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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
[
"applied-mathematics",
"applied-physics",
"computational-science",
"generative-ai",
"generative-modeling"
] | [
"data-assimilation",
"diffusion",
"inverse-problems",
"reconstruction"
] | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"science.inverse-problems"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-09-15T13:39:08Z | Official PyTorch implementation of the NeurIPS 2022 paper "Improving Diffusion Models for Inverse Problems using Manifold Constraints (https://arxiv.org/abs/2206.00941)" | [
"applied-mathematics",
"applied-physics",
"computational-science",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 24 | 537,044,410 | null | Python | Apache-2.0 | [
"data-assimilation",
"diffusion",
"inverse-problems",
"reconstruction"
] | hyungjin-chung/MCG_diffusion | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 6 | 2026-10-07T17:53:16Z | [] | 2022-10-26T03:50:00Z | [
"science.inverse-problems"
] | 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 | 270 | [] | 2026-09-24T12:36:30Z | https://github.com/hyungjin-chung/MCG_diffusion | null |
[
"automl",
"bayesian-ml",
"general-ml",
"probabilistic-ml"
] | [
"bayesian-inference",
"few-shot-learning",
"meta-learning",
"probabilistic-programming"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.meta-learning",
"general.probabilistic-programming"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-09-15T17:39:56Z | code for paper 'Squeezing More Utility via Adaptive Clipping on Deferentially Private Gradients in Federated Meta-Learning' ACSAC 2022 | [
"automl",
"general-ml"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-10T16:53:26Z | false | 1 | 537,140,591 | null | Python | null | [
"few-shot-learning",
"meta-learning"
] | ning-wang1/DPFedMeta | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-10-20T15:59:06Z | [] | 2022-10-09T01:51:08Z | [
"general.meta-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 5 | [] | 2023-09-26T12:05:43Z | https://github.com/ning-wang1/DPFedMeta | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-09-16T07:55:46Z | Official PyTorch implementation for Maximum Likelihood Training of Implicit Nonlinear Diffusion Model (INDM) in NeurIPS 2022. | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 3 | 537,360,424 | null | Python | Apache-2.0 | [
"diffusion"
] | byeonghu-na/INDM | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-09-30T16:47:43Z | [] | 2024-04-29T06:18:26Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 40 | [] | 2025-03-22T15:27:09Z | https://github.com/byeonghu-na/INDM | null |
[
"deep-learning",
"representation-learning"
] | [
"contrastive-learning",
"self-supervised-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-09-17T13:31:50Z | [COLING'22] Code for our paper: "COLO: A Contrastive Learning based Re-ranking Framework for One-Stage Summarization" | [
"deep-learning",
"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-15T15:20:52Z | false | 1 | 537,817,790 | null | Python | null | [
"contrastive-learning",
"self-supervised-learning"
] | ChenxinAn-fdu/CoLo | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-15T15:20:52Z | [] | 2022-10-21T05:40:58Z | [
"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 | 22 | [] | 2024-05-06T05:02:55Z | https://github.com/ChenxinAn-fdu/CoLo | 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 | [] | 2022-09-17T15:03:39Z | Code for our paper Data Leakage from Covariances in Federated Learning (Huth et al. , 2022). | [
"distributed-ml",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-07T18:07:52Z | false | 0 | 537,843,030 | null | Jupyter Notebook | null | [
"collaborative-learning",
"federated-learning"
] | manuhuth/Data-Leakage-From-Covariances | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-07T18:07:52Z | [] | 2023-04-03T13:50:23Z | [
"trust.federated-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 | [] | 2023-04-03T09:34:12Z | https://github.com/manuhuth/Data-Leakage-From-Covariances | null |
[
"generative-ai",
"generative-modeling",
"health-and-biomedicine",
"medical-imaging"
] | [
"diffusion",
"image-analysis"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"medical.medical-imaging",
"recall.name.diffusion-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-09-19T08:40:14Z | Medical Diffusion: This repository contains the code to our paper Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Synthesis | [
"generative-ai",
"generative-modeling",
"health-and-biomedicine",
"medical-imaging"
] | [
"machine-learning",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T18:03:22.392067Z | false | 78 | 538,408,555 | null | Jupyter Notebook | null | [
"diffusion",
"image-analysis"
] | FirasGit/medicaldiffusion | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 2 | 2026-10-07T17:53:16Z | [] | 2023-05-12T13:25:42Z | [
"medical.medical-imaging",
"recall.name.diffusion-model"
] | 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 | 495 | [
"ct",
"diffusion-models",
"machine-learning",
"mri"
] | 2026-09-30T02:51:37Z | https://github.com/FirasGit/medicaldiffusion | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"multimodal",
"vision"
] | [
"diffusion",
"image-generation",
"text-to-image"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"multimodal.text-to-image"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-09-19T08:40:35Z | [Journal of Artificial Intelligence Research] Source code for our paper "Exploiting Cultural Biases via Homoglyphs in Text-to-Image Synthesis". | [
"computer-vision",
"generative-ai",
"generative-modeling",
"multimodal",
"vision"
] | [
"deep-learning",
"generative-model",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 3 | 538,408,687 | https://arxiv.org/abs/2209.08891 | Python | MIT | [
"diffusion",
"image-generation",
"text-to-image"
] | LukasStruppek/Exploiting-Cultural-Biases-via-Homoglyphs | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 3 | 2026-10-07T17:53:16Z | [] | 2024-01-08T12:04:13Z | [
"multimodal.text-to-image"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 12 | [
"dall-e",
"dall-e2",
"deep-learning",
"generative-model",
"stable-diffusion",
"text-to-image-generation",
"text-to-image-synthesis"
] | 2025-11-19T11:28:39Z | https://github.com/LukasStruppek/Exploiting-Cultural-Biases-via-Homoglyphs | null |
[
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"forecasting",
"sequence-modeling"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"timeseries.forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-09-19T10:33:10Z | Code for paper titled "Learning Latent Seasonal-Trend Representations for Time Series Forecasting" in NeurIPS 2022 | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-06T16:55:56Z | false | 13 | 538,449,364 | null | Python | Apache-2.0 | [
"forecasting",
"sequence-modeling"
] | zhycs/LaST | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-12T20:31:19Z | [] | 2022-10-09T12:43:32Z | [
"timeseries.forecasting"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 83 | [] | 2026-05-29T11:41:13Z | https://github.com/zhycs/LaST | null |
[
"explainable-ai",
"generative-ai",
"generative-modeling",
"trustworthy-ml"
] | [
"counterfactual-explanations",
"diffusion",
"interpretability"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"trust.counterfactual-explanations"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-09-19T11:41:37Z | Official Code for the ACCV 2022 paper Diffusion Models for Counterfactual Explanations | [
"explainable-ai",
"generative-modeling",
"trustworthy-ml"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 7 | 538,473,445 | https://guillaumejs2403.github.io/projects/dime.html | Python | MIT | [
"counterfactual-explanations",
"diffusion",
"interpretability"
] | guillaumejs2403/DiME | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-07T17:53:16Z | [] | 2025-03-12T08:50:14Z | [
"trust.counterfactual-explanations"
] | 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 | [
"counterfactual-explanations",
"pytorch"
] | 2026-04-01T11:18:47Z | https://github.com/guillaumejs2403/DiME | null |
[
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"forecasting",
"multivariate-forecasting",
"neural-network",
"sequence-modeling"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"timeseries.forecasting",
"timeseries.multivariate"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-09-19T23:46:30Z | Application of new age Neural Networks and Traditional statistical approaches for Multivariate Time Series forecasting to predict price of agricultural commodities | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 0 | 538,737,256 | null | Jupyter Notebook | null | [
"forecasting",
"neural-network",
"sequence-modeling"
] | Nirman118/MA_THESIS_NIRMAN_SHARMA | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-13T18:05:26Z | [] | 2022-09-21T21:16:37Z | [
"timeseries.forecasting"
] | b79d846119d985fa4dd08d6af8a5da7cfc62de7f | 2026-10-01T19:11:32Z | gh-ml-readme-evidence-v2 | "b79d846119d985fa4dd08d6af8a5da7cfc62de7f" | [
"other"
] | [] | ok | 2026-10-01T19:11:32Z | Nirman118/MA_THESIS_NIRMAN_SHARMA | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [] | 2022-09-21T11:49:02Z | https://github.com/Nirman118/MA_THESIS_NIRMAN_SHARMA | null |
[
"computer-vision",
"information-retrieval",
"language",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"vision"
] | [
"question-answering",
"reading-comprehension",
"vision-language-model",
"visual-question-answering"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"multimodal.visual-question-answering",
"nlp.question-answering"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-09-21T06:53:02Z | [NeurIPS 2022] Official code for REVIVE: Regional Visual Representation Matters in Knowledge-Based Visual Question Answering | [
"computer-vision",
"information-retrieval",
"multimodal-learning",
"natural-language-processing"
] | [
"deep-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 14 | 539,347,572 | https://arxiv.org/abs/2206.01201 | Python | NOASSERTION | [
"question-answering",
"reading-comprehension"
] | yuanze-lin/REVIVE | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 11 | 2026-10-22T16:09:07Z | [] | 2025-04-06T16:38:09Z | [
"nlp.question-answering"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 105 | [
"computer-vision",
"deep-learning",
"gpt-3",
"knowledge-based",
"multimodal-deep-learning",
"neurips2022",
"ok-vqa",
"pytorch",
"question-answering",
"vision-and-languge",
"vqa"
] | 2026-01-12T08:21:40Z | https://github.com/yuanze-lin/REVIVE | null |
[
"deep-learning",
"graph-learning",
"representation-learning"
] | [
"contrastive-learning",
"graph-contrastive-learning",
"self-supervised-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning",
"graph.graph-contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-09-21T07:37:21Z | Code for NeurIPS 2022 paper "Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination" | [
"deep-learning",
"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 5 | 539,363,957 | null | Python | null | [
"contrastive-learning",
"self-supervised-learning"
] | zyzisastudyreallyhardguy/Graph-Group-Discrimination | [
"description",
"query-match",
"repository-metadata"
] | 5 | 2026-10-15T15:20:52Z | [] | 2023-03-06T09:34:01Z | [
"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 | 55 | [] | 2026-06-21T03:45:54Z | https://github.com/zyzisastudyreallyhardguy/Graph-Group-Discrimination | null |
[
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"neural-network",
"neural-network-pruning",
"pruning",
"structured-pruning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-09-22T06:34:10Z | Official code for the paper - Is Complexity Required for Neural Network Pruning? A Case Study on Global Magnitude Pruning | [
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-08T17:58:00Z | false | 0 | 539,833,653 | null | Python | MIT | [
"neural-network",
"neural-network-pruning",
"pruning",
"structured-pruning"
] | manasgupta-1/GlobalMP | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-15T15:20:52Z | [] | 2022-09-22T06:57:23Z | [
"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 | [] | 2023-11-14T15:56:25Z | https://github.com/manasgupta-1/GlobalMP | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-09-22T13:46:17Z | Official code implementation of MIDMs: Matching Interleaved Diffusion Models for Exemplar-based Image Translation (AAAI'23) | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 3 | 540,003,244 | null | Python | null | [
"diffusion"
] | cvlab-kaist/MIDMs | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-09-30T16:47:43Z | [] | 2023-01-08T11:02:20Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 71 | [] | 2025-07-28T19:21:16Z | https://github.com/cvlab-kaist/MIDMs | 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 | [] | 2022-09-24T16:10:46Z | Official code of the paper "Federated Learning in Heterogeneous Networks with Unreliable Communication" | [
"distributed-ml",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 5 | 540,888,409 | null | Jupyter Notebook | null | [
"collaborative-learning",
"federated-learning"
] | paulzhengfr/FedproxPER | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-04T15:58:58Z | [] | 2024-11-21T21:20:05Z | [
"trust.federated-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 3 | [] | 2025-10-12T11:46:41Z | https://github.com/paulzhengfr/FedproxPER | null |
[
"deep-learning",
"efficient-ml",
"general-ml",
"graph-learning",
"model-compression"
] | [
"distillation",
"graph-neural-network",
"knowledge-distillation",
"message-passing",
"neural-network"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation",
"general.neural-network",
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-09-25T06:20:43Z | Code for NeurIPS 2022 paper "Knowledge Distillation Improves Graph Structure Augmentation for Graph Neural Networks" | [
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"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 34 | 541,073,609 | null | Python | MIT | [
"distillation",
"graph-neural-network",
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"neural-network"
] | LirongWu/KDGA | [
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"query-match",
"repository-metadata"
] | 11 | 2026-10-08T17:58:00Z | [] | 2024-02-27T07:48:58Z | [
"general.neural-network"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 192 | [
"graph-augmentation",
"graph-neural-networks",
"graph-structure-learning",
"knowledge-distillation"
] | 2026-10-05T08:50:20Z | https://github.com/LirongWu/KDGA | null |
[
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] | [
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] | [
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] | [
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-09-25T09:12:20Z | code for paper "Two-Critic Deep Reinforcement Learning for Inverter-based Volt-Var Control in Active Distribution Networks" | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-06T16:55:56Z | false | 6 | 541,114,792 | null | Jupyter Notebook | null | [
"deep-reinforcement-learning",
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"reinforcement-learning"
] | ikelq/single-period-two-critic-DRL | [
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] | 2 | 2026-10-20T15:59:06Z | [] | 2024-04-10T03:12:50Z | [
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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 | 19 | [] | 2026-03-29T13:41:27Z | https://github.com/ikelq/single-period-two-critic-DRL | null |
[
"reinforcement-learning"
] | [
"batch-reinforcement-learning",
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] | [
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"query-match",
"repository-metadata"
] | [
"rl.offline"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-09-26T09:50:44Z | Code for NeurIPS 2022 paper "Robust offline Reinforcement Learning via Conservative Smoothing" | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 5 | 541,514,130 | null | Python | MIT | [
"batch-reinforcement-learning",
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"reinforcement-learning"
] | YangRui2015/RORL | [
"description",
"github-topics",
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"repository-metadata"
] | 8 | 2026-10-07T17:53:16Z | [] | 2023-02-15T16:58:08Z | [
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"ml-method-cue",
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] | include | ml-contribution-v5 | 23 | [
"offline-reinforcement-learning",
"reinforcement-learning",
"robust-reinforcement-learning"
] | 2026-08-14T13:15:15Z | https://github.com/YangRui2015/RORL | null |
[
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] | [
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] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-09-26T12:15:22Z | The implementation code of our paper "Learning Generalizable Models for Vehicle Routing Problems via Knowledge Distillation", accepted at NeurIPS2022. | [
"efficient-ml",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 10 | 541,569,512 | null | Python | MIT | [
"distillation",
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] | jieyibi/AMDKD | [
"description",
"github-topics",
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"query-match",
"repository-metadata"
] | 2 | 2026-10-07T17:53:16Z | [] | 2025-10-30T01:46:50Z | [
"efficiency.knowledge-distillation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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"ml-method-cue",
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 59 | [
"cross-distribution",
"cvrp",
"generalization",
"knowledge-distillation",
"multi-teacher-kd",
"neural-combinatorial-optimization",
"tsp",
"vehicle-routing-problem"
] | 2026-05-07T01:47:32Z | https://github.com/jieyibi/AMDKD | null |
[
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] | [
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] | [
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] | [
"general.contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-09-27T04:55:51Z | Official PyTorch implementation of "Energy-Based Contrastive Learning of Visual Representations", NeurIPS 2022 Oral Paper | [
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"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-15T15:20:52Z | false | 1 | 541,909,177 | https://arxiv.org/abs/2202.04933 | Python | null | [
"contrastive-learning",
"self-supervised-learning"
] | 1202kbs/EBCLR | [
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] | 1 | 2026-10-15T15:20:52Z | [] | 2022-10-02T16:09:47Z | [
"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 | 13 | [
"contrastive-learning",
"energy-based-model"
] | 2025-10-02T04:28:27Z | https://github.com/1202kbs/EBCLR | null |
[
"language",
"natural-language-processing"
] | [
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"language-modeling",
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] | [
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"repository-metadata"
] | [
"llm.language-model",
"nlp.language-modeling"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-09-27T07:44:57Z | The official PyTorch implementation of the NeurIPS2022 (spotlight) paper, Outlier Suppression: Pushing the Limit of Low-bit Transformer Language Models | [
"language",
"natural-language-processing"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-19T17:46:36Z | false | 4 | 541,963,746 | null | Python | null | [
"language-model",
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] | wimh966/outlier_suppression | [
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"repository-metadata"
] | 1 | 2026-10-19T17:46:36Z | [] | 2022-10-05T12:08:38Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 50 | [] | 2026-07-29T06:17:01Z | https://github.com/wimh966/outlier_suppression | null |
[
"generative-ai",
"generative-modeling"
] | [
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] | [
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"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"recall.name.diffusion-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-09-29T11:24:35Z | The official PyTorch implementation of the paper "Human Motion Diffusion Model" | [
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"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 465 | 543,082,997 | null | Python | MIT | [
"diffusion"
] | GuyTevet/motion-diffusion-model | [
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"license-metadata",
"query-match",
"repository-metadata"
] | 10 | 2026-10-23T15:59:01Z | [] | 2025-10-01T00:29:26Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 4,123 | [] | 2026-10-22T20:57:13Z | https://github.com/GuyTevet/motion-diffusion-model | null |
[
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
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"sequence-modeling"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"timeseries.forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-09-30T01:11:53Z | This repo contains the research work done towards LTCs, the novel architecture built on LTCs, time series forecasting & bitcoin forecasting | [
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"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-13T18:05:26Z | false | 0 | 543,380,760 | null | null | null | [
"forecasting",
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] | Ammar-Raneez/FYP_Research | [
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"query-match",
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] | 1 | 2026-10-13T18:05:26Z | [] | 2023-09-26T18:53:13Z | [
"timeseries.forecasting"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [] | 2023-02-15T06:09:18Z | https://github.com/Ammar-Raneez/FYP_Research | null |
[
"computer-vision",
"efficient-ml",
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] | [
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] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-09-30T04:33:25Z | An official implementation of "Decomposed Knowledge Distillation for Class-incremental Semantic Segmentation" (NeurIPS 2022) in PyTorch. | [
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"efficient-ml",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 11 | 543,430,157 | null | Python | GPL-3.0 | [
"distillation",
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] | cvlab-yonsei/DKD | [
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"query-match",
"repository-metadata"
] | 2 | 2026-10-07T17:53:16Z | [] | 2023-05-24T06:38:42Z | [
"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 | 68 | [] | 2026-04-05T14:55:58Z | https://github.com/cvlab-yonsei/DKD | null |
[
"reinforcement-learning"
] | [
"deep-reinforcement-learning",
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] | [
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"repository-metadata"
] | [
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-09-30T08:44:34Z | Code for the charginghub environment used in our paper "Adaptive Infinite-Horizon Control of Hybrid EV/FCEV Charging Hubs: A Large-Model Based Deep Reinforcement Learning Approach" | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-21T15:48:06Z | false | 2 | 543,507,772 | null | C++ | MIT | [
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] | XJTU-Power-AI/charginghub-env | [
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"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-21T15:48:06Z | [] | 2025-05-09T05:11:57Z | [
"rl.deep"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 3 | [] | 2025-11-10T12:30:32Z | https://github.com/XJTU-Power-AI/charginghub-env | null |
[
"generative-ai",
"generative-modeling"
] | [
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"self-attention"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-09-30T14:22:30Z | Official implementation of the paper "Improving Sample Quality of Diffusion Models Using Self-Attention Guidance" (ICCV`23) | [
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"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 14 | 543,638,018 | null | Jupyter Notebook | MIT | [
"diffusion",
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] | SusungHong/Self-Attention-Guidance | [
"description",
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"query-match",
"repository-metadata"
] | 1 | 2026-09-29T17:30:08Z | [] | 2024-08-23T12:46:47Z | [
"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 | 120 | [] | 2026-09-21T12:25:08Z | https://github.com/SusungHong/Self-Attention-Guidance | null |
[
"reinforcement-learning"
] | [
"batch-reinforcement-learning",
"offline-reinforcement-learning",
"reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"rl.offline"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-10-01T02:02:15Z | The official code of our top-25%-noble paper at ICLR'23: "Provably Efficient Neural Offline Reinforcement Learning via Perturbed Rewards" | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T14:50:46Z | false | 0 | 543,856,677 | null | Jupyter Notebook | null | [
"batch-reinforcement-learning",
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] | thanhnguyentang/neural-offline-rl | [
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] | 2 | 2026-10-07T17:53:16Z | [] | 2023-02-24T17:07:21Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 2 | [] | 2023-03-24T06:34:21Z | https://github.com/thanhnguyentang/neural-offline-rl | null |
[
"classical-ml",
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] | [
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] | [
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"github-topics",
"query-match",
"repository-metadata"
] | [
"general.metric-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-10-01T12:14:03Z | code for our BMVC 2022 paper "Positive Pair Distillation Considered Harmful: Continual Meta Metric Learning for Lifelong Object Re-Identification" | [
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"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-12T20:31:19Z | false | 1 | 544,015,034 | null | Python | null | [
"distillation",
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"meta-learning",
"metric-learning",
"representation-learning"
] | wangkai930418/DwoPP_code | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 1 | 2026-10-12T20:31:19Z | [] | 2022-10-05T10:15:49Z | [
"general.metric-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 | 9 | [
"bmvc",
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"incremental-learning",
"knowledge-distillation",
"meta-learning",
"meta-metric-learning",
"metric-learning",
"object-reid",
"person-reid"
] | 2024-03-04T08:54:24Z | https://github.com/wangkai930418/DwoPP_code | null |
[
"computer-vision"
] | [
"distillation",
"knowledge-distillation",
"representation-learning",
"self-supervised-learning",
"transformer"
] | [
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"query-match",
"repository-metadata"
] | [
"vision.self-supervised-vision"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-10-01T12:41:54Z | code for our paper "Attention Distillation: self-supervised vision transformer students need more guidance" in BMVC 2022 | [
"computer-vision"
] | [
"self-supervised-learning",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 0 | 544,023,165 | null | Python | null | [
"distillation",
"knowledge-distillation",
"representation-learning",
"self-supervised-learning",
"transformer"
] | wangkai930418/attndistill | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 4 | 2026-10-07T17:53:16Z | [] | 2022-10-04T07:44:05Z | [
"vision.self-supervised-vision"
] | 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 | 17 | [
"bmvc",
"knowledge-distillation",
"self-supervised-learning",
"teacher-student-learning",
"vision-transformers"
] | 2025-04-07T09:14:45Z | https://github.com/wangkai930418/attndistill | null |
[
"deep-learning",
"representation-learning"
] | [
"representation-learning",
"self-supervised-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.self-supervised-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-10-02T14:41:48Z | [MedIA 2023] The official implementation of MIA paper "Dive into the details of Self-supervised Learning for Medical Image Analysis" | [
"deep-learning",
"representation-learning"
] | [
"self-supervised-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 3 | 544,461,746 | null | Python | null | [
"representation-learning",
"self-supervised-learning"
] | EndoluminalSurgicalVision-IMR/Medical-SSL | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-08T17:58:00Z | [] | 2024-04-16T10:52:03Z | [
"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 | 33 | [] | 2026-02-27T07:23:36Z | https://github.com/EndoluminalSurgicalVision-IMR/Medical-SSL | null |
[
"classical-ml",
"generative-ai",
"generative-modeling",
"tabular-and-structured-data",
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] | [
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] | [
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"paper-reference",
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"repository-metadata"
] | [
"applied.tabular-ml",
"llm.diffusion-phrase",
"probable.classical-catboost-readme"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-10-02T23:01:07Z | [ICML 2023] The official implementation of the paper "TabDDPM: Modelling Tabular Data with Diffusion Models" | [
"classical-ml",
"generative-modeling",
"tabular-and-structured-data",
"tabular-ml"
] | [
"deep-learning",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 139 | 544,618,040 | https://arxiv.org/abs/2209.15421 | Python | MIT | [
"diffusion",
"ensemble-learning",
"gradient-boosting"
] | yandex-research/tab-ddpm | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 12 | 2026-10-28T17:27:44Z | [] | 2024-07-13T04:02:14Z | [
"probable.classical-catboost-readme"
] | 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 | 567 | [
"ai",
"deep-learning",
"diffusion-models",
"pytorch",
"synthetic-data",
"tabular"
] | 2026-10-23T19:27:40Z | https://github.com/yandex-research/tab-ddpm | null |
[
"computer-vision",
"image-processing",
"low-level-vision"
] | [
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"restoration",
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"transformer"
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] | [
"recall26.image-denoising",
"vision.image-restoration",
"vision.image-super-resolution"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-10-03T03:42:00Z | PyTorch code for our ICLR 2023 paper "Accurate Image Restoration with Attention Retractable Transformer". | [
"computer-vision"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 18 | 544,690,014 | null | Python | Apache-2.0 | [
"restoration",
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] | gladzhang/ART | [
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] | 7 | 2026-10-17T13:17:44Z | [] | 2023-02-08T13:12:22Z | [
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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 | 163 | [] | 2026-10-06T08:12:12Z | https://github.com/gladzhang/ART | null |
[
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] | [
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] | true | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-10-03T14:28:41Z | Code for paper "A Unified Analysis of Federated Learning with Arbitrary Client Participation" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 3 | 544,955,457 | null | Python | MIT | [
"collaborative-learning",
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] | IBM/fl-arbitrary-participation | [
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] | 1 | 2026-10-04T15:58:58Z | [] | 2025-05-08T01:01:02Z | [
"trust.federated-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 3 | [] | 2025-09-03T00:29:12Z | https://github.com/IBM/fl-arbitrary-participation | null |
[
"deep-learning",
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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 | [] | 2022-10-04T10:05:33Z | [ICLR 2023] One Transformer Can Understand Both 2D & 3D Molecular Data (official implementation) | [
"deep-learning",
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] | [
"neural-network",
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:27:52Z | false | 26 | 545,406,093 | https://arxiv.org/abs/2210.01765 | Python | MIT | [
"graph-neural-network",
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] | lsj2408/Transformer-M | [
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] | 8 | 2026-10-08T17:58:00Z | [] | 2023-03-31T12:36:34Z | [
"graph.topic-gnn"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 219 | [
"general-purpose-molecular-model",
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] | 2026-10-08T07:57:40Z | https://github.com/lsj2408/Transformer-M | null |
[
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] | [
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] | [
"general.contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-10-05T04:21:18Z | [ICRA 2023] Official implementation of the paper "ViPFormer: Efficient Vision-and-Pointcloud Transformer for Unsupervised Pointcloud Understanding" | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-15T15:20:52Z | false | 5 | 545,843,536 | https://arxiv.org/abs/2303.14376 | Jupyter Notebook | null | [
"contrastive-learning",
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"transformer"
] | auniquesun/ViPFormer | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-10-15T15:20:52Z | [] | 2026-03-13T08:08:55Z | [
"general.contrastive-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 24 | [
"3d-shape-part-segmentation",
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"unsupervised-le... | 2026-03-13T08:08:59Z | https://github.com/auniquesun/ViPFormer | null |
[
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] | [
"general.contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-10-05T08:02:57Z | [SIGIR 2023 Oral] This is our Pytorch implementation for the paper: "Meta-optimized Contrastive Learning for Sequential Recommendation". | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-15T15:20:52Z | false | 8 | 545,924,921 | https://arxiv.org/abs/2304.07763v1 | Python | MIT | [
"contrastive-learning",
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] | QinHsiu/MCLRec | [
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] | 1 | 2026-10-15T15:20:52Z | [] | 2024-01-05T02:40:17Z | [
"general.contrastive-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 27 | [
"contrastive-learning",
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"recommendation-system",
"sequential-recommendation"
] | 2026-09-02T02:17:28Z | https://github.com/QinHsiu/MCLRec | null |
[
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] | [
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] | [
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] | [
"general.contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-10-06T09:17:42Z | [BMVC 2022] This is the official code of our Paper "Revisiting Self-Supervised Contrastive Learning for Facial Expression Recognition" | [
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] | [
"deep-learning",
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-15T15:20:52Z | false | 2 | 546,560,185 | https://claudiashu.github.io/publications/2022-BMVC/ | Python | null | [
"contrastive-learning",
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] | ClaudiaShu/SSL-FER | [
"description",
"github-topics",
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"repository-metadata"
] | 1 | 2026-10-15T15:20:52Z | [] | 2024-07-08T13:13:37Z | [
"general.contrastive-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 25 | [
"contrastive-learning",
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] | 2026-09-27T15:47:27Z | https://github.com/ClaudiaShu/SSL-FER | 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 | [] | 2022-10-06T22:50:35Z | A pipeline to extend Jazz music using a novel Latent Diffusion Model architecture. | [
"general-ml",
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 0 | 546,944,706 | null | Python | null | [
"diffusion",
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] | keonroohparvar/ContinuerPipeline | [
"description",
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] | 3 | 2026-10-08T17:58:00Z | [] | 2026-01-13T16:39:51Z | [
"general.model-architecture"
] | 75e840f5f7cc8ce015842655975d0fcb8208adb9 | 2026-09-29T18:47:14Z | gh-ml-readme-evidence-v2 | "75e840f5f7cc8ce015842655975d0fcb8208adb9" | [
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] | [
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] | ok | 2026-09-29T18:47:14Z | keonroohparvar/ContinuerPipeline | specific-method-with-novelty-claim | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-01-13T16:39:51Z | https://github.com/keonroohparvar/ContinuerPipeline | null |
[
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-10-07T05:19:07Z | Official Algorithm Implementation of ICML'23 Paper "VIMA: General Robot Manipulation with Multimodal Prompts" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 99 | 547,079,255 | null | Python | MIT | [
"multimodal-learning",
"paper-implementation"
] | vimalabs/VIMA | [
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] | 10 | 2026-10-23T15:59:01Z | [] | 2024-04-18T18:57:22Z | [
"general.arxiv",
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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 | 858 | [] | 2026-10-23T09:22:02Z | https://github.com/vimalabs/VIMA | null |
[
"embodied-ai",
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] | [
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] | [
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] | [
"recall.name.multimodal-model",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-10-07T05:26:21Z | Official Task Suite Implementation of ICML'23 Paper "VIMA: General Robot Manipulation with Multimodal Prompts" | [
"multimodal",
"multimodal-learning",
"robotics-and-control"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 39 | 547,082,219 | null | Python | MIT | [
"multimodal-learning"
] | vimalabs/VIMABench | [
"description",
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] | 8 | 2026-10-08T17:58:00Z | [] | 2023-09-26T05:35:36Z | [
"recall.name.multimodal-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 328 | [] | 2026-09-22T16:58:44Z | https://github.com/vimalabs/VIMABench | null |
[
"deep-learning",
"information-retrieval",
"language",
"multimodal",
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] | [
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] | [
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] | [
"general.contrastive-learning",
"multimodal.visual-question-answering",
"nlp.question-answering"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-10-08T17:39:01Z | Code for our EMNLP-2022 paper: "Towards Robust Visual Question Answering: Making the Most of Biased Samples via Contrastive Learning" | [
"information-retrieval",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 2 | 547,981,527 | null | Python | null | [
"contrastive-learning",
"question-answering",
"reading-comprehension"
] | PhoebusSi/MMBS | [
"description",
"query-match",
"repository-metadata"
] | 5 | 2026-10-22T16:09:07Z | [] | 2023-02-22T04:29:17Z | [
"nlp.question-answering"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 16 | [] | 2025-06-26T07:49:11Z | https://github.com/PhoebusSi/MMBS | null |
[
"deep-learning",
"graph-learning",
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] | [
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] | [
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] | [
"general.contrastive-learning",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-10-09T14:26:47Z | Source code for NeurIPS 2022 paper "Uncovering the Structural Fairness in Graph Contrastive Learning" | [
"deep-learning",
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 6 | 548,417,524 | null | Python | null | [
"contrastive-learning",
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] | BUPT-GAMMA/Uncovering-the-Structural-Fairness-in-Graph-Contrastive-Learning | [
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] | 5 | 2026-10-15T15:20:52Z | [] | 2022-10-25T12:56:58Z | [
"general.contrastive-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 29 | [] | 2025-01-17T06:30:38Z | https://github.com/BUPT-GAMMA/Uncovering-the-Structural-Fairness-in-Graph-Contrastive-Learning | null |
[
"bioinformatics",
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] | [
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"chem-property-prediction",
"general.contrastive-learning",
"general.self-supervised-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-10-10T11:30:54Z | Official Code Repository for the paper "Graph Self-supervised Learning with Accurate Discrepancy Learning" (NeurIPS 2022) | [
"deep-learning",
"representation-learning"
] | [
"self-supervised-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 2 | 548,910,674 | null | Python | null | [
"contrastive-learning",
"self-supervised-learning"
] | DongkiKim95/D-SLA | [
"description",
"query-match",
"repository-metadata"
] | 13 | 2026-10-15T15:20:52Z | [] | 2022-10-10T11:41:32Z | [
"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 | 18 | [] | 2025-04-15T21:15:17Z | https://github.com/DongkiKim95/D-SLA | null |
[
"natural-language-processing"
] | [
"machine-translation",
"neural-machine-translation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"nlp.machine-translation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-10-11T01:42:40Z | The official code for our EMNLP 2022 long paper [Breaking the Representation Bottleneck of Chinese Characters: Neural Machine Translation with Stroke Sequence Modeling] | [
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-06T16:55:56Z | false | 3 | 549,306,238 | null | Python | null | [
"machine-translation",
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] | zjwang21/StrokeNet | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-10-12T20:31:19Z | [] | 2025-09-10T03:19:40Z | [
"nlp.machine-translation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-context-only",
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 27 | [] | 2026-07-01T16:23:48Z | https://github.com/zjwang21/StrokeNet | null |
[
"reinforcement-learning"
] | [
"hierarchical-reinforcement-learning",
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] | [
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"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.hierarchical"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-10-11T05:33:04Z | Official code for "DHRL: A Graph-Based Approach for Long-Horizon and Sparse Hierarchical Reinforcement Learning" (NeurIPS 2022 Oral) | [
"reinforcement-learning"
] | [
"deep-learning",
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 5 | 549,387,567 | null | Python | MIT | [
"hierarchical-reinforcement-learning",
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] | jayLEE0301/dhrl_official | [
"description",
"github-topics",
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"query-match",
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] | 4 | 2026-10-06T16:55:56Z | [] | 2023-01-23T06:58:24Z | [
"rl.hierarchical"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 34 | [
"deep-learning",
"hierarchical-reinforcement-learning",
"pytorch",
"reinforcement-learning"
] | 2026-02-16T12:53:40Z | https://github.com/jayLEE0301/dhrl_official | null |
[
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] | [
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] | [
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"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-10-11T20:55:02Z | Code for our paper : Mixed-supervised segmentation: Confidence maximization helps knowledge distillation. https://arxiv.org/abs/2109.10902 | [
"efficient-ml",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 1 | 549,852,450 | null | Python | MIT | [
"distillation",
"knowledge-distillation"
] | by-liu/ConfKD | [
"description",
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"repository-metadata"
] | 1 | 2026-10-01T17:50:46Z | [] | 2022-10-11T21:04:33Z | [
"efficiency.knowledge-distillation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 6 | [] | 2025-07-11T14:12:00Z | https://github.com/by-liu/ConfKD | null |
[
"machine-learning"
] | [
"adaptation",
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] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"specialized.test-time-adaptation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-10-12T01:57:06Z | The official PyTorch Implementation of "NOTE: Robust Continual Test-time Adaptation Against Temporal Correlation (NeurIPS '22)" | [
"machine-learning"
] | [
"deep-learning",
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 7 | 549,959,213 | https://nmsl.kaist.ac.kr/projects/note/ | Python | MIT | [
"adaptation",
"test-time-adaptation"
] | TaesikGong/NOTE | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-07T17:53:16Z | [] | 2023-12-21T07:50:17Z | [
"specialized.test-time-adaptation"
] | 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 | 50 | [
"deep-learning",
"domain-adaptation",
"machine-learning",
"test-time-adaptation"
] | 2026-08-05T08:15:23Z | https://github.com/TaesikGong/NOTE | null |
[
"3d",
"computer-vision",
"human-motion"
] | [
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] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.3d-human-pose",
"vision.pose-estimation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-10-12T11:11:57Z | Official Implementation for WACV'23 paper "Uplift and Upsample: Efficient 3D Human Pose Estimation with Uplifting Transformers" | [
"computer-vision"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T14:28:44Z | false | 1 | 550,200,149 | null | Python | MIT | [
"keypoint-detection",
"pose-estimation"
] | goldbricklemon/uplift-upsample-3dhpe | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-12T20:31:19Z | [] | 2023-02-28T13:26:46Z | [
"vision.pose-estimation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 35 | [] | 2026-03-17T01:11:25Z | https://github.com/goldbricklemon/uplift-upsample-3dhpe | null |
[
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion",
"generative-modeling",
"video-generation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"vision.video-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-10-13T05:33:30Z | Code for paper: "Diffusion Probabilistic Modeling for Video Generation" | [
"computer-vision",
"generative-ai",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 0 | 550,653,591 | null | Python | null | [
"diffusion",
"generative-modeling",
"video-generation"
] | mandt-lab/RVD | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2022-10-13T05:33:38Z | [
"vision.video-generation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [] | 2023-01-09T09:51:14Z | https://github.com/mandt-lab/RVD | null |
[
"generative-modeling",
"health-and-biomedicine",
"medical-imaging",
"radiology"
] | [
"deep-learning",
"diffusion",
"image-reconstruction",
"inverse-problems"
] | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"medical.medical-image-reconstruction"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-10-13T17:39:26Z | Official PyTorch implementation of AdaDiff described in the paper (https://arxiv.org/abs/2207.05876). | [
"generative-modeling",
"health-and-biomedicine",
"medical-imaging",
"radiology"
] | [
"deep-learning",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 9 | 551,012,280 | null | Python | null | [
"deep-learning",
"diffusion",
"image-reconstruction",
"inverse-problems"
] | icon-lab/AdaDiff | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | 6 | 2026-10-07T17:53:16Z | [] | 2024-03-26T05:53:48Z | [
"medical.medical-image-reconstruction"
] | 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 | 59 | [
"deep-learning",
"denoising-diffusion-models",
"diffusion-probabilistic",
"medical-imaging",
"mri-reconstruction"
] | 2026-05-25T14:00:57Z | https://github.com/icon-lab/AdaDiff | 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 | [] | 2022-10-13T19:04:43Z | Code for paper "A Spectral Analysis of Graph Neural Networks on Dense and Sparse Graphs" (https://arxiv.org/abs/2211.03231) | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 0 | 551,052,316 | null | Jupyter Notebook | null | [
"graph-neural-network",
"message-passing",
"neural-network"
] | nhuang37/GNN_community_detection | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-10-08T18:15:14Z | [] | 2024-01-16T07:08:25Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 2 | [] | 2023-11-25T19:37:37Z | https://github.com/nhuang37/GNN_community_detection | null |
[
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"neural-network-pruning",
"pruning",
"quantization",
"structured-pruning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning",
"efficiency.quantization"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-10-14T11:34:38Z | provide some new architecture, channel pruning and quantization methods for yolov5 | [
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-08T17:58:00Z | false | 5 | 551,427,670 | null | Python | GPL-3.0 | [
"neural-network-pruning",
"pruning",
"quantization",
"structured-pruning"
] | Cydia2018/YOLOv5-Light | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-15T15:20:52Z | [] | 2025-10-13T04:04:04Z | [
"efficiency.pruning"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 30 | [
"adaround",
"convnext",
"lsq",
"pruning",
"quantization",
"swin",
"tensorrt",
"yolo",
"yolov5"
] | 2026-03-13T13:51:05Z | https://github.com/Cydia2018/YOLOv5-Light | null |
[
"audio",
"computer-vision",
"speech"
] | [
"emotion-recognition",
"speech-understanding"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"audio-speech-emotion-recognition"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-10-14T13:32:19Z | Official implementation of the paper "SPEAKER VGG CCT: Cross-corpus Speech Emotion Recognition with Speaker Embedding and Vision Transformers, 2022" | [
"audio",
"computer-vision",
"speech"
] | [
"embedding",
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 4 | 551,480,133 | null | Python | null | [
"emotion-recognition",
"speech-understanding"
] | JabuMlDev/Speaker-VGG-CCT | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-10-05T19:54:32Z | [] | 2023-02-17T16:27:37Z | [
"audio-speech-emotion-recognition"
] | 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 | 25 | [] | 2026-03-09T15:22:51Z | https://github.com/JabuMlDev/Speaker-VGG-CCT | null |
[
"deep-learning",
"natural-language-processing",
"representation-learning"
] | [
"contrastive-learning",
"self-supervised-learning"
] | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-10-16T07:48:59Z | Fairseq Code for Neurips 2022 paper: "CoNT: Contrastive Neural Text Generation" | [
"deep-learning",
"natural-language-processing",
"representation-learning"
] | [
"deep-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-16T15:36:56Z | false | 0 | 552,272,721 | https://arxiv.org/pdf/2205.14690v2.pdf | Python | null | [
"contrastive-learning",
"self-supervised-learning"
] | ChenxinAn-fdu/CoNT | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-10-16T15:36:56Z | [] | 2022-10-28T09:45:14Z | [
"general.contrastive-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-context-only",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 9 | [
"deep-learning",
"fairseq",
"machine-translation"
] | 2023-12-28T04:27:32Z | https://github.com/ChenxinAn-fdu/CoNT | null |
[
"efficient-ml",
"model-compression"
] | [
"distillation",
"knowledge-distillation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-10-18T14:03:01Z | [WSDM 2023] This is the official PyTorch implementation for the paper: "Directed Acyclic Graph Factorization Machines for CTR Prediction via Knowledge Distillation" | [
"efficient-ml",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 6 | 553,626,735 | null | Python | null | [
"distillation",
"knowledge-distillation"
] | Ethan-TZ/DAGFM | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-07T17:53:16Z | [] | 2023-03-05T01:28:35Z | [
"efficiency.knowledge-distillation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 18 | [] | 2025-05-06T01:53:46Z | https://github.com/Ethan-TZ/DAGFM | null |
[
"computer-vision"
] | [
"restoration",
"super-resolution"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"vision.image-super-resolution"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-10-19T00:36:34Z | Official code of "Learning Deep Analysis Dictionaries for Image Super-Resolution" | [
"computer-vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:28:44Z | false | 4 | 553,906,301 | null | MATLAB | null | [
"restoration",
"super-resolution"
] | jjhuangcs/DeepAM | [
"description",
"query-match",
"repository-metadata"
] | 4 | 2026-10-17T13:17:44Z | [] | 2022-10-19T00:53:05Z | [
"vision.image-super-resolution"
] | a23cea636b44e53e2c1068699c17b83c61fdfee9 | 2026-09-26T20:42:46Z | gh-ml-readme-evidence-v2 | "a23cea636b44e53e2c1068699c17b83c61fdfee9" | [
"citation",
"other"
] | [
"method-contribution",
"ml-method-context",
"paper-reference"
] | ok | 2026-09-26T20:42:46Z | jjhuangcs/DeepAM | readme-supported-paper-method-implementation | [
"method-contribution",
"ml-method-context",
"paper-reference"
] | include | ml-contribution-v5 | 4 | [] | 2025-03-13T11:54:06Z | https://github.com/jjhuangcs/DeepAM | null |
[
"embodied-ai",
"robotics",
"robotics-and-control"
] | [
"manipulation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"robotics.manipulation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-10-19T13:11:21Z | Code for our IROS 2022 paper "Active Exploration for Robotic Manipulation" | [
"embodied-ai",
"robotics",
"robotics-and-control"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 1 | 554,227,088 | null | Python | MIT | [
"manipulation"
] | TimSchneider42/aerm | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-06T16:55:56Z | [] | 2022-11-01T17:03:01Z | [
"robotics.manipulation"
] | 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 | 9 | [] | 2025-06-26T02:59:59Z | https://github.com/TimSchneider42/aerm | null |
[
"language",
"multimodal",
"video"
] | [
"transformer",
"video-language-model",
"video-understanding"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.video-understanding"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-10-21T03:23:51Z | [MM23] Code for paper "Depth-Aware Sparse Transformer for Video-Language Learning" | [
"language",
"multimodal",
"video"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 0 | 555,153,916 | null | Python | null | [
"transformer",
"video-language-model",
"video-understanding"
] | zchoi/DAST | [
"description",
"query-match",
"repository-metadata"
] | 4 | 2026-10-07T17:53:16Z | [] | 2023-10-08T07:50:33Z | [
"multimodal.video-understanding"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 6 | [] | 2024-04-14T01:12:09Z | https://github.com/zchoi/DAST | null |
[
"deep-learning",
"efficient-ml",
"language",
"representation-learning"
] | [
"conditional-computation",
"mixture-of-experts",
"multi-task-learning",
"sparse-activation",
"transfer-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.mixture-of-experts",
"general.multi-task-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-10-22T11:26:50Z | Code for paper "Eliciting and Understanding Cross-task Skills with Task-level Mixture-of-Experts" (Findings of EMNLP 2022) | [
"deep-learning",
"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 1 | 555,806,326 | null | Python | null | [
"mixture-of-experts",
"multi-task-learning",
"transfer-learning"
] | INK-USC/CrossTaskMoE | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-10-17T13:17:44Z | [] | 2023-01-12T06:26:31Z | [
"general.multi-task-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 4 | [] | 2025-07-01T06:39:07Z | https://github.com/INK-USC/CrossTaskMoE | null |
[
"audio",
"speech-and-audio"
] | [
"architecture",
"generative-modeling",
"training"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"audio-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-10-29T09:27:19Z | Official implementation of the pipeline presented in I hear your true colors: Image Guided Audio Generation | [
"audio",
"speech-and-audio"
] | [
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 15 | 559,171,428 | null | Python | MIT | [
"architecture",
"generative-modeling",
"training"
] | RoySheffer/im2wav | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 7 | 2026-10-05T19:54:32Z | [] | 2023-01-18T17:23:43Z | [
"audio-generation"
] | f8afc45b5a68e527460ce0a09178e905d3f1c196 | 2026-09-26T20:42:46Z | gh-ml-readme-evidence-v2 | "f8afc45b5a68e527460ce0a09178e905d3f1c196" | [
"abstract",
"citation",
"installation",
"method",
"other",
"usage"
] | [
"method-contribution",
"ml-method-context",
"paper-reference"
] | ok | 2026-09-26T20:42:46Z | RoySheffer/im2wav | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-context-only",
"ml-method-context",
"paper-reference"
] | include | ml-contribution-v5 | 125 | [
"audio",
"audio-generation",
"image-to-audio",
"machine-learning",
"pytorch",
"video-to-audio"
] | 2026-03-27T02:57:13Z | https://github.com/RoySheffer/im2wav | null |
[
"deep-learning",
"representation-learning"
] | [
"contrastive-learning",
"k-means",
"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 | [] | 2022-10-30T16:15:30Z | We implement NNCLR and a novel clustering-based technique for contrastive learning that we call KMCLR. We show that applying a clustering technique to obtain prototype embeddings and using these prototypes to form positive pairs for contrastive loss can achieve performances on par with NNCLR on CIFAR-100 while storing ... | [
"deep-learning",
"representation-learning"
] | [
"self-supervised-learning",
"embedding"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-17T13:17:44Z | false | 1 | 559,602,410 | null | Python | MIT | [
"contrastive-learning",
"k-means",
"self-supervised-learning"
] | mwritescode/nnclr-cifar100 | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-17T13:17:44Z | [] | 2023-02-08T14:46:05Z | [
"general.contrastive-learning"
] | 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 | [
"cifar-100",
"clustering",
"contrastive-learning",
"k-means",
"nnclr",
"self-supervised-learning"
] | 2025-05-15T05:47:01Z | https://github.com/mwritescode/nnclr-cifar100 | null |
[
"efficient-ml",
"model-compression",
"natural-language-processing"
] | [
"distillation",
"knowledge-distillation",
"machine-translation",
"neural-machine-translation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation",
"nlp.machine-translation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-10-31T07:12:33Z | Implementation for our AAAI-23 paper "Selective Knowledge Distillation for Non-Autoregressive Neural Machine Translation". | [
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 0 | 559,827,203 | null | Python | MIT | [
"distillation",
"knowledge-distillation",
"machine-translation",
"neural-machine-translation"
] | NJUNLP/SelectiveKD | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-14T16:10:46Z | [] | 2023-11-22T15:24:42Z | [
"nlp.machine-translation"
] | 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 | [] | 2024-07-17T06:08:11Z | https://github.com/NJUNLP/SelectiveKD | null |
[
"geospatial",
"remote-sensing",
"satellite-imagery"
] | [
"image-classification",
"object-detection",
"segmentation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"geo.satellite-imagery"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-10-31T21:12:10Z | Official code repository for NeurIPS 2022 paper "SatMAE: Pretraining Transformers for Temporal and Multi-Spectral Satellite Imagery" | [
"geospatial",
"remote-sensing",
"satellite-imagery"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-07T17:53:16Z | false | 31 | 560,154,265 | null | Python | NOASSERTION | [
"image-classification",
"object-detection",
"segmentation"
] | sustainlab-group/SatMAE | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-22T16:09:07Z | [] | 2025-08-10T22:07:05Z | [
"geo.satellite-imagery"
] | 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 | 265 | [] | 2026-10-02T15:27:21Z | https://github.com/sustainlab-group/SatMAE | null |
[
"graph-learning",
"multi-agent-systems",
"reinforcement-learning"
] | [
"centralized-training",
"graph-neural-network",
"multi-agent-reinforcement-learning",
"neural-network",
"policy-learning",
"reinforcement-learning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
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] | [
"rl.core",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-11-02T20:27:59Z | Code for our paper: Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregation | [
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"reinforcement-learning"
] | [
"neural-network",
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 35 | 561,044,802 | https://nsidn98.github.io/InforMARL/ | Python | MIT | [
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"multi-agent-reinforcement-learning",
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] | nsidn98/InforMARL | [
"description",
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] | 7 | 2026-10-07T17:53:16Z | [] | 2025-07-08T23:02:01Z | [
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] | include | ml-contribution-v5 | 167 | [
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] | 2026-10-07T07:57:11Z | https://github.com/nsidn98/InforMARL | null |
[
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] | [
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] | [
"graph.gnn-description",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-11-03T03:35:37Z | code for paper SASTGCN：A Self-Adaptive Spatio-Temporal Graph Neural Network for Traffic Estimation | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 0 | 561,146,309 | null | Python | null | [
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] | liweiowl/SASTGCN | [
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] | 2 | 2026-10-07T18:07:52Z | [] | 2023-09-19T01:03:09Z | [
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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 | 4 | [] | 2026-04-09T06:51:46Z | https://github.com/liweiowl/SASTGCN | null |
[
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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 | [] | 2022-11-03T22:55:25Z | Data and code for paper "Relational Visual Information explains Human Social Inference: A Graph Neural Network model for Social Interaction Recognition" | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-06T17:10:23Z | false | 4 | 561,537,272 | null | Jupyter Notebook | MIT | [
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] | Isik-lab/SocialGNN | [
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] | 1 | 2026-10-06T17:10:23Z | [] | 2025-07-17T09:03:45Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 9 | [] | 2025-11-14T08:17:06Z | https://github.com/Isik-lab/SocialGNN | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
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] | [
"description",
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"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-11-04T06:18:54Z | This project is the official implementation of ``Self-Supervised Graph Neural Network for Multi-Source Domain Adaptation'' in PyTorch, which is accepted by ACM MM 2022. | [
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] | [
"neural-network",
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-06T17:10:23Z | false | 2 | 561,638,664 | null | Python | null | [
"graph-neural-network",
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] | a791702141/SSG | [
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] | 1 | 2026-10-06T17:10:23Z | [] | 2022-11-04T06:24:31Z | [
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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 | 12 | [] | 2025-06-06T08:44:27Z | https://github.com/a791702141/SSG | null |
[
"health-and-biomedicine",
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] | [
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] | [
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] | [
"medical.medical-image-reconstruction"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-11-04T13:38:48Z | Official PyTorch implementation of SwinGAN described in the paper ( SwinGAN: A Dual-domain Swin Transformer-based Generative Adversarial Network for MRI reconstruction). | [
"health-and-biomedicine",
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 4 | 561,784,313 | https://doi.org/10.1016/j.compbiomed.2022.106513 | Python | GPL-3.0 | [
"deep-learning",
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] | learnerzx/SwinGAN | [
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] | 6 | 2026-10-07T17:53:16Z | [] | 2024-04-11T02:07:14Z | [
"medical.medical-image-reconstruction"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 43 | [] | 2026-07-08T02:56:20Z | https://github.com/learnerzx/SwinGAN | null |
[
"computer-vision",
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] | [
"diffusion"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"repository-metadata"
] | [] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-11-04T14:06:30Z | [ICCV 2023] Source code for our paper "Rickrolling the Artist: Injecting Invisible Backdoors into Text-Guided Image Generation Models". | [
"computer-vision",
"generative-modeling"
] | [
"deep-learning",
"generative-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T18:08:49.004055Z | false | 9 | 561,795,391 | https://arxiv.org/abs/2211.02408 | Python | MIT | [
"diffusion"
] | LukasStruppek/Rickrolling-the-Artist | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"repository-metadata"
] | 1 | 2026-10-02T18:08:49.004055Z | [] | 2023-11-20T09:54:16Z | [] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 63 | [
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] | 2026-09-21T07:04:51Z | https://github.com/LukasStruppek/Rickrolling-the-Artist | {"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["stable-diffusion"]} |
[
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] | [
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] | [
"description",
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] | [
"general.novel-method"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-11-10T16:05:37Z | This repo provides the source code and data for the paper Emotion-Cause Relationship Between Clauses Prediction: a Novel Method Based on BERT for Emotion-Cause Pair Extraction (2022 IEEE BigData). | [
"general-ml"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 0 | 564,391,171 | null | Python | null | [
"novel-method"
] | rf-x/ECRP-BERT | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-09-28T18:50:00Z | [] | 2023-03-08T06:02:59Z | [
"general.novel-method"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
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] | include | ml-contribution-v5 | 2 | [] | 2025-12-30T03:15:37Z | https://github.com/rf-x/ECRP-BERT | 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 | [] | 2022-11-11T22:20:47Z | Official code for the paper "Predicting Properties of Quantum Systems with Conditional Generative Models" | [
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"quantum-physics"
] | [
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"machine-learning",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 10 | 564,948,486 | null | Jupyter Notebook | Apache-2.0 | [
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"transformer"
] | PennyLaneAI/generative-quantum-states | [
"description",
"github-topics",
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] | 5 | 2026-10-07T17:53:16Z | [] | 2022-12-02T08:17:20Z | [
"science.quantum-physics"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 36 | [
"generative-model",
"machine-learning",
"quantum-computing",
"quantum-machine-learning",
"quantum-many-body",
"transformer"
] | 2026-06-22T09:15:02Z | https://github.com/PennyLaneAI/generative-quantum-states | null |
[
"machine-learning",
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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 | [] | 2022-11-12T05:57:02Z | [ICCV 2023] An official implementation for "Part-Aware Transformer for Generalizable Person Re-identification" | [
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"representation-learning"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-08T17:58:00Z | false | 4 | 565,034,545 | null | Python | null | [
"domain-generalization",
"transfer-learning",
"transformer"
] | liyuke65535/Part-Aware-Transformer | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-12T20:31:19Z | [] | 2024-12-02T08:29:04Z | [
"probable.adaptation-domain-generalization-readme"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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] | include | ml-contribution-v5 | 45 | [] | 2026-07-02T12:13:44Z | https://github.com/liyuke65535/Part-Aware-Transformer | null |
[
"general-ml",
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] | [
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] | [
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"github-topics",
"query-match",
"repository-metadata"
] | [
"general.neural-network"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-11-13T06:24:06Z | Code for paper "JMDC: A Joint Model and Data Compression System for Deep Neural Networks Collaborative Computing in Edge-Cloud Networks" | [
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] | [
"deep-learning",
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-19T16:36:56Z | false | 2 | 565,360,972 | https://fangvv.github.io/JMDC/ | Python | null | [
"neural-network",
"pruning",
"quantization"
] | fangvv/JMDC | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 2 | 2026-10-22T14:58:59Z | [] | 2026-10-08T14:53:56Z | [
"general.neural-network"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 25 | [
"ai-models",
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"inference",
"mobilenet",
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"pruning",
"quantization"
] | 2026-10-08T14:54:02Z | https://github.com/fangvv/JMDC | null |
[
"computer-vision",
"general-ml",
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] | [
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] | [
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] | [
"general.arxiv",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-11-14T09:47:12Z | Official Implementation of Paella https://arxiv.org/abs/2211.07292v2 | [
"computer-vision",
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"multimodal"
] | [
"generative-model",
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 53 | 565,772,050 | null | Jupyter Notebook | MIT | [
"diffusion",
"image-captioning",
"vision-language"
] | dome272/Paella | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 5 | 2026-10-23T15:59:01Z | [] | 2023-10-04T20:38:26Z | [
"vision.image-captioning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 748 | [
"diffusion-models",
"generative-model"
] | 2026-10-20T07:27:23Z | https://github.com/dome272/Paella | null |
[
"3d",
"computer-vision",
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] | [
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] | [
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] | [
"multimodal.3d-generation",
"multimodal.text-to-3d"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-11-14T16:22:53Z | Official Implementation for "Latent-NeRF for Shape-Guided Generation of 3D Shapes and Textures" | [
"3d",
"computer-vision",
"generative-ai",
"multimodal"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 51 | 565,934,354 | null | Python | MIT | [
"3d-generation",
"text-to-3d"
] | eladrich/latent-nerf | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 7 | 2026-10-07T17:53:16Z | [] | 2023-01-26T18:11:40Z | [
"multimodal.text-to-3d"
] | eb113f9bbce78d5579a8f695ca54f063a94a1963 | 2026-09-26T20:42:46Z | gh-ml-readme-evidence-v2 | "eb113f9bbce78d5579a8f695ca54f063a94a1963" | [
"citation",
"installation",
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] | [
"method-contribution",
"ml-method-context",
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] | ok | 2026-09-26T20:42:46Z | eladrich/latent-nerf | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-method-context",
"paper-reference"
] | include | ml-contribution-v5 | 712 | [] | 2026-09-02T01:56:00Z | https://github.com/eladrich/latent-nerf | null |
[
"classical-ml",
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] | [
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"metric-learning",
"representation-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.metric-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-11-15T09:22:50Z | [VI 2024]The official code of Visual Intelligence 2024 paper "Improving deep metric learning via self-distillation and online batch diffusion process" | [
"classical-ml",
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"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-13T18:05:26Z | false | 0 | 566,248,591 | null | Python | null | [
"diffusion",
"distillation",
"metric-learning",
"representation-learning"
] | ZelongZeng/OBD-SD_Pytorch | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-13T18:05:26Z | [] | 2024-07-04T04:39:22Z | [
"general.metric-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 1 | [] | 2025-05-21T16:42:06Z | https://github.com/ZelongZeng/OBD-SD_Pytorch | null |
[
"deep-learning",
"graph-learning"
] | [
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"message-passing"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.graph-transformer"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-11-15T20:07:25Z | Official code for Periodic Graph Transformers for Crystal Material Property Prediction (NeurIPS 2022) | [
"deep-learning",
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] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 11 | 566,501,934 | null | Python | MIT | [
"graph-transformer",
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] | YKQ98/Matformer | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-06T16:55:56Z | [] | 2023-12-15T06:45:10Z | [
"graph.graph-transformer"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 114 | [] | 2026-09-02T13:13:23Z | https://github.com/YKQ98/Matformer | null |
[
"deep-learning",
"graph-learning",
"representation-learning"
] | [
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] | [
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"query-match",
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] | [
"general.contrastive-learning",
"graph.graph-contrastive-learning",
"graph.representation-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-11-16T01:12:20Z | An official PyTorch implementation of "Certifiably Robust Graph Contrastive Learning" (NeurIPS 2023) | [
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"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 0 | 566,581,593 | null | Python | Apache-2.0 | [
"contrastive-learning",
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] | ventr1c/RES-GCL | [
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] | 6 | 2026-10-15T15:20:52Z | [] | 2024-01-22T08:04:57Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 11 | [] | 2025-10-07T16:03:30Z | https://github.com/ventr1c/RES-GCL | null |
[
"applied-mathematics",
"applied-physics",
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] | [
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] | [
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"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"science.inverse-problems"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-11-16T03:12:29Z | An official pytorch implementation of "Parallel Diffusion Model of Operator and Image for Blind Inverse Problems" (CVPR 2023) | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 16 | 566,612,354 | https://blinddps.github.io/blind-dps-project/ | Python | null | [
"data-assimilation",
"diffusion",
"inverse-problems",
"reconstruction"
] | BlindDPS/blind-dps | [
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] | include | ml-contribution-v5 | 143 | [
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[
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 0 | 567,767,876 | https://aclanthology.org/2022.acl-long.520.pdf | Python | null | [
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] | 2 | 2026-10-06T16:55:56Z | [] | 2023-02-12T14:47:28Z | [
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] | include | ml-contribution-v5 | 1 | [] | 2025-04-18T06:45:57Z | https://github.com/sweta20/EditingCL | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-11-18T19:26:22Z | Official code implementation for our paper -- Direct Inversion: Optimization-Free Text-Driven Real Image Editing with Diffusion Models. | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 3 | 567,869,946 | null | Python | null | [
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[
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 17 | 568,006,468 | null | Python | Apache-2.0 | [
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] | include | ml-contribution-v5 | 121 | [
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] | 2026-10-03T12:34:04Z | https://github.com/ZhouYuxuanYX/Hyperformer | null |
[
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] | [
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] | [] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-11-19T07:18:10Z | Official implementation for "Diffusion-Based Scene Graph to Image Generation with Masked Contrastive Pre-Training" https://arxiv.org/abs/2211.11138 | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-30T18:03:22.392067Z | false | 18 | 568,017,011 | null | Python | null | [
"diffusion"
] | YangLing0818/SGDiff | [
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] | 1 | 2026-09-30T18:03:22.392067Z | [] | 2024-12-25T04:52:26Z | [] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 78 | [
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] | 2026-08-07T08:57:25Z | https://github.com/YangLing0818/SGDiff | {"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["diffusion-models","text-to-image"]} |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-11-20T05:55:50Z | A novel Text-VQA method with multi-modal OCR Token-Word Contrastive learning | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-16T15:36:56Z | false | 0 | 568,315,449 | null | Python | MIT | [
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] | 1 | 2026-10-16T15:36:56Z | [] | 2023-08-01T09:30:51Z | [
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] | include | ml-contribution-v5 | 9 | [] | 2026-04-28T03:14:50Z | https://github.com/xiaojino/TWA | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-11-21T11:05:41Z | Official Implementation of the paper "Exemplar-free Continual Learning of Vision Transformers via Gated Class-Attention and Cascaded Feature Drift Compensation" | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-09T17:31:36Z | false | 0 | 568,763,087 | https://arxiv.org/pdf/2211.12292.pdf | Python | null | [
"continual-learning",
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] | OcraM17/GCAB-CFDC | [
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] | 1 | 2026-10-09T17:31:36Z | [] | 2023-03-13T13:46:38Z | [
"general.continual-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 12 | [
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] | 2025-03-24T02:07:12Z | https://github.com/OcraM17/GCAB-CFDC | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-11-21T11:40:13Z | Official Pytorch Implementation of the paper: Wavelet Diffusion Models are fast and scalable Image Generators (CVPR'23) | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 39 | 568,776,116 | null | Python | AGPL-3.0 | [
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] | VinAIResearch/WaveDiff | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | 2026-10-02T15:27:23Z | https://github.com/VinAIResearch/WaveDiff | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-11-21T20:18:06Z | NeurIPS 2022, Revisiting Heterophily For Graph Neural Networks, official PyTorch implementation for Adaptive Channel Mixing (ACM) GNN framework | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 11 | 568,976,266 | null | Python | MIT | [
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] | SitaoLuan/ACM-GNN | [
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] | 4 | 2026-10-10T16:53:26Z | [] | 2024-11-21T02:34:38Z | [
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] | include | ml-contribution-v5 | 91 | [
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] | 2026-08-20T21:12:41Z | https://github.com/SitaoLuan/ACM-GNN | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-11-22T03:53:55Z | The implementation for our paper, "Improving Simultaneous Machine Translation with Monolingual Data," accepted to AAAI 2023. 🎉 | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-06T16:55:56Z | false | 3 | 569,091,318 | https://doi.org/10.1609/aaai.v37i11.26497 | Python | Apache-2.0 | [
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] | 3 | 2026-10-12T20:31:19Z | [] | 2023-07-19T05:15:51Z | [
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] | include | ml-contribution-v5 | 12 | [
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] | 2025-05-23T15:28:13Z | https://github.com/hexuandeng/Mono4SiMT | null |
[
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-11-22T11:52:36Z | This is the official implementation for our AAAI 2023 paper: "Towards Fine-grained Explainability for Heterogeneous Graph Neural Network" | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-06T17:10:23Z | false | 2 | 569,242,082 | null | Python | null | [
"graph-neural-network",
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] | LITONG99/xPath | [
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] | 1 | 2026-10-06T17:10:23Z | [] | 2022-11-24T10:51:30Z | [
"graph.gnn-description"
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] | include | ml-contribution-v5 | 7 | [] | 2026-06-02T17:31:24Z | https://github.com/LITONG99/xPath | null |
[
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] | [
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-11-22T13:48:17Z | [AAAI 2023 Oral] Official code for "PiCor: Multi-Task Deep Reinforcement Learning with Policy Correction". | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-20T15:59:06Z | false | 0 | 569,287,283 | null | Python | MIT | [
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] | 1 | 2026-10-20T15:59:06Z | [] | 2025-07-26T06:55:50Z | [
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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 | 22 | [] | 2026-10-09T04:55:09Z | https://github.com/ChangWinde/PiCor | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-11-23T16:22:20Z | Official implementation of the paper “Inversion-Based Style Transfer with Diffusion Models” (CVPR 2023) | [
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] | include | ml-contribution-v5 | 588 | [] | 2026-09-29T09:11:38Z | https://github.com/zyxElsa/InST | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-11-27T12:21:34Z | Official Pytorch Implementation of CMLO in the paper ”When to Update Your Model: Constrained Model-based Reinforcement Learning“ | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 1 | 571,176,180 | null | Python | null | [
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] | include | ml-contribution-v5 | 9 | [] | 2026-09-04T18:31:53Z | https://github.com/jity16/When-to-Update-Your-Model-Constrained-Model-based-Reinforcement-Learning | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-11-28T03:43:59Z | [CVPR 2023] Official implementation of "DATID-3D: Diversity-Preserved Domain Adaptation Using Text-to-Image Diffusion for 3D Generative Model" | [
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"description",
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] | 3 | 2026-10-07T17:53:16Z | [] | 2024-07-01T14:44:45Z | [
"multimodal.text-to-image"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 232 | [] | 2026-02-17T07:25:54Z | https://github.com/gwang-kim/DATID-3D | null |
[
"deep-learning",
"natural-language-processing",
"representation-learning"
] | [
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"sentence-embeddings",
"text-embeddings"
] | [
"description",
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"repository-metadata"
] | [
"general.contrastive-learning",
"nlp.sentence-embeddings"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-11-28T11:25:41Z | This repositoy contains the official implementation of AAAI 2023 paper: Instance Smoothed Contrastive Learning for Unsupervised Sentence Embedding | [
"deep-learning",
"representation-learning"
] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 1 | 571,548,320 | null | Python | null | [
"contrastive-learning",
"self-supervised-learning"
] | dll-wu/IS-CSE | [
"description",
"query-match",
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] | 4 | 2026-10-16T15:36:56Z | [] | 2023-05-15T09:12:42Z | [
"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 | 9 | [] | 2025-03-13T09:02:12Z | https://github.com/dll-wu/IS-CSE | null |
[
"reinforcement-learning"
] | [
"batch-reinforcement-learning",
"offline-reinforcement-learning",
"reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"rl.offline"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-11-29T06:41:35Z | Code for our AAAI 2023 paper: Behavior Estimation from Multi-Source Data for Offline Reinforcement Learning | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 0 | 571,913,822 | null | Python | null | [
"batch-reinforcement-learning",
"offline-reinforcement-learning",
"reinforcement-learning"
] | Altriaex/multi_source_behavior_modeling | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-07T17:53:16Z | [] | 2023-02-06T08:46:47Z | [
"rl.offline"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 6 | [] | 2025-06-01T15:04:18Z | https://github.com/Altriaex/multi_source_behavior_modeling | null |
[
"computer-vision",
"distributed-ml",
"privacy-and-federated-learning"
] | [
"collaborative-learning",
"distillation",
"federated-learning",
"knowledge-distillation",
"neural-network"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-11-29T10:12:17Z | (NeurIPS 2022) Official Implementation of "Preservation of the Global Knowledge by Not-True Distillation in Federated Learning" | [
"computer-vision",
"distributed-ml",
"privacy-and-federated-learning"
] | [
"deep-learning",
"machine-learning",
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-03T15:45:52Z | false | 14 | 571,986,199 | null | Python | MIT | [
"collaborative-learning",
"distillation",
"federated-learning",
"knowledge-distillation",
"neural-network"
] | Lee-Gihun/FedNTD | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-07T17:53:16Z | [] | 2023-02-24T02:08:02Z | [
"trust.federated-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 94 | [
"artificial-intelligence",
"computer-vision",
"continual-learning",
"deep-learning",
"deep-neural-networks",
"distributed-learning",
"fedavg",
"fedavgm",
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"federated-learning",
"fednova",
"fedntd",
"fedprox",
"knowledge-distillation",
"machine-learning",
"moon",
... | 2026-08-26T12:32:59Z | https://github.com/Lee-Gihun/FedNTD | null |
[
"computer-vision",
"efficient-ml",
"model-compression"
] | [
"distillation",
"knowledge-distillation"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-11-30T14:39:56Z | [AAAI 2023] Official PyTorch Code for "Curriculum Temperature for Knowledge Distillation" | [
"computer-vision",
"efficient-ml",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 16 | 572,561,818 | https://zhengli97.github.io/CTKD/ | Python | Apache-2.0 | [
"distillation",
"knowledge-distillation"
] | zhengli97/CTKD | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 7 | 2026-10-07T17:53:16Z | [] | 2024-12-03T09:09:39Z | [
"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 | 180 | [
"aaai2023",
"cifar100",
"computer-vision",
"curriculum-learning",
"dynamic-temperature",
"imagenet",
"knowledge-distillation"
] | 2026-07-14T08:01:12Z | https://github.com/zhengli97/CTKD | null |
[
"deep-learning",
"efficient-ml",
"graph-learning",
"model-compression"
] | [
"distillation",
"graph-neural-network",
"knowledge-distillation",
"message-passing",
"neural-network"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation",
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-12-01T11:43:05Z | The official implementation for "Geometric Knowledge Distillation: Topology Compression for Graph Neural Networks" which is accepted to NeurIPS 2022. | [
"efficient-ml",
"graph-learning",
"model-compression"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 2 | 572,953,828 | null | Python | null | [
"distillation",
"graph-neural-network",
"knowledge-distillation",
"neural-network"
] | chr26195/GKD | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-10-07T17:53:16Z | [] | 2024-05-07T06:49:07Z | [
"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 | 22 | [] | 2026-02-05T10:55:48Z | https://github.com/chr26195/GKD | null |
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