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"
] | [
"generative-modeling",
"video-generation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.video-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-01-27T13:46:28Z | Official Pytorch implementation of "Playable Video Generation", CVPR 2022 (Oral) | [
"computer-vision",
"generative-ai"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 25 | 333,434,439 | null | Python | NOASSERTION | [
"generative-modeling",
"video-generation"
] | willi-menapace/PlayableVideoGeneration | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-11T16:35:57Z | [] | 2023-01-20T19:05:13Z | [
"vision.video-generation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 160 | [] | 2025-12-01T10:20:06Z | https://github.com/willi-menapace/PlayableVideoGeneration | null |
[
"computational-science",
"computer-vision",
"physics"
] | [
"neural-operator",
"neural-operators",
"surrogate-modeling"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"science.neural-operators"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-01-29T15:16:07Z | [CVPR 2021] Involution: Inverting the Inherence of Convolution for Visual Recognition, a brand new neural operator | [
"computational-science",
"computer-vision",
"physics"
] | [
"classifier"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 175 | 334,181,506 | https://arxiv.org/abs/2103.06255 | Python | MIT | [
"neural-operator",
"neural-operators",
"surrogate-modeling"
] | d-li14/involution | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 4 | 2026-10-10T16:40:02Z | [] | 2021-07-16T06:01:08Z | [
"science.neural-operators"
] | e799f7498df0e116a931546972efb35fb822500d | 2026-09-26T15:53:33Z | gh-ml-readme-evidence-v2 | "e799f7498df0e116a931546972efb35fb822500d" | [
"method",
"other",
"usage"
] | [
"ml-method-context",
"paper-reference",
"reproduction-cue"
] | ok | 2026-09-26T15:53:33Z | d-li14/involution | specific-method-with-novelty-claim | [
"contribution-language",
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue",
"paper-reference",
"reproduction-cue"
] | include | ml-contribution-v5 | 1,309 | [
"cvpr2021",
"image-classification",
"instance-segmentation",
"involution",
"object-detection",
"operator",
"pre-trained-model",
"pytorch",
"semantic-segmentation"
] | 2026-10-08T12:02:37Z | https://github.com/d-li14/involution | null |
[
"deep-learning",
"forecasting",
"representation-learning",
"time-series",
"time-series-and-forecasting"
] | [
"forecasting",
"multi-task-learning",
"sequence-modeling",
"transfer-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.multi-task-learning",
"timeseries.forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-02-04T03:08:52Z | [KDD 2021] Official Code of the paper "ST-Norm: Spatial and Temporal Normalization for Multi-variate Time Series Forecasting" | [
"deep-learning",
"representation-learning",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 13 | 335,823,176 | null | Python | null | [
"multi-task-learning",
"transfer-learning"
] | JLDeng/ST-Norm | [
"description",
"query-match",
"repository-metadata"
] | 5 | 2026-10-11T16:35:57Z | [] | 2024-06-13T02:48:02Z | [
"general.multi-task-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 | 65 | [] | 2026-05-12T11:01:18Z | https://github.com/JLDeng/ST-Norm | null |
[
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-02-04T10:37:05Z | Code for paper "Hessian-Aware Pruning and Optimal Neural Implant" | [
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-12T20:31:19Z | false | 0 | 335,920,879 | null | Python | MIT | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | ICML2021Submission1958/Hessian-Aware-Pruning-and-Optimal-Neural-Implant | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-12T20:31:19Z | [] | 2021-02-04T12:59:38Z | [
"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 | 1 | [] | 2025-10-08T12:33:39Z | https://github.com/ICML2021Submission1958/Hessian-Aware-Pruning-and-Optimal-Neural-Implant | null |
[
"multi-agent-systems",
"reinforcement-learning"
] | [
"centralized-training",
"multi-agent-reinforcement-learning",
"reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"rl.multiagent"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-02-04T21:37:41Z | Code for our GECCO 2021 paper : A Coevolutionary Approach to Deep Multi-agent Reinforcement Learning | [
"multi-agent-systems",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 3 | 336,089,117 | null | Python | null | [
"centralized-training",
"multi-agent-reinforcement-learning",
"reinforcement-learning"
] | daanklijn/neurocoevolution | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-10T16:40:02Z | [] | 2021-10-04T19:17:02Z | [
"rl.multiagent"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 15 | [] | 2025-02-19T12:28:10Z | https://github.com/daanklijn/neurocoevolution | null |
[
"deep-learning",
"efficient-ml",
"model-compression",
"representation-learning"
] | [
"contrastive-learning",
"distillation",
"knowledge-distillation",
"self-supervised-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation",
"general.contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-02-06T14:58:15Z | [ICASSP-2021] Official implementations of Multi-View Contrastive Learning for Online Knowledge Distillation (MCL-OKD) | [
"efficient-ml",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 4 | 336,562,653 | null | Python | null | [
"contrastive-learning",
"distillation",
"knowledge-distillation"
] | winycg/MCL-OKD | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-10-10T16:40:02Z | [] | 2021-04-07T08:42:20Z | [
"efficiency.knowledge-distillation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 27 | [] | 2025-03-26T11:01:33Z | https://github.com/winycg/MCL-OKD | null |
[
"distributed-ml",
"privacy-and-federated-learning"
] | [
"collaborative-learning",
"federated-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-02-07T09:29:46Z | Official code implementation for "Personalized Federated Learning using Hypernetworks" [ICML 2021] | [
"distributed-ml",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 31 | 336,749,029 | null | Python | null | [
"collaborative-learning",
"federated-learning"
] | AvivSham/pFedHN | [
"description",
"query-match",
"repository-metadata"
] | 6 | 2026-10-11T16:35:57Z | [] | 2023-02-14T08:05:23Z | [
"trust.federated-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 199 | [] | 2026-08-26T19:40:10Z | https://github.com/AvivSham/pFedHN | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-02-07T14:27:35Z | code for paper TDGIA:Effective Injection Attacks on Graph Neural Networks (KDD 2021, research track) | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 1 | 336,806,540 | null | Python | null | [
"graph-neural-network",
"message-passing",
"neural-network"
] | THUDM/tdgia | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-02T16:59:40Z | [] | 2021-11-05T14:06:39Z | [
"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 | 22 | [] | 2026-08-04T22:52:37Z | https://github.com/THUDM/tdgia | null |
[
"embodied-ai",
"robotics",
"robotics-and-control"
] | [
"manipulation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"robotics.manipulation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-02-08T05:48:30Z | This package presents a novel dexterous robotic manipulation technique for picking thin objects called Scooping. | [
"embodied-ai",
"robotics",
"robotics-and-control"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T14:50:46Z | false | 0 | 336,973,413 | null | Jupyter Notebook | null | [
"manipulation"
] | JS-RML/Scooping-Manipulation | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-10T16:40:02Z | [] | 2021-08-27T02:25:38Z | [
"robotics.manipulation"
] | 9f21fa9016058d8e2097e78d6338410c3dfcd443 | 2026-09-26T15:53:33Z | gh-ml-readme-evidence-v2 | "9f21fa9016058d8e2097e78d6338410c3dfcd443" | [
"other",
"overview"
] | [] | ok | 2026-09-26T15:53:33Z | JS-RML/Scooping-Manipulation | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 4 | [] | 2026-08-05T07:09:05Z | https://github.com/JS-RML/Scooping-Manipulation | null |
[
"automl",
"general-ml",
"recommender-systems"
] | [
"few-shot-learning",
"meta-learning"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"applied.recommender-systems",
"general.meta-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-02-08T06:25:46Z | This is an official implementation for "Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising"(KDD2021). | [
"automl",
"general-ml",
"recommender-systems"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:28:44Z | false | 10 | 336,980,673 | null | Python | null | [
"few-shot-learning",
"meta-learning"
] | easezyc/MetaHeac | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 5 | 2026-10-11T16:35:57Z | [] | 2022-02-22T12:36:47Z | [
"applied.recommender-systems",
"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"
] | include | ml-contribution-v5 | 60 | [
"advertising",
"campaign",
"marketing",
"meta-learning",
"recommendation",
"transfer-learning"
] | 2026-08-01T12:09:42Z | https://github.com/easezyc/MetaHeac | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-02-08T23:53:33Z | Code for paper "Scalable Heterogeneous Graph Neural Networks for Predicting High-potential Early-stage Startups" | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-03T15:45:52Z | false | 1 | 337,242,626 | null | Python | null | [
"graph-neural-network",
"message-passing",
"neural-network"
] | mmichaelzhang/SHGMNN | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-03T15:45:52Z | [] | 2021-06-02T09:51:54Z | [
"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 | [] | 2022-10-10T13:36:21Z | https://github.com/mmichaelzhang/SHGMNN | null |
[
"efficient-ml",
"model-compression"
] | [
"distillation",
"knowledge-distillation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-02-09T07:27:55Z | Official implementation for (Show, Attend and Distill: Knowledge Distillation via Attention-based Feature Matching, AAAI-2021) | [
"efficient-ml",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 21 | 337,325,366 | null | Python | Apache-2.0 | [
"distillation",
"knowledge-distillation"
] | clovaai/attention-feature-distillation | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 7 | 2026-10-10T16:40:02Z | [] | 2021-02-09T07:30:49Z | [
"efficiency.knowledge-distillation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 122 | [] | 2026-08-07T03:59:31Z | https://github.com/clovaai/attention-feature-distillation | null |
[
"deep-learning",
"graph-learning",
"natural-language-processing"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-02-09T17:11:07Z | Official implementation of GraphMask as presented in our paper Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking. | [
"deep-learning",
"graph-learning",
"natural-language-processing"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 8 | 337,477,833 | null | Python | MIT | [
"graph-neural-network",
"message-passing",
"neural-network"
] | MichSchli/GraphMask | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-01T17:50:46Z | [] | 2021-04-23T10:37:22Z | [
"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 | 48 | [] | 2026-05-28T14:44:13Z | https://github.com/MichSchli/GraphMask | null |
[
"generative-ai",
"language"
] | [
"language-model",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.transformers"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-02-11T11:50:33Z | Official code repository of the paper Linear Transformers Are Secretly Fast Weight Programmers. | [
"generative-ai",
"language"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-22T16:09:07Z | false | 13 | 338,014,454 | null | Jupyter Notebook | MIT | [
"language-model",
"transformer"
] | ischlag/fast-weight-transformers | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-22T16:09:07Z | [] | 2021-06-10T13:22:22Z | [
"llm.transformers"
] | 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 | 120 | [] | 2026-10-05T02:02:27Z | https://github.com/ischlag/fast-weight-transformers | null |
[
"language",
"linguistics",
"natural-language-processing"
] | [
"language-model",
"language-modeling"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"llm.language-model",
"nlp.language-modeling",
"recall.computational-linguistics"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-02-12T13:38:30Z | Code for paper: "Numeracy Enhances the Literacy of Language Models" | [
"linguistics",
"natural-language-processing"
] | [
"machine-learning",
"representation-learning",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-12T20:31:19Z | false | 1 | 338,329,291 | null | Python | null | [
"language-modeling"
] | avijit-thawani/numeracy-literacy | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 2 | 2026-10-19T17:46:36Z | [] | 2021-12-07T02:00:53Z | [
"recall.computational-linguistics"
] | 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 | [
"bert",
"encoder",
"huggingface-transformers",
"language-model",
"language-modelling",
"machine-learning",
"nlp",
"numbers",
"perplexity",
"pytorch-lightning",
"representation-learning"
] | 2022-12-21T12:25:10Z | https://github.com/avijit-thawani/numeracy-literacy | null |
[
"computer-vision",
"general-ml",
"generative-ai",
"health-and-biomedicine",
"language",
"medical-imaging"
] | [
"image-analysis",
"language-model",
"segmentation",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.transformers",
"medical.medical-image-segmentation",
"medical.medical-imaging",
"recall.name.transformer-model",
"vision.segmentation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-02-14T03:00:14Z | Official Pytorch Code for "Medical Transformer: Gated Axial-Attention for Medical Image Segmentation" - MICCAI 2021 | [
"computer-vision",
"health-and-biomedicine",
"medical-imaging"
] | [
"deep-learning",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:27:52Z | false | 174 | 338,717,688 | null | Python | MIT | [
"image-analysis",
"transformer"
] | jeya-maria-jose/Medical-Transformer | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 16 | 2026-10-25T15:11:36Z | [] | 2023-02-23T08:42:53Z | [
"medical.medical-imaging"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 860 | [
"attention",
"deep-learning",
"medical-image-analysis",
"medical-imaging",
"pytorch",
"segmentation",
"transformer",
"transformers"
] | 2026-10-13T13:51:47Z | https://github.com/jeya-maria-jose/Medical-Transformer | null |
[
"automl",
"general-ml"
] | [
"few-shot-learning",
"meta-learning"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"general.meta-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-02-15T14:43:27Z | This is the official implementation for the paper "Learning to Scaffold: Optimizing Model Explanations for Teaching" | [
"automl",
"general-ml"
] | [
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T18:08:49.004055Z | false | 5 | 339,107,629 | null | Jupyter Notebook | null | [
"few-shot-learning",
"meta-learning"
] | CoderPat/learning-scaffold | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 2 | 2026-10-10T16:53:26Z | [] | 2022-05-19T04:05:36Z | [
"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 | 20 | [
"explainable-ml",
"interpretable-machine-learning",
"meta-learning"
] | 2026-03-27T09:07:19Z | https://github.com/CoderPat/learning-scaffold | null |
[
"deep-learning",
"graph-learning",
"health-and-biomedicine"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-02-16T15:52:43Z | BridgeDPI: A Novel Graph Neural Network for Predicting Drug-Protein Interaction | [
"deep-learning",
"graph-learning",
"health-and-biomedicine"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-03T15:45:52Z | false | 0 | 339,449,474 | null | null | null | [
"graph-neural-network",
"message-passing",
"neural-network"
] | StijnOudshoorn/BridgeDPI | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-03T15:45:52Z | [] | 2021-02-16T15:49:38Z | [
"graph.gnn-description"
] | 7e21d31b331047d665f6c7e6a112198fc6de5770 | 2026-10-03T17:01:43Z | gh-ml-readme-evidence-v2 | "7e21d31b331047d665f6c7e6a112198fc6de5770" | [
"other",
"usage"
] | [
"ml-method-context"
] | ok | 2026-10-03T17:01:43Z | StijnOudshoorn/BridgeDPI | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue"
] | include | ml-contribution-v5 | 1 | [] | 2023-08-07T01:27:35Z | https://github.com/StijnOudshoorn/BridgeDPI | null |
[
"deep-learning",
"graph-learning",
"health-and-biomedicine"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-02-18T04:24:00Z | BridgeDPI: A Novel Graph Neural Network for Predicting Drug-Protein Interactions | [
"deep-learning",
"graph-learning",
"health-and-biomedicine"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 7 | 339,934,546 | null | Python | Apache-2.0 | [
"graph-neural-network",
"message-passing",
"neural-network"
] | enai4bio/BridgeDPI | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-09T17:16:21Z | [] | 2024-12-25T09:48:48Z | [
"graph.gnn-description"
] | bebd3fd202305fcb0c751b895227f5c4b6bec20c | 2026-10-01T19:11:32Z | gh-ml-readme-evidence-v2 | "bebd3fd202305fcb0c751b895227f5c4b6bec20c" | [
"method",
"other",
"usage"
] | [
"ml-method-context"
] | ok | 2026-10-01T19:11:32Z | enai4bio/BridgeDPI | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue"
] | include | ml-contribution-v5 | 20 | [] | 2026-08-29T09:53:56Z | https://github.com/enai4bio/BridgeDPI | null |
[
"deep-learning",
"graph-learning",
"recommender-systems"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-02-19T03:42:14Z | Official code implementation for paper A Motif-Based Graph Neural Network to Reciprocal Recommendation for Online Dating | [
"deep-learning",
"graph-learning",
"recommender-systems"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 1 | 340,250,333 | null | Python | null | [
"graph-neural-network",
"message-passing",
"neural-network"
] | RManLuo/MotifGNN | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-02T16:59:40Z | [] | 2022-06-14T03:03:07Z | [
"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 | 9 | [] | 2026-07-28T17:51:48Z | https://github.com/RManLuo/MotifGNN | null |
[
"3d",
"computer-vision",
"robotics"
] | [
"3d-object-detection",
"3d-perception"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.3d-object-detection"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-02-20T09:21:17Z | The official code release of LidarMTL, a simple and efficient multi-task network for 3D object detection and road understanding | [
"3d",
"computer-vision",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:28:44Z | false | 13 | 340,612,863 | null | Python | Apache-2.0 | [
"3d-object-detection",
"3d-perception"
] | frankfengdi/LidarMTL | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-09T17:16:21Z | [] | 2021-03-09T17:21:40Z | [
"multimodal.3d-object-detection"
] | 61d5fc3d008e658bdea98403bd8cafb8dd0a9f91 | 2026-09-26T11:23:39Z | gh-ml-readme-evidence-v1 | "61d5fc3d008e658bdea98403bd8cafb8dd0a9f91" | [
"citation",
"installation",
"method",
"other",
"results"
] | [
"method-contribution",
"ml-method-context",
"official-implementation-claim"
] | ok | 2026-09-26T11:23:39Z | frankfengdi/LidarMTL | readme-supported-paper-method-implementation | [
"method-contribution",
"ml-method-context",
"official-implementation-claim"
] | include | ml-contribution-v5 | 110 | [] | 2026-05-29T02:58:03Z | https://github.com/frankfengdi/LidarMTL | null |
[
"distributed-ml",
"efficient-ml",
"machine-learning-systems",
"model-compression",
"privacy-and-federated-learning"
] | [
"collaborative-learning",
"federated-learning",
"neural-network",
"neural-network-pruning",
"pruning",
"quantization",
"structured-pruning"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning",
"efficiency.quantization",
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-02-22T09:04:01Z | Code for paper "Accelerating Federated Learning for IoT in Big Data Analytics with Pruning, Quantization and Selective Updating" | [
"efficient-ml",
"machine-learning-systems",
"model-compression",
"privacy-and-federated-learning"
] | [
"neural-network",
"model-training"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 3 | 341,138,944 | https://fangvv.github.io/FL-PQSU/ | Python | null | [
"federated-learning",
"neural-network",
"pruning",
"quantization"
] | fangvv/FL-PQSU | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 7 | 2026-10-19T16:36:56Z | [] | 2026-10-08T14:52:05Z | [
"efficiency.quantization"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 13 | [
"alexnet",
"alexnet-model",
"cifar10",
"deep-neural-networks",
"edge-computing",
"federated-learning",
"internet-of-things",
"mnist",
"model-training",
"pruning",
"quantization",
"quantization-algorithms",
"selective-updates",
"structural-pruning",
"vgg",
"vgg16-model"
] | 2026-10-08T14:52:10Z | https://github.com/fangvv/FL-PQSU | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-transformer",
"message-passing",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.graph-transformer"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-02-22T14:11:39Z | Source code for paper "LET: Linguistic Knowledge Enhanced Graph Transformer for Chinese Short Text Matching", AAAI2021. | [
"deep-learning",
"graph-learning"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 3 | 341,222,382 | null | Python | MIT | [
"graph-transformer",
"message-passing",
"transformer"
] | lbe0613/LET | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-10T16:40:02Z | [] | 2021-06-04T08:47:32Z | [
"graph.graph-transformer"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 48 | [] | 2024-09-14T10:08:58Z | https://github.com/lbe0613/LET | null |
[
"embodied-ai",
"robotics",
"robotics-and-control"
] | [
"manipulation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"robotics.manipulation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-02-23T08:32:53Z | This package presents a novel dexterous robotic manipulation technique for picking thin objects called Scooping. | [
"embodied-ai",
"robotics",
"robotics-and-control"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 0 | 341,483,527 | null | Jupyter Notebook | null | [
"manipulation"
] | Terryxyz/Scooping | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-10T16:40:02Z | [] | 2021-08-27T02:26:54Z | [
"robotics.manipulation"
] | a8ae99ae78c179ed9f4b19729ff8a93114f1eda7 | 2026-09-27T17:16:20Z | gh-ml-readme-evidence-v2 | "a8ae99ae78c179ed9f4b19729ff8a93114f1eda7" | [
"other",
"overview"
] | [] | ok | 2026-09-27T17:16:20Z | Terryxyz/Scooping | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [] | 2021-08-27T02:27:00Z | https://github.com/Terryxyz/Scooping | null |
[
"deep-learning",
"representation-learning"
] | [
"representation-learning",
"self-supervised-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.self-supervised-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-02-26T12:40:03Z | Code for CVPR 2021 paper: Revamping Cross-Modal Recipe Retrieval with Hierarchical Transformers and Self-supervised Learning | [
"deep-learning",
"representation-learning"
] | [
"self-supervised-learning",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T14:50:46Z | false | 25 | 342,571,959 | null | Python | Apache-2.0 | [
"representation-learning",
"self-supervised-learning"
] | amzn/image-to-recipe-transformers | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-11T16:35:57Z | [] | 2021-03-24T09:05:25Z | [
"general.self-supervised-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 88 | [] | 2026-08-05T03:21:26Z | https://github.com/amzn/image-to-recipe-transformers | null |
[
"embodied-ai",
"reinforcement-learning",
"robotics",
"robotics-and-control"
] | [
"manipulation",
"sim-to-real"
] | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"robotics.manipulation",
"robotics.sim2real"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-03-01T13:10:44Z | Official code (simulation part) for paper Sim-to-Real Transfer for Robotic Manipulation with Tactile Sensory Zihan Ding, Ya-Yen Tsai, Wang Wei Lee, Bidan Huang International Conference on Intelligent Robots and Systems (IROS) 2021 | [
"reinforcement-learning",
"robotics",
"robotics-and-control"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 4 | 343,420,830 | https://arxiv.org/abs/2103.00410 | Python | null | [
"sim-to-real"
] | quantumiracle/Robotic_Door_Opening_with_Tactile_Simulation | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | 6 | 2026-10-11T16:35:57Z | [] | 2022-02-11T15:07:52Z | [
"robotics.sim2real"
] | 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 | 25 | [] | 2026-08-18T03:50:27Z | https://github.com/quantumiracle/Robotic_Door_Opening_with_Tactile_Simulation | null |
[
"reinforcement-learning"
] | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.core",
"rl.deep"
] | true | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-03-02T09:54:42Z | Official implementation of the ICLR 2021 paper "Differentiable Trust Region Layers for Deep Reinforcement Learning" | [
"reinforcement-learning"
] | [
"deep-learning",
"machine-learning",
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-09T17:16:21Z | false | 10 | 343,724,658 | null | Python | AGPL-3.0 | [
"policy-learning",
"reinforcement-learning"
] | boschresearch/trust-region-layers | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-23T15:59:01Z | [] | 2023-08-23T13:46:38Z | [
"rl.core"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 11 | [
"bcai",
"deep-learning",
"iclr2021",
"machine-learning",
"paper-resource",
"projections",
"reinforcement-learning",
"trust-region-methods"
] | 2025-01-20T06:37:49Z | https://github.com/boschresearch/trust-region-layers | null |
[
"efficient-ml",
"model-compression"
] | [
"distillation",
"knowledge-distillation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-03-05T05:13:47Z | Official implementation for (Refine Myself by Teaching Myself : Feature Refinement via Self-Knowledge Distillation, CVPR-2021) | [
"efficient-ml",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 26 | 344,701,952 | null | Python | Apache-2.0 | [
"distillation",
"knowledge-distillation"
] | MingiJi/FRSKD | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 7 | 2026-10-10T16:40:02Z | [] | 2024-04-30T01:05:09Z | [
"efficiency.knowledge-distillation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 104 | [] | 2026-05-15T09:40:10Z | https://github.com/MingiJi/FRSKD | null |
[
"computer-vision",
"generative-ai",
"health-and-biomedicine",
"language",
"medical-imaging",
"multimodal",
"multimodal-learning"
] | [
"image-analysis",
"language-model",
"multimodal-learning",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"llm.transformers",
"medical.medical-imaging",
"recall.name.multimodal-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-03-05T10:43:04Z | This repo provides the official code for : 1) TransBTS: Multimodal Brain Tumor Segmentation Using Transformer (https://arxiv.org/abs/2103.04430) , accepted by MICCAI2021. 2) TransBTSV2: Towards Better and More Efficient Volumetric Segmentation of Medical Images(https://arxiv.org/abs/2201.12785). | [
"computer-vision",
"health-and-biomedicine",
"medical-imaging",
"multimodal-learning"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:27:52Z | false | 93 | 344,778,476 | null | Python | Apache-2.0 | [
"image-analysis",
"transformer"
] | Rubics-Xuan/TransBTS | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 15 | 2026-10-26T17:25:59Z | [] | 2024-03-11T10:27:01Z | [
"medical.medical-imaging"
] | 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 | 453 | [
"medical-image-segmentation",
"pytorch",
"transformer"
] | 2026-10-10T13:07:00Z | https://github.com/Rubics-Xuan/TransBTS | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-03-06T07:31:35Z | A novel graph neural networks for disease prediction | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-03T15:45:52Z | false | 0 | 345,033,731 | null | null | null | [
"graph-neural-network",
"message-passing",
"neural-network"
] | ystephenai/GRNNS | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-03T15:45:52Z | [] | 2021-03-06T07:31:36Z | [
"graph.gnn-description"
] | 39ee5489c2819671479aa0f1c625c4fdabcf4c35 | 2026-10-03T17:01:43Z | gh-ml-readme-evidence-v2 | "39ee5489c2819671479aa0f1c625c4fdabcf4c35" | [
"other"
] | [
"ml-method-context"
] | ok | 2026-10-03T17:01:43Z | ystephenai/GRNNS | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [] | 2021-03-06T07:31:38Z | https://github.com/ystephenai/GRNNS | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-03-08T13:42:32Z | The official implementation of ''On Graph Neural Networks versus Graph-Augmented MLPs'' ICLR 2021 | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 0 | 345,669,839 | null | Python | null | [
"graph-neural-network",
"message-passing",
"neural-network"
] | leichen2018/GNN_vs_GAMLP | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-02T16:59:40Z | [] | 2021-05-05T08:22:16Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 5 | [] | 2021-11-22T15:02:12Z | https://github.com/leichen2018/GNN_vs_GAMLP | null |
[
"computer-vision",
"efficient-ml",
"model-compression"
] | [
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] | [
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"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-03-09T08:14:07Z | The official implementation of ICLR2021 paper "Improve Object Detection with Feature-based Knowledge Distillation: Towards Accurate and Efficient Detectors". | [
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"efficient-ml",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 6 | 345,931,083 | null | null | MIT | [
"distillation",
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] | ArchipLab-LinfengZhang/Object-Detection-Knowledge-Distillation-ICLR2021 | [
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"query-match",
"repository-metadata"
] | 2 | 2026-10-10T16:40:02Z | [] | 2021-06-16T01:17:01Z | [
"efficiency.knowledge-distillation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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] | include | ml-contribution-v5 | 62 | [] | 2026-07-04T16:13:40Z | https://github.com/ArchipLab-LinfengZhang/Object-Detection-Knowledge-Distillation-ICLR2021 | null |
[
"computer-vision",
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] | [
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] | [
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"paper-reference",
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"repository-metadata"
] | [
"llm.transformers"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-03-09T09:33:48Z | (ICLR 2022 Spotlight) Official PyTorch implementation of "How Do Vision Transformers Work?" | [
"computer-vision",
"generative-ai",
"language"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-22T16:09:07Z | false | 78 | 345,953,439 | https://arxiv.org/abs/2202.06709 | Python | Apache-2.0 | [
"language-model",
"self-attention",
"transformer"
] | xxxnell/how-do-vits-work | [
"description",
"github-topics",
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"repository-metadata"
] | 2 | 2026-10-23T15:59:01Z | [] | 2022-07-14T18:53:23Z | [
"llm.transformers"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 820 | [
"loss-landscape",
"pytorch",
"self-attention",
"transformer",
"vision-transformer"
] | 2026-10-17T15:30:03Z | https://github.com/xxxnell/how-do-vits-work | null |
[
"reinforcement-learning"
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"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-03-14T14:54:17Z | Official implementation of paper "Learning to Solve Multiple-TSP with Time Window and Rejections via Deep Reinforcement Learning" | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-09T17:16:21Z | false | 7 | 347,665,801 | null | Python | null | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | zcaicaros/manager-worker-mtsptwr | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-15T15:20:52Z | [] | 2022-09-26T07:34:19Z | [
"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 | 48 | [] | 2026-04-15T01:35:03Z | https://github.com/zcaicaros/manager-worker-mtsptwr | null |
[
"deep-learning",
"representation-learning"
] | [
"representation-learning",
"self-supervised-learning"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"general.self-supervised-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-03-15T11:51:02Z | Code for our CVPR 2021 Paper "Self-Supervised Learning for Semi-Supervised Temporal Action Proposal". | [
"deep-learning",
"representation-learning"
] | [
"self-supervised-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T14:50:46Z | false | 8 | 347,948,911 | null | Python | null | [
"representation-learning",
"self-supervised-learning"
] | wangxiang1230/SSTAP | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 2 | 2026-10-11T16:35:57Z | [] | 2021-06-29T07:38:15Z | [
"general.self-supervised-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 72 | [
"action",
"cvpr2021",
"semi-supervised"
] | 2025-07-11T08:42:19Z | https://github.com/wangxiang1230/SSTAP | null |
[
"efficient-ml",
"model-compression"
] | [
"distillation",
"knowledge-distillation"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-03-17T02:52:40Z | Official implementation of AAAI22 paper "ADD: Frequency Attention and Multi-View based Knowledge Distillation to Detect Low-Quality Compressed Deepfake Images" | [
"efficient-ml",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 0 | 348,560,763 | null | Python | null | [
"distillation",
"knowledge-distillation"
] | Leminhbinh0209/AAAI22-ADD | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 2 | 2026-09-27T16:09:59Z | [] | 2024-03-01T06:08:38Z | [
"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 | 10 | [
"compress-images",
"deepfake-detection",
"knowledge-distillation"
] | 2026-04-05T09:59:49Z | https://github.com/Leminhbinh0209/AAAI22-ADD | null |
[
"3d",
"computer-vision",
"generative-ai",
"human-motion",
"language"
] | [
"3d-pose-estimation",
"keypoint-detection",
"language-model",
"pose-estimation",
"transformer"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.transformers",
"multimodal.3d-human-pose",
"vision.pose-estimation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-03-17T13:22:29Z | The project is an official implementation of our paper "3D Human Pose Estimation with Spatial and Temporal Transformers". | [
"generative-ai",
"language"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 79 | 348,718,235 | null | Python | null | [
"language-model",
"transformer"
] | zczcwh/PoseFormer | [
"description",
"query-match",
"repository-metadata"
] | 12 | 2026-10-23T15:59:01Z | [] | 2023-11-09T15:05:02Z | [
"llm.transformers"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 584 | [] | 2026-10-16T05:41:58Z | https://github.com/zczcwh/PoseFormer | null |
[
"automl",
"general-ml"
] | [
"few-shot-learning",
"meta-learning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.meta-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-03-18T08:05:13Z | A PyTorch implementation of our INTERSPEECH2021 paper 'A meta-learning approach for user-defined spoken term classification with varying classes and examples.' | [
"automl",
"general-ml"
] | [
"classifier"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-11T16:48:52Z | false | 0 | 348,989,603 | null | Python | MIT | [
"few-shot-learning",
"meta-learning"
] | Codelegant92/STC-ProtoNet | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-11T16:48:52Z | [] | 2022-07-06T06:16:57Z | [
"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 | 3 | [
"few-shot-learning",
"keyword-spotting",
"meta-learning",
"pytorch"
] | 2026-04-15T12:51:22Z | https://github.com/Codelegant92/STC-ProtoNet | null |
[
"generative-ai",
"language"
] | [
"language-model",
"transformer"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"llm.transformers"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-03-18T14:55:18Z | An English to French translator, implemented from scratch, based on the novel Transformer architecture as described in the 2017 paper "Attention Is All You Need" by Vaswani et. al. | [
"generative-ai",
"language"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-24T14:31:13Z | false | 0 | 349,113,237 | null | Python | null | [
"language-model",
"transformer"
] | tuhinnn-py/Marie | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 1 | 2026-10-24T14:31:13Z | [] | 2021-03-18T19:18:19Z | [
"llm.transformers"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
"contribution-language",
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 2 | [
"pytorch",
"transformer",
"translator"
] | 2022-05-25T12:15:25Z | https://github.com/tuhinnn-py/Marie | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-03-22T18:17:13Z | Code for our paper "Binary Graph Neural Networks", CVPR 2021 | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 7 | 350,445,314 | null | Python | MIT | [
"graph-neural-network",
"message-passing",
"neural-network"
] | mbahri/binary_gnn | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-09T17:16:21Z | [] | 2021-04-08T23:30:37Z | [
"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 | 38 | [] | 2025-11-28T10:47:00Z | https://github.com/mbahri/binary_gnn | null |
[
"generative-ai",
"language",
"multimodal",
"multimodal-learning"
] | [
"language-model",
"multimodal-learning",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.transformers",
"recall.name.multimodal-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-03-23T06:50:55Z | [CVPR 2021] "Multimodal Motion Prediction with Stacked Transformers": official code implementation and project page. | [
"generative-ai",
"language",
"multimodal-learning"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-09T17:31:36Z | false | 52 | 350,608,620 | null | Python | Apache-2.0 | [
"language-model",
"transformer"
] | decisionforce/mmTransformer | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-10-23T15:59:01Z | [] | 2021-12-03T04:50:23Z | [
"llm.transformers"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 377 | [
"cvpr",
"cvpr2021",
"paper"
] | 2026-07-31T16:03:03Z | https://github.com/decisionforce/mmTransformer | null |
[
"general-ml",
"generative-ai",
"interpretability-and-safety",
"language"
] | [
"language-model",
"novel-method",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.novel-method",
"llm.transformers",
"recall.name.transformer-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-03-23T22:11:18Z | [ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA. | [
"generative-ai",
"interpretability-and-safety",
"language"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 118 | 350,871,478 | null | Jupyter Notebook | MIT | [
"language-model",
"transformer"
] | hila-chefer/Transformer-MM-Explainability | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 7 | 2026-10-23T15:59:01Z | [] | 2023-08-24T17:45:14Z | [
"llm.transformers"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"method-tied-novelty-claim",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 914 | [
"clip",
"detr",
"explainability",
"explainable-ai",
"interpretability",
"lxmert",
"transformer",
"transformers",
"visualbert",
"visualization",
"vqa"
] | 2026-09-29T17:04:17Z | https://github.com/hila-chefer/Transformer-MM-Explainability | null |
[
"generative-ai",
"language",
"robotics-and-control",
"time-series-and-forecasting"
] | [
"language-model",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.transformers"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-03-24T16:40:46Z | [ICCV 2021] Official PyTorch Implementation of "AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting". | [
"generative-ai",
"language",
"robotics-and-control",
"time-series-and-forecasting"
] | [
"deep-learning",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 69 | 351,153,378 | https://www.ye-yuan.com/agentformer/ | Python | MIT | [
"language-model",
"transformer"
] | Khrylx/AgentFormer | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-23T15:59:01Z | [] | 2023-10-27T10:21:45Z | [
"llm.transformers"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 317 | [
"autonomous-driving",
"deep-learning",
"iccv2021",
"multi-agent-forecasting",
"pytorch",
"trajectory-forecasting",
"trajectory-prediction",
"transformer"
] | 2026-09-03T02:34:18Z | https://github.com/Khrylx/AgentFormer | null |
[
"language",
"linguistics",
"natural-language-processing"
] | [
"language-model",
"language-modeling"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"llm.language-model",
"nlp.language-modeling",
"recall.computational-linguistics"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-03-25T00:45:27Z | The official implementation of "Distilling Relation Embeddings from Pre-trained Language Models, EMNLP 2021 main conference", a high-quality relation embedding based on language models. | [
"linguistics",
"natural-language-processing"
] | [
"embedding"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-11T16:48:52Z | false | 4 | 351,265,006 | https://arxiv.org/abs/2110.15705 | Python | MIT | [
"language-modeling"
] | asahi417/relbert | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 2 | 2026-10-21T15:48:06Z | [] | 2024-12-02T13:00:01Z | [
"recall.computational-linguistics"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 48 | [
"bert",
"nlp",
"relation-extraction"
] | 2026-07-13T09:56:52Z | https://github.com/asahi417/relbert | null |
[
"generative-ai",
"language"
] | [
"language-model",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.transformers"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-03-28T10:33:00Z | This is the official PyTorch implementation of the paper "TransFG: A Transformer Architecture for Fine-grained Recognition" (Ju He, Jie-Neng Chen, Shuai Liu, Adam Kortylewski, Cheng Yang, Yutong Bai, Changhu Wang, Alan Yuille). | [
"generative-ai",
"language"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 94 | 352,300,645 | null | Python | MIT | [
"language-model",
"transformer"
] | TACJu/TransFG | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-23T15:59:01Z | [] | 2022-10-04T06:39:47Z | [
"llm.transformers"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 422 | [
"fine-grained-recognition"
] | 2026-10-12T13:59:27Z | https://github.com/TACJu/TransFG | null |
[
"program-analysis",
"software-engineering"
] | [
"machine-learning",
"program-repair"
] | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"growth26.software-automated-program-repair"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-03-29T08:32:50Z | Code of our paper Applying CodeBERT for Automated Program Repair of Java Simple Bugs which is accepted to MSR 2021. | [
"program-analysis",
"software-engineering"
] | [
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T22:51:24Z | false | 17 | 352,570,857 | https://arxiv.org/abs/2103.11626 | Python | null | [
"machine-learning",
"program-repair"
] | EhsanMashhadi/MSR2021-ProgramRepair | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-09-26T22:51:24Z | [] | 2022-11-27T15:57:04Z | [
"growth26.software-automated-program-repair"
] | 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 | 52 | [
"bert-model",
"machine-learning",
"mining-software-repositories",
"program-repair"
] | 2025-12-09T13:44:28Z | https://github.com/EhsanMashhadi/MSR2021-ProgramRepair | null |
[
"reinforcement-learning"
] | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-03-29T22:23:04Z | Code for our CVPR-2021 paper on Combining Semantic Guidance and Deep Reinforcement Learning For Generating Human Level Paintings. | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-15T15:20:52Z | false | 2 | 352,800,315 | null | Python | null | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | 1jsingh/semantic-guidance | [
"description",
"github-topics",
"query-match",
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-09T17:16:21Z | false | 14 | 355,776,787 | null | Python | null | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 51 | 359,744,542 | https://arxiv.org/abs/2105.02412 | Python | MIT | [
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] | Green-Wood/BTTR | [
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] | include | ml-contribution-v5 | 129 | [
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] | 2026-08-02T05:54:09Z | https://github.com/Green-Wood/BTTR | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-04-21T05:54:31Z | Official Pytorch Implementation of: "ImageNet-21K Pretraining for the Masses"(NeurIPS, 2021) paper | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T14:28:44Z | false | 71 | 360,047,919 | null | Python | MIT | [
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] | Alibaba-MIIL/ImageNet21K | [
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] | 2 | 2026-10-11T16:35:57Z | [] | 2023-01-11T11:27:25Z | [
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] | include | ml-contribution-v5 | 780 | [
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] | 2026-09-25T18:13:16Z | https://github.com/Alibaba-MIIL/ImageNet21K | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-04-22T06:13:30Z | This is the official implementation of the paper "Text2Gestures: A Transformer-Based Network for Generating Emotive Body Gestures for Virtual Agents". | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-24T14:31:13Z | false | 0 | 360,411,310 | https://gamma.umd.edu/t2g/ | null | MIT | [
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] | 1suancaiyu/Text2Gestures | [
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] | 1 | 2026-10-24T14:31:13Z | [] | 2021-03-06T06:36:05Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2022-10-28T06:04:43Z | https://github.com/1suancaiyu/Text2Gestures | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-04-22T08:15:10Z | A novel architecture and training strategy for graph neural networks (GNN). The proposed architecture, named as Autoencoder-Aided GNN (AA-GNN), compresses the convolutional features at multiple hidden layers, hinging on a novel end-to-end training procedure that learns different graph representations per each layer. As... | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 2 | 360,442,972 | null | Python | GPL-3.0 | [
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] | lrnzgiusti/Autoencoder-Aided-Graph-Convolutional-Neural-Network | [
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] | 1 | 2026-10-04T15:58:58Z | [] | 2024-05-14T19:52:53Z | [
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] | include | ml-contribution-v5 | 10 | [
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] | 2026-05-28T20:19:45Z | https://github.com/lrnzgiusti/Autoencoder-Aided-Graph-Convolutional-Neural-Network | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-04-24T03:26:07Z | Pytorch implementation of our paper accepted by IEEE TNNLS, 2022 — Carrying out CNN Channel Pruning in a White Box | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-11T16:35:57Z | false | 2 | 361,060,870 | null | Python | null | [
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] | zyxxmu/White-Box | [
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] | 2 | 2026-10-11T16:48:52Z | [] | 2022-02-15T07:29:49Z | [
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] | include | ml-contribution-v5 | 18 | [] | 2024-07-17T06:28:16Z | https://github.com/zyxxmu/White-Box | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-04-27T01:20:00Z | Implementation of the Neuromodulated Transformer (NeMoT), a new transformer architecture that entwines neuromodulation with the transformer architecture. | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 0 | 361,941,337 | null | Python | MIT | [
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] | Strong-AI-Lab/Neuromodulated-Transformer | [
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] | 1 | 2026-10-23T15:59:01Z | [] | 2022-06-01T08:16:32Z | [
"llm.transformers"
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] | include | ml-contribution-v5 | 0 | [] | 2022-06-01T08:19:15Z | https://github.com/Strong-AI-Lab/Neuromodulated-Transformer | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-05-02T07:51:20Z | Code for paper "Computation Offloading Optimization for UAV-assisted Mobile Edge Computing: A Deep Deterministic Policy Gradient Approach" | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 94 | 363,596,187 | https://fangvv.github.io/UAV-DDPG/ | Python | null | [
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] | fangvv/UAV-DDPG | [
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] | 12 | 2026-10-24T14:31:13Z | [] | 2026-10-08T14:57:48Z | [
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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 | 726 | [
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[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-05-02T22:56:19Z | Official implementation of the paper "AvaTr: One-Shot Speaker Extraction with Transformers" | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-25T15:11:36Z | false | 1 | 363,767,029 | null | Python | null | [
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] | cajal/AvaTr | [
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] | 1 | 2026-10-25T15:11:36Z | [] | 2023-07-19T19:11:46Z | [
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] | include | ml-contribution-v5 | 3 | [] | 2023-07-20T08:58:38Z | https://github.com/cajal/AvaTr | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-05-03T10:49:19Z | The code for paper "Exploiting diverse information in Pre-trained Language Model for Multi-choice Machine Reading Comprehension" | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-12T20:31:19Z | false | 0 | 363,897,995 | null | Python | null | [
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] | include | ml-contribution-v5 | 4 | [] | 2022-12-05T20:33:36Z | https://github.com/bestbzw/Multi-Decision-Transformer | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-05-03T14:12:19Z | This repository contains the official code for the paper : Realistic Data Augmentation Framework for Enhancing Tabular Reasoning (Findings-EMNLP, 2022). | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-03T15:45:52Z | false | 1 | 363,952,021 | https://autotnli.github.io/ | HTML | Apache-2.0 | [
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] | Dibyakanti/AutoTNLI-code | [
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] | 1 | 2026-10-03T15:45:52Z | [] | 2024-11-26T18:19:08Z | [
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[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-05-07T15:16:33Z | Code for ACL 2021 paper "Unsupervised Out-of-Domain Detection via Pre-trained Transformers" | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-21T15:48:06Z | false | 1 | 365,273,750 | null | Python | MIT | [
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] | rivercold/BERT-unsupervised-OOD | [
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] | 2 | 2026-10-25T15:11:36Z | [] | 2021-08-20T15:35:15Z | [
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] | include | ml-contribution-v5 | 30 | [] | 2024-09-17T07:53:44Z | https://github.com/rivercold/BERT-unsupervised-OOD | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-05-08T02:49:53Z | Code for our paper on doing resource allocation with graph neural networks | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 2 | 365,403,860 | null | Python | Apache-2.0 | [
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] | MilesCranmer/gnn_resource_allocation | [
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] | 1 | 2026-10-02T16:59:40Z | [] | 2021-06-24T09:03:54Z | [
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] | include | ml-contribution-v5 | 29 | [
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"graph-neural-networks",
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"machine-learning",
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] | 2026-07-29T08:43:47Z | https://github.com/MilesCranmer/gnn_resource_allocation | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-05-10T07:44:04Z | [IJCAI-2021&&TNNLS-2022] Official implementation of Hierarchical Self-supervised Augmented Knowledge Distillation | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 15 | 365,959,403 | null | Python | Apache-2.0 | [
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] | 7 | 2026-10-10T16:40:02Z | [] | 2024-03-22T16:11:06Z | [
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] | include | ml-contribution-v5 | 78 | [] | 2025-12-28T07:40:04Z | https://github.com/winycg/HSAKD | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-05-11T01:20:08Z | Official Pytorch implementation of IJCAI'21 paper "GraphMI: Extracting Private Graph Data from Graph Neural Networks" | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 8 | 366,219,916 | null | Python | null | [
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] | zaixizhang/GraphMI | [
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] | 1 | 2026-10-01T17:50:46Z | [] | 2021-11-19T03:17:13Z | [
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] | include | ml-contribution-v5 | 13 | [] | 2024-09-12T08:50:21Z | https://github.com/zaixizhang/GraphMI | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-05-11T08:47:42Z | [ICCV2021] Official PyTorch implementation of Segmenter: Transformer for Semantic Segmentation | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-11T16:35:57Z | false | 182 | 366,315,313 | null | Python | MIT | [
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] | rstrudel/segmenter | [
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] | 6 | 2026-10-24T14:31:13Z | [] | 2024-04-21T19:30:30Z | [
"llm.transformers"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 907 | [
"segmentation",
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] | 2026-10-21T14:00:47Z | https://github.com/rstrudel/segmenter | null |
[
"deep-learning",
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] | [
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"self-supervised-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-05-11T11:34:00Z | Official Implementation for "Self-Gudied Contrastive Learning for BERT Sentence Representations (ACL 2021)" | [
"deep-learning",
"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 5 | 366,359,249 | null | Python | null | [
"contrastive-learning",
"self-supervised-learning"
] | galsang/SG-BERT | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-02T16:59:40Z | [] | 2022-06-09T10:31:44Z | [
"general.contrastive-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 27 | [] | 2025-02-06T18:24:49Z | https://github.com/galsang/SG-BERT | null |
[
"generative-ai",
"language"
] | [
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] | [
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"paper-reference",
"query-match",
"repository-metadata"
] | [
"llm.transformers"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-05-11T13:37:53Z | Official implementation of the Keyword Transformer: https://arxiv.org/abs/2104.00769 | [
"generative-ai",
"language"
] | [
"machine-learning",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 40 | 366,394,443 | null | Jupyter Notebook | Apache-2.0 | [
"language-model",
"transformer"
] | ARM-software/keyword-transformer | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 2 | 2026-10-24T14:31:13Z | [] | 2022-04-29T20:33:35Z | [
"llm.transformers"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 149 | [
"keyword-spotting",
"machine-learning",
"transformer"
] | 2026-10-22T12:32:23Z | https://github.com/ARM-software/keyword-transformer | null |
[
"green-ai",
"natural-language-processing",
"sustainable-computing"
] | [
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] | [
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"query-match",
"repository-metadata"
] | [
"nlp.text-generation",
"specialized.carbon-footprint-training"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-05-11T20:20:11Z | Code for Paper BERT & Family Eat Word Salad: Experiments with Text Understanding at AAAI 2021 | [
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 0 | 366,503,361 | null | Python | Apache-2.0 | [
"text-generation"
] | ashim95/wordsalad | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-26T17:25:59Z | [] | 2021-05-11T20:22:24Z | [
"nlp.text-generation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2021-05-11T20:25:37Z | https://github.com/ashim95/wordsalad | null |
[
"computer-vision",
"generative-ai",
"language"
] | [
"language-model",
"transformer"
] | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"llm.transformers"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-05-17T06:42:03Z | The implementation of our paper: Towards Robust Vision Transformer (CVPR2022) | [
"computer-vision",
"generative-ai",
"language"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-24T14:31:13Z | false | 16 | 368,082,560 | https://arxiv.org/abs/2105.07926 | Python | null | [
"language-model",
"transformer"
] | vtddggg/Robust-Vision-Transformer | [
"description",
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"query-match",
"repository-metadata"
] | 1 | 2026-10-24T14:31:13Z | [] | 2022-08-16T10:23:43Z | [
"llm.transformers"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 142 | [] | 2025-03-05T06:32:18Z | https://github.com/vtddggg/Robust-Vision-Transformer | null |
[
"reinforcement-learning"
] | [
"neural-network",
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"reinforcement-learning"
] | [
"description",
"github-topics",
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"query-match",
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] | [
"rl.core"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-05-17T22:18:38Z | Official implementation of paper "A neural active inference model of perceptual-motor learning" published on Computational Neuroscience in 2023. | [
"reinforcement-learning"
] | [
"machine-learning",
"neural-network",
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-25T15:11:36Z | false | 0 | 368,335,483 | https://www.frontiersin.org/articles/10.3389/fncom.2023.1099593/full | Python | BSD-3-Clause | [
"neural-network",
"policy-learning",
"reinforcement-learning"
] | George614/neural_active_inference | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-10-25T15:11:36Z | [] | 2024-08-24T21:27:39Z | [
"rl.core"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
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] | include | ml-contribution-v5 | 7 | [
"active-inference",
"artificial-intelligence",
"cognitive-services",
"computational-neuroscience",
"machine-learning",
"neural-networks",
"off-policy-learning",
"reinforcement-learning"
] | 2025-12-19T18:32:15Z | https://github.com/George614/neural_active_inference | null |
[
"natural-language-processing"
] | [
"machine-translation",
"neural-machine-translation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"nlp.machine-translation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-05-22T03:58:20Z | Code for our paper "A Document-Level Neural Machine Translation Model with Dynamic Caching Guided by Theme-Rheme Information", appear at COLING2020. | [
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-11T16:48:52Z | false | 0 | 369,710,006 | null | null | MIT | [
"machine-translation",
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] | zgzjdx/DocNMT | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-11T16:48:52Z | [] | 2021-12-02T13:55:49Z | [
"nlp.machine-translation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-context-only",
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [] | 2021-12-02T13:55:52Z | https://github.com/zgzjdx/DocNMT | null |
[
"computer-vision",
"multimodal",
"multimodal-learning"
] | [
"image-captioning",
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] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.image-captioning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-05-24T07:42:49Z | Official Code for 'RSTNet: Captioning with Adaptive Attention on Visual and Non-Visual Words' (CVPR 2021) | [
"computer-vision",
"multimodal",
"multimodal-learning"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-09T17:16:21Z | false | 25 | 370,269,106 | null | Python | BSD-3-Clause | [
"image-captioning",
"transformer",
"vision-language"
] | zhangxuying1004/RSTNet | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-09T17:16:21Z | [] | 2022-12-17T04:19:19Z | [
"vision.image-captioning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 123 | [
"cvpr2021",
"image-captioning",
"multimodal",
"python",
"pytorch",
"transformer"
] | 2026-07-21T08:21:18Z | https://github.com/zhangxuying1004/RSTNet | null |
[
"reinforcement-learning"
] | [
"policy-learning",
"reinforcement-learning"
] | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"rl.core"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-05-25T03:28:59Z | [CVPR 2021] Official Implementation of VAI: Unsupervised Visual Attention and Invariance for Reinforcement Learning | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-24T14:31:13Z | false | 3 | 370,552,767 | https://arxiv.org/abs/2104.02921 | Python | null | [
"policy-learning",
"reinforcement-learning"
] | TonyLianLong/VAI-ReinforcementLearning | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-10-24T14:31:13Z | [] | 2022-05-03T08:40:33Z | [
"rl.core"
] | 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 | 27 | [
"attention-mechanism",
"reinforcement-learning"
] | 2025-05-23T08:22:35Z | https://github.com/TonyLianLong/VAI-ReinforcementLearning | null |
[
"deep-learning",
"language",
"natural-language-processing",
"representation-learning"
] | [
"contrastive-learning",
"language-model",
"language-modeling",
"self-supervised-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning",
"llm.language-model",
"nlp.language-modeling"
] | true | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-05-26T04:26:25Z | Source code for ACL 2021 paper "ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning" | [
"language",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 13 | 370,905,081 | null | Python | null | [
"contrastive-learning",
"language-model",
"language-modeling"
] | thunlp/ERICA | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-11T16:48:52Z | [] | 2021-05-26T04:26:40Z | [
"llm.language-model",
"nlp.language-modeling"
] | 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 | 85 | [] | 2026-02-18T15:37:56Z | https://github.com/thunlp/ERICA | null |
[
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-05-27T01:44:39Z | PDAS (Progressive Differentiable Architecture Search) is a novel network pruning algorithm, which aims to automatically find an appropriate layer width for each layer to reduce network redundancy. | [
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-11T16:48:52Z | false | 2 | 371,215,287 | null | Python | null | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | CGCL-codes/PDAS | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-11T16:48:52Z | [] | 2021-05-27T02:50:38Z | [
"efficiency.pruning"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 2 | [] | 2024-04-24T16:14:47Z | https://github.com/CGCL-codes/PDAS | null |
[
"linguistics",
"natural-language-processing"
] | [
"dependency-parsing",
"language-modeling",
"syntactic-parsing"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"nlp.dependency-parsing",
"recall.computational-linguistics"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-05-27T19:48:32Z | Official code of our work, Syntax-augmented Multilingual BERT for Cross-lingual Transfer [ACL 2021]. | [
"linguistics",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-09T17:16:21Z | false | 3 | 371,487,557 | null | Python | GPL-3.0 | [
"language-modeling"
] | wasiahmad/Syntax-MBERT | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-21T15:48:06Z | [] | 2021-12-02T06:47:06Z | [
"recall.computational-linguistics"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 16 | [] | 2024-06-17T03:47:02Z | https://github.com/wasiahmad/Syntax-MBERT | null |
[
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"neural-network",
"neural-network-pruning",
"pruning",
"structured-pruning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-05-28T12:15:59Z | Pytorch implementation of our paper RGP: Neural Network Pruning Through Regular Graph With Edges Swapping accepted by TNNLS. | [
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-11T16:48:52Z | false | 1 | 371,690,174 | https://ieeexplore.ieee.org/document/10149178 | Python | MIT | [
"neural-network",
"neural-network-pruning",
"pruning",
"structured-pruning"
] | JingyangXiang/Neural-Network-Pruning-through-its-RegularGraph-Structure | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-11T16:48:52Z | [] | 2021-09-26T08:47:51Z | [
"efficiency.pruning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 10 | [] | 2024-05-24T12:15:34Z | https://github.com/JingyangXiang/Neural-Network-Pruning-through-its-RegularGraph-Structure | null |
[
"linguistics",
"natural-language-processing",
"reinforcement-learning"
] | [
"language-modeling",
"reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"recall.computational-linguistics"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-05-28T17:03:28Z | PyTorch code for ACL 2021 paper Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards | [
"linguistics",
"natural-language-processing",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-21T15:48:06Z | false | 6 | 371,767,898 | null | Python | null | [
"language-modeling",
"reinforcement-learning"
] | shwetanlp/CHQ-Summ | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-21T15:48:06Z | [] | 2021-08-05T21:01:05Z | [
"recall.computational-linguistics"
] | 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 | 22 | [] | 2025-07-18T20:29:26Z | https://github.com/shwetanlp/CHQ-Summ | null |
[
"deep-learning",
"natural-language-processing",
"representation-learning"
] | [
"contrastive-learning",
"self-supervised-learning",
"text-generation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning",
"nlp.text-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-05-31T00:53:00Z | Code for our paper "SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization", ACL 2021 | [
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 25 | 372,345,402 | null | Python | null | [
"contrastive-learning",
"text-generation"
] | yixinL7/SimCLS | [
"description",
"query-match",
"repository-metadata"
] | 6 | 2026-10-24T14:31:13Z | [] | 2021-06-15T22:12:17Z | [
"nlp.text-generation"
] | 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 | 188 | [] | 2026-06-09T18:33:16Z | https://github.com/yixinL7/SimCLS | null |
[
"generative-ai",
"language",
"natural-language-processing"
] | [
"language-model",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.transformers"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-05-31T12:09:27Z | Source Code For ACL 2021 Paper "Mention Flags (MF): Constraining Transformer-based Text Generators" | [
"generative-ai",
"language",
"natural-language-processing"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-25T15:11:36Z | false | 5 | 372,493,570 | https://aclanthology.org/2021.acl-long.9/ | Python | Apache-2.0 | [
"language-model",
"transformer"
] | GaryYufei/ACL2021MF | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-25T15:11:36Z | [] | 2021-10-04T16:20:31Z | [
"llm.transformers"
] | 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 | [
"acl2021",
"nlg",
"nlp"
] | 2023-11-11T11:23:16Z | https://github.com/GaryYufei/ACL2021MF | null |
[
"reinforcement-learning"
] | [
"distributional-reinforcement-learning",
"neural-network",
"reinforcement-learning",
"value-based-reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"rl.distributional"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-06-01T11:26:34Z | Official implementation of the UMDQN algorithm presented in the scientific research paper entitled "Distributional Reinforcement Learning with Unconstrained Monotonic Neural Networks". | [
"reinforcement-learning"
] | [
"neural-network",
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T14:28:44Z | false | 2 | 372,804,957 | null | Python | null | [
"distributional-reinforcement-learning",
"neural-network",
"reinforcement-learning",
"value-based-reinforcement-learning"
] | ThibautTheate/Unconstrained-Monotonic-Deep-Q-Network-algorithm | [
"description",
"query-match",
"repository-metadata"
] | 4 | 2026-10-09T17:16:21Z | [] | 2022-06-03T08:54:58Z | [
"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 | 11 | [] | 2025-03-21T16:10:13Z | https://github.com/ThibautTheate/Unconstrained-Monotonic-Deep-Q-Network-algorithm | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-06-02T08:21:30Z | This repository contains the code of our SIGIR 2020 paper "Item Tagging for Information Retrieval: A Tripartite Graph Neural Network based Approach" | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-03T15:45:52Z | false | 0 | 373,094,037 | null | Python | null | [
"graph-neural-network",
"message-passing",
"neural-network"
] | kyriemao/TagGNN-SIGIR | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-03T15:45:52Z | [] | 2023-01-26T16:28:46Z | [
"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 | 7 | [] | 2024-07-09T08:39:55Z | https://github.com/kyriemao/TagGNN-SIGIR | null |
[
"computer-vision",
"general-ml",
"generative-ai",
"image-processing",
"language",
"low-level-vision"
] | [
"image-denoising",
"image-restoration",
"language-model",
"restoration",
"self-supervised-learning",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.transformers",
"recall.name.transformer-model",
"recall26.image-denoising",
"vision.image-restoration"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-06-03T07:44:44Z | [CVPR 2022] Official implementation of the paper "Uformer: A General U-Shaped Transformer for Image Restoration". | [
"generative-ai",
"language"
] | [
"deep-learning",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 133 | 373,423,349 | null | Python | MIT | [
"language-model",
"transformer"
] | ZhendongWang6/Uformer | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 15 | 2026-10-24T14:31:13Z | [] | 2025-03-21T08:08:03Z | [
"llm.transformers"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 948 | [
"deep-learning",
"image-deblurring",
"image-demoireing",
"image-denoising",
"image-deraining",
"image-restoration",
"pytorch"
] | 2026-10-21T15:03:59Z | https://github.com/ZhendongWang6/Uformer | 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 | [] | 2021-06-04T01:37:31Z | Official code for the ICML 2021 paper "Generative Causal Explanations for Graph Neural Networks." | [
"deep-learning",
"graph-learning"
] | [
"deep-learning",
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 8 | 373,688,613 | null | Jupyter Notebook | null | [
"graph-neural-network",
"message-passing",
"neural-network"
] | wanyu-lin/ICML2021-Gem | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 2 | 2026-10-09T17:16:21Z | [] | 2022-04-07T23:04:42Z | [
"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 | 64 | [
"graph-neural-networks",
"interpretable-deep-learning"
] | 2026-09-29T06:44:29Z | https://github.com/wanyu-lin/ICML2021-Gem | null |
[
"data-centric-ai",
"deep-learning",
"graph-learning",
"machine-learning"
] | [
"active-learning",
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"message-passing",
"neural-network",
"sample-selection"
] | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"general.active-learning",
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-06-04T06:58:27Z | Official Code for TMLR 2023 Paper: Partition-Based Active Learning for Graph Neural Networks | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 0 | 373,750,385 | https://arxiv.org/abs/2201.09391 | Jupyter Notebook | null | [
"graph-neural-network",
"message-passing",
"neural-network"
] | Mars-tin/GraphPart | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | 2 | 2026-10-04T15:58:58Z | [] | 2026-01-14T20:20:42Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [] | 2026-01-14T20:20:49Z | https://github.com/Mars-tin/GraphPart | null |
[
"automl",
"efficient-ml",
"general-ml",
"model-compression"
] | [
"distillation",
"few-shot-learning",
"knowledge-distillation",
"meta-learning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation",
"general.meta-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-06-06T07:42:59Z | Code for ACL 2022 paper "BERT Learns to Teach: Knowledge Distillation with Meta Learning". | [
"automl",
"general-ml"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 17 | 374,298,630 | null | Python | MIT | [
"distillation",
"few-shot-learning",
"knowledge-distillation",
"meta-learning"
] | JetRunner/MetaDistil | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 13 | 2026-10-11T16:35:57Z | [] | 2022-08-04T20:53:48Z | [
"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 | 87 | [
"knowledge-distillation",
"transfer-learning"
] | 2026-10-08T12:59:23Z | https://github.com/JetRunner/MetaDistil | null |
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