing"
] | [
"lexical-retrieval",
"rag",
"sparse-retrieval"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"nlp-sparse-retrieval"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-21T03:56:54Z | Code for our paper"When Retrieval Doesn't Help: A Large-Scale Study of Biomedical RAG" | [
"health-and-biomedicine",
"information-retrieval",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-19T17:46:36Z | false | 0 | 1,216,594,408 | null | Python | null | [
"lexical-retrieval",
"rag",
"sparse-retrieval"
] | erfan-nourbakhsh/BioMedicalRAG | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-19T17:46:36Z | [] | 2026-04-21T04:08:50Z | [
"nlp-sparse-retrieval"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 4 | [] | 2026-09-22T04:07:11Z | https://github.com/erfan-nourbakhsh/BioMedicalRAG | null |
[
"deep-learning",
"efficient-ml",
"language",
"robust-ml",
"time-series-and-forecasting",
"trustworthy-ml"
] | [
"conditional-computation",
"distribution-shift-detection",
"domain-generalization",
"mixture-of-experts",
"sparse-activation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.mixture-of-experts",
"trust.distribution-shift"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-25T06:07:45Z | This is the official PyTorch implementation for the paper "A Spatial-Temporal Distribution Shift Aware Mixture-of-Experts Model for Urban Atmospheric Forecasting". | [
"deep-learning",
"efficient-ml",
"language",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 0 | 1,220,636,209 | null | Python | MIT | [
"conditional-computation",
"mixture-of-experts",
"sparse-activation"
] | Lucas-Fu/DiS-MoE | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-19T16:36:56Z | [] | 2026-10-08T04:17:14Z | [
"efficiency.mixture-of-experts"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [] | 2026-10-08T04:17:18Z | https://github.com/Lucas-Fu/DiS-MoE | null |
[
"classical-ml",
"computer-vision",
"representation-learning"
] | [
"action-recognition",
"contrastive-learning",
"metric-learning",
"representation-learning",
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] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.metric-learning",
"vision.action-recognition"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-26T12:12:29Z | Official Code for Paper WeCo-OSAR: Weighted Contrastive Learning for Open-set Skeleton-based Action Recognition with pseudo-OOD Samples | [
"classical-ml",
"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-09T17:31:36Z | false | 0 | 1,221,582,202 | null | Python | NOASSERTION | [
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"metric-learning",
"representation-learning"
] | cyoungx/WeCo-OSAR | [
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"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-31T16:49:26Z | [] | 2026-07-09T01:00:19Z | [
"general.metric-learning"
] | 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 | 1 | [] | 2026-07-14T12:25:52Z | https://github.com/cyoungx/WeCo-OSAR | null |
[
"deep-learning",
"graph-learning"
] | [
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"neural-network"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-28T08:08:24Z | This repository will have the code to work with graph neural network algorithms, it will also be used to develop novel algorithms. | [
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] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-25T15:11:36Z | false | 0 | 1,223,327,807 | null | Jupyter Notebook | null | [
"graph-neural-network",
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"neural-network"
] | cs24d0002-jpg/graph_neural_networks | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-25T15:11:36Z | [] | 2026-06-11T04:38:21Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
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] | Sam343-mir/Recall_based_KD | [
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] | 1 | 2026-10-10T16:53:26Z | [] | 2026-04-30T04:57:22Z | [
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] | include | ml-contribution-v5 | 1 | [] | 2026-04-30T16:13:56Z | https://github.com/Sam343-mir/Recall_based_KD | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-30T07:25:45Z | Official implementation repository of IJCB2026 paper IdentiFace: Multi-Modal Iterative Diffusion Framework for Identifiable Suspect Face Generation in Crime Investigations | [
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] | 1 | 2026-10-04T15:58:58Z | [] | 2026-09-02T07:44:55Z | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-09-02T07:47:35Z | https://github.com/uglasses/IdentiFace | null |
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] | [
"multimodal.world-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-04-30T22:05:04Z | A new generation of world models. | [
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] | 1 | 2026-10-08T18:15:14Z | [] | 2026-04-30T22:30:26Z | [
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[
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-10T16:53:26Z | false | 0 | 1,226,036,892 | null | Python | null | [
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] | WanyuGroup/ICML2026-DKMP | [
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] | 2 | 2026-10-17T13:17:44Z | [] | 2026-05-19T10:58:54Z | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-05-28T11:52:33Z | https://github.com/WanyuGroup/ICML2026-DKMP | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-01T08:06:21Z | [ICML 2026] Official implementation of "CombinationTS: A Modular Framework for Understanding Time-Series Forecasting Models" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 0 | 1,226,298,457 | https://arxiv.org/abs/2605.01231 | Python | null | [
"machine-learning"
] | BenchCouncil/CombinationTS | [
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] | 6 | 2026-10-14T15:02:21Z | [] | 2026-09-27T16:24:45Z | [
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] | include | ml-contribution-v5 | 6 | [] | 2026-10-06T03:27:09Z | https://github.com/BenchCouncil/CombinationTS | null |
[
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] | [
"rl.multiagent"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-01T09:26:32Z | This repository is the official implementation of the paper: **"Optimistic $\epsilon$-Greedy Exploration for Cooperative Multi-Agent Reinforcement Learning"**. | [
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"centralized-training",
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] | qxqxtxdy/OptimisticExploration | [
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] | 1 | 2026-10-05T20:10:04Z | [] | 2026-05-01T09:29:07Z | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-05-01T09:29:11Z | https://github.com/qxqxtxdy/OptimisticExploration | null |
[
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] | [
"general.machine-learning-readme",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-01T12:42:11Z | [ICML 2026] official code release for our paper "Recovering Hidden Reward in Diffusion-Based Policies". | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-22T14:58:59Z | false | 0 | 1,226,466,741 | null | Python | null | [
"diffusion",
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] | sotaagi/EnergyFlow | [
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] | 3 | 2026-10-31T15:48:22Z | [] | 2026-10-02T08:52:29Z | [
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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 | 3 | [] | 2026-10-11T07:18:23Z | https://github.com/sotaagi/EnergyFlow | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-01T16:48:55Z | Official implementation for Order Matters in Retrosynthesis: Structure-aware Generation via Reaction-Center-Guided Discrete Flow Matching, ICML2026 | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 0 | 1,226,639,024 | null | Python | MIT | [
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] | 3 | 2026-09-29T17:30:08Z | [] | 2026-08-19T06:31:34Z | [
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] | include | ml-contribution-v5 | 4 | [] | 2026-08-19T14:32:48Z | https://github.com/LOGO-CUHKSZ/RetroDiT | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-01T20:50:29Z | Context-Anchored Recurrent Transformer - a new AI model architecture | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 1,226,790,004 | null | Python | MIT | [
"model-architecture",
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] | ccapps42/CART | [
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] | 2 | 2026-10-07T18:07:52Z | [] | 2026-06-02T11:30:03Z | [
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] | [
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] | ok | 2026-10-04T17:18:56Z | ccapps42/CART | specific-method-with-novelty-claim | [
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] | include | ml-contribution-v5 | 1 | [] | 2026-07-16T12:19:59Z | https://github.com/ccapps42/CART | null |
[
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] | [
"multimodal.world-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-02T03:40:29Z | Official implementation of "Identifiable Token Correspondence for World Models" (ICML'26) | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 2 | 1,226,976,346 | null | Python | MIT | [
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] | include | ml-contribution-v5 | 6 | [] | 2026-10-04T20:53:16Z | https://github.com/snu-mllab/Identifiable-Token-Correspondence | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-02T09:35:53Z | The official implementation of the paper "Toward Extremely Efficient Infrared Small Target Detection via Universal U-Net-based Knowledge Distillation" in PyTorch. | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 0 | 1,227,156,768 | null | Python | Apache-2.0 | [
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] | 3 | 2026-10-09T17:31:36Z | [] | 2026-07-14T08:18:26Z | [
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] | include | ml-contribution-v5 | 4 | [] | 2026-07-14T08:18:30Z | https://github.com/RuiminHuang/LW-DSTransNet | null |
[
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] | [
"description",
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"query-match",
"repository-metadata"
] | [
"multimodal.text-to-image"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-02T19:44:54Z | Official code for "Responsible Text-to-Image Diffusion" (ICML 2026) | [
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"vision"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-19T17:46:36Z | false | 0 | 1,227,515,849 | null | Python | MIT | [
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] | Moslem-Sh21/responsible-t2i-diffusion | [
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] | 1 | 2026-10-19T17:46:36Z | [] | 2026-05-02T20:00:27Z | [
"multimodal.text-to-image"
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] | include | ml-contribution-v5 | 0 | [] | 2026-05-02T20:00:30Z | https://github.com/Moslem-Sh21/responsible-t2i-diffusion | null |
[
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"paper-reference",
"query-match",
"repository-metadata"
] | [
"timeseries.forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-02T22:05:40Z | [ICML 2026] Official PyTorch implementation of From Observations to States: Latent Time Series Forecasting | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 4 | 1,227,580,497 | https://arxiv.org/abs/2602.00297 | Python | NOASSERTION | [
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] | Muyiiiii/LatentTSF | [
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"github-topics",
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"query-match",
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] | 4 | 2026-10-30T17:16:29Z | [] | 2026-05-23T05:38:14Z | [
"timeseries.forecasting"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 41 | [
"icml2026",
"time-series-forecasting"
] | 2026-10-29T22:59:51Z | https://github.com/Muyiiiii/LatentTSF | null |
[
"multi-agent-systems",
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"multi-agent-reinforcement-learning",
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] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.multiagent"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-04T06:20:50Z | Code for paper "DeComm: Decoupling Communication in Multi-Agent Reinforcement Learning" | [
"multi-agent-systems",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-03T15:45:52Z | false | 1 | 1,228,555,883 | null | Python | MIT | [
"centralized-training",
"multi-agent-reinforcement-learning",
"reinforcement-learning"
] | Shuxin-Wang/DeComm | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-03T15:45:52Z | [] | 2026-05-06T03:05:56Z | [
"rl.multiagent"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-05-06T03:05:59Z | https://github.com/Shuxin-Wang/DeComm | null |
[
"computer-vision",
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"description",
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"repository-metadata"
] | [
"multimodal.vision-language",
"robotics.visual-language-action"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-04T12:33:27Z | The official source code for Test-Time Training for Visual Foresight Vision-Language-Action Models, accepted at ICML 2026 Workshop on Contunual Adaptation at Scale (CATS). | [
"computer-vision",
"language",
"multimodal",
"multimodal-learning",
"vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T14:50:46Z | false | 0 | 1,228,835,648 | null | Python | null | [
"contrastive-learning",
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] | sangwu99/T3VF | [
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"query-match",
"repository-metadata"
] | 2 | 2026-10-21T15:48:06Z | [] | 2026-06-04T07:14:31Z | [
"multimodal.vision-language"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 11 | [] | 2026-10-12T10:43:23Z | https://github.com/sangwu99/T3VF | null |
[
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"medical-imaging",
"multimodal-learning",
"pathology",
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] | [
"description",
"license-metadata",
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"repository-metadata"
] | [
"bio.transcriptomics",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-05T03:38:31Z | Official implementation for the paper “Understanding When Flow Matching Helps Deterministic Multimodal Prediction: A Spatial Transcriptomics Study" | [
"biology",
"computational-biology",
"generative-modeling",
"multimodal-learning",
"transcriptomics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 0 | 1,229,441,134 | null | Python | MIT | [
"flow-matching",
"gene-expression-modeling",
"representation-learning"
] | hrterry/Understanding-When-Flow-Matching-Helps-Deterministic-Multimodal-Prediction | [
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"query-match",
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] | 2 | 2026-10-20T15:59:06Z | [] | 2026-05-05T05:06:46Z | [
"bio.transcriptomics"
] | 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 | [] | 2026-05-05T05:06:50Z | https://github.com/hrterry/Understanding-When-Flow-Matching-Helps-Deterministic-Multimodal-Prediction | null |
[
"general-ml"
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"repository-metadata"
] | [
"recall.name.transformer-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-05T12:32:36Z | Official code for “Beyond Missing Rates: Rethinking Incomplete Multi-View Clustering with Protocol Divergence” (NeurIPS 2026). | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-14T15:02:21Z | false | 0 | 1,229,835,102 | https://arxiv.org/abs/2606.04857 | Python | null | [
"transformer"
] | dk23lhl/CRAFT | [
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] | 1 | 2026-10-14T15:02:21Z | [] | 2026-09-29T17:39:42Z | [
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] | include | ml-contribution-v5 | 0 | [
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] | 2026-10-01T01:08:36Z | https://github.com/dk23lhl/CRAFT | null |
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-06T20:31:35Z | [ICML 2026] Official Implementation of "See What Matters: Differentiable Grid Sample Pruning for Generalizable Vision-Language-Action Model" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T16:16:53Z | false | 3 | 1,231,295,031 | https://fediory.github.io/Grid-Sampler/ | Python | null | [
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] | Fediory/Grid-Sampler | [
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] | 7 | 2026-10-29T17:49:51Z | [] | 2026-06-26T15:32:00Z | [
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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 | 68 | [] | 2026-10-21T10:34:38Z | https://github.com/Fediory/Grid-Sampler | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-07T13:24:04Z | "Official Implementation of the paper A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge", Published in Computing, Springer Nature 2026 | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-11T16:48:52Z | false | 0 | 1,231,979,880 | null | null | NOASSERTION | [
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] | rizwanqureshi123/Layered-Self-Supervised-Knowledge-Distillation | [
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] | 1 | 2026-10-11T16:48:52Z | [] | 2026-05-07T14:41:58Z | [
"efficiency.knowledge-distillation"
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] | include | ml-contribution-v5 | 0 | [] | 2026-05-07T14:55:40Z | https://github.com/rizwanqureshi123/Layered-Self-Supervised-Knowledge-Distillation | null |
[
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"generative-modeling",
"machine-learning",
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] | [
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"recall.name.diffusion-model",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-07T13:49:54Z | Official implementation of paper: Kinetic-Optimal Scheduling with Moment Correction for Metric-Induced Discrete Flow Matching in Zero-Shot Text-to-Speech | [
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"machine-learning",
"speech-and-audio"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-02T15:56:46Z | false | 1 | 1,232,003,954 | null | Python | MIT | [
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] | ydqmkkx/GibbsTTS | [
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"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-10-29T16:41:07Z | [] | 2026-10-06T09:51:46Z | [
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] | 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 | 12 | [] | 2026-10-12T07:24:18Z | https://github.com/ydqmkkx/GibbsTTS | null |
[
"computer-vision",
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] | [
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] | [
"description",
"query-match",
"repository-metadata"
] | [
"timeseries.state-space-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-07T17:50:49Z | Official code repo of ICML'26 paper: Deformba: Vision State Space Model with Adaptive State Fusion | [
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"sequence-modeling",
"time-series"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 0 | 1,232,215,213 | null | Python | null | [
"sequence-modeling",
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] | amai-gsu/Deformba | [
"description",
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"repository-metadata"
] | 3 | 2026-10-03T15:45:52Z | [] | 2026-05-20T14:44:38Z | [
"timeseries.state-space-models"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 4 | [] | 2026-09-03T19:52:05Z | https://github.com/amai-gsu/Deformba | null |
[
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] | [
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] | [
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"repository-metadata"
] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-08T08:59:53Z | PyTorch-based open-source code for paper "SOD: Step-wise On-policy Distillation for Small Language Model Agents" | [
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"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 12 | 1,232,776,063 | null | Python | Apache-2.0 | [
"distillation",
"language-model"
] | YoungZ365/SOD | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-09-26T10:12:50Z | [] | 2026-05-22T05:29:34Z | [
"llm.language-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 165 | [] | 2026-09-24T02:04:12Z | https://github.com/YoungZ365/SOD | null |
[
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"generative-modeling",
"geospatial-science",
"remote-sensing",
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] | [
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] | [
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] | [
"llm.diffusion-phrase",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-08T12:57:48Z | This repository contains the official PyTorch implementation of the paper "TAIS-Net: Time Adaptive Implicit Sampling Diffusion Model for Arbitrary-Scale UAV Video Super-Resolution", which has been accepted by the ISPRS Journal of Photogrammetry and Remote Sensing (ISPRS&RS). | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-30T17:16:29Z | false | 0 | 1,232,963,838 | null | Jupyter Notebook | null | [
"diffusion"
] | WenKLee/TAIS-Net | [
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"query-match",
"repository-metadata"
] | 2 | 2026-11-01T17:09:41Z | [] | 2026-08-03T06:41:12Z | [
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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 | 16 | [] | 2026-08-03T06:41:43Z | https://github.com/WenKLee/TAIS-Net | null |
[
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"generative-ai",
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-08T21:35:54Z | Official implementation for "Towards a Unified Self-Distillation Framework for Large Language Models" (https://arxiv.org/abs/2605.06597). | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 54 | 1,233,349,595 | null | Python | Apache-2.0 | [
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] | Ahren09/UniSD | [
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"query-match",
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] | 11 | 2026-11-01T17:09:41Z | [] | 2026-06-13T18:53:52Z | [
"general.contrastive-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 887 | [] | 2026-11-01T09:16:56Z | https://github.com/Ahren09/UniSD | null |
[
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] | [
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] | [
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] | [
"specialized.test-time-adaptation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-09T12:52:40Z | MCP-3D: Multi-Cache Matrix for 3D Point Cloud Test-time Adaptation (Point-Cache + MCP fork + new method skeleton) | [
"machine-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 0 | 1,233,856,984 | null | Jupyter Notebook | null | [
"adaptation",
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] | TanLinJi/MCM-PC | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-08T18:15:14Z | [] | 2026-05-13T02:13:04Z | [
"specialized.test-time-adaptation"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-05-13T02:13:08Z | https://github.com/TanLinJi/MCM-PC | null |
[
"computer-vision",
"deep-learning",
"general-ml",
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-10T01:18:04Z | Code for paper "Automated Spatial-Temporal Graph Neural Network Search for Skeleton-based Human Action Recognition on Edge Devices" | [
"deep-learning",
"graph-learning"
] | [
"deep-learning",
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-07T18:07:52Z | false | 0 | 1,234,279,166 | https://fangvv.github.io/Edge-STGNN/ | null | null | [
"graph-neural-network",
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] | fangvv/Edge-STGNN | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 6 | 2026-10-24T14:31:13Z | [] | 2026-10-08T14:49:02Z | [
"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 | [
"deep-learning-algorithms",
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"hardware-aware-nas",
"human-activity-recognition",
"inference-acceleration",
"jetson-nano",
"nas",
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"stgnn"
] | 2026-10-08T14:49:07Z | https://github.com/fangvv/Edge-STGNN | null |
[
"computer-vision",
"embodied-ai",
"language",
"multimodal",
"multimodal-learning",
"robotics",
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] | [
"contrastive-learning",
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] | [
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"query-match",
"repository-metadata"
] | [
"multimodal.vision-language",
"robotics.visual-language-action"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-10T03:10:29Z | Official Implementation of SCALE: Self-uncertainty Conditioned Adaptive Looking and Execution for Vision-Language-Action Models (ICML'26 Spotlight) | [
"computer-vision",
"language",
"multimodal",
"multimodal-learning",
"vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T16:16:53Z | false | 2 | 1,234,322,743 | null | Python | MIT | [
"contrastive-learning",
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] | snumprlab/scale | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-21T15:48:06Z | [] | 2026-06-29T04:41:30Z | [
"multimodal.vision-language"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 19 | [] | 2026-09-11T22:32:03Z | https://github.com/snumprlab/scale | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"machine-learning"
] | [
"diffusion",
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] | [
"description",
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"license-metadata",
"query-match",
"repository-metadata"
] | [
"probable.adaptation-finetuning-readme",
"recall.name.diffusion-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-10T13:10:06Z | Official Implementation of "Colored Noise Diffusion Sampling" - NeurIPS 2026 Oral | [
"computer-vision",
"generative-modeling",
"machine-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-30T18:03:22.392067Z | false | 3 | 1,234,646,690 | https://hadardavidson.github.io/CNS/ | Jupyter Notebook | MIT | [
"diffusion",
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] | HadarDavidson/colored-noise-sampling | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-09T16:23:55Z | [] | 2026-09-26T17:08:56Z | [
"probable.adaptation-finetuning-readme"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 45 | [
"diffusion",
"flow-matching",
"image-generation",
"sampling-methods",
"sde"
] | 2026-10-05T16:02:10Z | https://github.com/HadarDavidson/colored-noise-sampling | null |
[
"deep-learning",
"representation-learning"
] | [
"representation-learning",
"self-supervised-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.self-supervised-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-10T15:27:35Z | Official implementation for 'Understanding the Robustness of Distributed Self-Supervised Learning Frameworks Against Non-IID Data' (ICLR 2026). | [
"deep-learning",
"representation-learning"
] | [
"self-supervised-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-06T17:10:23Z | false | 1 | 1,234,741,392 | null | Python | null | [
"representation-learning",
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] | xuanyuLawrence/FedMAR-DecMAR | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-06T17:10:23Z | [] | 2026-05-11T04:28:01Z | [
"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 | 0 | [] | 2026-05-11T04:28:06Z | https://github.com/xuanyuLawrence/FedMAR-DecMAR | null |
[
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion",
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] | [
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] | [
"llm.diffusion-phrase",
"vision.image-restoration"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-11T10:12:23Z | This is the official implementation of "Image Restoration via Diffusion Models with Dynamic Resolution " (ICML 2026). | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 0 | 1,235,429,830 | null | Python | null | [
"diffusion"
] | StarNextDay/SubDAPS | [
"description",
"query-match",
"repository-metadata"
] | 6 | 2026-11-01T17:09:41Z | [] | 2026-05-12T09:42:43Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 6 | [] | 2026-10-09T07:43:29Z | https://github.com/StarNextDay/SubDAPS | null |
[
"computer-vision",
"generative-ai",
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] | [
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] | [
"description",
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"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.video-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-11T10:41:01Z | Official Pytorch Code of the Paper "FashionChameleon: Towards Real-Time and Interactive Human-Garment Video Customization" | [
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-02T15:56:46Z | false | 16 | 1,235,452,828 | https://quanjiansong.github.io/projects/FashionChameleon/ | Python | Apache-2.0 | [
"diffusion",
"generative-modeling",
"video-generation"
] | QuanjianSong/FashionChameleon | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-04T14:53:29Z | [] | 2026-09-25T07:25:42Z | [
"vision.video-generation"
] | 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 | 268 | [
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"sft",
"streaming",
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] | 2026-09-29T13:15:54Z | https://github.com/QuanjianSong/FashionChameleon | null |
[
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"description",
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"repository-metadata"
] | [
"timeseries.state-space-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-11T11:09:31Z | [MICCAI'26] Official Pytorch Code for Spectral-Temporal State Space Modeling on Functional Brain Networks | [
"sequence-modeling",
"time-series"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 1 | 1,235,475,484 | null | Python | null | [
"mamba",
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"state-space-model"
] | JaeyoonSSim/SpecT-Mamba | [
"description",
"query-match",
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] | 3 | 2026-10-03T15:45:52Z | [] | 2026-05-11T11:29:49Z | [
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] | include | ml-contribution-v5 | 4 | [] | 2026-07-27T06:30:36Z | https://github.com/JaeyoonSSim/SpecT-Mamba | null |
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] | include | ml-contribution-v5 | 49 | [
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] | 2026-10-06T10:56:55Z | https://github.com/h6kplus/PhyMotion | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-12T13:04:26Z | Source code for paper "DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices". | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 1,236,661,934 | null | Python | NOASSERTION | [
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] | 1 | 2026-10-04T15:58:58Z | [] | 2026-05-12T13:13:10Z | [
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] | include | ml-contribution-v5 | 2 | [] | 2026-05-23T14:20:10Z | https://github.com/thunlp/DECO | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-13T18:03:53Z | Official implementation of "SEGA: Spectral-Energy Guided Attention for Resolution Extrapolation in Diffusion Transformers" [NeurIPS 2026] | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T16:16:53Z | false | 6 | 1,238,001,435 | null | Python | null | [
"diffusion",
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] | rajabi2001/sega | [
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] | 11 | 2026-10-15T14:18:24Z | [] | 2026-09-30T15:25:41Z | [
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] | include | ml-contribution-v5 | 75 | [] | 2026-10-14T19:36:50Z | https://github.com/rajabi2001/sega | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-14T07:08:41Z | [EMNLP 2026 Oral] This repo is the official implementation of "IntentVLA: Short-Horizon Intent Modeling for Aliased Robot Manipulation" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 0 | 1,238,497,331 | null | Python | null | [
"multimodal-learning"
] | ZGC-EmbodyAI/IntentVLA | [
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] | 5 | 2026-10-05T18:59:49Z | [] | 2026-09-25T07:36:39Z | [
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] | include | ml-contribution-v5 | 14 | [] | 2026-09-25T07:36:43Z | https://github.com/ZGC-EmbodyAI/IntentVLA | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-14T07:37:25Z | The code for paper AT-VLA: Adaptive Tactile Injection for Enhanced Feedback Reaction in Vision-Language-Action Models | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T14:50:46Z | false | 1 | 1,238,521,534 | null | Python | MIT | [
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] | clorislili/AT-VLA | [
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] | include | ml-contribution-v5 | 21 | [] | 2026-09-15T12:36:45Z | https://github.com/clorislili/AT-VLA | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-14T21:36:18Z | Official repository for SumGCN: A novel Graph Neural Network architecture based on the anti-Laplacian operator. Includes theoretical foundations, source code, and full reproducibility for node/graph classification experiments and ADHD detection. | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-06-01T15:19:12Z | https://github.com/MauricioAguasFonseca/SumGCN-Research | null |
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-15T16:48:46Z | NeurIPS 2026: Official implementation of “VISD: Enhancing Video Reasoning via Structured Self-Distillation”. | [
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] | include | ml-contribution-v5 | 26 | [] | 2026-10-05T13:10:06Z | https://github.com/Koreyoshi01/VISD | null |
[
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] | [] | [
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] | [] | false | unknown | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-18T05:21:37Z | This is the official implementation of "Learning Latent Protein Languages for Autoregressive Generation" paper. | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-06T18:33:52Z | false | 0 | 1,242,067,542 | null | Python | MIT | [] | mahdip72/latent_protein_languages | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-18T06:40:13Z | Official Implementation of NeurIPS 25 Paper "Confounding Robust Deep Reinforcement Learning: A Causal Approach" | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-11-01T17:09:41Z | false | 0 | 1,242,123,571 | null | Python | MIT | [
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] | include | ml-contribution-v5 | 2 | [] | 2026-10-08T07:56:33Z | https://github.com/CausalAILab/Confounding-Robust-Deep-Reinforcement-Learning | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-18T09:38:45Z | Official implementation of the ICML 2026 paper "Structure Abstraction and Generalization in a Hippocampal-Entorhinal Inspired World Model". | [
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[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-18T09:39:34Z | Official implementation of the ICML 2026 paper "DiLA: Disentangled Latent Action World Models". | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 1 | 1,242,287,943 | https://disentangled-latent-action-world-models.github.io/ | Jupyter Notebook | null | [
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] | include | ml-contribution-v5 | 19 | [
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[
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[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-19T06:28:10Z | Official implementation of ICML 2026 paper Temporal-aware Flow Matching for Video Generation with Temporally Coherent Motion | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T13:50:07Z | false | 0 | 1,243,208,019 | https://pzrain.github.io/tfm/ | Python | Apache-2.0 | [
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] | 2 | 2026-09-28T17:46:51Z | [] | 2026-09-23T12:04:25Z | [
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] | include | ml-contribution-v5 | 4 | [
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] | 2026-09-23T17:19:11Z | https://github.com/pzrain/TFM | null |
[
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] | include | ml-contribution-v5 | 0 | [] | 2026-05-19T11:44:16Z | https://github.com/hyeon1996/hyeon1996.github.io | null |
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] | include | ml-contribution-v5 | 0 | [] | 2026-05-20T03:12:29Z | https://github.com/icantnamemyself/DisenTS | null |
[
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"automl",
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-18T13:47:34Z | false | 2 | 1,244,179,169 | https://arxiv.org/abs/2605.16567 | Python | MIT | [
"ensemble-learning",
"few-shot-learning",
"meta-learning"
] | ph-phuc/MetaEns | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 6 | 2026-10-29T17:49:51Z | [] | 2026-10-09T14:46:24Z | [
"general.meta-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 2 | [
"anomaly-detection",
"ensemble-learning",
"icml-2026",
"meta-learning",
"model-selection",
"outlier-detection",
"unsupervised-learning"
] | 2026-10-11T10:03:38Z | https://github.com/ph-phuc/MetaEns | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-20T06:20:10Z | Official implementation of [GP2F: Cross-Domain Graph Prompting with Adaptive Fusion of Pre-trained Graph Neural Networks](https://arxiv.org/abs/2602.11629), accepted by ICML 2026. | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-25T15:11:36Z | false | 0 | 1,244,295,579 | null | Python | null | [
"graph-neural-network",
"message-passing",
"neural-network"
] | hedongxiao-tju/GP2F | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-10-25T15:11:36Z | [] | 2026-05-20T06:20:59Z | [
"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 | 1 | [] | 2026-05-29T06:18:52Z | https://github.com/hedongxiao-tju/GP2F | null |
[
"embodied-ai",
"generative-modeling",
"interpretability-and-safety",
"robotics"
] | [
"diffusion",
"diffusion-policy",
"imitation-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"robotics.diffusion-policy"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-20T09:47:15Z | [ICML 2026 Spotlight] Official implementation of "PACT: Self-Evolving Physical Safety Alignment for Diffusion Policies in Embodied Manipulation". | [
"embodied-ai",
"generative-modeling",
"interpretability-and-safety",
"robotics"
] | [
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 1 | 1,244,472,355 | https://ethan-iai.github.io/pact/ | Python | Apache-2.0 | [
"diffusion",
"diffusion-policy",
"imitation-learning"
] | thu-ml/PACT | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-09-25T16:01:25Z | [] | 2026-06-16T02:31:42Z | [
"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 | 14 | [] | 2026-08-27T20:14:53Z | https://github.com/thu-ml/PACT | null |
[
"machine-learning-systems",
"natural-language-processing",
"sequence-modeling",
"time-series"
] | [
"mamba",
"quantization",
"sequence-modeling",
"state-space-model",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"timeseries.state-space-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-21T04:25:35Z | TESM (Token-Entangled State Machine) - A novel LLM architecture based on state entanglement theory, combining State Space Models with local entanglement mechanisms for efficient long-sequence modeling | [
"machine-learning-systems",
"natural-language-processing",
"sequence-modeling",
"time-series"
] | [
"deep-learning",
"large-language-model",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 0 | 1,245,271,340 | https://github.com/WANGLILIN360/tesm | Python | MIT | [
"mamba",
"quantization",
"sequence-modeling",
"state-space-model",
"transformer"
] | WANGLILIN360/tesm | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2026-06-08T01:10:30Z | [
"timeseries.state-space-models"
] | 725fb42c6bc55251a697beedff3481ce89820a8e | 2026-10-05T21:36:53Z | gh-ml-readme-evidence-v2 | "725fb42c6bc55251a697beedff3481ce89820a8e" | [
"other"
] | [] | ok | 2026-10-05T21:36:53Z | WANGLILIN360/tesm | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 1 | [
"cuda",
"deep-learning",
"entanglement",
"int2-quantization",
"large-language-model",
"llm",
"mamba",
"nlp",
"pytorch",
"quantum-entanglement",
"sequence-modeling",
"ssm",
"state-space-model",
"token-entanglement",
"transformer-alternative",
"triton"
] | 2026-06-03T04:29:36Z | https://github.com/WANGLILIN360/tesm | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-21T07:16:54Z | [NeurIPS 2026] An official implementation of Reward Score Matching: Unifying Reward-based Fine-tuning for Flow and Diffusion Models | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-30T17:16:29Z | false | 0 | 1,245,390,819 | https://arxiv.org/abs/2604.17415 | Python | MIT | [
"diffusion"
] | jaylee2000/rsm | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 2 | 2026-11-01T17:09:41Z | [] | 2026-06-19T23:42:50Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 18 | [
"diffusion-rl"
] | 2026-10-28T03:19:09Z | https://github.com/jaylee2000/rsm | null |
[
"machine-learning"
] | [
"deep-kernel-learning",
"kernel-methods"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"specialized26.deep-kernel-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-21T12:37:23Z | Code for paper “Continuous Bayesian Machine Learning for High-Dimensional Analog and Mixed-signal Circuit Yield Analysis and Optimization” | [
"machine-learning"
] | [
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T22:33:16Z | false | 0 | 1,245,659,859 | null | Python | Apache-2.0 | [
"deep-kernel-learning",
"kernel-methods"
] | JingKou129/SBYA | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-09-26T22:33:16Z | [] | 2026-05-21T13:18:53Z | [
"specialized26.deep-kernel-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-05-22T02:34:26Z | https://github.com/JingKou129/SBYA | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-21T12:52:20Z | Repository containing code to our ICML26 paper "Certifying Graph Neural Networks Against Label and Structure Poisoning" | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-25T15:11:36Z | false | 1 | 1,245,673,075 | null | Python | MIT | [
"graph-neural-network",
"message-passing",
"neural-network"
] | p-CXTN/ST-GPA | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-25T15:11:36Z | [] | 2026-08-30T09:22:26Z | [
"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 | 1 | [] | 2026-08-30T17:53:47Z | https://github.com/p-CXTN/ST-GPA | null |
[
"general-ml"
] | [
"novel-method",
"transformer"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.novel-method"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-21T20:50:51Z | Soft Clustering and Weighted Transformer-Based Novel Method for Missing Data Imputation | [
"general-ml"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 0 | 1,246,064,184 | null | Python | null | [
"novel-method",
"transformer"
] | caferbudak/Soft-Clustering-and-Weighted-Transformer-Based-Novel-Method-for-Missing-Data-Imputation- | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-09-27T16:09:59Z | [] | 2026-05-21T22:00:55Z | [
"general.novel-method"
] | 20a1f05f06dc08b64fd8edc287fc783b789dc604 | 2026-09-26T15:53:33Z | gh-ml-readme-evidence-v2 | "20a1f05f06dc08b64fd8edc287fc783b789dc604" | [
"dataset",
"other"
] | [
"ml-method-context",
"paper-reference"
] | ok | 2026-09-26T15:53:33Z | caferbudak/Soft-Clustering-and-Weighted-Transformer-Based-Novel-Method-for-Missing-Data-Imputation- | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue",
"paper-reference"
] | include | ml-contribution-v5 | 0 | [] | 2026-05-21T22:00:59Z | https://github.com/caferbudak/Soft-Clustering-and-Weighted-Transformer-Based-Novel-Method-for-Missing-Data-Imputation- | null |
[
"generative-ai",
"generative-modeling",
"natural-language-processing"
] | [
"diffusion",
"language-model"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"recall.name.diffusion-model",
"recall.name.language-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-22T00:59:44Z | [NeurIPS 2026] Official implementation of Contrastive Distribution Matching for Amortized Sequential Monte Carlo in Discrete Diffusion | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-01T19:00:27.900159Z | false | 1 | 1,246,194,140 | https://cdm-smc.github.io/ | Python | NOASSERTION | [
"diffusion"
] | KAIST-Visual-AI-Group/CDM | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-20T14:47:52Z | [] | 2026-10-01T14:58:40Z | [
"recall.name.diffusion-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 15 | [
"diffusion-models",
"generative-ai",
"inference"
] | 2026-10-01T14:58:47Z | https://github.com/KAIST-Visual-AI-Group/CDM | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"multimodal",
"vision"
] | [
"diffusion",
"generative-modeling",
"image-generation",
"text-to-image"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.text-to-image",
"vision.image-diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-22T15:50:04Z | Official implementation for "Cert-LAS: Toward Certified Model Ownership Verification for Text-to-Image Diffusion Models via Layer-Adaptive Smoothing" (ICML 2026). | [
"generative-ai",
"generative-modeling",
"multimodal",
"vision"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-16T15:36:56Z | false | 0 | 1,246,831,619 | null | Python | Apache-2.0 | [
"diffusion",
"image-generation",
"text-to-image"
] | Leyi-Qi/Cert-LAS | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-20T15:59:06Z | [] | 2026-06-19T09:52:28Z | [
"multimodal.text-to-image"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 1 | [] | 2026-06-19T09:52:32Z | https://github.com/Leyi-Qi/Cert-LAS | null |
[
"applied-mathematics",
"applied-physics",
"computational-science",
"generative-modeling"
] | [
"data-assimilation",
"diffusion",
"inverse-problems",
"reconstruction"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"science.inverse-problems"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-23T14:05:03Z | Official code for ICML 2026 paper "Stage-wise Distortion–Perception Traversal in Zero-shot Inverse Problems with Diffusion Models". | [
"applied-mathematics",
"applied-physics",
"computational-science",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 0 | 1,247,582,384 | null | Python | null | [
"data-assimilation",
"diffusion",
"inverse-problems",
"reconstruction"
] | weigerzan/MAP_RPS | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-02T16:59:40Z | [] | 2026-05-26T14:13:01Z | [
"science.inverse-problems"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [] | 2026-09-26T18:09:01Z | https://github.com/weigerzan/MAP_RPS | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-23T14:42:01Z | implementation for neurosemantic signature-a novel graph neural network framework that integrates spectral temporal EEG feature with high level linguistic features derived from same subjects. | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-25T15:11:36Z | false | 0 | 1,247,607,475 | null | Jupyter Notebook | GPL-3.0 | [
"graph-neural-network",
"message-passing",
"neural-network"
] | kash78675/kkt-alzheimer-neuro-rep | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-25T15:11:36Z | [] | 2026-05-26T09:02:10Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
"contribution-language",
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-05-26T09:02:15Z | https://github.com/kash78675/kkt-alzheimer-neuro-rep | null |
[
"multi-agent-systems",
"reinforcement-learning"
] | [
"centralized-training",
"multi-agent-reinforcement-learning",
"reinforcement-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.multiagent"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-23T16:58:39Z | Code for paper "HPS: Hyperspherical Parameter Sharing for Efficient Multi-Agent Reinforcement Learning" | [
"multi-agent-systems",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 1 | 1,247,699,938 | null | Python | Apache-2.0 | [
"centralized-training",
"multi-agent-reinforcement-learning",
"reinforcement-learning"
] | stimulus1594/HPS | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-03T15:45:52Z | [] | 2026-05-26T15:05:51Z | [
"rl.multiagent"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 13 | [] | 2026-08-24T08:30:39Z | https://github.com/stimulus1594/HPS | null |
[
"deep-learning",
"graph-learning",
"trustworthy-ml"
] | [
"calibration",
"graph-neural-network",
"message-passing",
"neural-network",
"uncertainty-quantification"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description",
"trust.uncertainty-quantification"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-24T13:07:35Z | This repository contains the official implementation for the paper: X-EviProbe: Post-hoc Parameter-free Evidential Uncertainty Quantification for Frozen Graph Neural Networks | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-07T18:07:52Z | false | 0 | 1,248,297,342 | null | HTML | NOASSERTION | [
"graph-neural-network",
"message-passing",
"neural-network"
] | chenghuaguo/X-EviProbe | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-25T15:11:36Z | [] | 2026-05-24T13:15:05Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-05-24T13:15:30Z | https://github.com/chenghuaguo/X-EviProbe | null |
[
"generative-ai",
"generative-modeling",
"language",
"multimodal",
"multimodal-learning",
"natural-language-processing"
] | [
"diffusion",
"language-model",
"language-modeling",
"large-language-model",
"multimodal-learning",
"pretraining"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.language-model",
"llm.large-language-model",
"nlp.language-modeling",
"recall.name.diffusion-model",
"recall.name.language-model",
"recall.name.large-language-model",
"recall.name.multimodal-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-25T08:23:03Z | Official implementation of paper “Visual-Redundancy-Controlled Parallel Decoding for Diffusion-Based Multimodal Large Language Models” | [
"generative-modeling",
"multimodal-learning",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T15:56:46Z | false | 2 | 1,248,977,394 | null | Python | Apache-2.0 | [
"diffusion",
"language-model"
] | infiniteYuanyl/VRCD | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-10-09T16:23:55Z | [] | 2026-09-25T09:11:27Z | [
"recall.name.language-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 9 | [] | 2026-10-06T16:10:44Z | https://github.com/infiniteYuanyl/VRCD | null |
[
"deep-learning",
"graph-learning",
"representation-learning"
] | [
"contrastive-learning",
"graph-contrastive-learning",
"self-supervised-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning",
"graph.graph-contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-25T09:06:30Z | Official PyTorch implementation of SPGCL in 'Revisiting Positive Samples in Graph Contrastive Learning: From the Perspective of Message Passing' (ICML 2026). | [
"graph-learning",
"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 0 | 1,249,013,174 | null | Python | MIT | [
"contrastive-learning",
"graph-contrastive-learning",
"self-supervised-learning"
] | hedongxiao-tju/SPGCL | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-09-26T10:28:13Z | [] | 2026-05-29T00:55:19Z | [
"graph.graph-contrastive-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 7 | [] | 2026-09-13T06:01:38Z | https://github.com/hedongxiao-tju/SPGCL | null |
[
"language",
"multimodal",
"natural-language-processing",
"video"
] | [
"distillation",
"video-question-answering",
"video-understanding"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.video-question-answering"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-25T11:56:28Z | The official code of the paper PEGS: Progressive Event Grounding with Self-Distillation for Video Question Answering | [
"language",
"multimodal",
"natural-language-processing",
"video"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 0 | 1,249,147,302 | null | Python | null | [
"distillation",
"video-question-answering",
"video-understanding"
] | XizeWu/PEGS | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-09-30T16:47:43Z | [] | 2026-05-25T11:57:02Z | [
"multimodal.video-question-answering"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-05-25T11:57:49Z | https://github.com/XizeWu/PEGS | null |
[
"audio",
"generative-modeling",
"interpretability-and-safety",
"multimodal",
"speech-and-audio"
] | [
"flow-matching",
"generative-modeling",
"training"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"audio-text-to-audio"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-26T04:26:05Z | Official code for AG-REPA: Causal Layer Selection for Representation Alignment in Audio Flow Matching (ICML 2026). Unified TTS+TTA Flow Matching with BiT-C / LASP / FoG-A diagnostics. | [
"audio",
"generative-modeling",
"interpretability-and-safety",
"multimodal",
"speech-and-audio"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-21T15:48:06Z | false | 2 | 1,249,829,675 | null | Python | MIT | [
"flow-matching",
"generative-modeling",
"training"
] | zpforlove/AG-REPA | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-21T15:48:06Z | [] | 2026-05-27T02:35:16Z | [
"audio-text-to-audio"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 7 | [] | 2026-10-16T05:40:07Z | https://github.com/zpforlove/AG-REPA | null |
[
"computer-vision",
"embodied-ai",
"interpretability-and-safety",
"language",
"multimodal",
"multimodal-learning",
"robotics",
"vision"
] | [
"contrastive-learning",
"robot-learning",
"vision-language-action",
"vision-language-model"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.vision-language",
"robotics.visual-language-action"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-26T07:01:28Z | The official code for paper "LARA: Latent Action Representation Alignment for Vision-Language-Action Models" | [
"computer-vision",
"interpretability-and-safety",
"language",
"multimodal",
"multimodal-learning",
"vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T14:50:46Z | false | 1 | 1,249,946,507 | null | Python | null | [
"contrastive-learning",
"vision-language-model"
] | lmy1001/LARA | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-21T15:48:06Z | [] | 2026-08-03T07:43:51Z | [
"multimodal.vision-language"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 10 | [] | 2026-10-12T08:33:11Z | https://github.com/lmy1001/LARA | null |
[
"efficient-ml",
"machine-learning",
"model-compression"
] | [
"distillation",
"knowledge-distillation",
"machine-unlearning",
"privacy"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation",
"specialized.machine-unlearning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-26T07:11:43Z | Official code for Paper “Learning on Unlearned Data Can Help Unlearning: Guided Machine Unlearning via Multi-Model Knowledge Distillation” | [
"efficient-ml",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 0 | 1,249,954,938 | null | Python | null | [
"distillation",
"knowledge-distillation"
] | SPHelixLab/GMUUD | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-10T16:53:26Z | [] | 2026-05-26T07:26:48Z | [
"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 | [] | 2026-05-26T07:26:53Z | https://github.com/SPHelixLab/GMUUD | null |
[
"computer-vision",
"general-ml",
"generative-ai",
"generative-modeling"
] | [
"diffusion",
"generative-modeling",
"transformer",
"video-generation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"recall.name.diffusion-model",
"recall.name.transformer-model",
"vision.video-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-05-26T07:12:06Z | [ICML 2026] This is the official PyTorch implementation of "⚡ LearniBridge: Learnable Calibration of Feature Caching for Diffusion Model Acceleration". | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-01T16:56:07Z | false | 1 | 1,249,955,293 | null | Python | Apache-2.0 | [
"diffusion"
] | Iiiiiiirene/LearniBridge | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 10 | 2026-11-01T17:09:41Z | [] | 2026-10-02T03:53:06Z | [
"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 | 9 | [] | 2026-10-20T03:33:03Z | https://github.com/Iiiiiiirene/LearniBridge | null |
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