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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
[
"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 | [] | 2022-12-01T14:56:12Z | Official implementation for the paper "Automatic Neural Network Pruning that Efficiently Preserves the Model Accuracy". | [
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-08T17:58:00Z | false | 2 | 573,032,825 | null | Python | Apache-2.0 | [
"neural-network",
"neural-network-pruning",
"pruning",
"structured-pruning"
] | nota-github/autobot_AAAI23 | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-15T15:20:52Z | [] | 2023-06-08T13:45:02Z | [
"efficiency.pruning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 10 | [] | 2026-06-30T16:32:20Z | https://github.com/nota-github/autobot_AAAI23 | null |
[
"anomaly-detection",
"deep-learning",
"graph-learning",
"representation-learning"
] | [
"anomaly-detection",
"contrastive-learning",
"graph-anomaly-detection",
"graph-neural-network",
"neural-network",
"self-supervised-learning"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning",
"graph.graph-anomaly-detection"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-12-02T08:26:07Z | An official source code for paper "Graph Anomaly Detection via Multi-Scale Contrastive Learning Networks with Augmented View", accepted by AAAI 2023. | [
"deep-learning",
"graph-learning",
"representation-learning"
] | [
"deep-learning",
"machine-learning",
"neural-network",
"self-supervised-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T14:28:44Z | false | 6 | 573,340,079 | null | Python | null | [
"contrastive-learning",
"graph-neural-network",
"neural-network",
"self-supervised-learning"
] | FelixDJC/GRADATE | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 6 | 2026-10-15T15:20:52Z | [] | 2023-12-01T08:36:12Z | [
"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 | 68 | [
"aaai2023",
"anomaly-detection",
"data-mining",
"deep-learning",
"graph-anomaly-detection",
"graph-contrastive-learning",
"graph-neural-networks",
"machine-learning",
"self-supervised-learning",
"unsupervised-learning"
] | 2026-09-22T09:31:29Z | https://github.com/FelixDJC/GRADATE | null |
[
"computer-vision",
"information-retrieval",
"multimodal-learning"
] | [
"image-retrieval",
"metric-learning",
"transformer"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"vision.image-retrieval"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-12-03T11:07:11Z | The implementation of our AAAI 2022 paper "TVT: Three-Way Vision Transformer through Multi-Modal Hypersphere Learning for Zero-Shot Sketch-Based Image Retrieval." | [
"computer-vision",
"information-retrieval",
"multimodal-learning"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-17T13:17:44Z | false | 0 | 573,764,151 | null | Python | null | [
"image-retrieval",
"metric-learning",
"transformer"
] | CFM-MSG/Code_TVT | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-17T13:17:44Z | [] | 2022-12-03T11:22:57Z | [
"vision.image-retrieval"
] | 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 | 7 | [] | 2026-05-10T05:44:14Z | https://github.com/CFM-MSG/Code_TVT | null |
[
"computer-vision",
"distributed-ml",
"privacy-and-federated-learning"
] | [
"collaborative-learning",
"federated-learning",
"restoration",
"super-resolution"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"trust.federated-learning",
"vision.image-super-resolution"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-12-04T20:35:29Z | PyTorch code for our paper "Resource-Adaptive Federated Learning with All-In-One Neural Composition" (NeurIPS2022) | [
"computer-vision",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 2 | 574,233,511 | null | Python | null | [
"federated-learning",
"restoration",
"super-resolution"
] | HarukiYqM/All-In-One-Neural-Composition | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-17T13:17:44Z | [] | 2022-12-04T20:50:16Z | [
"vision.image-super-resolution"
] | 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 | 19 | [] | 2025-10-28T04:45:30Z | https://github.com/HarukiYqM/All-In-One-Neural-Composition | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-12-06T00:36:33Z | [NeurIPS 2023] Official implementation of "A Neural Collapse Perspective on Feature Evolution in Graph Neural Networks" | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-06T17:10:23Z | false | 2 | 574,733,517 | https://arxiv.org/abs/2307.01951 | Python | Apache-2.0 | [
"graph-neural-network",
"message-passing",
"neural-network"
] | kvignesh1420/gnn_collapse | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-10-06T17:10:23Z | [] | 2026-07-23T08:58:45Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 6 | [
"community-detection",
"graph-neural-networks",
"neural-collapse",
"optimization",
"stochastic-block-model"
] | 2026-07-23T08:57:57Z | https://github.com/kvignesh1420/gnn_collapse | null |
[
"distributed-ml",
"efficient-ml",
"machine-learning-systems",
"model-compression",
"privacy-and-federated-learning"
] | [
"collaborative-learning",
"federated-learning",
"neural-network-pruning",
"pruning",
"structured-pruning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning",
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-12-06T04:14:05Z | code for paper "Complement Sparsification: Low-Overhead Model Pruning for Federated Learning" (AAAI2023) | [
"efficient-ml",
"machine-learning-systems",
"model-compression",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 0 | 574,786,473 | null | Python | null | [
"federated-learning",
"neural-network-pruning",
"pruning",
"structured-pruning"
] | XJ8/complement-sparsification-FL | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-10-15T15:20:52Z | [] | 2022-12-06T04:19:56Z | [
"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 | 6 | [] | 2025-10-12T13:46:38Z | https://github.com/XJ8/complement-sparsification-FL | null |
[
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion",
"generative-modeling",
"video-generation"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"vision.video-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-12-08T01:30:53Z | [AAAI23 Oral] Official implementations of Video Implicit Diffusion Models | [
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"generative-model",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 4 | 575,646,284 | null | Python | MIT | [
"diffusion",
"generative-modeling",
"video-generation"
] | MKFMIKU/vidm | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-08T17:58:00Z | [] | 2023-11-01T22:12:35Z | [
"vision.video-generation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 68 | [
"diffusion-models",
"generative-models",
"pytorch"
] | 2025-04-17T06:33:31Z | https://github.com/MKFMIKU/vidm | 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 | [] | 2022-12-08T10:37:25Z | Official code for paper “PointSmile: 3D Point Self-supervised Learning via Curriculum Mutual Information” | [
"deep-learning",
"representation-learning"
] | [
"self-supervised-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 0 | 575,804,751 | null | null | null | [
"representation-learning",
"self-supervised-learning"
] | vidulgi/PointSmile | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-01T17:50:46Z | [] | 2022-12-08T10:37:26Z | [
"general.self-supervised-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 2 | [] | 2023-04-09T13:46:20Z | https://github.com/vidulgi/PointSmile | null |
[
"biology",
"chemistry",
"computational-biology",
"science-and-engineering"
] | [
"force-field",
"scientific-machine-learning",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"bio.molecular-force-fields"
] | true | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-12-09T20:38:23Z | (ARCHIVED) Official PyTorch implementation of "Comprehensive Molecular Representation from Equivariant Transformer" paper https://arxiv.org/abs/2308.10752. Made in Cardiff University. | [
"biology",
"chemistry",
"computational-biology",
"science-and-engineering"
] | [
"machine-learning",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 1 | 576,425,528 | https://arxiv.org/abs/2308.10752 | Python | GPL-3.0 | [
"force-field",
"scientific-machine-learning",
"transformer"
] | Augus1999/torch_CMRET | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 5 | 2026-10-07T17:53:16Z | [] | 2025-04-08T15:15:59Z | [
"bio.molecular-force-fields"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 3 | [
"chemistry",
"force-fields",
"machine-learning",
"pytorch",
"transformer"
] | 2025-10-16T06:09:06Z | https://github.com/Augus1999/torch_CMRET | null |
[
"computer-vision",
"deep-learning",
"graph-learning"
] | [
"change-detection",
"graph-neural-network",
"image-analysis",
"message-passing",
"neural-network"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description",
"vision.change-detection"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-12-10T00:38:00Z | Code for paper "Semantic Change Detection with Graph Neural Networks" | [
"computer-vision",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 0 | 576,473,460 | null | Python | null | [
"change-detection",
"graph-neural-network",
"image-analysis",
"neural-network"
] | imgremlin/sem-gnn | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-24T14:31:13Z | [] | 2022-12-12T17:30:26Z | [
"vision.change-detection"
] | 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 | [] | 2023-01-05T13:20:08Z | https://github.com/imgremlin/sem-gnn | null |
[
"multi-agent-systems",
"reinforcement-learning"
] | [
"centralized-training",
"multi-agent-reinforcement-learning",
"reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"rl.multiagent"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-12-11T02:30:23Z | This repository records the source code for paper 2023 related Multi-Agent Reinforcement Learning project | [
"multi-agent-systems",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 0 | 576,799,811 | null | Python | null | [
"centralized-training",
"multi-agent-reinforcement-learning",
"reinforcement-learning"
] | chun-chih-lin/2023_DRF | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-01T17:50:46Z | [] | 2025-04-22T00:33:32Z | [
"rl.multiagent"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2025-11-29T11:11:25Z | https://github.com/chun-chih-lin/2023_DRF | null |
[
"computer-vision",
"efficient-ml",
"model-compression"
] | [
"distillation",
"knowledge-distillation",
"neural-network"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-12-12T16:42:10Z | Official code for our paper - "Melanoma classification from dermatoscopy images using knowledge distillation for highly imbalanced data". | [
"computer-vision",
"efficient-ml",
"model-compression"
] | [
"neural-network",
"classifier"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 2 | 577,398,387 | null | Jupyter Notebook | null | [
"distillation",
"knowledge-distillation",
"neural-network"
] | anil-adepu/Melanoma-Classification-using-Knowledge-Distillation-for-Highly-Imbalanced-Data | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 2 | 2026-10-07T17:53:16Z | [] | 2023-01-31T18:10:23Z | [
"efficiency.knowledge-distillation"
] | 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 | [
"computer-vision",
"cost-sensitive-learning",
"deep-neural-networks",
"deeplearning",
"efficientnet",
"isic-2020",
"knowledge-distillation",
"medical-image-analysis",
"melanoma-classification",
"skin-lesions"
] | 2025-07-26T02:05:04Z | https://github.com/anil-adepu/Melanoma-Classification-using-Knowledge-Distillation-for-Highly-Imbalanced-Data | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"multimodal",
"natural-language-processing",
"vision"
] | [
"diffusion",
"generative-modeling",
"image-generation",
"text-to-image"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"multimodal.text-to-image",
"vision.image-diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-12-14T20:16:53Z | Official implementation of the paper "Uncovering the Disentanglement Capability in Text-to-Image Diffusion Models | [
"generative-ai",
"generative-modeling",
"multimodal",
"natural-language-processing",
"vision"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 10 | 578,335,933 | null | Jupyter Notebook | NOASSERTION | [
"diffusion",
"image-generation",
"text-to-image"
] | UCSB-NLP-Chang/DiffusionDisentanglement | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-07T17:53:16Z | [] | 2023-10-08T17:54:52Z | [
"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",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 174 | [] | 2026-07-26T05:11:44Z | https://github.com/UCSB-NLP-Chang/DiffusionDisentanglement | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"image-processing",
"low-level-vision"
] | [
"diffusion",
"image-denoising",
"image-restoration",
"self-supervised-learning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"recall26.image-denoising"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-12-15T08:06:44Z | Official pytorch implementation of the paper: "SinDDM: A Single Image Denoising Diffusion Model" | [
"computer-vision",
"generative-modeling",
"image-processing",
"low-level-vision"
] | [
"generative-model",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 19 | 578,505,052 | https://matankleiner.github.io/sinddm/ | Python | MIT | [
"diffusion",
"image-denoising",
"image-restoration",
"self-supervised-learning"
] | fallenshock/SinDDM | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-11T16:48:52Z | [] | 2023-08-20T08:19:08Z | [
"recall26.image-denoising"
] | 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 | 119 | [
"diffusion",
"diffusion-models",
"generative-model",
"harmonization",
"official",
"sinddm",
"single-image",
"single-image-generation",
"style-transfer",
"text-guided-generation",
"text-guided-image-editing",
"text-guided-image-manipulation"
] | 2026-02-27T10:22:25Z | https://github.com/fallenshock/SinDDM | 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 | [] | 2022-12-18T05:11:18Z | Official code for Dual Contrastive Learning for Spatio-temporal Representation (ACM MM 2022) | [
"deep-learning",
"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-19T17:46:36Z | false | 0 | 579,563,321 | null | Python | null | [
"contrastive-learning",
"self-supervised-learning"
] | Mark12Ding/DCLR | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-19T17:46:36Z | [] | 2022-12-18T05:50:27Z | [
"general.contrastive-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 1 | [] | 2024-11-05T16:06:28Z | https://github.com/Mark12Ding/DCLR | null |
[
"deep-learning",
"graph-learning",
"information-retrieval",
"natural-language-processing"
] | [
"graph-transformer",
"message-passing",
"question-answering",
"reading-comprehension",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.graph-transformer",
"nlp.question-answering"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-12-21T11:43:37Z | Official pytorch implementation of 'Relation-aware Language-Graph Transformer for Question Answering' (AAAI 2023) | [
"information-retrieval",
"natural-language-processing"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 5 | 580,763,932 | null | Python | MIT | [
"question-answering",
"reading-comprehension",
"transformer"
] | mlvlab/QAT | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 8 | 2026-10-22T16:09:07Z | [] | 2023-04-25T16:22:15Z | [
"nlp.question-answering"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 18 | [
"aaai2023"
] | 2026-01-28T07:46:20Z | https://github.com/mlvlab/QAT | null |
[
"classical-ml",
"tabular-and-structured-data",
"tabular-ml"
] | [
"ensemble-learning",
"gradient-boosting"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.gradient-boosting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-12-22T03:06:46Z | Official code for paper: TRBoost: A Generic Gradient Boosting Machine based on Trust-region Method | [
"classical-ml",
"tabular-and-structured-data",
"tabular-ml"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-09T17:31:36Z | false | 0 | 581,010,221 | null | Jupyter Notebook | MIT | [
"ensemble-learning",
"gradient-boosting"
] | Luojiaqimath/TRBoost | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-09T17:31:36Z | [] | 2023-05-15T01:18:03Z | [
"general.gradient-boosting"
] | 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 | [] | 2023-04-11T06:40:26Z | https://github.com/Luojiaqimath/TRBoost | null |
[
"distributed-ml",
"privacy-and-federated-learning"
] | [
"collaborative-learning",
"federated-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-12-23T09:29:06Z | This is the implementation of our paper Improving Federated Learning for Aspect-based Sentiment Analysis via Topic Memories in EMNLP 2021 | [
"distributed-ml",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-07T18:07:52Z | false | 0 | 581,470,790 | null | null | MIT | [
"collaborative-learning",
"federated-learning"
] | synlp/ASA-TM | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-07T18:07:52Z | [] | 2022-12-23T09:29:07Z | [
"trust.federated-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2022-12-23T09:29:06Z | https://github.com/synlp/ASA-TM | null |
[
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"github-topics",
"repository-metadata"
] | [] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-12-27T10:02:34Z | Official PyTorch implementation of PDAE (NeurIPS 2022) | [
"generative-modeling"
] | [
"representation-learning",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-01T19:00:27.900159Z | false | 14 | 582,593,850 | null | Python | null | [
"diffusion"
] | ckczzj/PDAE | [
"description",
"github-topics",
"repository-metadata"
] | 1 | 2026-10-01T19:00:27.900159Z | [] | 2024-03-05T13:13:14Z | [] | 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 | 225 | [
"diffusion-models",
"representation-learning"
] | 2026-06-09T10:47:58Z | https://github.com/ckczzj/PDAE | {"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["diffusion-models"]} |
[
"audio",
"generative-modeling",
"speech"
] | [
"diffusion",
"neural-vocoder",
"speech-synthesis"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"cross26.audio-neural-vocoder"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2022-12-27T20:24:41Z | A novel diffusion-based model for synthesizing long-context, high-fidelity music efficiently. | [
"audio",
"generative-modeling",
"speech"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T22:17:16Z | false | 10 | 582,770,099 | null | Python | MIT | [
"diffusion",
"neural-vocoder",
"speech-synthesis"
] | Kinyugo/msanii | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-09-26T22:51:24Z | [] | 2023-04-27T08:47:48Z | [
"cross26.audio-neural-vocoder"
] | 9233522fde5f79aeffd6b0d87a9ecf05326809d8 | 2026-09-27T17:16:20Z | gh-ml-readme-evidence-v2 | "9233522fde5f79aeffd6b0d87a9ecf05326809d8" | [
"abstract",
"installation",
"other",
"usage"
] | [
"method-contribution",
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-09-27T17:16:20Z | Kinyugo/msanii | specific-method-with-novelty-claim | [
"method-contribution",
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 196 | [] | 2026-02-10T15:13:20Z | https://github.com/Kinyugo/msanii | null |
[
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-01-01T14:11:25Z | A new filter pruning method for medical image analysis on android devices | [
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-15T15:20:52Z | false | 0 | 584,132,650 | null | Jupyter Notebook | null | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | mohofar/Beta-Rank | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-15T15:20:52Z | [] | 2023-11-06T08:04:21Z | [
"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 | 5 | [] | 2024-08-30T14:13:48Z | https://github.com/mohofar/Beta-Rank | null |
[
"computer-vision",
"deep-learning",
"earth-science",
"geospatial",
"geospatial-science",
"remote-sensing",
"representation-learning",
"satellite-imagery",
"science-and-engineering"
] | [
"change-detection",
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"deep-learning",
"foundation-model",
"geospatial-learning",
"image-analysis",
"object-detection",
"remote-sensing",
"representation-learning",
"self-supervised-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning",
"general.self-supervised-learning",
"geo.object-detection",
"science.remote-sensing",
"vision.change-detection"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-01-03T14:25:29Z | The official implementation of paper "Unified Self-Supervised Learning Framework for Remote Sensing Images". | [
"computer-vision",
"science-and-engineering"
] | [
"self-supervised-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 8 | 584,791,471 | null | Python | null | [
"change-detection",
"image-analysis",
"self-supervised-learning"
] | NJU-LHRS/official-CMID | [
"description",
"query-match",
"repository-metadata"
] | 9 | 2026-10-22T16:09:07Z | [] | 2024-10-26T12:51:40Z | [
"vision.change-detection"
] | 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 | 103 | [] | 2026-04-27T05:33:14Z | https://github.com/NJU-LHRS/official-CMID | null |
[
"distributed-ml",
"privacy-and-federated-learning"
] | [
"collaborative-learning",
"federated-learning",
"neural-network"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-01-03T18:49:50Z | FedDebug is a novel testing and debugging technique that localizes faulty clients in Federated Learning. | [
"distributed-ml",
"privacy-and-federated-learning"
] | [
"deep-learning",
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 1 | 584,879,212 | null | Python | MIT | [
"collaborative-learning",
"federated-learning",
"neural-network"
] | SEED-VT/FedDebug | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2024-07-02T09:12:40Z | [
"trust.federated-learning"
] | a7eb9646e06f392dca8875883393060d17cf5d52 | 2026-10-05T21:36:53Z | gh-ml-readme-evidence-v2 | "a7eb9646e06f392dca8875883393060d17cf5d52" | [
"installation",
"other"
] | [
"paper-reference",
"reproduction-cue"
] | ok | 2026-10-05T21:36:53Z | SEED-VT/FedDebug | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue",
"paper-reference",
"reproduction-cue"
] | include | ml-contribution-v5 | 8 | [
"clients",
"debugging",
"deep-learning",
"deep-neural-networks",
"differential",
"engineering",
"feddebug",
"federated-learning",
"icse",
"icse2023",
"learning",
"machine",
"server",
"software",
"testing"
] | 2024-07-02T09:12:44Z | https://github.com/SEED-VT/FedDebug | 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 | [] | 2023-01-04T10:06:54Z | The source code for paper “MIRACLE: Mining Implicit Relationships with Multi-view Unsupervised Graph Contrastive Learning” | [
"deep-learning",
"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 0 | 585,097,589 | null | Python | null | [
"contrastive-learning",
"self-supervised-learning"
] | myjpgit/MIRACLE | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-10-19T17:46:36Z | [] | 2023-08-10T07:33:47Z | [
"general.contrastive-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [] | 2025-05-22T11:25:10Z | https://github.com/myjpgit/MIRACLE | null |
[
"control",
"reinforcement-learning",
"robotics",
"robotics-and-control"
] | [
"control",
"meta-learning",
"reinforcement-learning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"robotics.robot-control"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-01-04T16:55:14Z | This is the official code for the paper "Robotic Control Using Model Based Meta Adaption" accepted to ICRA 2023. | [
"control",
"reinforcement-learning",
"robotics",
"robotics-and-control"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 0 | 585,235,836 | https://wandb.ai/joelikels/mac/reports/Meta-Adaption-Controller---VmlldzozNjE1MzA0?accessToken=w8zdv6qrf1d6w0hywepu7pef3yxvoggejksistarcd54vx2wiyvd58qpr9sysht8 | Python | MIT | [
"control",
"meta-learning",
"reinforcement-learning"
] | jikels/mac | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2023-03-06T08:00:53Z | [
"robotics.robot-control"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [
"meta-learning",
"model-predictive-control",
"reinforcement-learning"
] | 2023-03-15T14:04:18Z | https://github.com/jikels/mac | null |
[
"reinforcement-learning"
] | [
"batch-reinforcement-learning",
"offline-reinforcement-learning",
"reinforcement-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.offline"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-01-09T01:45:16Z | Official code for "One Risk to Rule Them All: A Risk-Sensitive Perspective on Model-Based Offline Reinforcement Learning", NeurIPS 2023 | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 2 | 586,693,972 | null | Python | MIT | [
"batch-reinforcement-learning",
"offline-reinforcement-learning",
"reinforcement-learning"
] | marc-rigter/1R2R | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-04T15:44:14Z | [] | 2023-11-29T20:06:34Z | [
"rl.offline"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 11 | [] | 2026-03-30T20:31:30Z | https://github.com/marc-rigter/1R2R | null |
[
"deep-learning",
"earth-science",
"geospatial-science",
"remote-sensing",
"representation-learning",
"science-and-engineering"
] | [
"contrastive-learning",
"foundation-model",
"geospatial-learning",
"remote-sensing",
"self-supervised-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning",
"science.remote-sensing"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-01-11T12:42:35Z | This is the official implementation code of the paper "Weakly-supervised ROI Extraction Method Based on Contrastive Learning for Remote Sensing Images" | [
"deep-learning",
"representation-learning",
"science-and-engineering"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-10T16:53:26Z | false | 0 | 587,723,675 | null | Python | null | [
"contrastive-learning",
"self-supervised-learning"
] | HE-Lingfeng/ROI-Extraction | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-19T17:46:36Z | [] | 2025-05-24T04:58:38Z | [
"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 | 3 | [] | 2025-05-24T04:58:42Z | https://github.com/HE-Lingfeng/ROI-Extraction | 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 | [] | 2023-01-13T02:02:57Z | Official code for ICLR 2023 paper "Rethinking the Effect of Data Augmentation in Adversarial Contrastive Learning" | [
"deep-learning",
"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-20T15:59:06Z | false | 0 | 588,390,563 | null | Python | null | [
"contrastive-learning",
"self-supervised-learning"
] | Red-Fairy/DYNACL | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-20T15:59:06Z | [] | 2023-02-19T07:00:05Z | [
"general.contrastive-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [] | 2023-03-01T12:15:16Z | https://github.com/Red-Fairy/DYNACL | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-01-14T08:33:24Z | Code for paper "GAFExplainer: Global Explanation of Graph Neural Networks through Attribute Augmentation and Fusion Embedding" | [
"deep-learning",
"graph-learning"
] | [
"neural-network",
"embedding"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 1 | 588,853,104 | null | Python | MIT | [
"graph-neural-network",
"message-passing",
"neural-network"
] | wyhi/GAFExplainer | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-08T18:15:14Z | [] | 2023-05-05T02:58:22Z | [
"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 | 0 | [] | 2025-06-15T17:03:05Z | https://github.com/wyhi/GAFExplainer | 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 | [] | 2023-01-14T16:09:02Z | Code for our paper Mitigating Adversarial Attacks in Federated Learning with Trusted Execution Environments. | [
"distributed-ml",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 0 | 588,961,961 | null | Python | null | [
"collaborative-learning",
"federated-learning"
] | queyrusi/Pelta | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2023-07-20T18:54:10Z | [
"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 | 4 | [] | 2026-06-25T11:11:11Z | https://github.com/queyrusi/Pelta | null |
[
"3d",
"3d-vision",
"computer-vision",
"generative-ai",
"geometric-learning",
"language",
"robotics"
] | [
"3d-object-detection",
"3d-perception",
"language-model",
"point-cloud-learning",
"point-cloud-segmentation",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"graph.point-clouds",
"llm.transformers",
"multimodal.3d-object-detection"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-01-15T05:02:08Z | [CVPR2023] Official Implementation of "DSVT: Dynamic Sparse Voxel Transformer with Rotated Sets" | [
"3d",
"computer-vision",
"robotics"
] | [
"transformer"
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] | include | ml-contribution-v5 | 456 | [
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] | 2026-09-25T05:21:06Z | https://github.com/Haiyang-W/DSVT | null |
[
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T16:52:35Z | false | 1 | 589,262,342 | null | Python | null | [
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] | Mayueq/DropMix-Code | [
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] | 4 | 2026-10-19T17:46:36Z | [] | 2023-01-20T11:27:12Z | [
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] | include | ml-contribution-v5 | 1 | [] | 2024-05-13T13:58:25Z | https://github.com/Mayueq/DropMix-Code | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-01-16T07:01:45Z | [ICML 2023] Official code implementation of "Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning (https://proceedings.mlr.press/v202/dai23a)" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:44:14Z | false | 5 | 589,459,835 | null | Python | MIT | [
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] | ybdai7/Chameleon-durable-backdoor | [
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] | 3 | 2026-10-18T14:44:29Z | [] | 2025-09-09T13:18:45Z | [
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] | include | ml-contribution-v5 | 43 | [] | 2025-12-04T12:58:26Z | https://github.com/ybdai7/Chameleon-durable-backdoor | null |
[
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 0 | 589,582,983 | null | Jupyter Notebook | null | [
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] | arushi2509/Image-Quality-Enhancement-Using-Machine-Learning | [
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] | 6 | 2026-10-13T18:05:26Z | [] | 2023-10-01T22:17:09Z | [
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] | [
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] | ok | 2026-09-27T17:16:20Z | arushi2509/Image-Quality-Enhancement-Using-Machine-Learning | specific-method-with-novelty-claim | [
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] | include | ml-contribution-v5 | 0 | [
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] | 2024-06-23T16:59:16Z | https://github.com/arushi2509/Image-Quality-Enhancement-Using-Machine-Learning | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-01-16T14:04:51Z | Official code for the ICLR2024 paper -- Robust Angular Synchronization via Directed Graph Neural Networks | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-06T17:10:23Z | false | 2 | 589,602,715 | null | Python | MIT | [
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] | SherylHYX/GNN_Sync | [
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] | 1 | 2026-10-06T17:10:23Z | [] | 2024-01-20T12:13:08Z | [
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] | include | ml-contribution-v5 | 3 | [] | 2024-08-28T06:22:49Z | https://github.com/SherylHYX/GNN_Sync | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-01-17T12:04:33Z | [WSDM 2024 Oral] This is our Pytorch implementation for the paper: "Intent Contrastive Learning with Cross Subsequences for Sequential Recommendation". | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-18T14:44:29Z | false | 6 | 589,985,800 | https://arxiv.org/pdf/2310.14318.pdf | Python | MIT | [
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] | QinHsiu/ICSRec | [
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] | 1 | 2026-10-18T14:44:29Z | [] | 2024-01-07T07:38:25Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 42 | [
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] | 2026-09-02T02:17:38Z | https://github.com/QinHsiu/ICSRec | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-01-17T12:38:19Z | The official implementation of DMEL the method presented in the paper "DMEL: The differentiable log-Mel spectrogram as a trainable layer in neural networks". | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T16:44:32Z | false | 0 | 589,997,706 | null | Python | Apache-2.0 | [
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] | johnmartinsson/differentiable-mel-spectrogram | [
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] | 2 | 2026-10-04T15:58:58Z | [] | 2024-12-21T20:17:37Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 24 | [
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] | 2026-07-15T19:46:50Z | https://github.com/johnmartinsson/differentiable-mel-spectrogram | null |
[
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-01-18T05:53:14Z | Novel Approach for ML Model Hyperparameter Optimization Using Simulated Annealing | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 0 | 590,316,255 | null | Jupyter Notebook | GPL-3.0 | [
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] | 2 | 2026-10-04T15:44:14Z | [] | 2023-02-15T19:14:04Z | [
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] | include | ml-contribution-v5 | 1 | [] | 2023-10-20T01:56:32Z | https://github.com/yash-1708/SA_GA_HyperparameterTuning | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-01-18T15:50:43Z | [ICASSP-2022] Official implementation for "Cross-Modal Knowledge Distillation For Vision-To-Sensor Action Recognition" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 1 | 590,519,344 | null | Python | null | [
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] | jiany-ctrl/Crossmodal-KD-for-Vision-to-Sensor-HAR | [
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] | 3 | 2026-10-03T15:45:52Z | [] | 2024-04-29T01:47:19Z | [
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] | include | ml-contribution-v5 | 7 | [] | 2025-11-12T06:31:42Z | https://github.com/jiany-ctrl/Crossmodal-KD-for-Vision-to-Sensor-HAR | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-01-19T13:40:53Z | Official code repository for the paper: “Diffusion-SDF: Conditional Generative Modeling of Signed Distance Functions” | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T22:51:24Z | false | 31 | 590,902,803 | null | Python | MIT | [
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] | princeton-computational-imaging/Diffusion-SDF | [
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] | 1 | 2026-09-26T22:51:24Z | [] | 2023-07-28T15:23:14Z | [
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] | include | ml-contribution-v5 | 281 | [] | 2026-07-31T13:13:11Z | https://github.com/princeton-computational-imaging/Diffusion-SDF | null |
[
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] | [
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"repository-metadata"
] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-01-19T17:26:38Z | Source code for paper submitted to EMNLP2024 - Revisiting Supervised Contrastive Learning for Microblog Classification | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-19T17:46:36Z | false | 0 | 590,991,813 | null | Python | Apache-2.0 | [
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] | 2 | 2026-10-21T15:48:06Z | [] | 2024-10-01T10:00:36Z | [
"general.meta-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2024-10-01T10:03:21Z | https://github.com/semantic-systems/paper-revisiting-contrastive-learning-for-microblog-classification | null |
[
"computer-vision"
] | [
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"license-metadata",
"query-match",
"repository-metadata"
] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-01-20T09:43:41Z | Official PyTorch code for the paper "Towards Faster and Efficient Lightweight Image Super Resolution using Swin Transformers and Fourier Convolutions". | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-17T13:17:44Z | false | 1 | 591,250,298 | null | Python | MIT | [
"restoration",
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] | iVishalr/SWIFT | [
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] | 1 | 2026-10-17T13:17:44Z | [] | 2024-10-08T13:41:41Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 3 | [
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] | 2025-10-26T03:40:58Z | https://github.com/iVishalr/SWIFT | null |
[
"distributed-ml",
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] | [
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] | [
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] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-01-20T14:18:59Z | Official implementation of paper Evaluating Robustness of Subagging Algorithms against Data Poisoning and Its Applications to Federated Learning | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-07T18:07:52Z | false | 0 | 591,340,204 | null | null | null | [
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] | roy-ch/Certifiably-Robust-Subagging | [
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] | 1 | 2026-10-07T18:07:52Z | [] | 2023-01-20T14:19:00Z | [
"trust.federated-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2023-01-20T14:18:59Z | https://github.com/roy-ch/Certifiably-Robust-Subagging | null |
[
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] | [
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"query-match",
"repository-metadata"
] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-01-21T01:30:28Z | This project is the official implementation of 'Knowledge Distillation based Degradation Estimation for Blind Super-Resolution', ICLR2023 | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 2 | 591,527,743 | null | Python | null | [
"distillation",
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] | Zj-BinXia/KDSR | [
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] | 3 | 2026-10-17T13:17:44Z | [] | 2024-04-15T12:36:31Z | [
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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 | 131 | [
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"representation-learning",
"super-resolution"
] | 2026-08-31T03:32:05Z | https://github.com/Zj-BinXia/KDSR | null |
[
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] | [
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] | [
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"repository-metadata"
] | [
"chem-drug-discovery"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-01-21T05:16:00Z | null | [
"chemistry",
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"health-and-biomedicine"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-13T18:05:26Z | false | 0 | 591,564,196 | null | Jupyter Notebook | null | [
"ranking",
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"transformer",
"virtual-screening"
] | HongYuBinBin/Molecule-Generation-for-Drug-Discovery-with-New-Transformer-Architectures | [
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] | 1 | 2026-10-13T18:05:26Z | [] | 2023-04-13T12:47:35Z | [
"chem-drug-discovery"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
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"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [] | 2023-04-13T12:44:09Z | https://github.com/HongYuBinBin/Molecule-Generation-for-Drug-Discovery-with-New-Transformer-Architectures | null |
[
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"multimodal-learning",
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] | [
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"repository-metadata"
] | [
"general.contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-01-23T05:53:49Z | This is the official implementation for the paper "EMP: Emotion-guided Multi-modal Fusion and Contrastive Learning for Personality Traits Recognition". | [
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"multimodal-learning",
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-18T14:44:29Z | false | 2 | 592,180,151 | null | Python | null | [
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] | wykstc/PSR | [
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] | 1 | 2026-10-18T14:44:29Z | [] | 2023-12-26T03:00:37Z | [
"general.contrastive-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 7 | [] | 2025-05-28T13:06:17Z | https://github.com/wykstc/PSR | null |
[
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"trustworthy-ml"
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-01-24T02:29:11Z | The official implementation for ICLR23 paper "GNNSafe: Energy-based Out-of-Distribution Detection for Graph Neural Networks" | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 7 | 592,571,300 | null | Python | null | [
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] | qitianwu/GraphOOD-GNNSafe | [
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] | 10 | 2026-10-11T16:48:52Z | [] | 2023-07-27T11:43:00Z | [
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] | include | ml-contribution-v5 | 86 | [
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[
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] | 1 | 2026-09-30T16:47:43Z | [] | 2023-10-27T15:43:22Z | [
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[
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] | [
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] | [
"description",
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] | [] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-01-27T05:37:14Z | Official PyTorch implementation for the paper Minimizing Trajectory Curvature of ODE-based Generative Models, ICML 2023 | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-01T19:00:27.900159Z | false | 6 | 593,907,931 | null | Python | null | [
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] | sangyun884/fast-ode | [
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] | 2026-01-20T12:30:57Z | https://github.com/sangyun884/fast-ode | {"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["diffusion-models"]} |
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-01-27T10:43:40Z | [ICML 2023] official implementation for "Input Perturbation Reduces Exposure Bias in Diffusion Models" | [
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] | include | ml-contribution-v5 | 129 | [] | 2026-04-26T02:32:50Z | https://github.com/forever208/DDPM-IP | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-01-29T03:46:06Z | Official implementation of our paper titled "Fine-Tuning Deep Learning Model for Detecting Indonesian Traffic Accident Tweet" | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-14T16:10:46Z | false | 0 | 594,604,883 | null | Jupyter Notebook | null | [
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] | adirizq/indonesian-tweet-traffic-accident-detection | [
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] | 1 | 2026-10-14T16:10:46Z | [] | 2023-12-14T03:19:11Z | [
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] | include | ml-contribution-v5 | 1 | [
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] | 2024-06-14T08:26:11Z | https://github.com/adirizq/indonesian-tweet-traffic-accident-detection | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-01-29T14:51:54Z | A new technique proposed to improve performance of Federated Learning with unlabeled clients by using label propagation on top of FEDIRM technique | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-07T18:07:52Z | false | 0 | 594,755,818 | null | Python | null | [
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] | sallu08/FEDIRM-with-Label-Prop | [
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] | 1 | 2026-10-07T18:07:52Z | [] | 2023-01-29T14:58:39Z | [
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] | include | ml-contribution-v5 | 1 | [] | 2024-06-22T07:19:51Z | https://github.com/sallu08/FEDIRM-with-Label-Prop | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-01-29T16:33:35Z | Code for ICLR 2023 Paper, "Stable Target Field for Reduced Variance Score Estimation in Diffusion Models” | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 4 | 594,785,689 | null | Python | null | [
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] | Newbeeer/stf | [
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] | 1 | 2026-09-30T16:47:43Z | [] | 2023-06-06T15:43:25Z | [
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] | include | ml-contribution-v5 | 76 | [] | 2025-05-13T21:50:02Z | https://github.com/Newbeeer/stf | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-01-29T21:50:17Z | Official Pytorch Implementation for "MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation" presenting "MultiDiffusion" (ICML 2023) | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 62 | 594,869,373 | https://multidiffusion.github.io/ | Jupyter Notebook | null | [
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] | omerbt/MultiDiffusion | [
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] | 8 | 2026-10-03T15:32:04Z | [] | 2023-09-21T12:27:10Z | [
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] | include | ml-contribution-v5 | 1,070 | [
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] | 2026-09-30T22:25:55Z | https://github.com/omerbt/MultiDiffusion | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-01-30T10:34:17Z | Official code release for ICLR23 "Diminishing Return of Value Expansion Methods in Model-Based Reinforcement Learning" | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 0 | 595,071,372 | null | Python | MIT | [
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] | danielpalenicek/value_expansion | [
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] | 4 | 2026-10-04T15:44:14Z | [] | 2023-03-08T12:19:00Z | [
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] | include | ml-contribution-v5 | 16 | [
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] | 2026-02-09T11:36:23Z | https://github.com/danielpalenicek/value_expansion | null |
[
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] | [
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] | [
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"github-topics",
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] | [] | true | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-01-30T11:42:57Z | Official implementation for "Anti-Exploration by Random Network Distillation", ICML 2023 | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T20:57:28.489717Z | false | 5 | 595,096,303 | null | Python | Apache-2.0 | [
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] | tinkoff-ai/sac-rnd | [
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] | 4 | 2026-10-02T18:08:49.004055Z | [] | 2023-02-03T11:47:02Z | [] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 58 | [
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] | 2026-06-25T14:31:56Z | https://github.com/tinkoff-ai/sac-rnd | {"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["offline-reinforcement-learning"]} |
[
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] | [
"specialized.test-time-adaptation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-01-30T14:22:49Z | Code for ICLR 2023 paper (Oral) — Towards Stable Test-Time Adaptation in Dynamic Wild World | [
"machine-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 9 | 595,160,890 | null | Python | BSD-3-Clause | [
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] | mr-eggplant/SAR | [
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] | 5 | 2026-10-04T15:44:14Z | [] | 2023-09-08T08:49:11Z | [
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] | include | ml-contribution-v5 | 212 | [] | 2026-08-25T09:07:02Z | https://github.com/mr-eggplant/SAR | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-01-30T18:07:51Z | Official Implementation for "Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models" (SIGGRAPH 2023) | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 63 | 595,257,557 | https://yuval-alaluf.github.io/Attend-and-Excite/ | Jupyter Notebook | MIT | [
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] | yuval-alaluf/Attend-and-Excite | [
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] | 10 | 2026-10-04T15:44:14Z | [] | 2024-01-26T08:13:03Z | [
"multimodal.text-to-image"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 769 | [
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] | 2026-09-30T03:10:59Z | https://github.com/yuval-alaluf/Attend-and-Excite | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-01-31T08:14:47Z | This is the official implementation for IJCNN-23 paper "Bootstrap Decomposition Enhanced Orthogonal-Basis Projection Model for Long-Term Time Series Forecasting" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-12T20:31:19Z | false | 0 | 595,507,145 | null | Python | null | [
"forecasting",
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] | Nicholas0917/BDEOP-main | [
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] | 1 | 2026-10-12T20:31:19Z | [] | 2023-01-31T08:16:58Z | [
"timeseries.forecasting"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 3 | [] | 2025-04-17T12:51:49Z | https://github.com/Nicholas0917/BDEOP-main | null |
[
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] | [
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] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-01-31T11:01:57Z | Code for ICLR 2023 paper "Imitating Human Behaviour with Diffusion Models" | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 19 | 595,569,115 | null | Python | MIT | [
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] | microsoft/Imitating-Human-Behaviour-w-Diffusion | [
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"query-match",
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] | 1 | 2026-09-29T17:30:08Z | [] | 2024-08-31T00:12:07Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 156 | [] | 2026-07-24T19:03:07Z | https://github.com/microsoft/Imitating-Human-Behaviour-w-Diffusion | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-01-31T18:26:52Z | Code for Paper fair graph representation learning via diverse mixture-of-experts, WWW 2023 | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 1 | 595,749,771 | https://dl.acm.org/doi/abs/10.1145/3543507.3583207 | Python | null | [
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] | 2 | 2026-10-04T15:44:14Z | [] | 2023-11-04T00:06:20Z | [
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] | include | ml-contribution-v5 | 5 | [] | 2025-05-26T22:46:48Z | https://github.com/franciscoliu/GFAME | null |
[
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] | [
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"query-match",
"repository-metadata"
] | [
"nlp.information-extraction"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-02-01T05:18:55Z | Code for ICCV 2023 Paper : “ICL-D3IE: In-Context Learning with Diverse Demonstrations Updating for Document Information Extraction” | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 8 | 595,932,771 | null | Python | null | [
"information-extraction"
] | MAEHCM/ICL-D3IE | [
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"query-match",
"repository-metadata"
] | 3 | 2026-10-03T15:32:04Z | [] | 2023-08-08T09:02:37Z | [
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] | include | ml-contribution-v5 | 53 | [
"gpt-3",
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] | 2026-08-20T21:13:23Z | https://github.com/MAEHCM/ICL-D3IE | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-02-01T11:22:12Z | The code for paper "Cross Platforms Linguals and Models Social Bot Detection via Federated Adversarial Contrastive Knowledge Distillation." on Web Conference 2023 (WWW). | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 3 | 596,054,693 | null | Python | Apache-2.0 | [
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] | include | ml-contribution-v5 | 20 | [] | 2026-07-08T15:10:33Z | https://github.com/846468230/FedACK | null |
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-02-02T05:27:24Z | Official implementation of CVPR23 paper "Diffusion-based Generation, Optimization, and Planning in 3D Scenes" | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 29 | 596,406,333 | https://scenediffuser.github.io/ | Python | MIT | [
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] | scenediffuser/Scene-Diffuser | [
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] | 4 | 2026-10-04T15:44:14Z | [] | 2023-06-27T05:26:51Z | [
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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 | 407 | [
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] | 2026-05-25T07:13:53Z | https://github.com/scenediffuser/Scene-Diffuser | null |
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-02-04T01:34:44Z | Code for paper Dealing With Heterogeneous 3D MR Knee Images: A Federated Few-Shot Learning Method With Dual Knowledge Distillation | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 1 | 597,257,423 | null | Python | null | [
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] | hexiaoxiao-cs/fedml-knee | [
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] | 1 | 2026-09-30T16:47:43Z | [] | 2025-03-18T15:55:01Z | [
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] | include | ml-contribution-v5 | 8 | [] | 2025-05-15T02:09:58Z | https://github.com/hexiaoxiao-cs/fedml-knee | null |
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] | 4 | 2026-10-04T15:44:14Z | [] | 2024-03-21T15:40:00Z | [
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] | include | ml-contribution-v5 | 20 | [
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] | 2026-09-25T12:15:28Z | https://github.com/fregu856/regression_uncertainty | null |
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T22:51:24Z | false | 0 | 597,424,860 | null | Python | null | [
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] | include | ml-contribution-v5 | 11 | [] | 2026-06-09T07:39:55Z | https://github.com/wangshanyw/DCCDR | null |
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-02-05T23:36:59Z | An official PyTorch implementation of "Unnoticeable Backdoor Attacks on Graph Neural Networks" (WWW 2023) | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-03T15:32:04Z | false | 4 | 597,893,644 | null | Python | null | [
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] | ventr1c/UGBA | [
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] | 2 | 2026-10-05T20:10:04Z | [] | 2023-12-03T19:48:48Z | [
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] | include | ml-contribution-v5 | 62 | [] | 2026-09-23T10:58:00Z | https://github.com/ventr1c/UGBA | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-02-06T10:04:15Z | This repository contains the code for our paper "Probabilistic Contrastive Learning Recovers the Correct Aleatoric Uncertainty of Ambiguous Inputs". | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-17T13:17:44Z | false | 0 | 598,059,002 | null | Python | null | [
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] | mkirchhof/Probabilistic_Contrastive_Learning | [
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] | 2 | 2026-10-18T14:44:29Z | [] | 2023-04-25T09:36:28Z | [
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] | include | ml-contribution-v5 | 44 | [] | 2026-07-20T09:48:27Z | https://github.com/mkirchhof/Probabilistic_Contrastive_Learning | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-02-06T14:43:55Z | Official code for "Projection-Conditioned Point Cloud Diffusion for Single-Image 3D Reconstruction" (CVPR 2023) | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 20 | 598,166,178 | null | Python | MIT | [
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] | lukemelas/projection-conditioned-point-cloud-diffusion | [
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] | 5 | 2026-10-19T17:46:36Z | [] | 2025-09-23T02:18:59Z | [
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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 | 217 | [] | 2026-10-16T06:26:21Z | https://github.com/lukemelas/projection-conditioned-point-cloud-diffusion | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-02-06T14:46:43Z | An official implementation of CVPR 2023 "Self-Guided Diffusion Models" | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 2 | 598,167,463 | https://taohu.me/sgfm | Python | MIT | [
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] | dongzhuoyao/self-guided-diffusion-models | [
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] | 2 | 2026-09-30T16:47:43Z | [] | 2023-06-02T21:24:06Z | [
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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 | 28 | [
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] | 2026-07-07T13:56:05Z | https://github.com/dongzhuoyao/self-guided-diffusion-models | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-02-06T15:29:06Z | Official code for "RealFusion: 360° Reconstruction of Any Object from a Single Image" (CVPR 2023) | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 39 | 598,186,385 | null | Python | Apache-2.0 | [
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] | lukemelas/realfusion | [
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] | 6 | 2026-10-04T15:44:14Z | [] | 2024-06-02T07:04:54Z | [
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] | af8a14f85c3e3cdfc7e7a9e44caf38a9c34b93c7 | 2026-09-26T21:03:54Z | gh-ml-readme-evidence-v2 | "af8a14f85c3e3cdfc7e7a9e44caf38a9c34b93c7" | [
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] | [
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] | ok | 2026-09-26T21:03:54Z | lukemelas/realfusion | readme-supported-paper-method-implementation | [
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] | include | ml-contribution-v5 | 564 | [] | 2026-09-29T06:19:41Z | https://github.com/lukemelas/realfusion | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-02-06T17:00:48Z | The official implementation of our IPMI 2023 paper "SADM: Sequence-Aware Diffusion Model for Longitudinal Medical Image Generation" | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 5 | 598,224,149 | null | Python | MIT | [
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] | ubc-tea/SADM-Longitudinal-Medical-Image-Generation | [
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] | 2 | 2026-10-04T15:44:14Z | [] | 2023-04-10T19:40:49Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 75 | [] | 2026-08-16T07:54:46Z | https://github.com/ubc-tea/SADM-Longitudinal-Medical-Image-Generation | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-02-06T17:06:56Z | Code for paper: RACCER: Towards Reachable and Certain Counterfactual Explanations for Reinforcement Learning | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 0 | 598,226,656 | null | Python | null | [
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] | anonymous902109/RACCER | [
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] | 2 | 2026-10-04T15:44:14Z | [] | 2024-06-01T18:15:49Z | [
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] | include | ml-contribution-v5 | 2 | [] | 2025-02-10T20:14:37Z | https://github.com/anonymous902109/RACCER | null |
[
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] | [
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"github-topics",
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] | [] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-02-07T16:43:06Z | [ICCV-2023] Official code for work "HumanMAC: Masked Motion Completion for Human Motion Prediction". | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-01T19:00:27.900159Z | false | 20 | 598,705,038 | https://lhchen.top/Human-MAC | Python | MIT | [
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] | 1 | 2026-10-01T19:00:27.900159Z | [] | 2024-05-05T21:06:49Z | [] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 327 | [
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] | 2026-08-25T17:09:40Z | https://github.com/LinghaoChan/HumanMAC | {"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["diffusion-models"]} |
[
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] | [
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] | [
"vision.image-to-image-translation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-02-07T18:26:21Z | Official Pytorch Implementation for “Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation” (CVPR 2023) | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 64 | 598,748,375 | null | Python | null | [
"diffusion",
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] | MichalGeyer/plug-and-play | [
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] | 8 | 2026-10-17T13:17:44Z | [] | 2023-06-19T22:30:01Z | [
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] | include | ml-contribution-v5 | 1,001 | [] | 2026-09-25T19:07:43Z | https://github.com/MichalGeyer/plug-and-play | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-02-08T21:15:11Z | Official implementation for the paper Advancing Federated Learning in 6G: A Trusted Architecture with Graph-based Analysis accepted at GlobeCom2023 | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 2 | 599,298,524 | null | Python | null | [
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] | chendiqian/GNN4FL | [
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] | 1 | 2026-10-04T15:58:58Z | [] | 2023-10-05T20:52:07Z | [
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] | include | ml-contribution-v5 | 6 | [] | 2025-04-26T07:30:36Z | https://github.com/chendiqian/GNN4FL | null |
[
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"repository-metadata"
] | [
"graph.gnn-description",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-02-09T10:20:22Z | Code for our paper "Temporal Graph Neural Networks for Irregular Data" | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T14:22:14Z | false | 3 | 599,524,491 | null | Python | MIT | [
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] | joeloskarsson/tgnn4i | [
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] | 6 | 2026-10-05T20:10:04Z | [] | 2025-10-09T13:58:54Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 21 | [] | 2025-10-09T13:58:58Z | https://github.com/joeloskarsson/tgnn4i | null |
[
"graph-learning",
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] | [
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] | [
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"github-topics",
"query-match",
"repository-metadata"
] | [
"graph.representation-learning",
"graph.temporal-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-02-10T08:14:08Z | [AAAI 2023] Official implementation of FTM: A Frame-level Timeline Modeling Method for Temporal Graph Representation Learning | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T14:22:14Z | false | 1 | 599,941,951 | null | Python | null | [
"dynamic-graph-learning",
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"open-vocabulary",
"open-vocabulary-segmentation",
"open-vocabulary-semantic-segmentation",
"open-world-classification",
"open-world-object-detection",
"panoptic-segmentation",
"pytorch",
"semantic-segmentation",
"text-image-retriev... | 2026-08-18T06:39:46Z | https://github.com/NVlabs/ODISE | null |
[
"chemistry",
"deep-learning",
"drug-discovery",
"graph-learning",
"health-and-biomedicine",
"structural-biology"
] | [
"graph-neural-network",
"message-passing",
"neural-network",
"property-prediction",
"protein-ligand-modeling"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"chem-binding-affinity",
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-02-23T01:00:44Z | A novel graph neural network strategy with the Vina distance optimization terms to predict protein-ligand binding affinity | [
"deep-learning",
"graph-learning",
"health-and-biomedicine"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 0 | 605,349,171 | null | Python | GPL-3.0 | [
"graph-neural-network",
"message-passing",
"neural-network"
] | KailiWang1/GraphscoreDTA | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-07T18:07:52Z | [] | 2024-01-21T09:24:50Z | [
"graph.gnn-description"
] | ffda6dee7a8f2a25401581da6595041f5b8522ea | 2026-09-28T20:05:39Z | gh-ml-readme-evidence-v2 | "ffda6dee7a8f2a25401581da6595041f5b8522ea" | [
"installation",
"other",
"overview"
] | [
"ml-method-context",
"paper-reference"
] | ok | 2026-09-28T20:05:39Z | KailiWang1/GraphscoreDTA | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue",
"paper-reference"
] | include | ml-contribution-v5 | 5 | [] | 2025-09-04T06:54:52Z | https://github.com/KailiWang1/GraphscoreDTA | null |
[
"deep-learning",
"graph-learning",
"recommender-systems"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-02-23T11:50:40Z | Official source code for paper 《Int-GNN: a User Intention Aware Graph Neural Network for Session-Based Recommendation》accepted by ICASSP 2023 | [
"deep-learning",
"graph-learning",
"recommender-systems"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-07T18:07:52Z | false | 0 | 605,549,066 | null | Python | null | [
"graph-neural-network",
"message-passing",
"neural-network"
] | xuguangning1218/IntGNN_ICASSP2023 | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-07T18:07:52Z | [] | 2023-05-15T14:00:12Z | [
"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 | 6 | [] | 2026-03-20T13:18:27Z | https://github.com/xuguangning1218/IntGNN_ICASSP2023 | null |
[
"chemistry",
"deep-learning",
"drug-discovery",
"graph-learning",
"health-and-biomedicine",
"structural-biology"
] | [
"graph-neural-network",
"message-passing",
"neural-network",
"property-prediction",
"protein-ligand-modeling"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"chem-binding-affinity",
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-02-23T12:04:24Z | A novel graph neural network strategy with the Vina distance optimization terms to predict protein-ligand binding affinity | [
"deep-learning",
"graph-learning",
"health-and-biomedicine"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 11 | 605,554,209 | null | Python | GPL-3.0 | [
"graph-neural-network",
"message-passing",
"neural-network"
] | CSUBioGroup/GraphscoreDTA | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 10 | 2026-10-05T20:10:04Z | [] | 2024-05-16T13:47:46Z | [
"graph.gnn-description"
] | 963ec5bd7e19df647de8c6b9f7a8398a6bbd1c0c | 2026-09-26T15:53:33Z | gh-ml-readme-evidence-v2 | "963ec5bd7e19df647de8c6b9f7a8398a6bbd1c0c" | [
"citation",
"installation",
"other",
"overview"
] | [
"ml-method-context",
"paper-reference"
] | ok | 2026-09-26T15:53:33Z | CSUBioGroup/GraphscoreDTA | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue",
"paper-reference"
] | include | ml-contribution-v5 | 32 | [] | 2026-03-15T16:00:15Z | https://github.com/CSUBioGroup/GraphscoreDTA | null |
[
"automl",
"computer-vision",
"general-ml",
"language",
"multimodal-learning",
"natural-language-processing"
] | [
"few-shot-learning",
"language-model",
"language-modeling",
"meta-learning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.meta-learning",
"llm.language-model",
"nlp.language-modeling"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-02-24T17:48:32Z | [ICLR 2023] Official code repository for "Meta Learning to Bridge Vision and Language Models for Multimodal Few-Shot Learning" | [
"computer-vision",
"multimodal-learning",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:22:14Z | false | 2 | 606,145,422 | null | Python | MIT | [
"language-modeling",
"meta-learning"
] | ivonajdenkoska/multimodal-meta-learn | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-10-23T15:59:01Z | [] | 2023-06-06T22:46:03Z | [
"nlp.language-modeling"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 61 | [
"few-shot-learning",
"iclr-2023",
"image-captioning",
"meta-learning",
"vision-language",
"visual-question-answering"
] | 2026-03-14T01:00:08Z | https://github.com/ivonajdenkoska/multimodal-meta-learn | null |
[
"multimodal",
"reinforcement-learning",
"robotics"
] | [
"environment-modeling",
"world-model"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.world-models"
] | true | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-02-25T02:00:00Z | New World Models Extractpr | [
"multimodal",
"reinforcement-learning",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 1 | 606,271,570 | null | Ada | null | [
"environment-modeling",
"world-model"
] | giniedp/nw-models | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-05T19:54:32Z | [] | 2025-03-23T10:40:49Z | [
"multimodal.world-models"
] | ceb4b607e3cc1844284440859eec529e688de6a2 | 2026-09-28T20:05:39Z | gh-ml-readme-evidence-v2 | "ceb4b607e3cc1844284440859eec529e688de6a2" | [
"installation",
"other",
"usage"
] | [] | ok | 2026-09-28T20:05:39Z | giniedp/nw-models | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 5 | [] | 2025-03-23T10:41:05Z | https://github.com/giniedp/nw-models | null |
[
"computer-vision",
"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 | [] | 2023-02-27T05:40:16Z | The official implementation for the paper: "FedIIC: Towards Robust Federated Learning for Class-Imbalanced Medical Image Classification", which is accepted at MICCAI'23 (Early Accept, top 14% in total 2253 submissions). | [
"computer-vision",
"distributed-ml",
"privacy-and-federated-learning"
] | [
"classifier"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 6 | 607,007,957 | null | Python | null | [
"collaborative-learning",
"federated-learning"
] | wnn2000/FedIIC | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-04T15:58:58Z | [] | 2023-12-24T10:51:53Z | [
"trust.federated-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 19 | [] | 2026-07-13T01:03:52Z | https://github.com/wnn2000/FedIIC | null |
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