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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
[
"automl",
"deep-learning",
"general-ml",
"representation-learning"
] | [
"contrastive-learning",
"few-shot-learning",
"meta-learning",
"neural-network",
"self-supervised-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning",
"general.meta-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-10-26T19:10:30Z | This is an official PyTorch implementation of Task-Adaptive Neural Network Search with Meta-Contrastive Learning (NeurIPS 2021, Spotlight). | [
"automl",
"general-ml"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 7 | 421,553,089 | null | Python | null | [
"contrastive-learning",
"few-shot-learning",
"meta-learning",
"neural-network"
] | wyjeong/TANS | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-13T18:05:26Z | [] | 2023-11-24T15:57:54Z | [
"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 | 19 | [] | 2025-06-24T01:37:54Z | https://github.com/wyjeong/TANS | 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 | [] | 2021-10-26T19:45:06Z | The official implementation of DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks (NeurIPS 2021) | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 3 | 421,562,868 | null | Python | MIT | [
"graph-neural-network",
"message-passing",
"neural-network"
] | KarolisMart/DropGNN | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-02T16:59:40Z | [] | 2022-06-26T20:01:28Z | [
"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"
] | include | ml-contribution-v5 | 26 | [] | 2025-05-29T03:31:39Z | https://github.com/KarolisMart/DropGNN | null |
[
"chemistry",
"drug-discovery",
"health-and-biomedicine",
"natural-language-processing"
] | [
"property-prediction",
"qsar",
"ranking",
"representation-learning",
"supervised-learning",
"transformer",
"virtual-screening"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"chem-drug-discovery",
"chem-property-prediction"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-10-27T16:28:52Z | SELFIES-Transformer: Learning the Representation of Chemical Space for Discovering New Drugs using Transformers Architecture | [
"chemistry",
"drug-discovery",
"health-and-biomedicine",
"natural-language-processing"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-26T14:50:46Z | false | 3 | 421,893,548 | null | Python | null | [
"property-prediction",
"qsar",
"supervised-learning",
"transformer"
] | didar00/Final-Project | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 3 | 2026-10-09T17:16:21Z | [] | 2022-06-02T17:08:45Z | [
"chem-property-prediction"
] | 310869ae2910a574eb653b138cc630937bd44611 | 2026-09-26T15:53:33Z | gh-ml-readme-evidence-v2 | "310869ae2910a574eb653b138cc630937bd44611" | [
"installation",
"method",
"other",
"usage"
] | [
"method-contribution",
"ml-method-context",
"model-training-artifact",
"paper-reference"
] | ok | 2026-09-26T15:53:33Z | didar00/Final-Project | specific-method-with-novelty-claim | [
"method-contribution",
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue",
"model-training-artifact",
"paper-reference"
] | include | ml-contribution-v5 | 4 | [
"drug-discovery",
"nlp",
"transformers"
] | 2025-04-15T13:09:24Z | https://github.com/didar00/Final-Project | null |
[
"automl",
"optimization",
"time-series-and-forecasting"
] | [
"automl",
"feature-engineering"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.automated-machine-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-10-30T18:24:33Z | Code for paper "Short-term River Flood Forecasting using Composite Models and Automated Machine Learning: the Case Study of Lena River" | [
"automl",
"optimization",
"time-series-and-forecasting"
] | [
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-03T15:45:52Z | false | 0 | 422,959,977 | null | Python | null | [
"automl",
"feature-engineering"
] | ITMO-NSS-team/composite-flood-forecast | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-09T17:16:21Z | [] | 2021-12-09T20:13:01Z | [
"general.automated-machine-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-context-only",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 12 | [] | 2025-10-18T12:11:18Z | https://github.com/ITMO-NSS-team/composite-flood-forecast | null |
[
"linguistics",
"multimodal-learning",
"natural-language-processing"
] | [
"language-modeling",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"recall.computational-linguistics"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-11-01T02:03:45Z | This repository contains the official implementation code of the paper Transformer-based Feature Reconstruction Network for Robust Multimodal Sentiment Analysis, accepted at ACMMM 2021. | [
"linguistics",
"multimodal-learning",
"natural-language-processing"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-24T14:31:13Z | false | 6 | 423,308,881 | null | Python | MIT | [
"language-modeling",
"transformer"
] | thuiar/TFR-Net | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-24T14:31:13Z | [] | 2023-04-16T07:47:49Z | [
"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",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 46 | [] | 2026-09-23T06:43:11Z | https://github.com/thuiar/TFR-Net | null |
[
"reinforcement-learning",
"robotics"
] | [
"reinforcement-learning",
"robot-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"robotics.robot-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-11-01T10:51:54Z | Code for paper "Model-free Safe Control for Zero-Violation Reinforcement Learning" at Conference on Robot Learning (CoRL) 2021. | [
"reinforcement-learning",
"robotics"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T14:50:46Z | false | 1 | 423,429,947 | null | Python | MIT | [
"reinforcement-learning",
"robot-learning"
] | TairanHe/ISSA | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-10T16:40:02Z | [] | 2021-11-01T13:00:12Z | [
"robotics.robot-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 | 11 | [] | 2026-07-21T06:33:10Z | https://github.com/TairanHe/ISSA | null |
[
"generative-modeling",
"graph-learning",
"time-series"
] | [
"dynamic-graph-learning",
"temporal-graph-learning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.temporal-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-11-02T06:35:18Z | Official implementation of "Diverse Motion Stylization for Multiple Style Domains via Spatial-Temporal Graph-Based Generative Model" | [
"generative-modeling",
"graph-learning",
"time-series"
] | [
"generative-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T14:28:44Z | false | 5 | 423,732,317 | null | Python | MIT | [
"dynamic-graph-learning",
"temporal-graph-learning"
] | soomean/Diverse-Motion-Stylization | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-11T16:35:57Z | [] | 2022-03-07T17:49:54Z | [
"graph.temporal-learning"
] | c4bfa4fa178f7764b8c984fdeac61ef24634b85c | 2026-09-26T15:53:33Z | gh-ml-readme-evidence-v2 | "c4bfa4fa178f7764b8c984fdeac61ef24634b85c" | [
"abstract",
"citation",
"installation",
"method",
"other",
"usage"
] | [
"method-contribution",
"ml-method-context",
"paper-reference"
] | ok | 2026-09-26T15:53:33Z | soomean/Diverse-Motion-Stylization | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-method-context",
"paper-reference"
] | include | ml-contribution-v5 | 36 | [
"character-animation",
"generative-model",
"graph-convolutional-networks",
"motion-style-transfer",
"motion-synthesis"
] | 2026-07-05T12:39:14Z | https://github.com/soomean/Diverse-Motion-Stylization | null |
[
"deep-learning",
"graph-learning",
"recommender-systems",
"sequence-modeling"
] | [
"graph-neural-network",
"message-passing",
"neural-network",
"sequence-model",
"sequential-recommendation"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description",
"growth26.recsys-sequential-recommendation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-11-04T04:34:50Z | Official implementation of SIGIR'2021 paper: "Sequential Recommendation with Graph Neural Networks". | [
"deep-learning",
"graph-learning",
"recommender-systems"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T22:51:24Z | false | 27 | 424,472,715 | null | Python | MIT | [
"graph-neural-network",
"message-passing",
"neural-network"
] | tsinghua-fib-lab/SIGIR21-SURGE | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-09T17:16:21Z | [] | 2022-10-30T13:30:40Z | [
"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 | 85 | [
"graph-neural-networks",
"recommender-system",
"sequential-recommendation"
] | 2026-03-21T14:29:13Z | https://github.com/tsinghua-fib-lab/SIGIR21-SURGE | null |
[] | [
"distillation",
"knowledge-distillation"
] | [
"description",
"github-topics",
"repository-metadata"
] | [] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-11-07T03:22:21Z | Official PyTorch Implementation of MSDN (CVPR'22) | [] | [
"classifier"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-01T19:00:27.900159Z | false | 5 | 425,402,782 | null | Python | null | [
"distillation",
"knowledge-distillation"
] | shiming-chen/MSDN | [
"description",
"github-topics",
"repository-metadata"
] | 1 | 2026-10-01T19:00:27.900159Z | [] | 2022-12-03T01:48:43Z | [] | 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 | 54 | [
"knowledge-distillation",
"mutual-learning",
"semantic-distillation",
"zero-shot-classification",
"zero-shot-learning"
] | 2026-01-18T07:07:28Z | https://github.com/shiming-chen/MSDN | {"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["knowledge-distillation"]} |
[
"computer-vision",
"efficient-ml",
"model-compression"
] | [
"distillation",
"knowledge-distillation"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-11-09T10:36:15Z | This is the official implementation of the paper "Instance-conditional Knowledge Distillation for Object Detection", based on MegEngine and Pytorch. | [
"computer-vision",
"efficient-ml",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 5 | 426,187,297 | null | Python | NOASSERTION | [
"distillation",
"knowledge-distillation"
] | MegEngine/ICD | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-10T16:40:02Z | [] | 2022-08-07T03:17:12Z | [
"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 | 57 | [
"knowledge-distillation",
"megengine",
"object-detection",
"pytorch"
] | 2025-08-12T08:06:55Z | https://github.com/MegEngine/ICD | null |
[
"computer-vision",
"multimodal",
"multimodal-learning"
] | [
"multimodal-learning",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"recall.name.multimodal-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-11-12T12:00:09Z | Official implementation of History Aware Multimodal Transformer for Vision-and-Language Navigation (NeurIPS'21). | [
"computer-vision",
"multimodal",
"multimodal-learning"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-11T16:35:57Z | false | 14 | 427,344,270 | null | Python | MIT | [
"multimodal-learning",
"transformer"
] | cshizhe/VLN-HAMT | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-18T14:44:29Z | [] | 2023-06-14T07:29:05Z | [
"recall.name.multimodal-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 151 | [] | 2026-10-18T06:45:42Z | https://github.com/cshizhe/VLN-HAMT | null |
[
"deep-learning",
"graph-learning",
"information-retrieval",
"natural-language-processing"
] | [
"document-retrieval",
"graph-neural-network",
"information-retrieval",
"message-passing",
"neural-network"
] | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description",
"nlp-document-retrieval"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-11-13T09:47:50Z | [EMNLP 2021] Code for our EMNLP 2021 paper “Heterogeneous Graph Neural Networks for Keyphrase Generation” | [
"graph-learning",
"information-retrieval",
"natural-language-processing"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 2 | 427,625,481 | https://arxiv.org/pdf/2109.04703.pdf | Python | null | [
"document-retrieval",
"graph-neural-network",
"information-retrieval",
"neural-network"
] | jiacheng-ye/kg_gater | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | 3 | 2026-10-09T17:16:21Z | [] | 2021-11-13T12:09:21Z | [
"nlp-document-retrieval"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 13 | [
"heterogeneous-graph",
"keyphrase-extraction",
"keyphrase-generation"
] | 2026-09-28T01:38:12Z | https://github.com/jiacheng-ye/kg_gater | null |
[
"computer-vision",
"machine-learning",
"representation-learning"
] | [
"domain-generalization",
"transfer-learning",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"probable.adaptation-domain-generalization-readme"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-11-14T11:32:31Z | [CVPR22] Official Implementation of DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation | [
"computer-vision",
"machine-learning",
"representation-learning"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 99 | 427,910,655 | null | Python | NOASSERTION | [
"domain-generalization",
"transfer-learning",
"transformer"
] | lhoyer/DAFormer | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-09T17:31:36Z | [] | 2024-08-10T16:20:43Z | [
"probable.adaptation-domain-generalization-readme"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 575 | [
"cvpr2022",
"semantic-segmentation",
"transformer",
"unsupervised-domain-adaptation"
] | 2026-10-05T13:10:26Z | https://github.com/lhoyer/DAFormer | null |
[
"general-ml",
"language",
"natural-language-processing"
] | [
"language-model",
"language-modeling",
"novel-method",
"transformer"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.novel-method",
"llm.language-model",
"nlp.language-modeling"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-11-15T09:30:48Z | This M.Sc. thesis proposes a novel method for toponym resolution, using a Transformer-based pre-trained language model (namely the BERT and RoBERTa models), that builds a representation of the toponym to be disambiguated together with its surrounding context. Other toponyms, or even regular words appearing in the surro... | [
"language",
"natural-language-processing"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 0 | 428,200,245 | null | Python | null | [
"language-model",
"language-modeling",
"transformer"
] | diogoViegas/Toponym-Resolution-in-Text-with-Neural-Language-Models | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-10-15T15:20:52Z | [] | 2021-11-15T09:32:07Z | [
"llm.language-model",
"nlp.language-modeling"
] | 7f4922bc84f0f8baee657312bf4edb772f6b87e7 | 2026-09-29T18:47:14Z | gh-ml-readme-evidence-v2 | "7f4922bc84f0f8baee657312bf4edb772f6b87e7" | [
"other"
] | [
"method-contribution",
"ml-method-context"
] | ok | 2026-09-29T18:47:14Z | diogoViegas/Toponym-Resolution-in-Text-with-Neural-Language-Models | specific-method-with-novelty-claim | [
"contribution-language",
"method-contribution",
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [] | 2021-11-15T09:32:09Z | https://github.com/diogoViegas/Toponym-Resolution-in-Text-with-Neural-Language-Models | null |
[
"reinforcement-learning"
] | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-11-15T18:38:24Z | Code for Paper: Deep Reinforcement Learning for Inverse Inorganic Materials Design | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-18T14:44:29Z | false | 2 | 428,380,491 | https://arxiv.org/abs/2210.11931 | Jupyter Notebook | null | [
"deep-reinforcement-learning",
"policy-learning",
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[
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] | michelefontana92/HOLDA | [
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] | 2022-04-18T16:29:34Z | https://github.com/michelefontana92/HOLDA | null |
[
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] | 1 | 2026-10-04T15:58:58Z | [] | 2021-11-23T20:36:54Z | [
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[
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T14:50:46Z | false | 1 | 429,999,245 | null | Python | null | [
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] | 2026-10-17T21:19:26Z | https://github.com/mahaitongdae/Reachability_Constrained_RL | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-11-23T10:06:21Z | This is the official code for the paper Learning Represenations with Contrastive Self-Supervised Learning for Histopathology Applications | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 1 | 431,049,745 | null | Python | Apache-2.0 | [
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] | include | ml-contribution-v5 | 13 | [] | 2025-04-17T01:51:36Z | https://github.com/k-stacke/ssl-pathology | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-11-24T03:28:25Z | The project is an official implementation of our paper "A Lightweight Graph Transformer Network for Human Mesh Reconstruction from 2D Human Pose". | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 4 | 431,334,284 | null | Python | MIT | [
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] | 5 | 2026-10-10T16:40:02Z | [] | 2023-05-25T23:52:43Z | [
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[
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-26T14:50:46Z | false | 126 | 431,390,576 | null | Python | NOASSERTION | [
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] | 6 | 2026-10-21T15:48:06Z | [] | 2023-03-27T10:28:26Z | [
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] | include | ml-contribution-v5 | 866 | [] | 2026-10-13T03:57:45Z | https://github.com/gwang-kim/DiffusionCLIP | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-11-25T23:54:40Z | [MICCAI2022] This is an official PyTorch implementation for A Robust Volumetric Transformer for Accurate 3D Tumor Segmentation | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-10T16:40:02Z | false | 36 | 432,005,336 | null | Python | MIT | [
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] | 3 | 2026-10-22T16:09:07Z | [] | 2023-09-11T15:13:53Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T20:57:28.489717Z | false | 42 | 433,041,802 | https://omriavrahami.com/blended-diffusion-page/ | Jupyter Notebook | MIT | [
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] | omriav/blended-diffusion | [
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] | 4 | 2026-10-17T13:17:44Z | [] | 2024-06-04T11:59:37Z | [
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] | 5 | 2026-10-17T13:17:44Z | [] | 2022-08-20T13:01:44Z | [
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[
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 2 | 433,653,478 | https://arxiv.org/abs/2112.00656 | Python | null | [
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] | include | ml-contribution-v5 | 61 | [] | 2026-06-30T07:29:21Z | https://github.com/FingerRec/OA-Transformer | null |
[
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] | [] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-12-02T01:16:13Z | [EMNLP 2022] Official Pytorch implementation for "Tiny-NewsRec: Efficient and Effective PLM-based News Recommendation" | [
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] | 2026-08-27T02:14:33Z | https://github.com/yflyl613/Tiny-NewsRec | {"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["knowledge-distillation"]} |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-12-02T02:08:31Z | Official PyTorch implementation of our AAAI22 paper: TransMEF: A Transformer-Based Multi-Exposure Image Fusion Framework via Self-Supervised Multi-Task Learning. | [
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[
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 4 | 434,071,154 | null | Python | null | [
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[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-12-03T00:47:09Z | Official code for our ECCV'22 paper "A Fast Knowledge Distillation Framework for Visual Recognition" | [
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] | [
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"self-supervised-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 30 | 434,420,545 | null | Python | MIT | [
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] | 8 | 2026-10-10T16:40:02Z | [] | 2024-04-29T15:01:00Z | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 14 | 435,330,191 | null | Python | MIT | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T14:28:44Z | false | 71 | 435,921,975 | https://doi.org/10.1016/j.eswa.2023.119619 | Python | MIT | [
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[
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[
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[
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:28:44Z | false | 12 | 438,485,781 | null | Python | null | [
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] | include | ml-contribution-v5 | 13 | [] | 2024-10-11T13:25:07Z | https://github.com/24wenjie-li/FDIWN | null |
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[
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] | include | ml-contribution-v5 | 473 | [
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[
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] | 2 | 2026-10-10T16:40:02Z | [] | 2021-12-17T01:34:43Z | [
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] | include | ml-contribution-v5 | 5 | [] | 2024-10-04T03:49:22Z | https://github.com/shenao-zhang/MRA | null |
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-12-18T04:49:27Z | An official implementation of "An Efficient Combinatorial Optimization Model Using Learning-to-Rank Distillation" at AAAI 22 | [
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] | include | ml-contribution-v5 | 8 | [] | 2024-08-15T11:21:28Z | https://github.com/ita9naiwa/rlrd-aaai2022 | null |
[
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[
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] | include | ml-contribution-v5 | 43 | [
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] | 2026-08-04T02:25:47Z | https://github.com/UkcheolShin/ThermalMonoDepth | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-01-14T07:43:23Z | Source code for paper "Hard Negative Synthesis for Complementary Graph Contrastive Learning" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T16:52:35Z | false | 1 | 447,882,622 | null | Python | null | [
"contrastive-learning",
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] | kingloon/HNGCL | [
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] | 4 | 2026-10-07T18:07:52Z | [] | 2022-01-14T08:59:54Z | [
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] | include | ml-contribution-v5 | 1 | [] | 2022-04-26T03:36:18Z | https://github.com/kingloon/HNGCL | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-01-14T16:22:55Z | Official implementation of https://arxiv.org/abs/2106.03496 | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-13T18:05:26Z | false | 0 | 448,032,227 | null | Python | null | [
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] | FrancescoCappio/OSHOT-meta-learning | [
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] | include | ml-contribution-v5 | 15 | [] | 2022-12-02T12:06:39Z | https://github.com/FrancescoCappio/OSHOT-meta-learning | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-01-14T20:32:04Z | Parkinson’s disease is a progressive disorder of the central nervous system affecting movement and inducing tremors and stiffness. It has 5 stages to it and affects more than 1 million individuals every year in India. This is chronic and has no cure yet. It is a neurodegenerative disorder affecting dopamine-producing n... | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-06T17:10:23Z | false | 0 | 448,099,749 | null | Jupyter Notebook | null | [
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] | royabhishek99/parkinsonsdetectionusingXGBoost | [
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] | 1 | 2026-10-06T17:10:23Z | [] | 2022-01-14T20:32:24Z | [
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] | null | 2026-10-06T18:32:27Z | gh-ml-readme-evidence-v2 | null | [] | [] | missing | 2026-10-06T18:32:27Z | royabhishek99/parkinsonsdetectionusingXGBoost | specific-method-with-novelty-claim | [
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] | include | ml-contribution-v5 | 0 | [] | 2022-01-14T20:32:27Z | https://github.com/royabhishek99/parkinsonsdetectionusingXGBoost | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-01-14T22:02:55Z | Official code for the ICML2022 paper -- GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks | [
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] | [
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] | 2 | 2026-10-06T16:55:56Z | [] | 2022-11-27T21:50:10Z | [
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] | 2025-04-16T02:57:16Z | https://github.com/SherylHYX/GNNRank | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-01-17T05:32:07Z | MedViLL official code. (Published IEEE JBHI 2021) | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:28:44Z | false | 17 | 448,765,597 | null | Python | MIT | [
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] | include | ml-contribution-v5 | 110 | [] | 2026-06-16T22:45:50Z | https://github.com/SuperSupermoon/MedViLL | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-01-17T08:03:38Z | Code for our paper "Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation" | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T15:29:21Z | false | 30 | 448,803,521 | https://arxiv.org/abs/2201.08702 | Python | MIT | [
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] | 13 | 2026-10-17T13:17:44Z | [] | 2022-09-16T11:35:19Z | [
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] | include | ml-contribution-v5 | 169 | [
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] | 2026-10-03T01:07:42Z | https://github.com/hiyouga/Dual-Contrastive-Learning | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-01-17T12:31:58Z | Official implementations for "Communication-Efficient Diffusion Strategy for Performance Improvement of Federated Learning with Non-IID Data" paper. | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 3 | 448,886,076 | null | Python | null | [
"collaborative-learning",
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] | seyoungahn/FedDif | [
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] | 1 | 2026-09-29T17:30:08Z | [] | 2024-03-14T01:26:37Z | [
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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 | 22 | [
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] | 2025-05-09T21:58:15Z | https://github.com/seyoungahn/FedDif | null |
[
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] | [
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] | [
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] | [] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-01-17T14:15:38Z | [ICLR-2024] Official implementation of "KFC: Knowledge Reconstruction and Feedback Consolidation Enable Efficient and Effective Continual Generative Learning" | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-01T19:00:27.900159Z | false | 1 | 448,921,772 | null | Python | Apache-2.0 | [
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] | libo-huang/KFC | [
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] | 1 | 2026-10-01T19:00:27.900159Z | [] | 2024-02-18T07:40:23Z | [] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 28 | [
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] | 2026-07-02T07:31:20Z | https://github.com/libo-huang/KFC | {"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["knowledge-distillation"]} |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-01-17T16:40:15Z | Official Code Implementation of the paper Multi-View Contrastive Learning from Demonstrations. Obtain task-relevant image representations. Train context invariant policies. | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-07T18:07:52Z | false | 0 | 448,971,591 | null | Python | null | [
"contrastive-learning",
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] | andrerspcorreia/CLfD | [
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] | 1 | 2026-10-07T18:07:52Z | [] | 2023-12-27T04:53:45Z | [
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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 | [] | 2026-01-24T01:31:28Z | https://github.com/andrerspcorreia/CLfD | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-01-24T03:58:17Z | The official implementation for Pseudo Numerical Methods for Diffusion Models on Manifolds (PNDM, PLMS | ICLR2022) | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T20:57:28.489717Z | false | 30 | 451,309,277 | https://arxiv.org/abs/2202.09778 | Python | Apache-2.0 | [
"diffusion"
] | luping-liu/PNDM | [
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] | 5 | 2026-10-24T14:31:13Z | [] | 2023-04-25T08:00:04Z | [
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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 | 357 | [
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] | 2026-09-05T06:31:44Z | https://github.com/luping-liu/PNDM | null |
[
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] | [
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] | [
"general.self-supervised-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-01-24T14:07:15Z | Official implementation of the paper - "Ensemble and self-supervised learning for improved classification of seismic signals from the ̊Aknes rockslope" | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 1 | 451,489,181 | null | Jupyter Notebook | MIT | [
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] | ML4ITS/Aknes_clf | [
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] | 1 | 2026-10-02T16:59:40Z | [] | 2022-10-21T05:36:41Z | [
"general.self-supervised-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 2 | [] | 2026-03-31T05:07:17Z | https://github.com/ML4ITS/Aknes_clf | null |
[
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] | [
"multimodal.text-to-image",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-01-27T02:45:14Z | Official code for "Cross-Modal Coherence for Text-to-Image Retrieval" | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T14:28:44Z | false | 2 | 452,516,402 | null | Jupyter Notebook | null | [
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] | klory/Cross-Modal-Coherence-for-Text-to-Image-Retrieval | [
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] | 5 | 2026-10-15T15:20:52Z | [] | 2022-01-27T04:12:57Z | [
"vision.image-retrieval"
] | f69d2f5c1aa9934a5df33a59342e547b50c62881 | 2026-09-26T20:25:07Z | gh-ml-readme-evidence-v2 | "f69d2f5c1aa9934a5df33a59342e547b50c62881" | [
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"method",
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] | [
"method-contribution",
"ml-method-context",
"paper-code-relationship",
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] | ok | 2026-09-26T20:25:07Z | klory/Cross-Modal-Coherence-for-Text-to-Image-Retrieval | readme-supported-paper-method-implementation | [
"method-contribution",
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 2 | [] | 2023-12-16T12:39:53Z | https://github.com/klory/Cross-Modal-Coherence-for-Text-to-Image-Retrieval | null |
[
"distributed-ml",
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] | [
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] | [
"description",
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"repository-metadata"
] | [
"general.arxiv",
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-01-27T08:20:31Z | This repository is the official implementation of 'EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning' (ICML 2022). | [
"general-ml",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 1 | 452,597,657 | https://proceedings.mlr.press/v162/vargaftik22a.html | Jupyter Notebook | null | [
"federated-learning",
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] | amitport/EDEN-Distributed-Mean-Estimation | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-24T14:31:13Z | [] | 2026-05-05T13:41:35Z | [
"general.arxiv"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 18 | [] | 2026-05-06T10:44:09Z | https://github.com/amitport/EDEN-Distributed-Mean-Estimation | null |
[
"generative-ai",
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] | [
"diffusion"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"recall.name.diffusion-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-01-27T19:46:37Z | [NeurIPS 2022] Denoising Diffusion Restoration Models -- Official Code Repository | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-26T20:57:28.489717Z | false | 69 | 452,821,660 | null | Python | MIT | [
"diffusion"
] | bahjat-kawar/ddrm | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-24T14:31:13Z | [] | 2022-10-10T19:23:22Z | [
"recall.name.diffusion-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 674 | [
"deblurring",
"diffusion",
"diffusion-models",
"inpainting",
"inverse-problems",
"score-based",
"super-resolution",
"variational-inference"
] | 2026-10-07T09:36:30Z | https://github.com/bahjat-kawar/ddrm | null |
[
"classical-ml",
"computer-vision",
"information-retrieval",
"representation-learning"
] | [
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] | [
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"github-topics",
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"paper-reference",
"query-match",
"repository-metadata"
] | [
"general.metric-learning",
"vision.image-retrieval"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-01-28T11:23:30Z | This repo contains the official implementation of HAPPIER: Hierarchical Average Precision Training for Pertinent Image Retrieval (ECCV'22). | [
"computer-vision",
"information-retrieval"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:28:44Z | false | 3 | 453,041,125 | https://arxiv.org/abs/2207.04873 | Python | MIT | [
"image-retrieval",
"metric-learning"
] | elias-ramzi/HAPPIER | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 5 | 2026-10-15T15:20:52Z | [] | 2023-04-06T11:27:28Z | [
"vision.image-retrieval"
] | c4981914eec03c7a99ee217ad32c5f113d963bcf | 2026-09-26T20:25:07Z | gh-ml-readme-evidence-v2 | "c4981914eec03c7a99ee217ad32c5f113d963bcf" | [
"citation",
"dataset",
"other"
] | [
"ml-method-context",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-09-26T20:25:07Z | elias-ramzi/HAPPIER | readme-supported-paper-method-implementation | [
"contribution-language",
"ml-method-context",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 24 | [
"eccv2022",
"hierarchical-average-precision",
"hierarchical-image-retrieval",
"image-retrieval",
"ranking"
] | 2026-07-17T09:53:26Z | https://github.com/elias-ramzi/HAPPIER | 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 | [] | 2022-01-28T22:34:24Z | [The Lancet Digital Health, MICCAI2020 Oral] The official code of "Spatially-aware Graph Neural Networks and Cross-level Molecular Profile Prediction in Colon Cancer Histopathology: A Retrospective Multicentre Cohort Study" | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-06T17:10:23Z | false | 5 | 453,236,379 | null | Python | null | [
"graph-neural-network",
"message-passing",
"neural-network"
] | Cassie07/Review_Molecular_profile_prediction_GNN | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-06T17:10:23Z | [] | 2023-10-02T14:57:22Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 23 | [] | 2026-01-06T05:31:57Z | https://github.com/Cassie07/Review_Molecular_profile_prediction_GNN | null |
[
"reinforcement-learning"
] | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-01-29T14:40:19Z | TorchGRL is the source code for our paper Graph Convolution-Based Deep Reinforcement Learning for Multi-Agent Decision-Making in Mixed Traffic Environments. | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-06T16:55:56Z | false | 11 | 453,424,154 | null | Python | null | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | Jacklinkk/TorchGRL | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-20T15:59:06Z | [] | 2022-10-22T08:59:49Z | [
"rl.deep"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 105 | [] | 2026-07-30T12:26:54Z | https://github.com/Jacklinkk/TorchGRL | null |
[
"control",
"robotics",
"robotics-and-control"
] | [
"control"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"robotics.robot-control"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-01-31T16:13:47Z | Code for paper "Fundamental Performance Limits for Sensor-Based Robot Control and Policy Learning". | [
"control",
"robotics",
"robotics-and-control"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-03T15:45:52Z | false | 0 | 454,077,161 | null | Python | MIT | [
"control"
] | irom-princeton/performance-limits | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-07T17:53:16Z | [] | 2023-07-08T00:57:25Z | [
"robotics.robot-control"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 4 | [] | 2024-11-11T01:29:23Z | https://github.com/irom-princeton/performance-limits | null |
[
"reinforcement-learning"
] | [
"batch-reinforcement-learning",
"offline-reinforcement-learning",
"reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"rl.offline"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-02-01T13:17:39Z | Official code for the paper: Pareto Policy Pool for Model-based Offline Reinforcement Learning | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 0 | 454,394,624 | null | Python | null | [
"batch-reinforcement-learning",
"offline-reinforcement-learning",
"reinforcement-learning"
] | stevenyangyj/P3 | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-07T17:53:16Z | [] | 2023-06-02T16:16:45Z | [
"rl.offline"
] | 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 | [] | 2026-03-06T17:07:48Z | https://github.com/stevenyangyj/P3 | null |
[
"deep-learning",
"representation-learning"
] | [
"contrastive-learning",
"self-supervised-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-02-02T12:09:03Z | This is our implementation for the paper "Contrastive Learning for Knowledge Tracing" (TheWebConf 2022). | [
"deep-learning",
"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-09T17:31:36Z | false | 11 | 454,759,794 | null | Python | null | [
"contrastive-learning",
"self-supervised-learning"
] | UpstageAI/cl4kt | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-10T16:53:26Z | [] | 2024-05-03T20:30:29Z | [
"general.contrastive-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 32 | [] | 2026-07-20T12:23:28Z | https://github.com/UpstageAI/cl4kt | null |
[
"computer-vision"
] | [
"keypoint-detection",
"pose-estimation",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.pose-estimation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-02-05T02:00:42Z | [IJCAI 2022] Official PyTorch implementation of AggPose: Deep Aggregation Vision Transformer for Infant Pose Estimation | [
"computer-vision"
] | [
"deep-learning",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-07T17:53:16Z | false | 8 | 455,751,171 | null | Python | AGPL-3.0 | [
"keypoint-detection",
"pose-estimation",
"transformer"
] | PediaMedAI/AggPose | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-11T16:48:52Z | [] | 2022-07-20T00:30:10Z | [
"vision.pose-estimation"
] | 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 | 33 | [
"computer-vision",
"deep-learning",
"infant-health",
"pose-estimation",
"pytorch-implementation",
"transformers"
] | 2026-08-09T07:54:25Z | https://github.com/PediaMedAI/AggPose | null |
[
"natural-language-processing"
] | [
"conversational-ai",
"dialogue-modeling"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"nlp.dialogue-systems"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-02-05T03:11:41Z | Official Code for SIGIR 2022 "A Multi-task Based Neural Model to Simulate Users in Goal Oriented Dialogue Systems". User Simulator generates user-side utterance, predicts user's next action and satisfaction level. | [
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-06T16:55:56Z | false | 1 | 455,762,211 | null | Python | null | [
"conversational-ai",
"dialogue-modeling"
] | kimdanny/user-simulation-t5 | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 3 | 2026-10-09T17:31:36Z | [] | 2022-07-14T13:50:53Z | [
"nlp.dialogue-systems"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-context-only",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 37 | [
"nlp",
"pytorch",
"sigir2022",
"t5",
"task-oriented-dialogue",
"user-satisfaction",
"user-simulation",
"utterance-generation"
] | 2025-08-19T14:13:20Z | https://github.com/kimdanny/user-simulation-t5 | null |
[
"automl",
"general-ml",
"recommender-systems"
] | [
"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 | [] | 2022-02-05T03:14:52Z | Official code implementation for ICDE 23 paper MAMDR: A Model Agnostic Learning Method for Multi-Domain Recommendation | [
"automl",
"general-ml",
"recommender-systems"
] | [
"deep-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T14:28:44Z | false | 5 | 455,762,785 | null | Python | MIT | [
"few-shot-learning",
"meta-learning"
] | RManLuo/MAMDR | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-16T15:36:56Z | [] | 2023-11-27T03:38:56Z | [
"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 | 39 | [
"deep-learning",
"meta-learning",
"multi-domain-learning",
"recommender-system"
] | 2026-07-07T16:04:51Z | https://github.com/RManLuo/MAMDR | null |
[
"audio",
"machine-learning",
"speech",
"speech-and-audio"
] | [
"adaptation",
"architecture",
"evaluation",
"inference",
"test-time-adaptation",
"training"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"audio-asr",
"specialized.test-time-adaptation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-02-05T16:50:18Z | Test-time adaptation for speech recognition model by single utterance. The official implementation of "Listen, Adapt, Better WER: Source-free Single-utterance Test-time Adaptation for Automatic Speech Recognition" paper. | [
"audio",
"speech",
"speech-and-audio"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 6 | 455,927,971 | null | Python | null | [
"architecture",
"evaluation",
"inference",
"test-time-adaptation",
"training"
] | DanielLin94144/Test-time-adaptation-ASR-SUTA | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 8 | 2026-10-12T20:31:19Z | [] | 2022-04-01T08:36:53Z | [
"audio-asr"
] | 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 | 24 | [
"speech-recognition",
"test-time-adaptation"
] | 2026-09-12T11:23:13Z | https://github.com/DanielLin94144/Test-time-adaptation-ASR-SUTA | null |
[
"deep-learning",
"efficient-ml",
"graph-learning",
"machine-learning-systems",
"model-compression"
] | [
"graph-neural-network",
"message-passing",
"neural-network",
"quantization"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.quantization",
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-02-09T08:44:20Z | This repository contains the implementation of our paper: EPQuant: An Efficient Quantization Methodology for Graph Neural Networks with Enhanced Product Quantization. | [
"efficient-ml",
"graph-learning",
"machine-learning-systems",
"model-compression"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-07T18:07:52Z | false | 1 | 457,276,567 | null | Python | null | [
"graph-neural-network",
"neural-network",
"quantization"
] | Lyun-Huang/EPQuant | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-10-08T18:15:14Z | [] | 2022-02-17T07:54:58Z | [
"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 | 5 | [] | 2025-05-13T09:54:19Z | https://github.com/Lyun-Huang/EPQuant | null |
[
"deep-learning",
"graph-learning",
"recommender-systems"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"applied.recommender-systems",
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-02-10T04:55:59Z | Code for paper named "Large-scale Personalized Video Game Recommendation via Social-aware Contextualized Graph Neural Network" published on WWW 2022. Upcoming soon | [
"graph-learning",
"recommender-systems"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 8 | 457,635,549 | null | Python | null | [
"graph-neural-network",
"neural-network"
] | YangLiangwei/SCGRec | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-08T17:58:00Z | [] | 2022-04-28T05:22:08Z | [
"applied.recommender-systems"
] | 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 | [] | 2025-02-07T04:07:57Z | https://github.com/YangLiangwei/SCGRec | null |
[
"language",
"natural-language-processing"
] | [
"language-model",
"language-modeling"
] | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"llm.language-model",
"nlp.language-modeling"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-02-10T06:56:54Z | [ICLR 2022] Code for paper "Exploring Extreme Parameter Compression for Pre-trained Language Models"(https://arxiv.org/abs/2205.10036) | [
"language",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-17T13:17:44Z | false | 0 | 457,665,267 | null | Python | null | [
"language-model",
"language-modeling"
] | twinkle0331/Xcompression | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-10-17T13:17:44Z | [] | 2023-05-24T03:04:54Z | [
"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 | 22 | [
"bert",
"low-rank-approximation",
"model-compression",
"tensor-decomposition"
] | 2026-07-24T04:01:26Z | https://github.com/twinkle0331/Xcompression | null |
[
"reinforcement-learning"
] | [
"deep-reinforcement-learning",
"policy-gradient",
"policy-learning",
"policy-optimization",
"reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"rl.deep",
"rl.policy-gradient"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-02-10T07:14:34Z | All Actions Optimization (AAO) is a novel deep reinforcement learning algorithm that is more learning efficient compared to traditional policy gradient methods. | [
"reinforcement-learning"
] | [
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] | 10 | 2026-10-15T15:20:52Z | [] | 2022-11-11T22:17:00Z | [
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] | include | ml-contribution-v5 | 124 | [] | 2026-01-19T08:16:03Z | https://github.com/RUCAIBox/NCL | null |
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] | Daalma7/FairTreesAlgorithms | [
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] | 3 | 2026-10-17T13:17:44Z | [] | 2024-09-17T17:08:24Z | [
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] | include | ml-contribution-v5 | 2 | [
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] | include | ml-contribution-v5 | 12 | [
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] | 2025-09-27T12:18:35Z | https://github.com/AdamJelley/POEM | null |
[
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T14:28:44Z | false | 1 | 460,816,907 | null | Python | MIT | [
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] | alherit/cr-dqn | [
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] | 3 | 2026-10-06T16:55:56Z | [] | 2022-02-23T07:35:49Z | [
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] | include | ml-contribution-v5 | 0 | [] | 2022-02-21T15:02:11Z | https://github.com/alherit/cr-dqn | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-02-19T15:10:14Z | Official PyTorch Implementation of ADKF-IFT from Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction (ICLR 2023) | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 2 | 461,226,821 | https://openreview.net/forum?id=KXRSh0sdVTP | Jupyter Notebook | NOASSERTION | [
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] | Wenlin-Chen/ADKF-IFT | [
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] | 7 | 2026-10-17T13:17:44Z | [] | 2023-06-05T10:31:28Z | [
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] | include | ml-contribution-v5 | 11 | [] | 2026-08-14T02:35:52Z | https://github.com/Wenlin-Chen/ADKF-IFT | null |
[
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-02-24T08:30:30Z | [IEEE TIP 2022] This is Pytorch code for our paper "I2Transformer: Intra- and Inter-relation Embedding Transformer for TV Show Captioning". | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-24T14:31:13Z | false | 1 | 463,067,606 | null | Python | null | [
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] | tuyunbin/I2Transformer | [
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] | 1 | 2026-10-24T14:31:13Z | [] | 2024-12-12T01:10:30Z | [
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] | include | ml-contribution-v5 | 4 | [] | 2025-07-07T08:53:53Z | https://github.com/tuyunbin/I2Transformer | null |
[
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] | [
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] | [
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] | jpWang/LiLT | [
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] | 4 | 2026-10-26T17:25:59Z | [] | 2022-10-31T01:55:58Z | [
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] | include | ml-contribution-v5 | 371 | [
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] | 2026-10-06T00:02:48Z | https://github.com/jpWang/LiLT | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-03-01T20:41:43Z | Official implementation of the paper "IteraTeR: Understanding Iterative Revision from Human-Written Text" (ACL 2022) | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-06T16:55:56Z | false | 7 | 465,049,834 | null | Python | Apache-2.0 | [
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] | vipulraheja/iterater | [
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] | 2 | 2026-10-26T17:25:59Z | [] | 2023-11-15T21:55:06Z | [
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] | include | ml-contribution-v5 | 84 | [
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] | 2026-09-08T06:33:13Z | https://github.com/vipulraheja/iterater | null |
[
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] | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-10T16:53:26Z | false | 3 | 465,230,869 | null | Python | null | [
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] | myt517/DKT | [
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] | 1 | 2026-10-10T16:53:26Z | [] | 2022-07-23T09:53:25Z | [
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] | include | ml-contribution-v5 | 14 | [] | 2025-12-28T11:18:13Z | https://github.com/myt517/DKT | null |
[
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-03-02T12:19:26Z | Code for paper ConMAE: Bringing inductive bias to visual transformer by supervised contrastive learning | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-11T16:48:52Z | false | 0 | 465,293,602 | null | null | MIT | [
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] | longkukuhi/ConMAE | [
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] | 1 | 2026-10-11T16:48:52Z | [] | 2022-03-02T12:21:20Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 2 | [] | 2024-03-13T15:44:38Z | https://github.com/longkukuhi/ConMAE | null |
[
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-03-03T07:41:05Z | This is the official Pytorch implementation of "Affine Medical Image Registration with Coarse-to-Fine Vision Transformer" (CVPR 2022), written by Tony C. W. Mok and Albert C. S. Chung. | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 6 | 465,616,993 | null | Python | MIT | [
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] | cwmok/C2FViT | [
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] | 4 | 2026-10-07T17:53:16Z | [] | 2023-11-02T13:45:28Z | [
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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 | 155 | [] | 2026-08-27T12:59:34Z | https://github.com/cwmok/C2FViT | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-03-03T11:38:27Z | The official implementation of Autoregressive Image Generation using Residual Quantization (CVPR '22) | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:27:52Z | false | 112 | 465,694,201 | null | Jupyter Notebook | NOASSERTION | [
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] | kakaobrain/rq-vae-transformer | [
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] | 8 | 2026-10-08T18:15:14Z | [] | 2024-01-03T07:12:24Z | [
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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 | 1,028 | [] | 2026-10-02T15:14:21Z | https://github.com/kakaobrain/rq-vae-transformer | null |
[
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] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-03-03T13:01:36Z | Official implementation of "Disentangled Knowledge Transfer for OOD Intent Discovery with Unified Contrastive Learning", ACL2022 main conference | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-11T16:48:52Z | false | 0 | 465,720,793 | null | Python | null | [
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] | pris-nlp/DKT | [
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] | 1 | 2026-10-11T16:48:52Z | [] | 2022-03-03T13:04:57Z | [
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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 | [] | 2022-09-20T12:57:03Z | https://github.com/pris-nlp/DKT | null |
[
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] | [
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"repository-metadata"
] | [
"general.contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-03-04T12:46:12Z | Official code for "ContrastMask: Contrastive Learning to Segment Every Thing" (CVPR2022) | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-10T16:53:26Z | false | 3 | 466,120,069 | https://arxiv.org/abs/2203.09775 | Python | null | [
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"self-supervised-learning"
] | huiserwang/ContrastMask | [
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"repository-metadata"
] | 1 | 2026-10-10T16:53:26Z | [] | 2022-05-01T10:45:43Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 35 | [] | 2025-10-13T01:49:32Z | https://github.com/huiserwang/ContrastMask | null |
[
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] | [
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] | [
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"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.self-supervised-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-03-06T12:54:36Z | Official code for Self-supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detection (CVPR 2022 oral) | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 14 | 466,736,810 | null | Python | Apache-2.0 | [
"representation-learning",
"self-supervised-learning"
] | liangchen527/SLADD | [
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"github-topics",
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"query-match",
"repository-metadata"
] | 2 | 2026-10-08T17:58:00Z | [] | 2022-05-29T04:00:29Z | [
"general.self-supervised-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 141 | [
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] | 2026-08-14T13:16:23Z | https://github.com/liangchen527/SLADD | null |
[
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] | [
"general.contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-03-07T08:45:56Z | Code of our paper Multi-Level Contrastive Learning for Cross-Lingual Alignment in ICASSP 2022 | [
"deep-learning",
"interpretability-and-safety",
"representation-learning"
] | [
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-11T16:48:52Z | false | 1 | 467,012,235 | null | Python | Apache-2.0 | [
"contrastive-learning",
"self-supervised-learning"
] | Mckysse/ML-CTL | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-11T16:48:52Z | [] | 2022-05-16T12:39:15Z | [
"general.contrastive-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [] | 2022-03-07T08:47:21Z | https://github.com/Mckysse/ML-CTL | null |
[
"health-and-biomedicine",
"medical-imaging"
] | [
"image-analysis",
"transformer"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"medical.medical-imaging"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-03-07T15:46:09Z | [EMNLP-2020] The official implementation of Generating Radiology Reports via Memory-driven Transformer. | [
"health-and-biomedicine",
"medical-imaging"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-07T17:53:16Z | false | 34 | 467,157,571 | null | Python | null | [
"image-analysis",
"transformer"
] | zhjohnchan/R2Gen | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-07T17:53:16Z | [] | 2023-08-17T01:30:29Z | [
"medical.medical-imaging"
] | 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 | 137 | [] | 2026-09-15T17:21:30Z | https://github.com/zhjohnchan/R2Gen | null |
[
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"generative-modeling",
"video-generation"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.video-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-03-08T04:08:06Z | Official code for CVPR2022 paper: Depth-Aware Generative Adversarial Network for Talking Head Video Generation | [
"computer-vision",
"generative-ai",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 127 | 467,358,744 | https://harlanhong.github.io/publications/dagan.html | Python | NOASSERTION | [
"generative-modeling",
"video-generation"
] | harlanhong/CVPR2022-DaGAN | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-08T17:58:00Z | [] | 2023-12-04T12:14:44Z | [
"vision.video-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 | 997 | [
"deepfake",
"depth",
"face",
"face-reenactment",
"gan",
"image-animation",
"motion-transfer",
"talking-head"
] | 2026-09-29T19:31:45Z | https://github.com/harlanhong/CVPR2022-DaGAN | null |
[
"deep-learning",
"representation-learning"
] | [
"contrastive-learning",
"self-supervised-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-03-08T06:36:30Z | Official Code for ICLR2022 Paper: Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation Overlap | [
"deep-learning",
"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-10T16:53:26Z | false | 3 | 467,392,867 | null | Jupyter Notebook | null | [
"contrastive-learning",
"self-supervised-learning"
] | zhangq327/ARC | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-10T16:53:26Z | [] | 2025-09-28T03:41:45Z | [
"general.contrastive-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 | 28 | [] | 2025-09-28T03:41:48Z | https://github.com/zhangq327/ARC | null |
[
"computer-vision",
"health-and-biomedicine",
"medical-imaging"
] | [
"image-analysis",
"segmentation"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"medical.medical-image-segmentation",
"medical.medical-imaging",
"vision.segmentation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-03-08T15:57:29Z | Official Pytorch Code base for "UNeXt: MLP-based Rapid Medical Image Segmentation Network", MICCAI 2022 | [
"computer-vision",
"health-and-biomedicine"
] | [
"deep-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:27:52Z | false | 86 | 467,579,697 | https://jeya-maria-jose.github.io/UNext-web/ | Python | MIT | [
"segmentation"
] | jeya-maria-jose/UNeXt-pytorch | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 11 | 2026-10-17T13:17:44Z | [] | 2023-09-09T01:08:49Z | [
"vision.segmentation"
] | b5565a4331bf475b6785212ed7738cf429f83ea0 | 2026-09-26T20:25:07Z | gh-ml-readme-evidence-v2 | "b5565a4331bf475b6785212ed7738cf429f83ea0" | [
"citation",
"other",
"overview"
] | [
"ml-method-context",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-09-26T20:25:07Z | jeya-maria-jose/UNeXt-pytorch | readme-supported-paper-method-implementation | [
"ml-context-only",
"ml-method-context",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 571 | [
"deep-learning",
"medical-image-segmentation",
"medical-imaging",
"mlp",
"segmentation",
"unet",
"unet-image-segmentation"
] | 2026-10-12T23:46:35Z | https://github.com/jeya-maria-jose/UNeXt-pytorch | null |
[
"health-and-biomedicine",
"language",
"natural-language-processing"
] | [
"language-model",
"language-modeling"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"llm.language-model",
"nlp.language-modeling"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-03-09T08:24:37Z | Code for paper "Adversarial Attacks on Protein Language Models", Ginevra Carbone, Francesca Cuturello, Luca Bortolussi, Alberto Cazzaniga (2022). | [
"health-and-biomedicine",
"language",
"natural-language-processing"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-18T14:44:29Z | false | 0 | 467,838,650 | null | Python | null | [
"language-model",
"language-modeling"
] | ginevracoal/adversarial-protein-sequences | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 1 | 2026-10-18T14:44:29Z | [] | 2022-10-28T08:11:36Z | [
"llm.language-model",
"nlp.language-modeling"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 2 | [
"adversarial-attacks",
"protein-structure-prediction",
"transformers"
] | 2022-11-14T11:41:08Z | https://github.com/ginevracoal/adversarial-protein-sequences | null |
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