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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
[
"deep-learning",
"linguistics",
"natural-language-processing",
"representation-learning"
] | [
"contrastive-learning",
"language-modeling",
"self-supervised-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning",
"recall.computational-linguistics"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-03-10T14:59:42Z | Code for paper Sentence-aware Contrastive Learning for Open-Domain Passage Retrieval, Accepted by ACL2022 Main Conference, Long Paper | [
"linguistics",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-10T16:53:26Z | false | 2 | 468,390,034 | null | Python | NOASSERTION | [
"contrastive-learning",
"language-modeling"
] | chengzhipanpan/DCSR | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-28T17:27:44Z | [] | 2022-03-12T04:22:34Z | [
"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 | 30 | [] | 2024-03-07T12:58:47Z | https://github.com/chengzhipanpan/DCSR | 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 | [] | 2022-03-12T03:11:49Z | A PyTorch implementation of our paper Variance-Reduced Heterogeneous Federated Learning via Stratified Client Selection | [
"distributed-ml",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 4 | 468,965,519 | null | Python | null | [
"collaborative-learning",
"federated-learning"
] | Songdx2000/FL-stratified-client-selection | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-04T15:58:58Z | [] | 2023-11-08T02:57:35Z | [
"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",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 4 | [] | 2026-02-27T10:18:51Z | https://github.com/Songdx2000/FL-stratified-client-selection | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-03-12T03:16:07Z | Implement the DrBC approach from Learning to Identify High Betweenness Centrality Nodes from Scratch: A Novel Graph Neural Network Approach. paper : https://arxiv.org/pdf/1905.10418.pdf | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 0 | 468,966,247 | null | Jupyter Notebook | null | [
"graph-neural-network",
"message-passing",
"neural-network"
] | ChiungYunChang/DrBC | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-10-08T18:15:14Z | [] | 2025-03-25T05:30:45Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
"contribution-language",
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 2 | [] | 2025-03-25T05:30:48Z | https://github.com/ChiungYunChang/DrBC | null |
[
"computer-vision",
"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 | [] | 2022-03-12T04:02:20Z | [ICCV 2021] Official PyTorch Code for "Online Knowledge Distillation for Efficient Pose Estimation" | [
"computer-vision",
"efficient-ml",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 4 | 468,974,081 | https://zhengli97.github.io/OKDHP/ | Python | null | [
"distillation",
"knowledge-distillation"
] | zhengli97/OKDHP | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 3 | 2026-10-07T17:53:16Z | [] | 2024-03-08T04:38:26Z | [
"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 | 42 | [
"coco",
"computer-vision",
"human-pose-estimation",
"iccv2021",
"knowledge-distillation",
"mpii",
"online-knowledge-distillation"
] | 2026-04-14T06:33:29Z | https://github.com/zhengli97/OKDHP | 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 | [] | 2022-03-12T23:53:43Z | Code for ACL 2022 paper: Tackling Fake News Detection by Continually Improving Social Context Representations using Graph Neural Networks | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 3 | 469,227,595 | null | Python | Apache-2.0 | [
"graph-neural-network",
"message-passing",
"neural-network"
] | hockeybro12/FakeNews_Inference_Operators | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2023-12-19T01:52:00Z | [
"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 | 27 | [] | 2025-05-13T12:42:14Z | https://github.com/hockeybro12/FakeNews_Inference_Operators | null |
[
"computer-vision",
"multimodal-learning",
"natural-language-processing"
] | [
"neural-network",
"referring-expression-comprehension",
"vision-language-learning",
"visual-grounding"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"recall26.visual-grounding"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-03-14T07:40:55Z | This is the official implementation for our paper;"LAR:Look Around and Refer". | [
"computer-vision",
"multimodal-learning",
"natural-language-processing"
] | [
"deep-learning",
"machine-learning",
"neural-network",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 2 | 469,627,710 | null | C++ | MIT | [
"neural-network",
"referring-expression-comprehension",
"vision-language-learning",
"visual-grounding"
] | eslambakr/LAR-Look-Around-and-Refer | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-06T16:55:56Z | [] | 2022-12-01T16:21:45Z | [
"recall26.visual-grounding"
] | 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 | 30 | [
"3d",
"3dvisualgrounding",
"cnn",
"deep-neural-networks",
"deeplearning",
"geometry",
"grounding",
"machine-learning",
"multimodal-deep-learning",
"neurips",
"neurips-2022",
"projective-geometry",
"transformers"
] | 2025-01-18T11:50:26Z | https://github.com/eslambakr/LAR-Look-Around-and-Refer | null |
[
"computer-vision",
"generative-ai"
] | [
"generative-modeling",
"image-to-image-translation",
"transformer"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"vision.image-to-image-translation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-03-14T12:05:13Z | Official implementation for "Style Transformer for Image Inversion and Editing" (CVPR 2022) | [
"computer-vision",
"generative-ai"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-07T17:53:16Z | false | 11 | 469,715,423 | null | Python | null | [
"generative-modeling",
"image-to-image-translation",
"transformer"
] | sapphire497/style-transformer | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 3 | 2026-10-15T15:20:52Z | [] | 2023-01-13T09:07:50Z | [
"vision.image-to-image-translation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 181 | [
"attention-mechanism",
"generative-adversarial-network",
"stylegan2",
"transformer"
] | 2026-06-11T11:51:35Z | https://github.com/sapphire497/style-transformer | null |
[
"deep-learning",
"natural-language-processing",
"representation-learning"
] | [
"contrastive-learning",
"named-entity-recognition",
"self-supervised-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning",
"nlp.named-entity-recognition"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-03-14T15:53:09Z | Code for ACL 2022 paper "CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learning" | [
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-06T16:55:56Z | false | 19 | 469,802,542 | null | Python | MIT | [
"contrastive-learning",
"named-entity-recognition"
] | psunlpgroup/CONTaiNER | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-10-24T14:31:13Z | [] | 2022-06-14T05:28:41Z | [
"nlp.named-entity-recognition"
] | 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 | 118 | [] | 2026-07-08T09:32:04Z | https://github.com/psunlpgroup/CONTaiNER | null |
[
"deep-learning",
"representation-learning"
] | [
"contrastive-learning",
"self-supervised-learning"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-03-15T03:17:50Z | Official implementation for "QS-Attn: Query-Selected Attention for Contrastive Learning in I2I Translation" (CVPR 2022) | [
"deep-learning",
"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-06T16:55:56Z | false | 13 | 469,989,031 | null | Python | null | [
"contrastive-learning",
"self-supervised-learning"
] | sapphire497/query-selected-attention | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 4 | 2026-10-10T16:53:26Z | [] | 2023-01-13T09:09:18Z | [
"general.contrastive-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 85 | [
"contrastive-learning",
"generative-adversarial-network",
"image-to-image-translation"
] | 2026-08-28T07:06:03Z | https://github.com/sapphire497/query-selected-attention | null |
[
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning"
] | true | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-03-15T16:43:39Z | Official PyTorch implementation of the paper "SOSP: Efficiently Capturing Global Correlations by Second-Order Structured Pruning" | [
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-17T13:17:44Z | false | 0 | 470,248,632 | null | null | null | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | boschresearch/sosp | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 1 | 2026-10-17T13:17:44Z | [] | 2022-03-15T16:44:50Z | [
"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 | 1 | [
"paper-resource"
] | 2024-06-12T06:11:22Z | https://github.com/boschresearch/sosp | null |
[
"computer-vision",
"efficient-ml",
"model-compression"
] | [
"computer-vision",
"distillation",
"knowledge-distillation"
] | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation",
"vision.topic-computer-vision"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-03-16T09:03:42Z | The official implementation of [CVPR2022] Decoupled Knowledge Distillation https://arxiv.org/abs/2203.08679 and [ICCV2023] DOT: A Distillation-Oriented Trainer https://openaccess.thecvf.com/content/ICCV2023/papers/Zhao_DOT_A_Distillation-Oriented_Trainer_ICCV_2023_paper.pdf | [
"computer-vision"
] | [
"deep-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 130 | 470,505,142 | null | Python | null | [
"computer-vision",
"distillation",
"knowledge-distillation"
] | megvii-research/mdistiller | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | 5 | 2026-10-08T17:58:00Z | [] | 2023-11-05T09:23:59Z | [
"vision.topic-computer-vision"
] | 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 | 897 | [
"cifar",
"coco",
"computer-vision",
"cvpr2022",
"deep-learning",
"iccv2023",
"imagenet",
"knowledge-distillation",
"pytorch"
] | 2026-10-03T09:55:24Z | https://github.com/megvii-research/mdistiller | null |
[
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"forecasting",
"sequence-modeling"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"timeseries.forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-03-16T20:26:33Z | A novel time series forecasting model, called CEEMDAN-TCN. | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-06T16:55:56Z | false | 1 | 470,741,129 | null | null | null | [
"forecasting",
"sequence-modeling"
] | Ray-0403/superblack.github.io | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-09T17:31:36Z | [] | 2022-03-15T11:55:11Z | [
"timeseries.forecasting"
] | e8e4270bb1ccdba87f55e7540d39620d83f33884 | 2026-10-06T18:32:27Z | gh-ml-readme-evidence-v2 | "e8e4270bb1ccdba87f55e7540d39620d83f33884" | [] | [] | ok | 2026-10-06T18:32:27Z | Ray-0403/superblack.github.io | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 11 | [] | 2024-05-15T03:10:00Z | https://github.com/Ray-0403/superblack.github.io | null |
[
"3d",
"3d-vision",
"computer-vision",
"geometric-learning",
"robotics"
] | [
"3d-perception",
"point-cloud-learning",
"point-cloud-segmentation",
"transformer"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"graph.point-clouds",
"multimodal.point-cloud"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-03-17T07:49:57Z | Official implementation of the paper "Point Cloud Classification Using Content-based Transformer via Clustering in Feature Space" | [
"3d",
"computer-vision",
"robotics"
] | [
"classifier",
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 3 | 470,895,916 | null | Python | null | [
"3d-perception",
"point-cloud-learning",
"transformer"
] | yahuiliu99/PointConT | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 3 | 2026-10-20T15:59:06Z | [] | 2023-06-01T12:23:27Z | [
"multimodal.point-cloud"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 38 | [
"point-cloud",
"pointnet2",
"pytorch"
] | 2026-05-20T03:08:55Z | https://github.com/yahuiliu99/PointConT | null |
[
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-03-17T09:26:30Z | The official code for our ACCV2022 poster paper: Network Pruning via Feature Shift Minimization. | [
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-08T17:58:00Z | false | 0 | 470,926,446 | null | Python | null | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | lscgx/FSM | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 2 | 2026-10-15T15:20:52Z | [] | 2022-10-04T11:18:55Z | [
"efficiency.pruning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 5 | [
"network-pruning"
] | 2025-10-08T12:29:59Z | https://github.com/lscgx/FSM | null |
[
"general-ml"
] | [
"research-implementation"
] | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"general.research-code"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-03-18T00:24:20Z | Research code for CVPR 2022 paper "SwinBERT: End-to-End Transformers with Sparse Attention for Video Captioning" | [
"general-ml"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 36 | 471,184,610 | https://arxiv.org/abs/2111.13196 | Python | MIT | [
"research-implementation"
] | microsoft/SwinBERT | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 2 | 2026-09-24T15:29:21Z | [] | 2022-05-26T06:50:24Z | [
"general.research-code"
] | 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 | 251 | [] | 2026-07-20T18:32:08Z | https://github.com/microsoft/SwinBERT | null |
[
"computer-vision",
"generative-ai",
"language"
] | [
"fine-tuning",
"parameter-efficient-fine-tuning",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.parameter-efficient-finetuning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-03-19T06:58:24Z | Official code for our CVPR'22 paper “Vision Transformer Slimming: Multi-Dimension Searching in Continuous Optimization Space” | [
"computer-vision",
"generative-ai",
"language"
] | [
"transformer"
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] | Arnav0400/ViT-Slim | [
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] | 8 | 2026-10-07T17:53:16Z | [
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] | include | ml-contribution-v5 | 251 | [] | 2026-09-16T11:04:30Z | https://github.com/Arnav0400/ViT-Slim | null |
[
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] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-03-20T10:17:59Z | Official implementation of the paper "Robust and Resource-Efficient Data-Free Knowledge Distillation by Generative Pseudo Replay" (AAAI-22) | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 1 | 471,943,539 | null | Python | null | [
"distillation",
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] | kuluhan/PRE-DFKD | [
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] | 1 | 2026-09-30T16:47:43Z | [] | 2022-05-05T09:02:07Z | [
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] | include | ml-contribution-v5 | 17 | [] | 2026-09-23T13:35:36Z | https://github.com/kuluhan/PRE-DFKD | null |
[
"deep-learning",
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] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-03-22T01:26:39Z | Official code for the CVPR 2022 (oral) paper "OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks." | [
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"interpretability-and-safety"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 12 | 472,571,820 | null | Python | MIT | [
"graph-neural-network",
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] | WanyuGroup/CVPR2022-OrphicX | [
"description",
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] | 2 | 2026-10-06T16:55:56Z | [] | 2022-04-02T00:29:43Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 37 | [
"causality",
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] | 2026-09-10T06:21:46Z | https://github.com/WanyuGroup/CVPR2022-OrphicX | null |
[
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] | [
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-03-23T21:20:12Z | Code for paper publication: Deep reinforcement learning-based solution for a multi-objective online order batching problem | [
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] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-20T15:59:06Z | false | 5 | 473,361,852 | null | Python | null | [
"deep-reinforcement-learning",
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] | martijnbeeks/drl-order-batching | [
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] | 1 | 2026-10-20T15:59:06Z | [] | 2022-04-07T20:18:49Z | [
"rl.deep"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 16 | [] | 2026-03-17T09:23:58Z | https://github.com/martijnbeeks/drl-order-batching | null |
[
"generative-ai",
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] | [
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] | [
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"repository-metadata"
] | [
"recall.name.diffusion-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-03-24T06:25:42Z | [CVPR2022] Code for CVPR 2022 paper "Stochastic Trajectory Prediction via Motion Indeterminacy Diffusion" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 36 | 473,482,531 | null | Python | null | [
"diffusion"
] | Gutianpei/MID | [
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"repository-metadata"
] | 3 | 2026-10-25T15:11:36Z | [] | 2023-04-25T23:05:50Z | [
"recall.name.diffusion-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 235 | [] | 2026-08-11T16:37:42Z | https://github.com/Gutianpei/MID | null |
[
"computer-vision",
"deep-learning",
"representation-learning"
] | [
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] | [
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"repository-metadata"
] | [
"general.contrastive-learning",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-03-24T07:00:07Z | The code for paper FedCL: Federated Contrastive Learning for Multi-center Medical Image Classification. | [
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] | [
"classifier"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-06T16:55:56Z | false | 4 | 473,491,667 | null | Python | null | [
"contrastive-learning",
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] | Sulley-Boo/FedCL | [
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] | 2 | 2026-10-10T16:53:26Z | [] | 2023-06-08T12:29:17Z | [
"general.contrastive-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 24 | [] | 2026-01-01T11:20:28Z | https://github.com/Sulley-Boo/FedCL | null |
[
"machine-learning"
] | [
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] | [
"description",
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"repository-metadata"
] | [
"specialized.test-time-adaptation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-03-24T07:40:51Z | Official PyTorch implementation of Ev-TTA: Test-Time Adaptation for Event-Based Object Recognition (CVPR 2022) | [
"machine-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 1 | 473,503,537 | null | Python | null | [
"adaptation",
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] | 82magnolia/ev_tta | [
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] | 2 | 2026-10-07T17:53:16Z | [] | 2024-02-26T06:27:20Z | [
"specialized.test-time-adaptation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 9 | [] | 2026-03-30T09:07:39Z | https://github.com/82magnolia/ev_tta | null |
[
"deep-learning",
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] | [
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] | [
"general.self-supervised-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-03-24T08:31:16Z | [CVPR2022] Official Implementation of the paper 'Learning Where to Learn in Cross-View Self-Supervised Learning' | [
"deep-learning",
"representation-learning"
] | [
"self-supervised-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 3 | 473,518,525 | null | Python | NOASSERTION | [
"contrastive-learning",
"representation-learning",
"self-supervised-learning"
] | LayneH/LEWEL | [
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"github-topics",
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"query-match",
"repository-metadata"
] | 2 | 2026-10-08T17:58:00Z | [] | 2022-10-12T15:51:24Z | [
"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 | 29 | [
"contrastive-learning",
"self-supervised-learning"
] | 2025-08-20T01:12:17Z | https://github.com/LayneH/LEWEL | null |
[
"deep-learning",
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] | [
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] | [
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"query-match",
"repository-metadata"
] | [
"general.contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-03-26T00:29:13Z | [CVPR'22] Official Implementation of the CVPR 2022 paper "UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning" | [
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] | [
"deep-learning",
"machine-learning",
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-06T16:55:56Z | false | 17 | 474,201,764 | null | Python | MIT | [
"contrastive-learning",
"neural-network",
"self-supervised-learning"
] | nazmul-karim170/UNICON | [
"description",
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"query-match",
"repository-metadata"
] | 3 | 2026-10-10T16:53:26Z | [] | 2024-10-10T01:36:08Z | [
"general.contrastive-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 63 | [
"contrastive-learning",
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"deep-neural-networks",
"jensen-shannon-divergence",
"label-noise-robustness",
"machine-learning",
"noisy-labels",
"semi-supervised-learning"
] | 2026-03-05T07:19:21Z | https://github.com/nazmul-karim170/UNICON | null |
[
"bioinformatics",
"biology",
"genomics",
"language",
"natural-language-processing"
] | [
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-03-26T13:22:38Z | iEnhancer-BERT: A novel transfer learning architecture based on DNA-language model for identifying enhancers and their strength | [
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"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 1 | 474,354,460 | null | Python | MIT | [
"language-model"
] | lhy0322/iEnhancer-BERT | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-10-18T14:44:29Z | [] | 2022-12-08T07:58:41Z | [
"llm.language-model"
] | 566dc99c316858685f4ab80c2d0d1ff0ee91ea8b | 2026-09-26T15:53:33Z | gh-ml-readme-evidence-v2 | "566dc99c316858685f4ab80c2d0d1ff0ee91ea8b" | [
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"installation",
"method",
"other"
] | [
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"paper-reference"
] | ok | 2026-09-26T15:53:33Z | lhy0322/iEnhancer-BERT | specific-method-with-novelty-claim | [
"method-contribution",
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue",
"paper-reference"
] | include | ml-contribution-v5 | 5 | [] | 2025-03-25T16:43:28Z | https://github.com/lhy0322/iEnhancer-BERT | null |
[
"computer-vision",
"generative-ai"
] | [
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"generative-modeling",
"video-generation"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"vision.video-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-03-27T06:56:00Z | Official implementation of "Stereo Depth from Events Cameras: Concentrate and Focus on the Future" (CVPR 2022) | [
"computer-vision",
"generative-ai"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 6 | 474,559,691 | null | Python | null | [
"distillation",
"generative-modeling",
"video-generation"
] | yonseivnl/se-cff | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 2 | 2026-10-08T17:58:00Z | [] | 2023-01-12T04:37:09Z | [
"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 | 52 | [
"attention",
"cvpr",
"cvpr2022",
"distillation",
"event-camera",
"events",
"kld",
"stereo-vision"
] | 2026-03-30T09:06:06Z | https://github.com/yonseivnl/se-cff | null |
[
"automl",
"general-ml"
] | [
"few-shot-learning",
"meta-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.meta-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-03-28T08:31:04Z | Official code for our CVPR'22 paper “Dynamic Kernel Selection for Improved Generalization and Memory Efficiency in Meta-learning” | [
"automl",
"general-ml"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-17T13:17:44Z | false | 0 | 474,912,896 | null | Python | MIT | [
"few-shot-learning",
"meta-learning"
] | transmuteAI/MetaDOCK | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-17T13:17:44Z | [] | 2022-09-16T10:11:29Z | [
"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 | 5 | [] | 2023-07-24T09:22:37Z | https://github.com/transmuteAI/MetaDOCK | null |
[
"interpretability-and-safety"
] | [
"distillation",
"knowledge-distillation"
] | [
"description",
"github-topics",
"repository-metadata"
] | [] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-03-28T09:53:15Z | Official code of the CVPR 2022 paper "Proto2Proto: Can you recognize the car, the way I do?" | [
"interpretability-and-safety"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-01T19:00:27.900159Z | false | 1 | 474,941,113 | null | Python | null | [
"distillation",
"knowledge-distillation"
] | archmaester/proto2proto | [
"description",
"github-topics",
"repository-metadata"
] | 1 | 2026-10-01T19:00:27.900159Z | [] | 2022-05-02T14:11:06Z | [] | 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 | [
"interpretability",
"interpretability-methods",
"knowledge-distillation",
"prototypical-networks"
] | 2025-10-21T14:07:36Z | https://github.com/archmaester/proto2proto | {"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["knowledge-distillation"]} |
[
"deep-learning",
"interpretability-and-safety",
"representation-learning"
] | [
"contrastive-learning",
"self-supervised-learning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-03-29T01:44:19Z | Official PyTorch code for the CVPR 2022 paper - Consistent Explanations by Contrastive Learning | [
"deep-learning",
"interpretability-and-safety",
"representation-learning"
] | [
"deep-learning",
"self-supervised-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-10T16:53:26Z | false | 2 | 475,240,959 | null | Python | MIT | [
"contrastive-learning",
"self-supervised-learning"
] | UCDvision/CGC | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-10T16:53:26Z | [] | 2022-09-11T20:49:11Z | [
"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 | 20 | [
"cgc",
"contrastive-learning",
"deep-learning",
"explainable-ai",
"explainable-models",
"self-supervised-learning"
] | 2026-09-17T06:46:58Z | https://github.com/UCDvision/CGC | 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 | [] | 2022-03-29T03:29:52Z | A PyTorch implementation of Mugs proposed by our paper "Mugs: A Multi-Granular Self-Supervised Learning Framework". | [
"deep-learning",
"representation-learning"
] | [
"self-supervised-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 8 | 475,266,753 | null | Python | Apache-2.0 | [
"representation-learning",
"self-supervised-learning"
] | sail-sg/mugs | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-08T17:58:00Z | [] | 2024-02-13T13:51:09Z | [
"general.self-supervised-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 85 | [] | 2026-06-03T13:17:26Z | https://github.com/sail-sg/mugs | 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 | [] | 2022-03-29T12:32:26Z | [CVPR-2022] Official implementation for "Knowledge Distillation with the Reused Teacher Classifier". | [
"efficient-ml",
"model-compression"
] | [
"deep-learning",
"classifier"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 19 | 475,434,057 | null | Python | null | [
"distillation",
"knowledge-distillation"
] | DefangChen/SimKD | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 9 | 2026-10-07T17:53:16Z | [] | 2022-06-16T06:28:19Z | [
"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 | 101 | [
"deep-learning",
"knowledge-distillation"
] | 2026-09-29T02:14:40Z | https://github.com/DefangChen/SimKD | null |
[
"3d-vision",
"computer-vision",
"deep-learning",
"representation-learning"
] | [
"3d-perception",
"depth-estimation",
"representation-learning",
"self-supervised-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.self-supervised-learning",
"vision.depth-estimation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-03-30T13:27:38Z | Official PyTorch implementation of our WACV 2022 paper, "Self-Supervised Learning of Domain Invariant Features for Depth Estimation". | [
"3d-vision",
"computer-vision"
] | [
"self-supervised-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 0 | 475,891,150 | null | Python | MIT | [
"3d-perception",
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"self-supervised-learning"
] | hiroyasuakada/Self-Supervised-Learning-of-Domain-Invariant-Features-for-Depth-Estimation | [
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"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-12T20:31:19Z | [] | 2022-11-10T09:41:40Z | [
"vision.depth-estimation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
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] | include | ml-contribution-v5 | 7 | [] | 2025-12-10T14:48:54Z | https://github.com/hiroyasuakada/Self-Supervised-Learning-of-Domain-Invariant-Features-for-Depth-Estimation | null |
[
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-03-30T14:57:51Z | Official source code of Fast Point Transformer, CVPR 2022 | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T14:28:44Z | false | 43 | 475,928,299 | null | Python | MIT | [
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] | POSTECH-CVLab/FastPointTransformer | [
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] | 7 | 2026-10-08T18:15:14Z | [] | 2023-02-01T09:49:50Z | [
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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 | 293 | [
"3d-vision",
"computer-vision",
"cvpr2022",
"point-cloud",
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] | 2026-08-27T08:49:22Z | https://github.com/POSTECH-CVLab/FastPointTransformer | null |
[
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] | [
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] | [
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"query-match",
"repository-metadata"
] | [
"general.contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-03-31T20:00:03Z | [ICML 2023] Official implementation of "A randomized schur complement based graph augmentor" | [
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"graph-learning",
"representation-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-11T16:48:52Z | false | 1 | 476,459,044 | https://proceedings.mlr.press/v202/kothapalli23a.html | Python | MIT | [
"contrastive-learning",
"diffusion",
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"neural-network",
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] | kvignesh1420/rlap | [
"description",
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"repository-metadata"
] | 1 | 2026-10-11T16:48:52Z | [] | 2024-06-29T07:56:37Z | [
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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 | 8 | [
"contrastive-learning",
"graph-diffusion",
"graph-neural-networks",
"randomized-algorithms",
"schur-complement"
] | 2025-08-25T17:04:35Z | https://github.com/kvignesh1420/rlap | null |
[
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] | [
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] | [
"description",
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] | [
"general.meta-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-04-01T17:26:46Z | Code for ICCV 2021 paper: 'Query Adaptive Few-Shot Object Detection with Heterogeneous Graph Convolutional Networks' | [
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"general-ml"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-16T15:36:56Z | false | 6 | 476,811,404 | null | Python | null | [
"few-shot-learning",
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] | GuangxingHan/QA-FewDet | [
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] | 1 | 2026-10-16T15:36:56Z | [] | 2022-12-29T03:25:49Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 33 | [
"few-shot-object-detection",
"graph-convolutional-networks",
"meta-learning"
] | 2025-11-20T05:44:01Z | https://github.com/GuangxingHan/QA-FewDet | null |
[
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] | [
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] | [
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] | [
"robotics.robotics-topic"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-04-04T11:30:13Z | Official TensorFlow implementation of the paper "Automating Reinforcement Learning with Example-based Resets" | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 3 | 477,671,235 | null | Python | null | [
"reinforcement-learning",
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] | jigangkim/autoreset_rl | [
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] | 1 | 2026-10-23T15:59:01Z | [] | 2022-05-26T02:13:48Z | [
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] | include | ml-contribution-v5 | 5 | [
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] | 2025-11-06T11:35:31Z | https://github.com/jigangkim/autoreset_rl | null |
[
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] | [
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-04-04T22:48:58Z | [ECCV 2022]Code for paper "DaViT: Dual Attention Vision Transformer" | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-08T17:58:00Z | false | 34 | 477,899,290 | null | Python | MIT | [
"language-model",
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] | dingmyu/davit | [
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] | 1 | 2026-10-08T17:58:00Z | [] | 2024-02-13T09:59:10Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 379 | [] | 2026-09-23T15:46:09Z | https://github.com/dingmyu/davit | null |
[
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] | [
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] | [
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] | [
"graph.point-clouds"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-04-07T13:04:16Z | Official implementation of the Point Transformer-in-Transformer: https://arxiv.org/abs/2204.03957 | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-03T15:45:52Z | false | 2 | 478,988,366 | null | Python | MIT | [
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] | axeber01/point-tnt | [
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] | 1 | 2026-10-03T15:45:52Z | [] | 2022-04-11T08:52:43Z | [
"graph.point-clouds"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 19 | [
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] | 2026-04-04T22:34:52Z | https://github.com/axeber01/point-tnt | null |
[
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] | [
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] | [
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] | [
"recall.name.multimodal-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-04-07T13:33:04Z | This repo is official implementation of the paper "Multimodal transformer for Nurse Activity Recognition", published in CVPM2022, CVPRW. | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-25T15:11:36Z | false | 3 | 478,999,208 | null | Python | null | [
"multimodal-learning",
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] | Momilijaz96/MMT_for_NCRC | [
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] | 1 | 2026-10-25T15:11:36Z | [] | 2024-06-02T09:09:52Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 19 | [] | 2025-12-10T02:04:48Z | https://github.com/Momilijaz96/MMT_for_NCRC | null |
[
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-04-07T17:06:15Z | Official PyTorch implementation of TATS: A Long Video Generation Framework with Time-Agnostic VQGAN and Time-Sensitive Transformer (ECCV 2022) | [
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"speech-and-audio"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-26T20:57:28.489717Z | false | 21 | 479,077,177 | null | Python | MIT | [
"generative-modeling",
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] | songweige/TATS | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-08T17:58:00Z | [] | 2024-05-01T21:32:51Z | [
"vision.video-generation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 289 | [
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"pytorch",
"text-to-video",
"video-generation",
"video-manipulation",
"video-synthesizer"
] | 2026-09-26T05:30:42Z | https://github.com/songweige/TATS | null |
[
"distributed-ml",
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] | [
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"federated-learning"
] | [
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] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-04-12T03:08:27Z | Source code for paper 'An Improved Federated Learning Algorithm for Privacy-Preserving in Cybertwin-Driven 6G System' | [
"distributed-ml",
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 1 | 480,634,849 | null | Python | null | [
"collaborative-learning",
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] | ym1231/FL_CI | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2022-04-12T03:09:50Z | [
"trust.federated-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 7 | [] | 2025-04-26T07:30:21Z | https://github.com/ym1231/FL_CI | null |
[
"3d",
"3d-vision",
"computer-vision",
"graphics"
] | [
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] | [
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"repository-metadata"
] | [
"multimodal.neural-rendering",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-04-12T11:37:12Z | [CVPRW'22] This is the official implementation code repository of Underwater Light Field Retention : Neural Rendering for Underwater Imaging (Accepted by CVPR Workshop2022 NTIRE) | [
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"computer-vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 9 | 480,783,569 | null | Python | null | [
"3d-vision",
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] | Ephemeral182/UWNR | [
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"github-topics",
"query-match",
"repository-metadata"
] | 4 | 2026-10-07T17:53:16Z | [] | 2024-03-25T11:15:52Z | [
"vision.neural-rendering"
] | 79bdd872ad62f71525f12fc811ab80849f7ce4d4 | 2026-09-26T20:25:07Z | gh-ml-readme-evidence-v2 | "79bdd872ad62f71525f12fc811ab80849f7ce4d4" | [
"citation",
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] | [
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] | ok | 2026-09-26T20:25:07Z | Ephemeral182/UWNR | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-context-only",
"ml-method-context",
"paper-reference"
] | include | ml-contribution-v5 | 56 | [
"category-image-restoration",
"cvpr",
"image-restoration",
"neural-rendering",
"python",
"underwater-imaging"
] | 2026-09-11T01:35:49Z | https://github.com/Ephemeral182/UWNR | null |
[
"information-retrieval",
"language",
"multimodal",
"multimodal-learning",
"natural-language-processing",
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-04-13T06:07:15Z | Implementation of LaTr: Layout-aware transformer for scene-text VQA,a novel multimodal architecture for Scene Text Visual Question Answering (STVQA) | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 5 | 481,092,984 | https://uakarsh.github.io/latr/ | Python | MIT | [
"multimodal-learning",
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] | uakarsh/latr | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-10-25T15:11:36Z | [] | 2026-07-22T17:01:45Z | [
"recall.name.multimodal-model"
] | 20e5db765925819d8eed32305f97f5d9eba0c5e1 | 2026-09-28T20:05:39Z | gh-ml-readme-evidence-v2 | "20e5db765925819d8eed32305f97f5d9eba0c5e1" | [
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] | [
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] | ok | 2026-09-28T20:05:39Z | uakarsh/latr | specific-method-with-novelty-claim | [
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"method-tied-novelty-claim",
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] | include | ml-contribution-v5 | 56 | [
"deep-learning",
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] | 2026-07-22T17:03:19Z | https://github.com/uakarsh/latr | null |
[
"computational-physics",
"physics",
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] | [
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] | [
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"repository-metadata"
] | [
"science.physics-informed-neural-networks"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-04-14T20:13:33Z | Supporting code for paper MOPINNs: An Evolutionary Multi-Objective Approach to Physics-Informed Neural Networks | [
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"physics",
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 1 | 481,736,862 | null | Python | MIT | [
"neural-network",
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] | Inria-Chile/mopinns-gecco-2022 | [
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"repository-metadata"
] | 2 | 2026-10-07T17:53:16Z | [] | 2022-06-08T15:42:06Z | [
"science.physics-informed-neural-networks"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 3 | [] | 2024-12-11T12:01:05Z | https://github.com/Inria-Chile/mopinns-gecco-2022 | null |
[
"3d",
"computer-vision",
"robotics"
] | [
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"point-cloud-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.point-cloud"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-04-15T07:48:37Z | the Pytorch implementation for our ICMR 2022 paper "TransPCC: Towards Deep Point Cloud Compression via Transformers" | [
"3d",
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] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-20T15:59:06Z | false | 1 | 481,880,664 | null | Python | MIT | [
"3d-perception",
"point-cloud-learning"
] | jokieleung/TransPCC | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-20T15:59:06Z | [] | 2022-04-15T07:58:20Z | [
"multimodal.point-cloud"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
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] | include | ml-contribution-v5 | 14 | [] | 2026-03-20T21:38:05Z | https://github.com/jokieleung/TransPCC | null |
[
"computer-vision",
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] | [
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] | [
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"repository-metadata"
] | [
"general.contrastive-learning",
"vision.image-to-image-translation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-04-18T04:46:34Z | Official Pytorch implementation of "Exploring Patch-wise Semantic Relation for Contrastive Learning in Image-to-Image Translation Tasks" (CVPR 2022) | [
"computer-vision",
"generative-ai"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-07T17:53:16Z | false | 6 | 482,717,078 | null | Python | MIT | [
"contrastive-learning",
"generative-modeling",
"image-to-image-translation"
] | jcy132/Hneg_SRC | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-15T15:20:52Z | [] | 2024-01-29T05:58:32Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 68 | [] | 2026-10-08T06:58:49Z | https://github.com/jcy132/Hneg_SRC | null |
[
"multi-agent-systems",
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] | [
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] | [
"description",
"query-match",
"repository-metadata"
] | [
"rl.multiagent"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-04-18T17:28:55Z | Code for paper --- multi-agent reinforcement learning for communications | [
"multi-agent-systems",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 1 | 482,935,643 | null | Python | null | [
"centralized-training",
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"reinforcement-learning"
] | Farquhar13/RL_Transmission_Control | [
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"repository-metadata"
] | 2 | 2026-10-07T17:53:16Z | [] | 2022-05-16T14:25:21Z | [
"rl.multiagent"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 4 | [] | 2026-05-25T08:27:21Z | https://github.com/Farquhar13/RL_Transmission_Control | null |
[
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] | 2026-04-21T07:33:12Z | https://github.com/agrija9/ssl-sonar-images | null |
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-06T16:55:56Z | false | 4 | 483,595,293 | https://arxiv.org/pdf/2203.13167.pdf | Python | MIT | [
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] | 2026-04-30T12:48:26Z | https://github.com/srvCodes/continual_learning_with_vit | null |
[
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] | include | ml-contribution-v5 | 34 | [] | 2025-06-17T00:04:58Z | https://github.com/wzx0826/LBNet | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-04-21T08:23:55Z | [ECCV22] Official Implementation of HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-08T17:58:00Z | false | 34 | 483,967,267 | null | Python | NOASSERTION | [
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] | 2026-08-11T15:45:42Z | https://github.com/lhoyer/HRDA | null |
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-04-22T03:41:37Z | This is the official code of the paper "Differentiable Cross Modal Hashing via Multimodal Transformers" | [
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] | include | ml-contribution-v5 | 19 | [] | 2026-07-28T09:04:34Z | https://github.com/kalenforn/DCHMT | null |
[
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[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-04-22T12:20:30Z | Source code of our IJCAI 2022 paper 'Neural Subgraph Explorer: Reducing Noisy Information via Target-oriented Syntax Graph Pruning'' | [
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] | include | ml-contribution-v5 | 1 | [] | 2022-04-23T01:25:00Z | https://github.com/XingBowen714/Neural-Subgraph-Explorer | null |
[
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-06T16:55:56Z | false | 4 | 484,627,173 | https://www.ijcai.org/proceedings/2022/0224.pdf | Python | MIT | [
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] | zchoi/S2-Transformer | [
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] | 3 | 2026-10-24T14:31:13Z | [] | 2024-08-14T03:39:09Z | [
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] | 2026-05-25T09:24:16Z | https://github.com/zchoi/S2-Transformer | null |
[
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] | 2 | 2026-10-20T15:59:06Z | [] | 2022-08-31T11:58:23Z | [
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] | include | ml-contribution-v5 | 21 | [
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] | 2025-11-05T12:57:21Z | https://github.com/heng-hw/SpaCap3D | null |
[
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 85 | 485,320,771 | null | Python | Apache-2.0 | [
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] | 8 | 2026-10-07T17:53:16Z | [] | 2022-08-03T17:38:41Z | [
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] | include | ml-contribution-v5 | 951 | [
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] | 2026-10-06T06:01:12Z | https://github.com/zai-org/CogView2 | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-04-25T19:46:32Z | [ICLR'22] PyTorch code for our paper "Learning Efficient Image Super-Resolution Networks via Structure-Regularized Pruning" | [
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] | [
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] | MingSun-Tse/SRP | [
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] | 4 | 2026-10-15T15:20:52Z | [] | 2023-02-18T14:22:28Z | [
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] | include | ml-contribution-v5 | 29 | [
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] | 2025-07-18T11:18:39Z | https://github.com/MingSun-Tse/SRP | null |
[
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-11T16:48:52Z | false | 1 | 486,082,705 | null | Python | null | [
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] | iriscxy/Target-aware-RWG | [
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] | include | ml-contribution-v5 | 31 | [] | 2026-01-09T03:12:07Z | https://github.com/iriscxy/Target-aware-RWG | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-04-28T10:28:39Z | code for paper "Cross-modal Contrastive Learning for Speech Translation" (NAACL 2022) | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 5 | 486,547,757 | null | Python | MIT | [
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] | ReneeYe/ConST | [
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] | 10 | 2026-10-15T15:20:52Z | [] | 2022-05-25T13:40:33Z | [
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] | 2026-04-30T12:21:03Z | https://github.com/ReneeYe/ConST | null |
[
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-10T16:53:26Z | false | 3 | 486,831,793 | null | Python | null | [
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] | yueyu1030/actune | [
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] | 3 | 2026-10-28T17:27:44Z | [] | 2022-11-16T18:17:06Z | [
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] | include | ml-contribution-v5 | 15 | [
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] | 2025-07-23T04:01:10Z | https://github.com/yueyu1030/actune | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-04-30T21:51:39Z | Official code of the BMVC 2021 paper "Beyond Classification: Knowledge Distillation using Multi-Object Impressions" | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 0 | 487,388,775 | null | null | MIT | [
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] | archmaester/moikd | [
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] | 1 | 2026-10-02T16:59:40Z | [] | 2022-04-30T22:02:25Z | [
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] | include | ml-contribution-v5 | 0 | [] | 2022-04-30T21:58:09Z | https://github.com/archmaester/moikd | null |
[
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-05-01T11:41:21Z | This repository contains data and code for our ASE 2022 paper "Code comment generation based on graph neural network enhanced transformer model for code understanding in open-source software ecosystems". Code will be available soon. | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 0 | 487,521,821 | null | null | null | [
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] | zc-work/GTrans | [
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] | 1 | 2026-10-08T18:15:14Z | [] | 2022-08-11T17:29:57Z | [
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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 | 3 | [] | 2024-07-25T18:59:28Z | https://github.com/zc-work/GTrans | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-05-01T12:26:57Z | Code for paper "Nearest Neighbor Knowledge Distillation for Neural Machine Translation" by Zhixian Yang, Renliang Sun, and Xiaojun Wan. This paper is accepted by NAACL 2022 Main Conference. | [
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"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 2 | 487,531,607 | null | Python | MIT | [
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] | 5 | 2026-10-28T17:27:44Z | [] | 2022-07-16T08:04:42Z | [
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] | include | ml-contribution-v5 | 32 | [] | 2025-09-24T12:21:48Z | https://github.com/FadedCosine/kNN-KD | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-05-02T04:32:16Z | Official Code Repository for the paper "Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations" (ICML 2022) | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 28 | 487,716,282 | https://arxiv.org/abs/2202.02514 | Python | null | [
"diffusion"
] | harryjo97/GDSS | [
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] | 10 | 2026-10-25T15:11:36Z | [] | 2023-11-16T05:27:28Z | [
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] | include | ml-contribution-v5 | 195 | [
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] | 2026-10-21T12:05:53Z | https://github.com/harryjo97/GDSS | null |
[
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-06T17:10:23Z | false | 2 | 488,792,219 | null | Python | null | [
"graph-neural-network",
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] | GuetYe/anomaly_detection | [
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] | 2 | 2026-10-25T15:11:36Z | [] | 2025-06-26T14:45:00Z | [
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] | ok | 2026-10-06T18:32:27Z | GuetYe/anomaly_detection | specific-method-with-novelty-claim | [
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] | include | ml-contribution-v5 | 10 | [] | 2026-10-12T07:21:02Z | https://github.com/GuetYe/anomaly_detection | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-05-09T08:29:04Z | Official implementation for AAAI 2022 paper: "GeomGCL: Geometric Graph Contrastive Learning for Molecular Property Prediction". | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 2 | 490,197,468 | null | null | null | [
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] | 7 | 2026-10-13T18:05:26Z | [] | 2022-07-11T01:57:19Z | [
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] | include | ml-contribution-v5 | 22 | [] | 2026-09-21T11:05:14Z | https://github.com/agave233/GeomGCL | null |
[
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-05-09T08:31:54Z | Official implementation of the paper “Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learning”（SIGGRAPH 2022） | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T15:29:21Z | false | 23 | 490,198,369 | null | Python | Apache-2.0 | [
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] | zyxElsa/CAST_pytorch | [
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] | 8 | 2026-10-12T20:31:19Z | [] | 2023-10-16T05:12:35Z | [
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] | include | ml-contribution-v5 | 199 | [] | 2026-08-31T03:43:19Z | https://github.com/zyxElsa/CAST_pytorch | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-05-09T14:58:05Z | Source code for paper: Friends to Help: Saving Federated Learning from Client Dropout | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 1 | 490,329,191 | null | Python | MIT | [
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] | 1 | 2026-10-05T20:10:04Z | [] | 2023-11-21T15:34:04Z | [
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] | include | ml-contribution-v5 | 5 | [] | 2026-07-01T12:30:17Z | https://github.com/ystex/Federated-Learning-with-Friend-Discovery-and-Model-Substitution- | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-05-13T10:58:00Z | Official implementation of the paper "Exploring the Effectiveness of Self-supervised Learning and Classifier Chains in Emotion Recognition of Nonverbal Vocalizations" accepted by the Few-Shot track of the ICML Expressive Vocalizations (ExVo) Competition 2022. | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 1 | 491,862,692 | null | Python | MIT | [
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] | Aria-K-Alethia/ExVo | [
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] | 1 | 2026-10-02T16:59:40Z | [] | 2022-07-27T13:53:49Z | [
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] | include | ml-contribution-v5 | 6 | [] | 2024-10-25T08:59:30Z | https://github.com/Aria-K-Alethia/ExVo | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-05-15T06:07:14Z | Official PyTorch implementation of CASS, from the following paper: CASS: Cross Architectural Self-Supervision for Medical Image Analysis. arXiv 2022. | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-06T16:55:56Z | false | 6 | 492,402,256 | null | Jupyter Notebook | null | [
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] | pranavsinghps1/CASS | [
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] | 4 | 2026-10-28T17:27:44Z | [] | 2024-02-22T06:28:27Z | [
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] | include | ml-contribution-v5 | 20 | [
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] | 2026-01-27T08:16:21Z | https://github.com/pranavsinghps1/CASS | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-05-15T13:09:44Z | Official Implementation for the ICML2022 paper "Directed Acyclic Transformer for Non-Autoregressive Machine Translation" | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 17 | 492,490,402 | null | Python | NOASSERTION | [
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] | thu-coai/DA-Transformer | [
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] | 5 | 2026-10-12T20:31:19Z | [] | 2023-09-10T13:17:03Z | [
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] | include | ml-contribution-v5 | 138 | [
"machine-translation",
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"pytorch",
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] | 2026-07-02T05:56:32Z | https://github.com/thu-coai/DA-Transformer | null |
[
"deep-learning",
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-05-15T17:25:52Z | Official implementation of the paper "MV-MR: multi-views and multi-representations for self-supervised learning and knowledge distillation" | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 0 | 492,555,807 | null | Python | null | [
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] | vkinakh/mv-mr | [
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] | 2 | 2026-10-13T18:05:26Z | [] | 2024-06-02T13:03:40Z | [
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] | include | ml-contribution-v5 | 8 | [
"self-supervised-learning"
] | 2025-05-20T00:44:31Z | https://github.com/vkinakh/mv-mr | null |
[
"deep-learning",
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] | [
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] | [
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] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-05-18T22:50:45Z | Code for Paper "From Local to Global: Spectral-Inspired Graph Neural Networks" | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-06T17:10:23Z | false | 2 | 493,848,394 | null | Jupyter Notebook | null | [
"graph-neural-network",
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] | nhuang37/spectral-inspired-gnn | [
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] | 1 | 2026-10-06T17:10:23Z | [] | 2024-04-02T20:56:29Z | [
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] | include | ml-contribution-v5 | 4 | [] | 2024-09-25T16:14:57Z | https://github.com/nhuang37/spectral-inspired-gnn | null |
[
"classical-ml",
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-05-20T07:14:13Z | [ECCV 2022] Official PyTorch implementation of the paper - Graph Neural Network for Cell Tracking in Microscopy Videos | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T15:29:21Z | false | 12 | 494,360,402 | null | Python | NOASSERTION | [
"graph-neural-network",
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] | talbenha/cell-tracker-gnn | [
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] | 7 | 2026-10-11T16:48:52Z | [] | 2022-11-30T08:00:56Z | [
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] | include | ml-contribution-v5 | 82 | [
"deep-learning",
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] | 2026-08-18T08:22:34Z | https://github.com/talbenha/cell-tracker-gnn | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-05-21T07:02:49Z | Official implementation of paper "Knowledge Distillation from A Stronger Teacher", NeurIPS 2022 | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 23 | 494,706,929 | null | Python | Apache-2.0 | [
"distillation",
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] | hunto/DIST_KD | [
"description",
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] | 7 | 2026-10-07T17:53:16Z | [] | 2022-12-28T03:52:19Z | [
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"contribution-language",
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 159 | [
"detection",
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"imagenet",
"kd",
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] | 2026-10-07T07:45:37Z | https://github.com/hunto/DIST_KD | null |
[
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] | include | ml-contribution-v5 | 15 | [] | 2025-10-24T16:19:54Z | https://github.com/shaunyue/clare | null |
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-28T17:27:44Z | false | 29 | 497,104,444 | null | Python | MIT | [
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] | include | ml-contribution-v5 | 371 | [] | 2026-09-11T03:20:43Z | https://github.com/voletiv/mcvd-pytorch | null |
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] | include | ml-contribution-v5 | 20 | [] | 2025-04-16T14:07:11Z | https://github.com/Thinklab-SJTU/WSGNN | null |
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-05-30T02:52:42Z | Official implementation of the paper: Safe Model-Based Reinforcement Learning with an Uncertainty-Aware Reachability Certificate | [
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[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-05-30T08:27:27Z | Source code for paper: We Know You Are Living in Bali: Location Prediction of Twitter User Using BERT Language Model | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-18T14:44:29Z | false | 0 | 497,853,923 | null | null | null | [
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] | include | ml-contribution-v5 | 0 | [] | 2022-05-30T08:27:27Z | https://github.com/lihardofs/locationpredictionBERT | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-05-30T08:53:39Z | Code for ICML 2022 paper "Plan Your Target and Learn Your Skills: Transferable State-Only Imitation Learning via Decoupled Policy Optimization" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 4 | 497,862,884 | https://decoupled-policy-optimization.github.io/ | Python | null | [
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] | apexrl/DePO | [
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] | 2 | 2026-10-06T16:55:56Z | [] | 2022-11-13T14:58:31Z | [
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] | include | ml-contribution-v5 | 9 | [] | 2026-09-15T15:17:50Z | https://github.com/apexrl/DePO | null |
[
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] | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-08T17:58:00Z | false | 2 | 497,935,823 | https://doi.org/10.1016/j.cviu.2022.103445 | Python | null | [
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] | Mxbonn/anchor_pruning | [
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] | 2 | 2026-10-15T15:20:52Z | [] | 2023-08-22T13:04:25Z | [
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] | include | ml-contribution-v5 | 6 | [
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] | 2025-07-22T03:19:13Z | https://github.com/Mxbonn/anchor_pruning | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-05-30T20:37:45Z | An open source framework for my novel algorithm 'Clustered Federated Learning' - a fully decentralised learning technique. | [
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[
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] | 2025-03-06T04:35:12Z | https://github.com/zhongyu1998/PG-GNN | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-06-01T13:36:54Z | Official code for "Personalized Federated Learning through Local Memorization" (ICML'22) | [
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] | 2025-11-18T17:19:23Z | https://github.com/omarfoq/knn-per | null |
[
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"general.self-supervised-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-06-01T14:25:57Z | The data and source code for paper “Self-supervised Learning for Label Sparsity in Computational Drug Repositioning” | [
"deep-learning",
"representation-learning"
] | [
"self-supervised-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 0 | 498,769,382 | null | null | null | [
"representation-learning",
"self-supervised-learning"
] | NovaRick/SSLDR | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-02T16:59:40Z | [] | 2022-06-02T02:33:52Z | [
"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 | 0 | [] | 2022-06-01T14:25:57Z | https://github.com/NovaRick/SSLDR | null |
[
"reinforcement-learning"
] | [
"batch-reinforcement-learning",
"offline-reinforcement-learning",
"reinforcement-learning"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"rl.offline"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-06-02T01:45:41Z | The official implementation of "When Data Geometry Meets Deep Function: Generalizing Offline Reinforcement Learning" (ICLR2023) | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 2 | 498,955,058 | null | Python | null | [
"batch-reinforcement-learning",
"offline-reinforcement-learning",
"reinforcement-learning"
] | Facebear-ljx/DOGE | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 4 | 2026-10-07T17:53:16Z | [] | 2023-03-06T01:34:47Z | [
"rl.offline"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 44 | [
"generalization-ability",
"pytorch",
"reinforcement-learning"
] | 2025-03-26T12:48:24Z | https://github.com/Facebear-ljx/DOGE | 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 | [] | 2022-06-02T03:57:46Z | Open-source code for paper "On Structural Explanation of Bias in Graph Neural Networks". | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-06T17:10:23Z | false | 1 | 498,984,091 | null | Python | null | [
"graph-neural-network",
"message-passing",
"neural-network"
] | yushundong/REFEREE | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-06T17:10:23Z | [] | 2023-03-28T16:50:45Z | [
"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 | [] | 2023-04-13T09:35:06Z | https://github.com/yushundong/REFEREE | null |
[
"deep-learning",
"representation-learning",
"time-series-and-forecasting"
] | [
"contrastive-learning",
"self-supervised-learning"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning"
] | true | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-06-03T10:28:58Z | Official PyTorch implementation of the paper "Utilizing Expert Features for Contrastive Learning of Time-Series Representations" | [
"deep-learning",
"representation-learning",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-13T18:05:26Z | false | 0 | 499,469,905 | null | null | null | [
"contrastive-learning",
"self-supervised-learning"
] | boschresearch/expclr | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 1 | 2026-10-13T18:05:26Z | [] | 2023-01-31T12:29:10Z | [
"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 | 15 | [
"paper-resource"
] | 2026-07-21T22:41:03Z | https://github.com/boschresearch/expclr | null |
[
"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 | [] | 2022-06-04T03:58:33Z | [MICCAI 2022] The official code for "mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation" | [
"multimodal",
"multimodal-learning"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-08T17:58:00Z | false | 15 | 499,718,867 | null | Python | Apache-2.0 | [
"multimodal-learning",
"transformer"
] | YaoZhang93/mmFormer | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-26T17:25:59Z | [] | 2023-12-14T02:03:48Z | [
"recall.name.multimodal-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 | 138 | [] | 2026-07-04T13:22:31Z | https://github.com/YaoZhang93/mmFormer | null |
[
"generative-ai",
"generative-modeling",
"speech-and-audio"
] | [
"diffusion"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-06-05T19:37:49Z | This is the official implementation of the paper Voice Modification with Denoising Diffusion Models. | [
"generative-ai",
"generative-modeling",
"speech-and-audio"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 0 | 500,194,229 | null | null | Apache-2.0 | [
"diffusion"
] | alonlevko/Voice-Modification-with-Denoising-Diffusion-Models | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-02T16:59:40Z | [] | 2022-06-05T19:39:13Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [] | 2022-11-01T13:31:31Z | https://github.com/alonlevko/Voice-Modification-with-Denoising-Diffusion-Models | null |
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