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",
"generative-ai",
"language",
"multimodal",
"safety"
] | [
"alignment",
"diffusion",
"generative-modeling",
"motion-generation",
"preference-optimization"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.preference-optimization",
"multimodal.motion-generation",
"recall.name.diffusion-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-06-06T11:04:10Z | [NeurIPS 2022] Official Code for "Benchmarking 3D Pose and Shape Estimation Beyond Algorithms" | [
"generative-ai"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 4 | 500,398,517 | null | Python | Apache-2.0 | [
"diffusion"
] | MotrixLab/hmr-benchmarks | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-28T17:27:44Z | [] | 2026-02-13T02:52:02Z | [
"recall.name.diffusion-model"
] | 1c240a3fd916da823896deb71e7920a1b6f05788 | 2026-09-26T20:25:07Z | gh-ml-readme-evidence-v2 | "1c240a3fd916da823896deb71e7920a1b6f05788" | [
"citation",
"dataset",
"installation",
"other",
"overview",
"results",
"usage"
] | [
"method-contribution",
"ml-method-context",
"paper-reference"
] | ok | 2026-09-26T20:25:07Z | MotrixLab/hmr-benchmarks | readme-supported-paper-method-implementation | [
"method-contribution",
"ml-method-context",
"paper-reference"
] | include | ml-contribution-v5 | 133 | [] | 2026-08-04T15:21:25Z | https://github.com/MotrixLab/hmr-benchmarks | null |
[
"reinforcement-learning"
] | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-06-06T12:33:05Z | The official code for paper "α-Nego: Self-play Deep Reinforcement Learning for Negotiation Dialogues". | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-22T16:09:07Z | false | 0 | 500,426,009 | null | Python | MIT | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | Jinyi6/alpha-Nego | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-22T16:09:07Z | [] | 2022-06-07T01:43:56Z | [
"rl.deep"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2022-06-06T12:36:19Z | https://github.com/Jinyi6/alpha-Nego | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"multimodal",
"multimodal-learning",
"natural-language-processing"
] | [
"computer-vision",
"diffusion",
"multimodal-learning",
"text-generation",
"vision-language"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"nlp.text-generation",
"recall.name.diffusion-model",
"vision.topic-computer-vision",
"vision.vision-language"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-06-06T12:38:59Z | Official implementation for "Blended Latent Diffusion" [SIGGRAPH 2023] | [
"computer-vision",
"generative-ai",
"generative-modeling",
"multimodal-learning"
] | [
"deep-learning",
"generative-model",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T20:57:28.489717Z | false | 37 | 500,427,876 | https://omriavrahami.com/blended-latent-diffusion-page/ | Jupyter Notebook | MIT | [
"diffusion"
] | omriav/blended-latent-diffusion | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 9 | 2026-10-28T17:27:44Z | [] | 2024-06-04T11:38:45Z | [
"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 | 631 | [
"computer-vision",
"deep-learning",
"diffusion",
"diffusion-models",
"generative-model",
"image-generation",
"multimodal",
"multimodal-deep-learning",
"pytorch",
"text-driven-editing",
"text-guided-manipulation",
"text-to-image",
"text-to-image-synthesis"
] | 2026-09-26T10:46:37Z | https://github.com/omriav/blended-latent-diffusion | null |
[
"computer-vision",
"general-ml"
] | [
"detection",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"recall.name.transformer-model",
"vision.object-detection"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-06-06T16:02:01Z | [CVPR 2023] Official implementation of the paper "Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation" | [
"computer-vision",
"general-ml"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-07T17:53:16Z | false | 162 | 500,500,682 | null | Python | Apache-2.0 | [
"transformer"
] | IDEA-Research/MaskDINO | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-08T17:58:00Z | [] | 2023-12-20T07:05:03Z | [
"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 | 1,568 | [
"instance-segmentation",
"object-detection",
"panoptic-segmentation",
"semantic-segmentation"
] | 2026-10-06T14:19:12Z | https://github.com/IDEA-Research/MaskDINO | null |
[
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion",
"generative-modeling"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"recall.name.diffusion-model",
"vision.image-diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-06-07T10:53:23Z | Official PyTorch implementation of the paper Progressive Deblurring of Diffusion Models for Coarse-to-Fine Image Synthesis. | [
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"generative-model",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T20:57:28.489717Z | false | 9 | 500,800,680 | null | Python | MIT | [
"diffusion"
] | sangyun884/blur-diffusion | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-28T17:27:44Z | [] | 2022-09-11T02:15:28Z | [
"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 | 159 | [
"diffusion-models",
"generative-model",
"image-generation",
"image-synthesis",
"score-based-generative-modeling",
"score-based-generative-models",
"score-based-models"
] | 2026-09-03T01:27:09Z | https://github.com/sangyun884/blur-diffusion | null |
[
"computer-vision",
"information-retrieval",
"language",
"multimodal",
"video"
] | [
"image-captioning",
"image-retrieval",
"metric-learning",
"video-question-answering",
"video-understanding",
"vision-language"
] | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"multimodal.video-question-answering",
"vision.image-captioning",
"vision.image-retrieval"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-06-07T17:36:23Z | [ACL 2023] Official PyTorch code for Singularity model in "Revealing Single Frame Bias for Video-and-Language Learning" | [
"computer-vision",
"multimodal"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:28:44Z | false | 14 | 500,942,683 | https://arxiv.org/abs/2206.03428 | Python | MIT | [
"image-captioning",
"vision-language"
] | jayleicn/singularity | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 9 | 2026-10-26T17:25:59Z | [] | 2023-05-05T17:22:22Z | [
"vision.image-captioning"
] | 3fe6b0e12c3e3a5d806fd17fb2718622ae95f181 | 2026-09-26T20:25:07Z | gh-ml-readme-evidence-v2 | "3fe6b0e12c3e3a5d806fd17fb2718622ae95f181" | [
"installation",
"other",
"results"
] | [
"ml-method-context",
"official-implementation-claim",
"paper-reference"
] | ok | 2026-09-26T20:25:07Z | jayleicn/singularity | readme-supported-paper-method-implementation | [
"ml-method-context",
"official-implementation-claim",
"paper-reference"
] | include | ml-contribution-v5 | 136 | [] | 2025-10-20T13:38:34Z | https://github.com/jayleicn/singularity | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-06-08T23:17:31Z | Official code for the LoG2022 paper -- MSGNN: A Spectral Graph Neural Network Based on a Novel Magnetic Signed Laplacian. | [
"deep-learning",
"graph-learning"
] | [
"machine-learning",
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-06T17:10:23Z | false | 3 | 501,439,139 | null | Python | MIT | [
"graph-neural-network",
"message-passing",
"neural-network"
] | SherylHYX/MSGNN | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-06T17:10:23Z | [] | 2026-09-03T07:07:11Z | [
"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 | 14 | [
"directed-networks",
"graph-neural-networks",
"machine-learning",
"magnetic-laplacian",
"pytorch",
"signed-directed-networks",
"signed-networks"
] | 2026-09-03T07:09:13Z | https://github.com/SherylHYX/MSGNN | null |
[
"computer-vision",
"deep-learning",
"imbalanced-learning",
"machine-learning",
"reliable-ml",
"representation-learning",
"robust-ml",
"trustworthy-ml"
] | [
"classification",
"contrastive-learning",
"long-tailed-learning",
"out-of-distribution-detection",
"recognition",
"self-supervised-learning",
"uncertainty-estimation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning",
"probable.evaluation-ood-readme",
"recall26.long-tailed-recognition",
"trust.out-of-distribution-detection"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-06-10T00:03:31Z | Official implementation for "Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition" (ICML'22 Long Presentation) | [
"deep-learning",
"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 6 | 501,844,557 | https://proceedings.mlr.press/v162/wang22aq/wang22aq.pdf | Python | Apache-2.0 | [
"contrastive-learning",
"self-supervised-learning"
] | amazon-science/long-tailed-ood-detection | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 10 | 2026-10-12T20:31:19Z | [] | 2023-05-15T14:26:15Z | [
"general.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 | 42 | [] | 2025-12-10T01:56:02Z | https://github.com/amazon-science/long-tailed-ood-detection | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-06-10T23:49:54Z | PyTorch code for our CIKM 2022 paper "Calibrate Automated Graph Neural Network via Hyperparameter Uncertainty" | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 0 | 502,208,421 | null | Python | null | [
"graph-neural-network",
"message-passing",
"neural-network"
] | xyang2316/HyperU-GCN | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 1 | 2026-10-08T18:15:14Z | [] | 2022-10-20T05:15:46Z | [
"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 | [
"gnn",
"hypernetworks",
"uncertainty-estimation"
] | 2024-10-23T03:45:45Z | https://github.com/xyang2316/HyperU-GCN | null |
[
"reinforcement-learning"
] | [
"batch-reinforcement-learning",
"offline-reinforcement-learning",
"reinforcement-learning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.offline"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-06-11T15:05:45Z | [AAAI 2022] The official implementation of CPQ in "Constraints Penalized Q-learning for Safe Offline Reinforcement Learning" | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 2 | 502,379,796 | null | Python | MIT | [
"batch-reinforcement-learning",
"offline-reinforcement-learning",
"reinforcement-learning"
] | ryanxhr/CPQ | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-07T17:53:16Z | [] | 2023-01-26T14:06:03Z | [
"rl.offline"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 12 | [
"constrained-reinforcement-learning",
"offline-reinforcement-learning"
] | 2025-01-13T06:44:58Z | https://github.com/ryanxhr/CPQ | null |
[
"automl",
"deep-learning",
"general-ml",
"representation-learning"
] | [
"continual-learning",
"distillation",
"few-shot-learning",
"knowledge-distillation",
"lifelong-learning",
"meta-learning"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"general.continual-learning",
"general.meta-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-06-11T20:58:53Z | code for our paper "Incremental Meta-Learning via Episodic Replay Distillation for Few-Shot Image Recognition" in CVPR 2022 3rd CLVISION continual learning workshop | [
"automl",
"general-ml"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-01T19:00:27.900159Z | false | 0 | 502,454,987 | null | Python | null | [
"distillation",
"few-shot-learning",
"knowledge-distillation",
"meta-learning"
] | wangkai930418/ERD | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 3 | 2026-10-17T13:17:44Z | [] | 2022-06-11T21:30:08Z | [
"general.meta-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 4 | [
"continual-learning",
"few-shot-learning",
"incremental-learning",
"knowledge-distillation",
"meta-learning"
] | 2026-02-20T09:49:08Z | https://github.com/wangkai930418/ERD | 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 | [] | 2022-06-13T22:48:49Z | A new adversarial purification method that uses the forward and reverse processes of diffusion models to remove adversarial perturbations. | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 50 | 503,137,361 | null | Python | NOASSERTION | [
"diffusion"
] | NVlabs/DiffPure | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-28T17:27:44Z | [] | 2023-01-29T23:01:03Z | [
"recall.name.diffusion-model"
] | 9fc47a91d81b8d0e11b716a4406c121678f91fb0 | 2026-09-29T18:47:14Z | gh-ml-readme-evidence-v2 | "9fc47a91d81b8d0e11b716a4406c121678f91fb0" | [
"citation",
"installation",
"other"
] | [
"method-contribution",
"ml-method-context",
"model-training-artifact",
"paper-reference"
] | ok | 2026-09-29T18:47:14Z | NVlabs/DiffPure | specific-method-with-novelty-claim | [
"method-contribution",
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue",
"model-training-artifact",
"paper-reference"
] | include | ml-contribution-v5 | 347 | [] | 2026-10-15T14:23:04Z | https://github.com/NVlabs/DiffPure | null |
[
"efficient-ml",
"machine-learning-systems",
"model-compression",
"robust-ml",
"trustworthy-ml"
] | [
"certified-robustness",
"neural-network",
"neural-network-pruning",
"pruning",
"structured-pruning",
"verification"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning",
"trust.robustness-certification"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-06-15T05:01:22Z | Code for paper "Can pruning improve certified robustness of deep neural networks?" | [
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 1 | 503,621,242 | null | Python | null | [
"neural-network",
"neural-network-pruning",
"pruning",
"structured-pruning"
] | VITA-Group/CertifiedPruning | [
"description",
"query-match",
"repository-metadata"
] | 5 | 2026-10-15T15:20:52Z | [] | 2023-03-27T17:32:02Z | [
"efficiency.pruning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 3 | [] | 2024-10-21T06:14:43Z | https://github.com/VITA-Group/CertifiedPruning | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"recall.name.diffusion-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-06-15T07:46:20Z | Official PyTorch implementation for paper: Diffusion-GAN: Training GANs with Diffusion | [
"generative-ai",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 75 | 503,666,542 | null | Python | MIT | [
"diffusion"
] | Zhendong-Wang/Diffusion-GAN | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-28T17:27:44Z | [] | 2023-03-27T20:30:39Z | [
"recall.name.diffusion-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 708 | [] | 2026-10-25T06:34:24Z | https://github.com/Zhendong-Wang/Diffusion-GAN | null |
[
"efficient-ml",
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-06-15T17:59:59Z | Official Code of The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another in Neural Networks[ICML2022] | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-08T17:58:00Z | false | 1 | 503,871,582 | null | Python | null | [
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] | yuxwind/CBS | [
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] | 2 | 2026-10-15T15:20:52Z | [] | 2022-09-20T05:30:50Z | [
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] | include | ml-contribution-v5 | 16 | [] | 2026-03-30T14:53:11Z | https://github.com/yuxwind/CBS | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-06-16T07:00:36Z | Official implementation of "Multi-Level Branched Regularization for Federated Learning" in ICML 2022 | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 5 | 504,047,246 | null | Python | null | [
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] | jinkyu032/FedMLB | [
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] | 2 | 2026-10-07T17:53:16Z | [] | 2024-03-12T02:46:20Z | [
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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 | 21 | [] | 2026-03-27T08:02:48Z | https://github.com/jinkyu032/FedMLB | null |
[
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] | [
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] | [
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] | [
"recall.name.diffusion-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-06-16T12:11:31Z | Official code for "Maximum Likelihood Training for Score-Based Diffusion ODEs by High-Order Denoising Score Matching" (ICML 2022) | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-28T17:27:44Z | false | 8 | 504,141,798 | null | Python | null | [
"diffusion"
] | LuChengTHU/mle_score_ode | [
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] | 1 | 2026-10-28T17:27:44Z | [] | 2022-09-14T08:56:50Z | [
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] | include | ml-contribution-v5 | 67 | [] | 2026-06-28T12:23:25Z | https://github.com/LuChengTHU/mle_score_ode | null |
[
"efficient-ml",
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] | [
"efficiency.pruning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-06-16T16:24:12Z | Code for ICML 2022 paper "SPDY: Accurate Pruning with Speedup Guarantees" | [
"efficient-ml",
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-08T17:58:00Z | false | 4 | 504,227,049 | https://arxiv.org/abs/2201.13096 | Python | null | [
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] | IST-DASLab/spdy | [
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] | 2 | 2026-10-15T15:20:52Z | [] | 2023-05-03T08:16:23Z | [
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] | include | ml-contribution-v5 | 20 | [] | 2025-07-20T07:04:14Z | https://github.com/IST-DASLab/spdy | null |
[
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] | [
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-06-17T00:31:42Z | Code for ICLR 2022 Paper (HyperDQN: A Randomized Exploration Method for Deep Reinforcement Learning) | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-20T15:59:06Z | false | 1 | 504,345,368 | null | Python | null | [
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] | liziniu/HyperDQN | [
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] | 1 | 2026-10-20T15:59:06Z | [] | 2023-11-28T09:10:26Z | [
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] | include | ml-contribution-v5 | 12 | [
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] | 2025-06-10T16:54:41Z | https://github.com/liziniu/HyperDQN | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-06-17T12:08:27Z | Official implementation for "Towards Safe Reinforcement Learning via Constraining Conditional Value at Risk" (IJCAI 2022) | [
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"safety"
] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 3 | 504,520,583 | https://www.ijcai.org/proceedings/2022/0510 | Python | MIT | [
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"reinforcement-learning",
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] | yingchengyang/CPPO | [
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] | 4 | 2026-10-07T17:53:16Z | [] | 2024-08-29T06:12:35Z | [
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] | include | ml-contribution-v5 | 27 | [
"cvar",
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"risk-sensitive-reinforcement-learning",
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] | 2026-04-02T01:51:26Z | https://github.com/yingchengyang/CPPO | null |
[
"data-centric-ai",
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] | [
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] | [
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] | true | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-06-17T19:26:58Z | This repository contains code for our ACL 2022 Findings paper `Efficient Argument Structure Extraction with Transfer Learning and Active Learning`. We implement an argument structure extraction method based on a pre-trained Transformer model.` | [
"data-centric-ai",
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-06T17:10:23Z | false | 0 | 504,650,000 | null | Python | Apache-2.0 | [
"active-learning",
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] | bloomberg/argument-relation-transformer-acl2022 | [
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] | 1 | 2026-10-06T17:10:23Z | [] | 2023-06-12T21:32:34Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 7 | [] | 2026-07-01T21:02:09Z | https://github.com/bloomberg/argument-relation-transformer-acl2022 | null |
[
"computer-vision",
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] | [
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] | [
"llm.transformers"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-06-18T00:44:21Z | [ICML 2023] Official PyTorch implementation of Global Context Vision Transformers | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-08T17:58:00Z | false | 52 | 504,708,536 | https://arxiv.org/abs/2206.09959 | Python | NOASSERTION | [
"language-model",
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] | NVlabs/GCVit | [
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] | 1 | 2026-10-08T17:58:00Z | [] | 2023-12-22T13:04:04Z | [
"llm.transformers"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 449 | [
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] | 2026-09-30T12:45:30Z | https://github.com/NVlabs/GCVit | null |
[
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] | [
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-06-18T08:03:40Z | TEMPPO is a new Deep Reinforcement Learning algorithm. This new method is built upon PPO algorithm and also got some tricks like TD3 algorithm. The main challenges that TEMPPO is trying to encounter is overestimation error in Actor-Critic methods and using the entropy in a more efficient and intelligent way. | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-22T16:09:07Z | false | 0 | 504,782,217 | null | Python | null | [
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] | ahmadshahrokhi/TEMPPO-Twin-Entropy-Maximized-Proximal-Policy-Optimization- | [
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] | 1 | 2026-10-22T16:09:07Z | [] | 2022-06-18T08:16:03Z | [
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] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
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] | include | ml-contribution-v5 | 0 | [] | 2023-03-05T10:27:37Z | https://github.com/ahmadshahrokhi/TEMPPO-Twin-Entropy-Maximized-Proximal-Policy-Optimization- | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-06-20T13:54:52Z | This is code for our WWWJ paper《HMGCL: Heterogeneous Multigraph Contrastive Learning for LBSN Friend Recommendation》 | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-13T18:05:26Z | false | 2 | 505,462,779 | null | Python | null | [
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] | liyongkang123/HMGCL | [
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] | 1 | 2026-10-13T18:05:26Z | [] | 2025-03-03T18:43:25Z | [
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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 | 5 | [] | 2026-08-19T19:43:22Z | https://github.com/liyongkang123/HMGCL | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-06-20T16:11:48Z | In this repository, based on the latest NLP pappers, we researched on sequential data and time series and developed tasks in NLP such as stock price prediction, time series prediction, sentiment analysis from text and We developed the language model and so on. This research is based on recurrent neural networks, LSTM n... | [
"language",
"natural-language-processing",
"time-series-and-forecasting"
] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-18T14:44:29Z | false | 4 | 505,512,029 | null | Jupyter Notebook | null | [
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] | M-Taghizadeh/NLP-from-RNN-to-Transformer | [
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] | 1 | 2026-10-18T14:44:29Z | [] | 2024-05-11T18:08:33Z | [
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] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
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] | include | ml-contribution-v5 | 14 | [
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"sentiment-analysis",
"stock-price-prediction",
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] | 2026-08-08T20:23:50Z | https://github.com/M-Taghizadeh/NLP-from-RNN-to-Transformer | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-06-21T10:41:49Z | [IEEE TNNLS 2022] An official source code for paper Interpolation-based Contrastive Learning for Few-Label Semi-Supervised Learning. | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:28:44Z | false | 0 | 505,799,182 | null | Python | MIT | [
"contrastive-learning",
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] | xihongyang1999/ICL_SSL | [
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] | 4 | 2026-10-19T17:46:36Z | [] | 2025-03-06T09:15:07Z | [
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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 | 40 | [] | 2025-05-21T11:08:02Z | https://github.com/xihongyang1999/ICL_SSL | null |
[
"privacy-ml",
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-06-21T18:48:35Z | [USENIX Security 2022] Mitigating Membership Inference Attacks by Self-Distillation Through a Novel Ensemble Architecture | [
"privacy-ml",
"trustworthy-ml"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:28:44Z | false | 6 | 505,966,143 | null | Python | MIT | [
"distillation",
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] | inspire-group/MIAdefenseSELENA | [
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] | 4 | 2026-10-07T17:53:16Z | [] | 2022-08-29T22:07:12Z | [
"trust.membership-inference"
] | 6fe0e10b4c9f145127823fae0115b430b3f9f401 | 2026-09-26T15:53:33Z | gh-ml-readme-evidence-v2 | "6fe0e10b4c9f145127823fae0115b430b3f9f401" | [
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] | [
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"method-tied-novelty-claim",
"ml-method-cue",
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] | include | ml-contribution-v5 | 16 | [] | 2026-02-13T05:14:14Z | https://github.com/inspire-group/MIAdefenseSELENA | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-06-21T22:18:10Z | Open source code for paper "Denoised MDPs: Learning World Models Better Than the World Itself" | [
"general-ml"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 13 | 506,020,299 | null | Python | NOASSERTION | [
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] | facebookresearch/denoised_mdp | [
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] | 3 | 2026-10-28T17:27:44Z | [] | 2026-10-14T22:43:29Z | [
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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 | 143 | [] | 2026-09-23T20:33:47Z | https://github.com/facebookresearch/denoised_mdp | null |
[
"agriculture-and-food",
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] | [
"robotics.robotics-topic"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-06-23T11:02:41Z | Official TensorFlow code for the paper "Waypoint Generation in Row-based Crops with Deep Learning and Contrastive Clustering" presented at ECML PKDD 2022. | [
"agriculture-and-food",
"robotics",
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 1 | 506,604,771 | https://doi.org/10.48550/arXiv.2206.11623 | Python | Apache-2.0 | [
"robot-learning"
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] | 1 | 2026-10-23T15:59:01Z | [] | 2022-08-26T10:55:00Z | [
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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 | 10 | [
"deep-learning",
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"precision-agriculture",
"robotics"
] | 2025-09-24T12:21:47Z | https://github.com/fsalv/ClusterWay | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-06-25T02:36:59Z | Official PyTorch implementation of the paper ‘TDRNet: Transformer-Based Dual-Branch Restoration Network for Geometry Based Point Cloud Compression Artifacts’ (ICME'2022 Oral) | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-20T15:59:06Z | false | 0 | 507,195,872 | null | null | null | [
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] | gbliao/TDRNet | [
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] | 1 | 2026-10-20T15:59:06Z | [] | 2024-05-02T08:33:15Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 7 | [] | 2025-03-21T07:21:56Z | https://github.com/gbliao/TDRNet | null |
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] | [
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"query-match",
"repository-metadata"
] | [
"general.meta-learning",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-06-27T17:18:39Z | [MICCAI 2022] Official Implementation for "Meta-hallucinator: Towards few-shot cross-modality cardiac image segmentation" | [
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] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-17T13:17:44Z | false | 0 | 507,999,024 | null | null | MIT | [
"convolutional-neural-network",
"few-shot-learning",
"meta-learning",
"neural-network",
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] | jacobzhaoziyuan/Meta-Hallucinator | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-17T13:17:44Z | [] | 2023-05-12T06:31:26Z | [
"general.meta-learning",
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 5 | [
"convolutional-neural-networks",
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"image-segmentation",
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"meta-learning",
"pytorch",
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"ssl",
"uda",
"unet",
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] | 2023-08-30T07:38:39Z | https://github.com/jacobzhaoziyuan/Meta-Hallucinator | null |
[
"classical-ml",
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] | [
"distillation",
"metric-learning",
"representation-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.metric-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-06-27T18:20:51Z | Official code repository for the paper "Improving Generalization of Metric Learning via Listwise Self-distillation" in Pytorch | [
"classical-ml",
"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-13T18:05:26Z | false | 1 | 508,017,905 | null | Python | null | [
"distillation",
"metric-learning",
"representation-learning"
] | ZelongZeng/Improving-Generalization-of-Metric-Learning-via-Listwise-Self-distillation | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-13T18:05:26Z | [] | 2022-06-27T18:26:53Z | [
"general.metric-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 1 | [] | 2023-12-08T05:36:18Z | https://github.com/ZelongZeng/Improving-Generalization-of-Metric-Learning-via-Listwise-Self-distillation | null |
[
"language",
"multimodal",
"natural-language-processing",
"video"
] | [
"language-model",
"language-modeling",
"video-language-model",
"video-understanding"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.language-model",
"multimodal.video-language-model",
"multimodal.video-understanding",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-06-27T20:31:53Z | ViLMA: A Zero-Shot Benchmark for Linguistic and Temporal Grounding in Video-Language Models (ICLR 2024, Official Implementation) | [
"language",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 2 | 508,053,484 | https://cyberiada.github.io/ViLMA/ | Python | MIT | [
"language-model",
"language-modeling"
] | ilkerkesen/ViLMA | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-17T13:17:44Z | [] | 2024-01-18T19:40:14Z | [
"llm.language-model",
"nlp.language-modeling"
] | f233c630010f43cc71ad0ed8a84f4780659040c1 | 2026-09-26T20:42:46Z | gh-ml-readme-evidence-v2 | "f233c630010f43cc71ad0ed8a84f4780659040c1" | [
"citation",
"dataset",
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"other",
"overview",
"results"
] | [
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"ml-method-context",
"paper-reference"
] | ok | 2026-09-26T20:42:46Z | ilkerkesen/ViLMA | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-method-context",
"paper-reference"
] | include | ml-contribution-v5 | 16 | [] | 2025-10-06T09:14:54Z | https://github.com/ilkerkesen/ViLMA | null |
[
"deep-learning",
"graph-learning"
] | [
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"message-passing",
"neural-network"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-06-28T15:47:34Z | Source code for paper "Learning the Solution Operator of Boundary Value Problems using Graph Neural Networks" | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 3 | 508,361,974 | null | Python | MIT | [
"graph-neural-network",
"message-passing",
"neural-network"
] | merantix-momentum/gnn-bvp-solver | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2024-07-25T10:27:17Z | [
"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 | 20 | [] | 2026-03-05T08:27:40Z | https://github.com/merantix-momentum/gnn-bvp-solver | null |
[
"distributed-ml",
"general-ml",
"privacy-and-federated-learning"
] | [
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] | [
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] | [
"general.novel-method",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-06-28T19:00:56Z | Code for novel methods for one-shot Federated Learning under high statistical heterogeneity. | [
"general-ml",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 3 | 508,421,680 | null | Python | MIT | [
"federated-learning",
"novel-method"
] | ceh-2000/fed_cvae | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-10-08T17:58:00Z | [] | 2023-10-02T16:42:27Z | [
"general.novel-method"
] | 7600d47e0bfee5a0be892202ba8dd2d4109a1ca6 | 2026-09-26T15:53:33Z | gh-ml-readme-evidence-v2 | "7600d47e0bfee5a0be892202ba8dd2d4109a1ca6" | [
"other",
"overview"
] | [
"ml-method-context",
"model-training-artifact",
"paper-reference"
] | ok | 2026-09-26T15:53:33Z | ceh-2000/fed_cvae | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue",
"model-training-artifact",
"paper-reference"
] | include | ml-contribution-v5 | 19 | [] | 2026-06-21T15:35:14Z | https://github.com/ceh-2000/fed_cvae | null |
[
"generative-ai",
"language",
"reinforcement-learning",
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] | [
"code-generation",
"deep-reinforcement-learning",
"language-model",
"policy-learning",
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] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm-code-generation",
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-06-30T02:54:36Z | This is the official code for the paper CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning (NeurIPS22). | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 69 | 508,912,853 | null | Python | BSD-3-Clause | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | salesforce/CodeRL | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-20T15:59:06Z | [] | 2026-06-02T18:14:33Z | [
"rl.deep"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 578 | [
"ai",
"codegeneration",
"languagemodel",
"machinelearning",
"programsynthesis",
"reinforcementlearning"
] | 2026-10-16T13:01:09Z | https://github.com/salesforce/CodeRL | null |
[
"forecasting",
"geometric-learning",
"graph-learning",
"time-series",
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] | [
"continuous-depth-learning",
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"graph-neural-network",
"graph-neural-ode",
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] | [
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] | [
"graph.neural-ode-graph",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-06-30T13:13:35Z | [TKDE 2022] The official PyTorch implementation of the paper "Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs". | [
"graph-learning",
"time-series",
"time-series-and-forecasting"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 29 | 509,086,754 | null | Python | MIT | [
"graph-neural-network",
"neural-network",
"sequence-modeling"
] | TrustAGI-Lab/MTGODE | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 12 | 2026-10-15T15:20:52Z | [] | 2022-11-10T02:29:40Z | [
"timeseries.topic"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 88 | [
"graph-neural-networks",
"neural-odes",
"spatial-temporal-data",
"spatial-temporal-forecasting",
"time-series",
"time-series-forecasting"
] | 2026-07-16T07:32:54Z | https://github.com/TrustAGI-Lab/MTGODE | null |
[
"applied-mathematics",
"computational-science",
"generative-modeling",
"scientific-computing"
] | [
"differentiable-programming",
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] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"science.scientific-machine-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-06-30T19:22:23Z | Code for paper "Learning Green's Functions of Linear Reaction-Diffusion Equations with Application to Fast Numerical Solver" | [
"applied-mathematics",
"computational-science",
"generative-modeling",
"scientific-computing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 2 | 509,206,725 | null | Python | MIT | [
"differentiable-programming",
"diffusion",
"learned-solvers",
"surrogate-modeling"
] | sloooWTYK/GF-Net | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-08T17:58:00Z | [] | 2022-11-28T18:06:03Z | [
"science.scientific-machine-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 | 5 | [] | 2025-11-19T11:43:52Z | https://github.com/sloooWTYK/GF-Net | null |
[
"reinforcement-learning"
] | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-07-04T13:43:56Z | [KI'22] Official implementation of the paper "Solving the Traveling Salesperson Problem with Precedence Constraints (TSPPC) by Deep Reinforcement Learning" | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-20T15:59:06Z | false | 4 | 510,375,871 | null | Python | MIT | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | christianll9/tsppc-drl | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-20T15:59:06Z | [] | 2022-09-19T17:37:09Z | [
"rl.deep"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 13 | [] | 2026-02-01T13:25:44Z | https://github.com/christianll9/tsppc-drl | null |
[
"automl",
"distributed-ml",
"general-ml",
"privacy-and-federated-learning"
] | [
"collaborative-learning",
"federated-learning",
"few-shot-learning",
"meta-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.meta-learning",
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-07-05T07:02:16Z | Official Pytorch implementation for the paper titled "Few-Round Learning for Federated Learning" presented on NeurIPS 2021. | [
"automl",
"general-ml",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 1 | 510,623,642 | null | Python | MIT | [
"federated-learning",
"few-shot-learning",
"meta-learning"
] | MoonLab-YH/Few-Round-Learning-for-Federated-Learning | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-17T13:17:44Z | [] | 2022-07-14T05:05:45Z | [
"general.meta-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 5 | [] | 2024-06-26T06:23:01Z | https://github.com/MoonLab-YH/Few-Round-Learning-for-Federated-Learning | null |
[
"computer-vision",
"efficient-ml",
"model-compression"
] | [
"distillation",
"face-analysis",
"face-recognition",
"knowledge-distillation"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation",
"vision.face-recognition"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-07-06T02:20:59Z | Official Implementation of the ECCV 22 paper, "Teaching Where to Look: Attention Similarity Knowledge Distillation for Low Resolution Face Recognition". | [
"efficient-ml",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:28:44Z | false | 9 | 510,958,577 | null | Python | Apache-2.0 | [
"distillation",
"knowledge-distillation"
] | gist-ailab/teaching-where-to-look | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-07T17:53:16Z | [] | 2023-04-25T15:18:05Z | [
"efficiency.knowledge-distillation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 64 | [
"a-skd",
"face-recognition",
"low-resolution",
"teaching-where-to-look"
] | 2026-09-30T20:18:11Z | https://github.com/gist-ailab/teaching-where-to-look | null |
[
"embodied-ai",
"robotics"
] | [
"imitation-learning",
"teleoperation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"robotics.teleoperation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-07-06T03:57:06Z | Official implementation for VIOLA | [
"embodied-ai",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:28:44Z | false | 7 | 510,980,714 | null | Python | MIT | [
"imitation-learning",
"teleoperation"
] | UT-Austin-RPL/VIOLA | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-07T17:53:16Z | [] | 2023-06-18T01:43:05Z | [
"robotics.teleoperation"
] | 6f2cef71d416d4d53d9de3f7575d5fde0b1231f4 | 2026-09-26T20:42:46Z | gh-ml-readme-evidence-v2 | "6f2cef71d416d4d53d9de3f7575d5fde0b1231f4" | [
"dataset",
"installation",
"other",
"overview",
"results",
"usage"
] | [
"method-contribution",
"ml-method-context",
"paper-reference"
] | ok | 2026-09-26T20:42:46Z | UT-Austin-RPL/VIOLA | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-method-context",
"paper-reference"
] | include | ml-contribution-v5 | 123 | [] | 2026-10-07T12:17:55Z | https://github.com/UT-Austin-RPL/VIOLA | null |
[
"ai-safety",
"reinforcement-learning",
"safety"
] | [
"constrained-reinforcement-learning",
"reinforcement-learning",
"safe-learning",
"safe-reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"rl.safe",
"trust.safe-reinforcement-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-07-06T22:03:43Z | This is the code for our NeurIPS 2022 paper "DOPE: DOUBLY OPTIMISTIC AND PESSIMISTIC EXPLORATION FOR SAFE REINFORCEMENT LEARNING" | [
"reinforcement-learning",
"safety"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 2 | 511,304,841 | null | Python | null | [
"constrained-reinforcement-learning",
"reinforcement-learning",
"safe-reinforcement-learning"
] | archanabura/DOPE-DoublyOptimisticPessimisticExploration | [
"description",
"query-match",
"repository-metadata"
] | 4 | 2026-10-07T17:53:16Z | [] | 2025-06-24T22:01:25Z | [
"rl.safe"
] | 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 | [] | 2025-06-24T22:01:29Z | https://github.com/archanabura/DOPE-DoublyOptimisticPessimisticExploration | null |
[
"computer-vision",
"health-and-biomedicine",
"medical-imaging"
] | [
"image-analysis",
"segmentation",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"medical.medical-image-segmentation",
"medical.medical-imaging",
"vision.segmentation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-07-10T03:36:21Z | [IEEE Transactions on Medical Imaging/TMI 2024] This repository is the official implementation of the paper ScribFormer: Transformer Makes CNN Work Better for Scribble-based Medical Image Segmentation. | [
"computer-vision",
"health-and-biomedicine"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 9 | 512,336,570 | null | Python | MIT | [
"segmentation",
"transformer"
] | HUANGLIZI/ScribFormer | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-17T13:17:44Z | [] | 2024-02-08T16:25:28Z | [
"vision.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 | 92 | [] | 2026-10-10T03:41:37Z | https://github.com/HUANGLIZI/ScribFormer | null |
[
"computer-vision",
"language",
"multimodal",
"multimodal-learning",
"vision"
] | [
"contrastive-learning",
"vision-language-model"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"multimodal.vision-language"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-07-11T22:21:46Z | [ECCV 2022] Official Pytorch Implementation of the paper : " Zero-Shot Temporal Action Detection via Vision-Language Prompting " | [
"computer-vision",
"language",
"multimodal",
"multimodal-learning",
"vision"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 11 | 512,924,327 | https://sauradip.github.io/project_pages/STALE/ | Python | null | [
"contrastive-learning",
"vision-language-model"
] | sauradip/STALE | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 2 | 2026-10-08T17:58:00Z | [] | 2023-08-03T18:35:34Z | [
"multimodal.vision-language"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 117 | [
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] | 2026-08-28T20:14:38Z | https://github.com/sauradip/STALE | null |
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-07-12T01:22:20Z | The official implementation of BadHash | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:28:44Z | false | 1 | 512,957,903 | null | Python | MIT | [
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] | CGCL-codes/BadHash | [
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] | 4 | 2026-10-15T15:20:52Z | [] | 2022-09-01T16:09:56Z | [
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] | include | ml-contribution-v5 | 58 | [] | 2025-12-12T15:19:03Z | https://github.com/CGCL-codes/BadHash | null |
[
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] | [
"multimodal.point-cloud"
] | true | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-07-13T09:12:18Z | Official implementation of the Context-Matching-Guided Transformer (CMT) Tracker for Point Clouds (ECCV22) | [
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] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-20T15:59:06Z | false | 0 | 513,454,227 | null | Python | MIT | [
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] | jasongzy/CMT | [
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] | 1 | 2026-10-20T15:59:06Z | [] | 2023-03-12T09:47:36Z | [
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] | include | ml-contribution-v5 | 7 | [] | 2024-11-26T06:53:43Z | https://github.com/jasongzy/CMT | null |
[
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] | [
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] | [
"robotics.autonomous-driving"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-07-14T12:32:58Z | A generic repository for LiDAR 3D perception in autonomous driving scenarios such as segmentation, detection, etc. Also the official implementation of ECCV2022 paper (Self-Distillation for Robust LiDAR Semantic Segmentation in Autonomous Driving). | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-06T17:10:23Z | false | 1 | 513,894,240 | null | null | Apache-2.0 | [
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] | jialeli1/lidarperception3d | [
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] | 1 | 2026-10-06T17:10:23Z | [] | 2022-07-19T08:58:22Z | [
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] | include | ml-contribution-v5 | 1 | [] | 2023-03-17T00:52:49Z | https://github.com/jialeli1/lidarperception3d | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-07-15T01:26:09Z | The Code for Paper “Relay Hindsight Experience Replay: Self-Guided Continual Reinforcement Learning for Sequential Object Manipulation Tasks with Sparse Rewards” | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T14:28:44Z | false | 9 | 514,095,693 | null | Python | MIT | [
"reinforcement-learning",
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] | kaixindelele/RHER | [
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] | 5 | 2026-10-11T16:48:52Z | [] | 2024-07-10T12:06:16Z | [
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] | include | ml-contribution-v5 | 160 | [] | 2026-08-17T08:26:03Z | https://github.com/kaixindelele/RHER | null |
[
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-07-15T02:30:41Z | The official code for our ECCV22 oral paper: tracking objects as pixel-wise distributions. | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-07T17:53:16Z | false | 4 | 514,109,201 | null | Python | NOASSERTION | [
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] | JIA-Lab-research/ECCV22-P3AFormer-Tracking-Objects-as-Pixel-wise-Distributions | [
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] | 1 | 2026-10-07T17:53:16Z | [] | 2022-09-21T01:56:48Z | [
"vision.object-tracking"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 159 | [
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] | 2026-03-11T16:28:53Z | https://github.com/JIA-Lab-research/ECCV22-P3AFormer-Tracking-Objects-as-Pixel-wise-Distributions | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-07-16T05:05:56Z | This is the official code for our paper title "Generalizable Memory-driven Transformer for Multivariate Long Sequence Time-series Forecasting" | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-09T17:31:36Z | false | 0 | 514,482,969 | null | Python | MIT | [
"forecasting",
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] | mlii0117/CLMFormer | [
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] | 1 | 2026-10-09T17:31:36Z | [] | 2023-07-17T02:15:26Z | [
"timeseries.forecasting"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 8 | [] | 2026-10-09T02:38:12Z | https://github.com/mlii0117/CLMFormer | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-07-17T19:52:39Z | Official implementation of Graph Neural Network Query Executor (ICML 2022) | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-03T15:45:52Z | false | 13 | 514,941,547 | null | Python | MIT | [
"graph-neural-network",
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] | DeepGraphLearning/GNN-QE | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-10-08T17:58:00Z | [] | 2023-09-05T13:10:14Z | [
"general.neural-network"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 89 | [
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] | 2026-04-10T19:12:30Z | https://github.com/DeepGraphLearning/GNN-QE | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-07-18T04:00:00Z | The official implementation for ECCV22 paper: "FOSTER: Feature Boosting and Compression for Class-Incremental Learning" in PyTorch. | [
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"tabular-and-structured-data",
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-01T19:00:27.900159Z | false | 5 | 515,028,691 | null | Python | MIT | [
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] | G-U-N/ECCV22-FOSTER | [
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] | 2 | 2026-10-08T17:58:00Z | [] | 2022-11-29T12:29:47Z | [
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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 | 63 | [
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"eccv2022",
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] | 2026-07-22T02:55:23Z | https://github.com/G-U-N/ECCV22-FOSTER | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-07-18T05:34:53Z | [ECCV 2022] Official pytorch implementation of the paper, "PointMixer: MLP-Mixer for Point Cloud Understanding" | [
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] | [
"classifier",
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 12 | 515,047,895 | null | Python | null | [
"3d-perception",
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] | LifeBeyondExpectations/ECCV22-PointMixer | [
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] | 4 | 2026-10-19T17:46:36Z | [] | 2023-10-25T06:53:49Z | [
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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 | 111 | [
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] | 2026-03-16T01:04:21Z | https://github.com/LifeBeyondExpectations/ECCV22-PointMixer | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-07-18T05:49:33Z | [ECCV2022] Official implementation of "Object Discovery via Contrastive Learning for Weakly Supervised Object Detection" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-12T20:31:19Z | false | 7 | 515,051,280 | null | Python | null | [
"contrastive-learning",
"self-supervised-learning"
] | jinhseo/OD-WSCL | [
"description",
"github-topics",
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] | 1 | 2026-10-12T20:31:19Z | [] | 2024-01-16T06:24:15Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 50 | [
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"weakly-supervised-object-detection"
] | 2026-08-16T06:54:25Z | https://github.com/jinhseo/OD-WSCL | null |
[
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-07-18T13:18:33Z | The official code of KDD22 paper "FLDetecotor: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-03T15:45:52Z | false | 19 | 515,192,386 | null | Python | null | [
"collaborative-learning",
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] | zaixizhang/FLDetector | [
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] | 3 | 2026-10-07T17:53:16Z | [] | 2023-02-23T14:37:55Z | [
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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 | 87 | [
"adversarial-attacks",
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"federated-learning"
] | 2026-04-17T10:52:50Z | https://github.com/zaixizhang/FLDetector | null |
[
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] | [
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] | [
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"query-match",
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] | [
"vision.ocr"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-07-19T07:44:36Z | The official code of CornerTransformer (ECCV 2022, Oral) on top of MMOCR. | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-07T17:53:16Z | false | 15 | 515,485,694 | null | Python | Apache-2.0 | [
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] | xdxie/WordArt | [
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] | 3 | 2026-10-12T20:31:19Z | [] | 2023-03-06T11:51:43Z | [
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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 | 148 | [
"artistic-text-recognition",
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"corner",
"dataset",
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"transformer"
] | 2026-06-24T05:58:31Z | https://github.com/xdxie/WordArt | null |
[
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] | [
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-07-19T07:46:08Z | This repo contains the official implementation of paper "Layered Controllabel Video Generation". | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 0 | 515,486,205 | null | Python | null | [
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] | Gabriel-Huang/Layered-Controllable-Video-Generation | [
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] | 2 | 2026-10-08T17:58:00Z | [] | 2022-10-31T16:04:10Z | [
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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 | 14 | [] | 2026-08-24T10:25:13Z | https://github.com/Gabriel-Huang/Layered-Controllable-Video-Generation | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-07-19T09:51:09Z | Official PyTorch implementation of the paper MSF3DDETR: Multi-Sensor Fusion 3D Detection Transformer for Autonomous Driving | [
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] | [
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] | 2 | 2026-10-08T18:15:14Z | [] | 2024-05-19T15:38:36Z | [
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] | include | ml-contribution-v5 | 4 | [] | 2025-04-09T17:08:26Z | https://github.com/gopi-erabati/MSF3DDETR | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-07-20T01:21:19Z | Official Implementation of OCR-free Document Understanding Transformer (Donut) and Synthetic Document Generator (SynthDoG), ECCV 2022 | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:27:52Z | false | 563 | 515,792,028 | https://arxiv.org/abs/2111.15664 | Python | MIT | [
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] | clovaai/donut | [
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] | 12 | 2026-10-12T20:31:19Z | [] | 2024-07-11T15:33:26Z | [
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] | include | ml-contribution-v5 | 6,928 | [
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] | 2026-10-07T05:39:16Z | https://github.com/clovaai/donut | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-07-20T01:38:08Z | An official PyTorch implementation of "Bi-directional Contrastive Learning for Domain Adaptive Semantic Segmentation", ECCV 2022. | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-09T17:31:36Z | false | 9 | 515,795,640 | null | Python | GPL-3.0 | [
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] | cvlab-yonsei/DASS | [
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] | 2 | 2026-10-12T20:31:19Z | [] | 2023-02-17T07:31:59Z | [
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] | include | ml-contribution-v5 | 44 | [] | 2025-11-17T05:20:48Z | https://github.com/cvlab-yonsei/DASS | null |
[
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] | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 2 | 515,867,989 | null | Python | null | [
"contrastive-learning",
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] | pantheon5100/DeACL | [
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] | 3 | 2026-10-13T18:05:26Z | [] | 2024-07-15T04:29:52Z | [
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] | include | ml-contribution-v5 | 19 | [
"adversarial-attacks",
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] | 2025-01-09T08:33:31Z | https://github.com/pantheon5100/DeACL | null |
[
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] | [
"efficiency.quantization"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-07-20T11:15:09Z | Official implementation for ECCV 2022 paper LIMPQ - "Mixed-Precision Neural Network Quantization via Learned Layer-wise Importance" | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-08T17:58:00Z | false | 6 | 515,949,950 | null | Python | MIT | [
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] | 2 | 2026-10-08T18:15:14Z | [] | 2023-03-19T19:52:32Z | [
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] | include | ml-contribution-v5 | 63 | [] | 2026-09-11T07:13:55Z | https://github.com/1hunters/LIMPQ | null |
[
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 5 | 516,051,596 | null | Python | MIT | [
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] | leekyungmoon/XDED | [
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] | 7 | 2026-10-12T20:31:19Z | [] | 2024-12-11T08:47:31Z | [
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] | include | ml-contribution-v5 | 25 | [] | 2025-06-24T14:21:40Z | https://github.com/leekyungmoon/XDED | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-07-20T19:15:06Z | Official code in PyTorch for the paper "Bitwidth-Adaptive Quantization-Aware Neural Network Training: A Meta-Learning Approach", to appear in ECCV 2022 | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 0 | 516,106,377 | null | Python | null | [
"few-shot-learning",
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] | jsjs0369/MEBQAT | [
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] | 2 | 2026-10-17T13:17:44Z | [] | 2022-07-20T19:18:34Z | [
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] | include | ml-contribution-v5 | 10 | [] | 2024-01-24T14:51:54Z | https://github.com/jsjs0369/MEBQAT | null |
[
"machine-learning",
"representation-learning",
"robust-ml",
"trustworthy-ml"
] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-07-21T07:08:11Z | SBDG (ICIP 2021): Official Project Webpage This repository provides the official PyTorch implementation of the following paper: Self-balanced Learning For Domain Generalization | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 0 | 516,263,459 | null | Python | MIT | [
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] | Genie-Kim/SBDG | [
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] | 3 | 2026-10-13T18:05:26Z | [] | 2022-08-03T16:17:35Z | [
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] | include | ml-contribution-v5 | 2 | [
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] | 2023-02-16T07:02:33Z | https://github.com/Genie-Kim/SBDG | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-07-21T10:47:36Z | a novel transformer-based architecture named CSTTN for traffic prediction | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 1 | 516,331,337 | null | Python | Apache-2.0 | [
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] | 0shelter0/CSTTN | [
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] | 1 | 2026-10-02T16:59:40Z | [] | 2023-08-22T14:23:01Z | [
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] | f6251f286f294a2173b44e7ba7b31bf3652998d6 | 2026-10-02T18:21:54Z | gh-ml-readme-evidence-v2 | "f6251f286f294a2173b44e7ba7b31bf3652998d6" | [
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] | [
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] | ok | 2026-10-02T18:21:54Z | 0shelter0/CSTTN | specific-method-with-novelty-claim | [
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"ml-method-cue",
"official-implementation-claim",
"paper-reference"
] | include | ml-contribution-v5 | 6 | [
"graph-convolutional-network",
"spatial-temporal-forecasting",
"traffic-prediction"
] | 2024-09-11T07:11:18Z | https://github.com/0shelter0/CSTTN | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-07-27T09:41:22Z | The Source code for paper "Optimal Energy System Scheduling Combining Mixed-Integer Programming and Deep Reinforcement Learning". Safe reinforcement learning, energy management | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 36 | 518,396,914 | null | Python | MIT | [
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] | 7 | 2026-10-20T15:59:06Z | [] | 2026-03-27T00:39:02Z | [
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] | include | ml-contribution-v5 | 190 | [
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] | 2026-10-03T11:17:05Z | https://github.com/EnergyQuantResearch/Optimal-Energy-System-Scheduling-Combining-Mixed-Integer-Programming-and-Deep-Reinforcement-Learning | null |
[
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-07-29T15:19:37Z | [NeurIPS 2022 Oral] The official implementation of POR in "A Policy-Guided Imitation Approach for Offline Reinforcement Learning" | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 7 | 519,260,658 | null | Python | MIT | [
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] | ryanxhr/POR | [
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"query-match",
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] | 8 | 2026-10-07T17:53:16Z | [] | 2023-04-06T09:16:29Z | [
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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 | 58 | [
"offline-reinforcement-learning",
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] | 2026-05-20T09:31:37Z | https://github.com/ryanxhr/POR | null |
[
"deep-learning",
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] | [
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] | [
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"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.graph-transformer"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-08-03T07:36:03Z | The project is an official implementation of our paper "A Lightweight Graph Transformer Network for Human Mesh Reconstruction from 2D Human Pose". | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 0 | 520,782,483 | null | null | MIT | [
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] | 5AF1/GTRS | [
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"query-match",
"repository-metadata"
] | 2 | 2026-10-06T16:55:56Z | [] | 2022-06-03T00:09:22Z | [
"graph.graph-transformer"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2022-08-03T08:34:14Z | https://github.com/5AF1/GTRS | null |
[
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] | [
"llm.diffusion-phrase",
"recall.name.diffusion-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-08-05T10:19:24Z | Official PyTorch implementation for the paper "CARD: Classification and Regression Diffusion Models" | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 43 | 521,593,554 | https://arxiv.org/abs/2206.07275 | Python | null | [
"diffusion"
] | XzwHan/CARD | [
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] | 4 | 2026-10-28T17:27:44Z | [] | 2023-03-21T23:18:04Z | [
"recall.name.diffusion-model"
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"contribution-language",
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] | include | ml-contribution-v5 | 241 | [] | 2026-10-27T05:26:56Z | https://github.com/XzwHan/CARD | null |
[
"general-ml",
"generative-ai",
"generative-modeling"
] | [
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] | [
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"repository-metadata"
] | [
"general.topic-machine-learning",
"recall.name.diffusion-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-08-07T10:25:42Z | Official code for "DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps" (Neurips 2022 Oral) | [
"generative-ai",
"generative-modeling"
] | [
"generative-model",
"machine-learning",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T20:57:28.489717Z | false | 136 | 522,180,634 | null | Python | MIT | [
"diffusion"
] | LuChengTHU/dpm-solver | [
"description",
"github-topics",
"license-metadata",
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"repository-metadata"
] | 5 | 2026-10-28T17:27:44Z | [] | 2024-02-06T20:38:31Z | [
"recall.name.diffusion-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 1,859 | [
"diffusion-models",
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"stable-diffusion"
] | 2026-10-23T01:10:26Z | https://github.com/LuChengTHU/dpm-solver | null |
[
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] | [
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] | [
"description",
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"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-08-09T15:19:13Z | Design a novel Graph Neural Network for bus passenger flow prediction | [
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"time-series-and-forecasting"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 1 | 523,003,208 | null | Jupyter Notebook | null | [
"graph-neural-network",
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"neural-network"
] | mohamed-chaaben/Bus-passenger-flow-forecasting | [
"description",
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"repository-metadata"
] | 1 | 2026-10-08T18:15:14Z | [] | 2022-08-09T15:31:22Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
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] | include | ml-contribution-v5 | 0 | [] | 2023-11-12T01:44:31Z | https://github.com/mohamed-chaaben/Bus-passenger-flow-forecasting | null |
[
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] | [
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] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-08-10T09:27:07Z | Official pytorch implementation of the paper: "Similarity-Navigated Graph Neural Networks for Node Classification" | [
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] | [
"neural-network",
"classifier"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-06T17:10:23Z | false | 0 | 523,279,988 | null | Python | null | [
"graph-neural-network",
"message-passing",
"neural-network"
] | MinhZou/SNGNN | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-06T17:10:23Z | [] | 2025-07-18T03:00:36Z | [
"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 | 10 | [] | 2026-01-08T18:29:47Z | https://github.com/MinhZou/SNGNN | null |
[
"climate-science",
"earth-science",
"meteorology",
"science-and-engineering"
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] | [
"description",
"query-match",
"repository-metadata"
] | [
"science.climate-modeling"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-08-11T18:08:53Z | Official implementation of the experiments in the paper "Controllable Generation for Climate Modeling" presented at the Tackling Climate Change with Machine Learning, NeurIPS Workshop 2022. | [
"climate-science",
"earth-science",
"meteorology",
"science-and-engineering"
] | [
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-07T18:07:52Z | false | 0 | 523,823,177 | null | Python | null | [
"climate-modeling",
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"weather-forecasting"
] | MoulikChoraria/Climate_Generation | [
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] | 1 | 2026-10-07T18:07:52Z | [] | 2023-04-11T17:54:47Z | [
"science.climate-modeling"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 1 | [] | 2023-04-11T18:01:02Z | https://github.com/MoulikChoraria/Climate_Generation | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-08-16T02:30:17Z | Official released code for the paper entitled *Subarchitecture Ensemble Pruning in Neural Architecture Search (SAEP)* | [
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"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 0 | 525,205,323 | null | Python | MIT | [
"neural-network-pruning",
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] | 2026-09-02T09:04:11Z | https://github.com/zzxslp/RadBERT | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-08-19T01:47:35Z | [ECCV ISIC Workshop 2022 (best paper)] FairDisCo: Fairer AI in Dermatology via Disentanglement Contrastive Learning (an official implementation) | [
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] | 2026-03-02T14:41:12Z | https://github.com/siyi-wind/FairDisCo | null |
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-08-21T10:22:03Z | Official PyTorch implementation of SGEM: Test-Time Adaptation for Automatic Speech Recognition via Sequential-Level Generalized Entropy Minimization (INTERSPEECH 2023 Oral Presentation) | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 3 | 527,171,804 | https://arxiv.org/abs/2306.01981 | Python | MIT | [
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] | 6 | 2026-10-13T18:05:26Z | [] | 2024-08-27T02:24:18Z | [
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] | 2026-09-12T12:32:42Z | https://github.com/drumpt/SGEM | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-08-21T13:09:27Z | Official implementation of the ECCV-MCV workshop paper "Multi-Scale Multi-Task distillation for incremental 3D medical image segmentation" | [
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] | 2 | 2026-10-20T15:59:06Z | [] | 2022-08-21T13:12:18Z | [
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] | include | ml-contribution-v5 | 4 | [] | 2023-12-16T03:40:45Z | https://github.com/kevinmtian/msmt-medseg | null |
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-08-25T08:57:34Z | Code for paper: EEG-based Emotion Recognition via Efficient Convolutional Neural Network and Contrastive Learning | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-16T15:36:56Z | false | 1 | 528,770,133 | null | null | null | [
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] | Lin-Xuejuan/ECNN-C | [
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] | 1 | 2026-10-16T15:36:56Z | [] | 2022-08-26T09:56:52Z | [
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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 | 5 | [] | 2024-12-03T09:48:50Z | https://github.com/Lin-Xuejuan/ECNN-C | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-08-25T16:06:15Z | [Briefings in Bioinformatics 2024]* We propose a novel FusionGDA model, which uses a contrastive learning–based training strategy together with a fusion module to enrich the gene and disease semantic representations encoded by pre-trained language models. | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-12T20:31:19Z | false | 2 | 528,924,517 | null | Jupyter Notebook | null | [
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] | 3 | 2026-10-20T15:59:06Z | [] | 2026-03-04T13:17:56Z | [
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] | include | ml-contribution-v5 | 7 | [
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] | 2026-08-21T18:55:14Z | https://github.com/ZhaohanM/FusionGDA | null |
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-08-26T22:40:08Z | This is an official implementation for the ICLR2023 paper "The Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillation" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 5 | 529,422,562 | null | Python | null | [
"distillation",
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"knowledge-distillation"
] | CityChan/Federated-Hyper-Knowledge-Distillation | [
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] | 3 | 2026-10-07T17:53:16Z | [] | 2025-03-04T05:53:04Z | [
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] | include | ml-contribution-v5 | 24 | [] | 2026-07-22T13:29:39Z | https://github.com/CityChan/Federated-Hyper-Knowledge-Distillation | null |
[
"computer-vision",
"general-ml",
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-08-27T11:39:15Z | Research code for paper "Frido: Feature Pyramid Diffusion for Complex Scene Image Synthesis" | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 11 | 529,568,024 | https://arxiv.org/abs/2208.13753 | Jupyter Notebook | MIT | [
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] | chrisfan-wc/Frido | [
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] | 6 | 2026-10-06T16:55:56Z | [] | 2024-11-13T01:36:23Z | [
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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 | 114 | [
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] | 2026-03-23T01:16:06Z | https://github.com/chrisfan-wc/Frido | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-08-28T08:27:57Z | Official Implementation of RecFormer at EvalRS-CIKM-2022 | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:28:44Z | false | 0 | 529,804,483 | https://ceur-ws.org/Vol-3318/short9.pdf | Python | null | [
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] | wywyWang/RecFormer | [
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] | dd90ae5ecac617989ccc7e8e96d6083c676e0e7a | 2026-09-26T20:42:46Z | gh-ml-readme-evidence-v2 | "dd90ae5ecac617989ccc7e8e96d6083c676e0e7a" | [
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] | include | ml-contribution-v5 | 3 | [] | 2025-04-07T21:59:01Z | https://github.com/wywyWang/RecFormer | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-08-31T12:20:07Z | This is the official code for the baseline methods of the publised paper 'A Multi-action Deep Reinforcement Learning Framework for Flexible Job-shop Scheduling Problem' | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-06T16:55:56Z | false | 13 | 531,071,254 | null | Python | MIT | [
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] | include | ml-contribution-v5 | 114 | [
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] | 2026-07-25T14:37:47Z | https://github.com/Lei-Kun/Dispatching-rules-for-FJSP | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-09-01T03:05:30Z | This is the official code of the publised paper 'A Multi-action Deep Reinforcement Learning Framework for Flexible Job-shop Scheduling Problem' | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T18:03:22.392067Z | false | 85 | 531,343,118 | null | Python | MIT | [
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"neural-network",
"policy-learning",
"reinforcement-learning"
] | Lei-Kun/End-to-end-DRL-for-FJSP | [
"description",
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] | 4 | 2026-10-20T15:59:06Z | [] | 2023-03-27T05:55:38Z | [
"rl.deep"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | 2026-10-17T22:01:12Z | https://github.com/Lei-Kun/End-to-end-DRL-for-FJSP | null |
[
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] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-09-01T06:25:56Z | code is for our paper PoE: Poisoning Enhancement Through Label Smoothing in Federated Learning | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-07T18:07:52Z | false | 0 | 531,391,520 | null | Python | null | [
"collaborative-learning",
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] | Leon022/poisoning_enhancement | [
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] | 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 | [] | 2023-03-10T08:01:15Z | https://github.com/Leon022/poisoning_enhancement | null |
[
"computer-vision",
"deep-learning",
"representation-learning"
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-09-03T06:29:09Z | This repository provides the code for the methods and experiments presented in our paper 'SCL-Net: An End-to-End Supervised Contrastive Learning Network for Hyperspectral Image Classification'，under review by TGRS | [
"computer-vision",
"deep-learning",
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] | luting-hnu/SCL-Net | [
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"query-match",
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] | 1 | 2026-10-16T15:36:56Z | [] | 2022-09-03T06:29:10Z | [
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] | include | ml-contribution-v5 | 2 | [] | 2023-03-19T13:22:24Z | https://github.com/luting-hnu/SCL-Net | null |
[
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] | [
"causal-inference",
"double-machine-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"trust.instrumental-variable-ml"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-09-05T06:55:18Z | [ICCV 2023] Official PyTorch Implementation for "Mitigating Adversarial Vulnerability through Causal Parameter Estimation by Adversarial Double Machine Learning" | [
"causal-ml",
"economics"
] | [
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 3 | 532,779,304 | null | Python | MIT | [
"causal-inference",
"double-machine-learning"
] | ByungKwanLee/Double-Debiased-Adversary | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-07T17:53:16Z | [] | 2023-10-13T20:04:13Z | [
"trust.instrumental-variable-ml"
] | ece3db222fb91a1e66e6340ffe296bfedbcefd83 | 2026-09-26T20:42:46Z | gh-ml-readme-evidence-v2 | "ece3db222fb91a1e66e6340ffe296bfedbcefd83" | [
"other"
] | [
"method-contribution",
"ml-method-context",
"official-implementation-claim",
"paper-reference"
] | ok | 2026-09-26T20:42:46Z | ByungKwanLee/Double-Debiased-Adversary | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-context-only",
"ml-method-context",
"official-implementation-claim",
"paper-reference"
] | include | ml-contribution-v5 | 32 | [] | 2026-08-26T16:36:49Z | https://github.com/ByungKwanLee/Double-Debiased-Adversary | null |
[
"computer-vision",
"control",
"robotics",
"robotics-and-control"
] | [
"control"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"robotics.robot-control"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-09-06T15:37:06Z | Code for paper titled "Failure Prediction with Statistical Guarantees for Vision-Based Robot Control" | [
"computer-vision",
"control",
"robotics",
"robotics-and-control"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 1 | 533,390,853 | null | Python | MIT | [
"control"
] | irom-princeton/Failure-Prediction | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2022-09-15T16:12:53Z | [
"robotics.robot-control"
] | 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 | [] | 2022-09-06T23:50:24Z | https://github.com/irom-princeton/Failure-Prediction | null |
[
"reinforcement-learning"
] | [
"distributional-reinforcement-learning",
"reinforcement-learning",
"value-based-reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"rl.distributional"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-09-07T13:24:39Z | Official implementation of the algorithmic approach presented in the research paper entitled "Risk-Sensitive Policy with Distributional Reinforcement Learning". | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T14:28:44Z | false | 0 | 533,776,475 | null | Python | null | [
"distributional-reinforcement-learning",
"reinforcement-learning",
"value-based-reinforcement-learning"
] | ThibautTheate/Risk-Sensitive-Policy-with-Distributional-Reinforcement-Learning | [
"description",
"query-match",
"repository-metadata"
] | 4 | 2026-10-06T16:55:56Z | [] | 2022-12-19T13:18:21Z | [
"rl.distributional"
] | 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 | 16 | [] | 2026-07-05T12:42:24Z | https://github.com/ThibautTheate/Risk-Sensitive-Policy-with-Distributional-Reinforcement-Learning | null |
[
"automl",
"general-ml"
] | [
"few-shot-learning",
"meta-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.meta-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-09-09T02:24:42Z | This repository provides the code for our paper "Adaptive Regularized Warped Gradient Descent Enhances Model Generalization and Meta-learning for Few-shot learning" submitted to Neurocomputing. | [
"automl",
"general-ml"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-19T17:46:36Z | false | 0 | 534,468,814 | null | Python | Apache-2.0 | [
"few-shot-learning",
"meta-learning"
] | susan-Rao/ARWarpGrad | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-19T17:46:36Z | [] | 2023-01-12T08:02:23Z | [
"general.meta-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 3 | [] | 2023-07-20T04:37:26Z | https://github.com/susan-Rao/ARWarpGrad | null |
[
"3d",
"computer-vision",
"generative-modeling",
"robotics"
] | [
"3d-perception",
"diffusion",
"point-cloud-learning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.point-cloud"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-09-11T16:15:46Z | Official implementation of "Learning to Generate Realistic LiDAR Point Clouds" (ECCV 2022) | [
"3d",
"computer-vision",
"generative-modeling",
"robotics"
] | [
"generative-model",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T18:03:22.392067Z | false | 13 | 535,353,599 | https://www.zyrianov.org/lidargen/ | Python | MIT | [
"3d-perception",
"diffusion",
"point-cloud-learning"
] | vzyrianov/lidargen | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-19T17:46:36Z | [] | 2022-09-16T04:26:04Z | [
"multimodal.point-cloud"
] | 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 | 155 | [
"diffusion",
"diffusion-models",
"generative-model",
"lidar",
"lidar-generation",
"score-matching"
] | 2026-09-04T01:57:15Z | https://github.com/vzyrianov/lidargen | null |
[
"distributed-ml",
"privacy-and-federated-learning",
"privacy-ml"
] | [
"distributed-training",
"split-learning",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"growth26.privacy-split-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-09-11T21:52:56Z | The official implementatioin code of ICDM 2022 paper: "Privacy-Preserving Split Learning via Patch Shuffling over Transformers" | [
"distributed-ml",
"privacy-and-federated-learning",
"privacy-ml"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-26T22:51:24Z | false | 0 | 535,435,314 | null | Python | MIT | [
"distributed-training",
"split-learning",
"transformer"
] | dixiyao/Patch-Shuffling-Transformer | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-09-26T22:51:24Z | [] | 2023-11-27T19:43:30Z | [
"growth26.privacy-split-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 | 12 | [
"data-mining",
"encryption",
"transformers"
] | 2026-04-21T04:49:21Z | https://github.com/dixiyao/Patch-Shuffling-Transformer | null |
[
"deep-learning",
"reinforcement-learning",
"representation-learning"
] | [
"multi-task-learning",
"neural-network",
"reinforcement-learning",
"transfer-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.multi-task-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-09-12T12:57:56Z | Official PyTorch implementation of the paper "T3S: Improving Multi-Task Reinforcement Learning with Task-Specific Feature Selector and Scheduler" published in 2023 International Joint Conference on Neural Networks (IJCNN). | [
"deep-learning",
"reinforcement-learning",
"representation-learning"
] | [
"neural-network",
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-18T14:44:29Z | false | 0 | 535,667,506 | null | Python | MIT | [
"multi-task-learning",
"neural-network",
"reinforcement-learning",
"transfer-learning"
] | metaqiang/t3s-multitask-rl | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-18T14:44:29Z | [] | 2026-01-11T13:44:18Z | [
"general.multi-task-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 | 3 | [] | 2026-01-23T08:01:43Z | https://github.com/metaqiang/t3s-multitask-rl | 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 | [] | 2022-09-13T06:50:44Z | Information-Theoretic Diffusion: A brand new diffusion model / density estimator based on information theory. | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 2 | 535,995,806 | null | Python | MIT | [
"diffusion"
] | kxh001/ITdiffusion | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-09-30T16:47:43Z | [] | 2024-07-18T19:45:23Z | [
"llm.diffusion-phrase"
] | e3850e93b6a1d76e97cf758eee0b2671a86c57bc | 2026-09-30T18:16:34Z | gh-ml-readme-evidence-v2 | "e3850e93b6a1d76e97cf758eee0b2671a86c57bc" | [
"citation",
"installation",
"other",
"references",
"results",
"usage"
] | [
"method-contribution",
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-09-30T18:16:34Z | kxh001/ITdiffusion | specific-method-with-novelty-claim | [
"method-contribution",
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 39 | [] | 2026-08-21T17:32:10Z | https://github.com/kxh001/ITdiffusion | null |
[
"automl",
"deep-learning",
"general-ml",
"natural-language-processing",
"representation-learning"
] | [
"continual-learning",
"conversational-ai",
"dialogue-modeling",
"few-shot-learning",
"lifelong-learning",
"meta-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.continual-learning",
"general.meta-learning",
"nlp.dialogue-systems"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-09-15T02:41:24Z | Code for our paper "Balanced Meta Learning and Diverse Sampling for Lifelong Task-Oriented Dialogue Systems" (AAAI2023). | [
"automl",
"general-ml"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-09T17:31:36Z | false | 0 | 536,838,227 | null | Python | null | [
"few-shot-learning",
"meta-learning"
] | travis-xu/MetaLTDS | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-20T15:59:06Z | [] | 2024-10-06T14:22:00Z | [
"general.meta-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 2 | [] | 2026-01-09T09:05:36Z | https://github.com/travis-xu/MetaLTDS | null |
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