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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
[
"3d",
"autonomous-driving",
"computer-vision",
"generative-modeling",
"robotics",
"robotics-and-control"
] | [
"3d-object-detection",
"3d-perception",
"diffusion",
"perception",
"planning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.3d-object-detection",
"robotics.autonomous-driving"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-01T18:24:11Z | About [CVPR 2024] The official implementation of paper " Light the Night: A Multi-Condition Diffusion Framework for Unpaired Low-Light Enhancement in Autonomous Driving" | [
"3d",
"computer-vision",
"generative-modeling",
"robotics",
"robotics-and-control"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-07T18:07:52Z | false | 2 | 780,561,215 | null | Python | Apache-2.0 | [
"3d-object-detection",
"3d-perception",
"diffusion"
] | jinlong17/LightDiff | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-16T15:36:56Z | [] | 2024-09-20T22:07:46Z | [
"multimodal.3d-object-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 | 88 | [] | 2026-10-14T08:23:58Z | https://github.com/jinlong17/LightDiff | null |
[
"forecasting",
"foundation-models",
"generative-ai",
"natural-language-processing",
"time-series",
"time-series-and-forecasting"
] | [
"forecasting",
"multimodal",
"pretraining",
"probabilistic-forecasting",
"sequence-modeling",
"transformer",
"uncertainty-estimation"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.foundation-models",
"timeseries.forecasting",
"timeseries.foundation-models",
"timeseries.probabilistic-forecasting",
"timeseries.topic"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-01T22:22:40Z | The official code for "TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting (ICLR 2024)". TEMPO is one of the very first open source Time Series Foundation Models for forecasting task v1.0 version. | [
"natural-language-processing",
"time-series",
"time-series-and-forecasting"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 20 | 780,643,324 | null | Python | MIT | [
"sequence-modeling",
"transformer"
] | DC-research/TEMPO | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 13 | 2026-10-19T17:46:36Z | [] | 2025-02-23T21:38:07Z | [
"timeseries.topic"
] | 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 | 142 | [
"forecasting",
"forecasting-models",
"forecasting-time-series",
"foundation-models",
"gpt",
"pretrained-language-model",
"pretrained-models",
"time-series",
"time-series-analysis",
"transformer",
"transformers",
"transformers-models"
] | 2026-10-13T11:36:34Z | https://github.com/DC-research/TEMPO | 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 | [] | 2024-04-02T06:36:39Z | [TDSC 2024] Official code for our paper "FedTracker: Furnishing Ownership Verification and Traceability for Federated Learning Model" | [
"distributed-ml",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-01T17:35:19Z | false | 2 | 780,782,100 | null | Python | null | [
"collaborative-learning",
"federated-learning"
] | shaoshuo-ss/FedTracker | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-10T16:53:26Z | [] | 2025-05-14T06:31:07Z | [
"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 | 23 | [] | 2026-03-19T05:18:00Z | https://github.com/shaoshuo-ss/FedTracker | null |
[
"computer-vision",
"efficient-ml",
"language",
"machine-learning",
"machine-learning-systems",
"model-compression",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"vision"
] | [
"contrastive-learning",
"fine-tuning",
"image-text",
"language-model",
"language-modeling",
"lora",
"model-adaptation",
"multimodal-learning",
"neural-network-pruning",
"pruning",
"structured-pruning",
"vision-language",
"vision-language-model"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning",
"llm.language-model",
"multimodal.vision-language",
"multimodal.vlm",
"nlp.language-modeling",
"probable.adaptation-finetuning-readme",
"recall.name.language-model",
"recall.name.multimodal-model",
"vision.vision-language"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-02T18:49:20Z | The official implementation of the paper "Rethinking Pruning for Vision-Language Models: Strategies for Effective Sparsity". | [
"computer-vision",
"machine-learning-systems",
"multimodal-learning",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 1 | 781,102,199 | https://dl.acm.org/doi/10.1145/3764944.3764948 | Python | Apache-2.0 | [
"language-model",
"lora",
"pruning"
] | Shwai-He/VLM-Compression | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 12 | 2026-10-28T16:14:12Z | [] | 2026-10-02T20:32:14Z | [
"recall.name.language-model"
] | 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 | 17 | [
"lora-fine-tuning",
"model-compression",
"multimodal-models",
"sparsity"
] | 2026-10-02T20:32:18Z | https://github.com/Shwai-He/VLM-Compression | null |
[
"computer-vision"
] | [
"distillation",
"keypoint-detection",
"pose-estimation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.pose-estimation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-03T01:06:50Z | Official implementation for 'SDPose: Tokenized Pose Estimation via Circulation-Guide Self-Distillation' on CVPR 2024 | [
"computer-vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-17T13:17:44Z | false | 1 | 781,220,478 | null | Python | MIT | [
"distillation",
"keypoint-detection",
"pose-estimation"
] | MartyrPenink/SDPose | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-17T13:17:44Z | [] | 2024-05-15T17:02:10Z | [
"vision.pose-estimation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 18 | [] | 2025-11-29T03:27:38Z | https://github.com/MartyrPenink/SDPose | null |
[
"human-ai-interaction",
"reinforcement-learning",
"robotics-and-control"
] | [
"preference-learning",
"reinforcement-learning",
"reinforcement-learning-from-human-feedback"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.preference"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-04T14:37:29Z | Official code for ICML 2024 paper, "RIME: Robust Preference-based Reinforcement Learning with Noisy Preferences" (ICML 2024 Spotlight) | [
"human-ai-interaction",
"reinforcement-learning",
"robotics-and-control"
] | [
"deep-learning",
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 4 | 782,047,747 | null | Python | MIT | [
"preference-learning",
"reinforcement-learning",
"reinforcement-learning-from-human-feedback"
] | CJReinforce/RIME_ICML2024 | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-01T17:35:19Z | [] | 2024-10-15T15:06:36Z | [
"rl.preference"
] | 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 | 35 | [
"artificial-intelligence",
"deep-learning",
"locomotion",
"manipulation",
"preference-learning",
"reinforcement-learning",
"reinforcement-learning-from-human-feedback",
"robotics"
] | 2026-10-01T08:43:07Z | https://github.com/CJReinforce/RIME_ICML2024 | null |
[
"computer-vision",
"multimodal-learning"
] | [
"action-recognition",
"distillation",
"video-understanding"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.action-recognition"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-05T06:34:32Z | [ECCV 2024] Official code release for "Multimodal Cross-Domain Few-Shot Learning for Egocentric Action Recognition" | [
"computer-vision",
"multimodal-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-30T16:30:43Z | false | 2 | 782,360,942 | https://masashi-hatano.github.io/MM-CDFSL/ | Python | MIT | [
"action-recognition",
"distillation",
"video-understanding"
] | masashi-hatano/MM-CDFSL | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-02T16:59:40Z | [] | 2026-07-07T06:00:05Z | [
"vision.action-recognition"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 44 | [
"action-recognition",
"cross-domain-few-shot-learning",
"egocentric-vision",
"multi-modal-distillation"
] | 2026-09-26T08:39:30Z | https://github.com/masashi-hatano/MM-CDFSL | null |
[
"computer-vision",
"sequence-modeling",
"time-series"
] | [
"neural-network",
"recurrent-neural-network",
"sequence-modeling",
"state-space-model",
"transformer"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"timeseries.state-space-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-05T13:39:58Z | [CVPR'24 Spotlight] The official implementation of "State Space Models for Event Cameras" | [
"computer-vision",
"sequence-modeling",
"time-series"
] | [
"deep-learning",
"machine-learning",
"neural-network",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 13 | 782,533,473 | null | Python | null | [
"neural-network",
"recurrent-neural-network",
"sequence-modeling",
"state-space-model",
"transformer"
] | uzh-rpg/ssms_event_cameras | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 7 | 2026-10-01T17:35:19Z | [] | 2024-09-28T17:37:21Z | [
"timeseries.state-space-models"
] | 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 | 140 | [
"deep-learning",
"deep-neural-networks",
"event-camera",
"event-cameras",
"machine-learning",
"machine-learning-algorithms",
"neuromorphic",
"recurrent-neural-networks",
"rnn",
"rnn-pytorch",
"rnns",
"spotlight",
"ssm",
"ssms",
"state-space-model",
"state-space-models",
"state-space-... | 2026-09-24T22:30:44Z | https://github.com/uzh-rpg/ssms_event_cameras | null |
[
"reinforcement-learning"
] | [
"policy-gradient",
"policy-optimization"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"rl.policy-gradient"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-05T15:27:52Z | code for paper Provable policy gradient methods for average-reward Markov potential games | [
"reinforcement-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-01T17:35:19Z | false | 0 | 782,579,262 | null | Python | null | [
"policy-gradient",
"policy-optimization"
] | bluewoods127/AMPG | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-02T16:59:40Z | [] | 2024-04-07T04:52:38Z | [
"rl.policy-gradient"
] | 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 | [] | 2024-05-05T07:17:33Z | https://github.com/bluewoods127/AMPG | null |
[
"generative-ai",
"language",
"multimodal-learning",
"natural-language-processing"
] | [
"language-model",
"pretraining"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.large-language-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-06T03:26:11Z | The code for paper:Joint Visual and Text Prompting for Improved Object-Centric Perception with Multimodal Large Language Models | [
"generative-ai",
"language",
"multimodal-learning",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 1 | 782,788,553 | null | null | null | [
"language-model",
"pretraining"
] | jiangsongtao/VTprompt | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-23T15:59:01Z | [] | 2024-04-06T06:01:20Z | [
"llm.large-language-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 4 | [] | 2024-09-23T07:49:44Z | https://github.com/jiangsongtao/VTprompt | null |
[
"generative-ai"
] | [
"diffusion"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"recall.name.diffusion-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-06T17:07:34Z | [ ECCV 2024 ] MotionLCM: This repo is the official implementation of "MotionLCM: Real-time Controllable Motion Generation via Latent Consistency Model" | [
"generative-ai"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T13:50:07Z | false | 18 | 783,004,638 | null | Python | NOASSERTION | [
"diffusion"
] | Dai-Wenxun/MotionLCM | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-09-26T13:50:07Z | [
"2404.19759"
] | 2026-09-23T18:53:51Z | [
"recall.name.diffusion-model"
] | 2a8d88a1b8f5e61eabaa7dcd98ee02a6d2363f77 | 2026-10-01T19:11:32Z | gh-ml-readme-evidence-v2 | "2a8d88a1b8f5e61eabaa7dcd98ee02a6d2363f77" | [
"abstract",
"citation",
"method",
"other",
"usage"
] | [
"method-contribution",
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-10-01T19:11:32Z | Dai-Wenxun/MotionLCM | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 469 | [] | 2026-09-23T18:53:55Z | https://github.com/Dai-Wenxun/MotionLCM | null |
[
"geospatial",
"radar-imaging",
"remote-sensing"
] | [
"change-detection",
"image-classification",
"self-supervised-learning",
"transformer"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"geo.sar-imagery"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-07T07:53:54Z | This is a new SAR-ATR model by considering both CNN-based features and transformer-based features to further enhance the representation ability of SAR targets under limited samples. | [
"geospatial",
"radar-imaging",
"remote-sensing"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 0 | 783,197,710 | null | Python | null | [
"change-detection",
"image-classification",
"self-supervised-learning",
"transformer"
] | Crush0416/MANets | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-01T17:35:19Z | [] | 2024-06-21T08:23:34Z | [
"geo.sar-imagery"
] | ee3f58df9d6931ea7a4a940aa14014418299a903 | 2026-09-30T18:16:34Z | gh-ml-readme-evidence-v2 | "ee3f58df9d6931ea7a4a940aa14014418299a903" | [
"installation",
"other",
"usage"
] | [
"ml-method-context"
] | ok | 2026-09-30T18:16:34Z | Crush0416/MANets | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue"
] | include | ml-contribution-v5 | 8 | [] | 2025-12-06T10:49:40Z | https://github.com/Crush0416/MANets | null |
[
"deep-learning",
"graph-learning",
"recommender-systems"
] | [
"graph-transformer",
"message-passing",
"transformer"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.graph-transformer"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-08T07:48:02Z | The official PyTorch implementation for SIGIR 2024 paper "SIGformer: sign-aware graph transformer for recommendation". | [
"deep-learning",
"graph-learning",
"recommender-systems"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 3 | 783,595,355 | null | Python | null | [
"graph-transformer",
"message-passing",
"transformer"
] | StupidThree/SIGformer | [
"description",
"query-match",
"repository-metadata"
] | 5 | 2026-10-01T17:35:19Z | [] | 2024-09-04T02:55:55Z | [
"graph.graph-transformer"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 22 | [] | 2026-04-10T02:14:48Z | https://github.com/StupidThree/SIGformer | null |
[
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion",
"generative-modeling"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.image-diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-08T13:23:43Z | Official implementation of the ECCV paper "SwapAnything: Enabling Arbitrary Object Swapping in Personalized Visual Editing" | [
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-30T16:30:43Z | false | 16 | 783,736,494 | https://swap-anything.github.io | Python | MIT | [
"diffusion",
"generative-modeling"
] | UCSB-AI/swap-anything | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-04T15:58:58Z | [] | 2024-10-10T19:02:15Z | [
"vision.image-diffusion"
] | 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 | 262 | [
"diffusion-models",
"image-editing",
"personalization",
"photoswapping",
"subject-driven-generation",
"swap-anything"
] | 2026-08-20T21:18:28Z | https://github.com/UCSB-AI/swap-anything | null |
[
"generative-modeling",
"representation-learning",
"transfer-learning"
] | [
"contrastive-learning",
"domain-adaptation",
"neural-network",
"transfer-learning"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"general.domain-adaptation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-08T14:36:38Z | Official Implementation for the paper Deep Domain Adaptation: A Sim2Real Neural Approach for Improving Eye-Tracking System. | [
"generative-modeling",
"representation-learning",
"transfer-learning"
] | [
"deep-learning",
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-01T17:35:19Z | false | 2 | 783,770,812 | https://doi.org/10.1145/3654703 | Python | NOASSERTION | [
"contrastive-learning",
"domain-adaptation",
"neural-network",
"transfer-learning"
] | PerForm-Lab-RIT/domain-adaptation-eye-tracking | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 2 | 2026-10-05T20:10:04Z | [] | 2024-06-07T23:32:07Z | [
"general.domain-adaptation"
] | 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 | [
"contrastive-learning",
"cycle-gan",
"deep-learning",
"domain-adaptation",
"domain-generalization",
"eye-segmentation",
"neural-networks"
] | 2026-07-06T13:26:24Z | https://github.com/PerForm-Lab-RIT/domain-adaptation-eye-tracking | null |
[
"generative-ai",
"generative-modeling",
"graph-learning"
] | [
"diffusion",
"graph-diffusion",
"graph-generation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.graph-diffusion",
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-09T06:46:16Z | A novel molecular graph diffusion model | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 0 | 784,084,707 | null | Python | MIT | [
"diffusion"
] | zhang-xuan1314/MG-Diff | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-12T20:31:19Z | [] | 2024-04-09T06:48:19Z | [
"llm.diffusion-phrase"
] | 24cf4a10b23ce5550cafc7639989231c01d07037 | 2026-09-26T15:53:33Z | gh-ml-readme-evidence-v2 | "24cf4a10b23ce5550cafc7639989231c01d07037" | [
"other"
] | [
"ml-method-context"
] | ok | 2026-09-26T15:53:33Z | zhang-xuan1314/MG-Diff | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [] | 2024-04-09T06:48:22Z | https://github.com/zhang-xuan1314/MG-Diff | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-09T12:04:54Z | [WSDM 2022] The official implementation for paper: Ada-GNN: Adapting to Local Patterns for Improving Graph Neural Networks | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-13T18:05:26Z | false | 1 | 784,210,055 | null | Python | null | [
"graph-neural-network",
"message-passing",
"neural-network"
] | LuoZhhh/Ada-GNN | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-13T18:05:26Z | [] | 2024-06-27T10:57:12Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 2 | [] | 2025-11-13T15:49:50Z | https://github.com/LuoZhhh/Ada-GNN | null |
[
"sequence-modeling",
"time-series"
] | [
"sequence-modeling",
"state-space-model"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"timeseries.state-space-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-09T16:40:25Z | [NeurIPS 2024] Official implementation of MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection. | [
"sequence-modeling",
"time-series"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 12 | 784,334,947 | null | Python | null | [
"sequence-modeling",
"state-space-model"
] | lewandofskee/MambaAD | [
"description",
"query-match",
"repository-metadata"
] | 7 | 2026-10-01T17:35:19Z | [] | 2025-03-13T02:54:45Z | [
"timeseries.state-space-models"
] | 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 | 220 | [] | 2026-09-17T16:10:20Z | https://github.com/lewandofskee/MambaAD | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | true | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-10T07:17:53Z | The official code repo of this paper: ArtiDiffusion: A Targeted Backdoor Attack Against Diffusion Models for Image-to-Image Translation Tasks | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-13T18:05:26Z | false | 0 | 784,604,163 | null | Python | MIT | [
"diffusion"
] | saulchiu/ArtiDiffusion | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-13T18:05:26Z | [] | 2026-04-06T15:48:22Z | [
"llm.diffusion-phrase"
] | 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 | [] | 2026-09-21T12:07:06Z | https://github.com/saulchiu/ArtiDiffusion | null |
[
"computer-vision",
"language",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"vision"
] | [
"contrastive-learning",
"image-classification",
"image-text",
"multimodal-learning",
"vision-language",
"vision-language-model"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.vision-language",
"multimodal.vlm",
"vision.image-classification",
"vision.vision-language"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-10T08:11:16Z | PRCV'24 | Official PyTorch implementation of "Pseudo-Prompt Generating in Pre-trained Vision-Language Models for Multi-Label Medical Image Classification" | [
"computer-vision",
"multimodal",
"multimodal-learning",
"natural-language-processing"
] | [
"classifier"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 3 | 784,623,109 | null | Python | null | [
"multimodal-learning",
"vision-language"
] | fallingnight/PsPG | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-10-12T20:31:19Z | [] | 2024-07-09T18:21:15Z | [
"vision.vision-language"
] | 53015b5f553fb2c2df183aae37f648f2f0b1c940 | 2026-10-01T19:11:32Z | gh-ml-readme-evidence-v2 | "53015b5f553fb2c2df183aae37f648f2f0b1c940" | [
"dataset",
"other",
"usage"
] | [
"method-contribution",
"ml-method-context",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-10-01T19:11:32Z | fallingnight/PsPG | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-method-context",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 12 | [] | 2026-05-23T16:56:32Z | https://github.com/fallingnight/PsPG | null |
[
"distributed-ml",
"machine-learning-systems",
"privacy-and-federated-learning"
] | [
"collaborative-learning",
"federated-learning",
"pruning"
] | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-10T08:32:14Z | Official code for Breaking the Capacity Bottleneck in Model-Heterogeneous Federated Learning via Gradual Model Restoration, accepted at ICML 2026. Gradual Model Restoration re-activates bandw idth-constrained clients in the late stage of MHFL. Preprint: https://arxiv.org/abs/2512.05372 | [
"distributed-ml",
"machine-learning-systems",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-13T18:05:26Z | false | 0 | 784,631,202 | null | Jupyter Notebook | null | [
"collaborative-learning",
"federated-learning",
"pruning"
] | machengjie321/ICML2026-FedGMR | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-10-13T18:05:26Z | [] | 2026-05-13T03:31:19Z | [
"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 | 1 | [
"asynchronous",
"federated-learning",
"pruning"
] | 2026-07-24T05:11:35Z | https://github.com/machengjie321/ICML2026-FedGMR | null |
[
"machine-learning"
] | [
"adaptation",
"test-time-adaptation"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"specialized.test-time-adaptation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-10T08:38:02Z | PyTorch implementation of our CVPR 2024 paper "Unified Entropy Optimization for Open-Set Test-Time Adaptation" | [
"machine-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 0 | 784,633,697 | null | Python | MIT | [
"adaptation",
"test-time-adaptation"
] | gaozhengqing/UniEnt | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-01T17:35:19Z | [] | 2024-08-30T06:32:42Z | [
"specialized.test-time-adaptation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 34 | [
"open-set-recognition",
"test-time-adaptation"
] | 2026-09-05T09:01:54Z | https://github.com/gaozhengqing/UniEnt | null |
[
"generative-ai",
"language",
"natural-language-processing"
] | [
"language-model",
"pretraining",
"transformer"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"llm.language-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-10T14:17:26Z | Official implementation for our paper: "NICA at EXIST CLEF Tasks 2024", CLEF 2024: Conference and Labs of the Evaluation Forum, September 9–12, 2024, Grenoble, France | [
"generative-ai",
"language",
"natural-language-processing"
] | [
"large-language-model",
"classifier",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-10T16:53:26Z | false | 0 | 784,768,916 | null | Jupyter Notebook | null | [
"language-model",
"pretraining",
"transformer"
] | AylinNaebzadeh/NICA-at-CLEF-EXIST2024 | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 1 | 2026-10-10T16:53:26Z | [] | 2026-03-15T09:43:43Z | [
"llm.language-models"
] | 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 | [
"clip",
"large-language-models",
"nlp",
"text-classification",
"transformer"
] | 2026-03-15T09:43:47Z | https://github.com/AylinNaebzadeh/NICA-at-CLEF-EXIST2024 | null |
[
"computer-vision",
"deep-learning",
"multimodal",
"representation-learning"
] | [
"contrastive-learning",
"multimodal-learning",
"self-supervised-learning",
"vision-language"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning",
"vision.vision-language"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-11T08:43:31Z | Official Implementation of the ECCV 2024 Paper: "CLAP: Isolating Content from Style through Contrastive Learning with Augmented Prompts" | [
"deep-learning",
"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-30T16:30:43Z | false | 5 | 785,124,270 | https://yichaocai.com/clap_paper.github.io/ | Python | null | [
"contrastive-learning",
"self-supervised-learning"
] | YichaoCai1/CLAP | [
"description",
"query-match",
"repository-metadata"
] | 5 | 2026-10-30T17:16:29Z | [] | 2025-10-24T21:18:30Z | [
"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 | 56 | [] | 2026-04-06T16:52:11Z | https://github.com/YichaoCai1/CLAP | null |
[
"computer-vision",
"multimodal-learning",
"robotics"
] | [
"visual-localization",
"visual-place-recognition"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.visual-localization"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-11T14:50:09Z | official implementation of PRAM: Place Recognition Anywhere Model for Efficient Visual Localization | [
"computer-vision",
"multimodal-learning",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 6 | 785,271,694 | https://feixue94.github.io/pram-project/ | Python | NOASSERTION | [
"visual-localization",
"visual-place-recognition"
] | feixue94/pram | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-10-02T16:44:32Z | [] | 2024-12-10T16:33:53Z | [
"vision.visual-localization"
] | b8ceb745c82fd44f1ef2c1808ab3993fb4d3890c | 2026-10-01T19:11:32Z | gh-ml-readme-evidence-v2 | "b8ceb745c82fd44f1ef2c1808ab3993fb4d3890c" | [
"citation",
"dataset",
"method",
"other"
] | [
"method-contribution",
"ml-method-context",
"paper-reference"
] | ok | 2026-10-01T19:11:32Z | feixue94/pram | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-method-context",
"paper-reference"
] | include | ml-contribution-v5 | 73 | [
"multimodality-localization",
"self-defined-semantics",
"sparse-place-recognition",
"visual-localization"
] | 2026-05-04T16:49:14Z | https://github.com/feixue94/pram | null |
[
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion",
"generative-modeling"
] | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"vision.image-diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-12T05:38:48Z | Official PyTorch implementation for ZoDi: Zero-Shot Domain Adaptation with Diffusion-Based Image Transfer (ECCV2024 Workshop) | [
"computer-vision",
"generative-ai",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 1 | 785,550,330 | https://arxiv.org/abs/2403.13652 | Python | MIT | [
"diffusion",
"generative-modeling"
] | azuma164/ZoDi | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2024-09-25T06:26:19Z | [
"vision.image-diffusion"
] | 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 | 9 | [] | 2025-07-09T10:36:04Z | https://github.com/azuma164/ZoDi | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-12T09:02:31Z | Official Implementation of the paper 'Diffusion Models Encode the Dimension of the Data Manifold' | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-11T16:48:52Z | false | 3 | 785,619,959 | https://gbatzolis.github.io/ID-diff/ | Python | null | [
"diffusion"
] | GBATZOLIS/ID-diff | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-11T16:48:52Z | [] | 2024-05-07T12:57:04Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 11 | [] | 2026-09-09T09:03:28Z | https://github.com/GBATZOLIS/ID-diff | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"multimodal",
"vision"
] | [
"diffusion",
"generative-modeling",
"image-generation",
"text-to-image"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"multimodal.text-to-image",
"vision.image-diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-12T15:07:53Z | Official implementation of the papers "Blending Concepts with Text-to-Image Diffusion Models" and "How to Blend Concepts in Diffusion Models" presented at KMG@ECCV 2024. | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 1 | 785,760,284 | null | Python | null | [
"diffusion"
] | LorenzoOlearo/blending-diffusion-models | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-12T20:31:19Z | [] | 2025-12-22T15:16:49Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 2 | [] | 2025-12-22T15:17:45Z | https://github.com/LorenzoOlearo/blending-diffusion-models | null |
[
"efficient-ml",
"model-compression"
] | [
"distillation",
"knowledge-distillation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-12T19:18:13Z | A New Algorithm For Knowledge Distillation | [
"efficient-ml",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 0 | 785,857,093 | null | Python | null | [
"distillation",
"knowledge-distillation"
] | QiyouChang/TrainTimeKD | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-01T17:50:46Z | [] | 2024-05-04T01:11:46Z | [
"efficiency.knowledge-distillation"
] | a2eed7c56557bbf18b22aab75820fcd11e16e757 | 2026-10-01T19:11:32Z | gh-ml-readme-evidence-v2 | "a2eed7c56557bbf18b22aab75820fcd11e16e757" | [
"installation",
"method",
"other",
"usage"
] | [
"ml-method-context"
] | ok | 2026-10-01T19:11:32Z | QiyouChang/TrainTimeKD | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [] | 2024-05-04T01:11:50Z | https://github.com/QiyouChang/TrainTimeKD | null |
[
"computer-vision",
"generative-ai",
"health-and-biomedicine",
"language",
"medical-imaging"
] | [
"image-analysis",
"language-model",
"segmentation",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.transformers",
"medical.medical-image-segmentation",
"medical.medical-imaging",
"vision.segmentation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-12T20:07:26Z | Official Implementation of SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentation (CVPR 2024) | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 33 | 785,872,631 | https://osupcvlab.github.io/SegFormer3D/ | Python | GPL-3.0 | [
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] | 11 | 2026-10-22T16:09:07Z | [] | 2026-03-21T16:03:50Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 221 | [
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] | 2026-10-15T16:34:14Z | https://github.com/OSUPCVLab/SegFormer3D | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-12T22:59:20Z | [CVPRW 2024] Official PyTorch implementation for SkipPLUS: Skip the First Few Layers to Better Explain Vision Transformers, a novel method to enhance attribution methods on Vision Transformer models. We also do a comprehensive benchmark of the prominent white-box attribution methods applicable to Transformers. | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 1 | 785,918,776 | null | null | null | [
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] | NightMachinery/SkipPLUS-CVPR-2024 | [
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] | 2 | 2026-10-02T16:44:32Z | [] | 2024-11-24T17:37:01Z | [
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] | ok | 2026-09-28T20:05:39Z | NightMachinery/SkipPLUS-CVPR-2024 | specific-method-with-novelty-claim | [
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] | include | ml-contribution-v5 | 3 | [] | 2025-03-31T09:57:34Z | https://github.com/NightMachinery/SkipPLUS-CVPR-2024 | null |
[
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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 | [] | 2024-04-13T02:24:53Z | Code for paper "GPFL : Personalized Federated Learning with Client Collaboration Graphs for Non-IID Data" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-11T16:48:52Z | false | 0 | 785,957,584 | null | Python | null | [
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] | consult98/GPFL | [
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] | 1 | 2026-10-11T16:48:52Z | [] | 2024-04-13T10:17:04Z | [
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] | include | ml-contribution-v5 | 2 | [] | 2026-01-18T16:31:29Z | https://github.com/consult98/GPFL | null |
[
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] | [
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] | [
"description",
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"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-13T20:28:01Z | The official code for the paper: Enhancing Graph Neural Networks in Large-scale Traffic Incident Analysis with Concurrency Hypothesis | [
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"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-13T18:05:26Z | false | 0 | 786,233,475 | null | Python | null | [
"graph-neural-network",
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] | xiwenc1/Incident-GNN-CP | [
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] | 1 | 2026-10-13T18:05:26Z | [] | 2024-06-01T20:03:00Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 2 | [] | 2024-12-16T00:08:01Z | https://github.com/xiwenc1/Incident-GNN-CP | null |
[
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] | [
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] | [
"recall26.image-denoising",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-14T14:44:41Z | [Official Repo] WiTUnet: A New U-Shaped Architecture Integrating CNN and Transformer for Improved Feature Alignment and Fuse local and non-local Information. | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-16T15:36:56Z | false | 4 | 786,463,702 | null | Python | null | [
"image-denoising",
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] | woldier/WiTUnet | [
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] | 2 | 2026-10-17T13:17:44Z | [] | 2024-04-29T04:37:48Z | [
"recall26.image-denoising"
] | 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 | 16 | [] | 2026-03-18T09:02:33Z | https://github.com/woldier/WiTUnet | null |
[
"generative-modeling"
] | [
"diffusion"
] | [
"description",
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] | [] | false | unknown | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-15T13:38:16Z | [ICCV'25 Best Paper Candidate] Official Implementations for Paper: Dynamic Typography: Bringing Text to Life via Video Diffusion Prior | [
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-22T17:39:57Z | false | 16 | 786,883,707 | https://arxiv.org/abs/2404.11614 | Python | Apache-2.0 | [
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] | zliucz/animate-your-word | [
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] | 1 | 2026-10-22T17:39:57Z | [
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] | 2025-11-11T08:41:03Z | [] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 355 | [] | 2026-09-30T14:38:38Z | https://github.com/zliucz/animate-your-word | {"discovery_source":"hf_daily_papers","paper_evidence":"unverified","queryless":true} |
[
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] | [
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] | [
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] | [
"general.contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-15T15:22:06Z | the code for paper Forecasting the Clinical Status of a Patient Through Contrastive Learning | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-31T16:49:26Z | false | 0 | 786,933,817 | null | Python | null | [
"contrastive-learning",
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] | allhailzzq/CCF-CL | [
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] | 1 | 2026-10-31T16:49:26Z | [] | 2024-04-15T15:52:41Z | [
"general.contrastive-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2024-04-15T15:23:41Z | https://github.com/allhailzzq/CCF-CL | null |
[
"computer-vision",
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] | [
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] | [
"description",
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"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"vision.video-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-15T21:48:03Z | Official implementation of Ctrl-Adapter: An Efficient and Versatile Framework for Adapting Diverse Controls to Any Diffusion Model (ICLR 2025 Oral) | [
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"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-02T16:44:32Z | false | 14 | 787,090,239 | https://ctrl-adapter.github.io/ | Python | Apache-2.0 | [
"diffusion",
"generative-modeling",
"video-generation"
] | HL-hanlin/Ctrl-Adapter | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-10-16T15:36:56Z | [
"2404.09967"
] | 2025-02-11T15:49:28Z | [
"vision.video-generation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 474 | [] | 2026-09-08T09:22:48Z | https://github.com/HL-hanlin/Ctrl-Adapter | null |
[
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] | [
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] | [
"description",
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"repository-metadata"
] | [
"nlp.dialogue-systems"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-17T03:10:54Z | Code for our ECAI 2024 paper: "HAIformer: Human-AI Collaboration Framework for Disease Diagnosis via Doctor-Enhanced Transformer" | [
"natural-language-processing"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-15T15:20:52Z | false | 0 | 787,710,120 | null | Python | null | [
"conversational-ai",
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] | mercyzi/HAIformer | [
"description",
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"repository-metadata"
] | 1 | 2026-10-15T15:20:52Z | [] | 2024-08-21T11:52:29Z | [
"nlp.dialogue-systems"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 0 | [] | 2025-03-24T15:06:27Z | https://github.com/mercyzi/HAIformer | null |
[
"automl",
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] | [
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] | [
"description",
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"repository-metadata"
] | [
"general.meta-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-17T08:48:44Z | [IJCAI 2024] Official code for "Hacking Task Confounder in Meta-Learning". | [
"automl",
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-02T16:44:32Z | false | 0 | 787,822,562 | null | Python | null | [
"few-shot-learning",
"meta-learning"
] | WangJingyao07/MetaCRL | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-02T16:44:32Z | [] | 2024-04-18T12:32:52Z | [
"general.meta-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 9 | [] | 2025-07-04T07:36:41Z | https://github.com/WangJingyao07/MetaCRL | null |
[
"computer-vision",
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] | [
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] | [
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] | [
"medical.medical-image-segmentation",
"specialized.test-time-adaptation",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-18T14:09:13Z | This repository contains the pytorch code for our CVPRW 2024 paper "Test-Time Adaptation with SaLIP: A Cascade of SAM and CLIP for Zero-shot Medical Image Segmentation". | [
"computer-vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 7 | 788,487,858 | null | Python | MIT | [
"segmentation",
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] | aleemsidra/SaLIP | [
"description",
"license-metadata",
"query-match",
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] | 7 | 2026-10-22T16:09:07Z | [] | 2024-04-18T14:16:14Z | [
"vision.segmentation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 54 | [] | 2026-05-01T07:43:06Z | https://github.com/aleemsidra/SaLIP | 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 | [] | 2024-04-18T17:47:07Z | This repository is the official implementation of our paper Robust Diffusion Model-Generated Image Detection with CLIP, accepted by MIPR 2024 | [
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"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-12T20:31:19Z | false | 2 | 788,585,712 | null | Python | MIT | [
"diffusion"
] | Purdue-M2/Robust_DM_Generated_Image_Detection | [
"description",
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"query-match",
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] | 1 | 2026-10-12T20:31:19Z | [] | 2024-06-13T21:29:37Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 11 | [] | 2026-04-25T19:02:31Z | https://github.com/Purdue-M2/Robust_DM_Generated_Image_Detection | null |
[
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"machine-learning",
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] | [
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] | [
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] | [
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"general.contrastive-learning",
"probable.evaluation-ood-readme"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-19T01:09:45Z | This repository is an implementation of our paper "Contrastive Prior Enhances the Performance of Bayesian Neural Network-based Molecular Property Prediction" in PyTorch | [
"deep-learning",
"materials-science",
"representation-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 1 | 788,730,705 | null | Python | Apache-2.0 | [
"contrastive-learning",
"neural-network",
"self-supervised-learning"
] | ljatynu/CPBayesMPP | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-11-01T17:09:41Z | [] | 2025-12-11T07:48:20Z | [
"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",
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] | include | ml-contribution-v5 | 1 | [] | 2025-12-11T07:48:25Z | https://github.com/ljatynu/CPBayesMPP | null |
[
"scientific-computing"
] | [
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] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"probable.scientific-surrogate-modeling"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-19T07:30:37Z | [ICML 2024] Official PyTorch implementation of the Vectorized Conditional Neural Field. | [
"scientific-computing"
] | [
"deep-learning",
"machine-learning",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-08T17:58:00Z | false | 0 | 788,840,807 | null | Python | MIT | [
"scientific-machine-learning",
"surrogate-modeling",
"transformer"
] | jhagnberger/vcnef | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-11T16:48:52Z | [] | 2024-08-01T11:16:28Z | [
"probable.scientific-surrogate-modeling"
] | 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 | [
"deep-learning",
"machine-learning",
"partial-differential-equations",
"transformer-architecture"
] | 2026-04-14T08:40:57Z | https://github.com/jhagnberger/vcnef | null |
[
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"forecasting",
"sequence-modeling"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"timeseries.forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-19T09:33:56Z | [TKDE 2024] Official Code of the paper "Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting" | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-14T16:10:46Z | false | 2 | 788,889,445 | null | Python | MIT | [
"forecasting",
"sequence-modeling"
] | JLDeng/SCNN | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-17T13:17:44Z | [] | 2024-07-13T01:34:36Z | [
"timeseries.forecasting"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 33 | [] | 2026-05-22T13:25:14Z | https://github.com/JLDeng/SCNN | null |
[
"generative-ai",
"language",
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] | [
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] | [
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"query-match",
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] | [
"llm-code-generation",
"llm.evaluation",
"llm.language-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-20T03:50:25Z | Code for our paper, "BADGE: BADminton report Generation and Evaluation with LLM," presented at the IJCAI 2024 Workshop IT4PSS. | [
"generative-ai",
"language",
"natural-language-processing",
"software-engineering"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-06T17:10:23Z | false | 1 | 789,257,037 | https://arxiv.org/abs/2406.18116 | Python | MIT | [
"code-generation",
"language-model",
"pretraining"
] | AndyChiangSH/BADGE | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 2 | 2026-10-08T18:15:14Z | [] | 2024-07-22T10:26:32Z | [
"llm-code-generation",
"llm.language-models"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-context-only",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 8 | [
"badminton",
"evaluation",
"generation",
"large-language-models",
"report"
] | 2026-08-09T21:07:06Z | https://github.com/AndyChiangSH/BADGE | null |
[
"computer-vision",
"deep-learning",
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] | [
"contrastive-learning",
"self-supervised-learning"
] | [
"description",
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"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-20T09:12:40Z | Official implementation of the paper ``DeepFeatureX-SN: Generalization of deepfake detection via contrastive learning'', accepted for publication at the Springer journal 'Multimedia Tools and Application'. | [
"computer-vision",
"deep-learning",
"representation-learning"
] | [
"deep-learning",
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-31T16:49:26Z | false | 2 | 789,341,624 | https://doi.org/10.1007/s11042-025-21060-1 | Python | MIT | [
"contrastive-learning",
"self-supervised-learning"
] | opontorno/DeepFeatureX-SN | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-10-31T16:49:26Z | [] | 2025-10-24T14:23:58Z | [
"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 | 1 | [
"computer-vision",
"deep-learning",
"deepfakes",
"machine-learning",
"multimedia-forensics"
] | 2025-11-27T18:16:21Z | https://github.com/opontorno/DeepFeatureX-SN | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion",
"self-attention"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-21T04:54:28Z | Official implementation of the paper "Attentive Eraser: Unleashing Diffusion Model’s Object Removal Potential via Self-Attention Redirection Guidance" (AAAI 2025 Oral) | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-11T16:48:52Z | false | 10 | 789,625,925 | null | Jupyter Notebook | Apache-2.0 | [
"diffusion",
"self-attention"
] | Alibaba-VELLDEPTH/AttentiveEraser | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-11T16:48:52Z | [] | 2026-06-16T06:29:01Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 221 | [] | 2026-08-15T14:29:14Z | https://github.com/Alibaba-VELLDEPTH/AttentiveEraser | null |
[
"computer-vision",
"data-centric-ai",
"generative-modeling",
"health-and-biomedicine",
"machine-learning",
"medical-imaging"
] | [
"active-learning",
"diffusion",
"sample-selection",
"segmentation"
] | [
"description",
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"query-match",
"repository-metadata"
] | [
"general.active-learning",
"medical.medical-image-segmentation",
"vision.segmentation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-21T07:00:59Z | PyTorch implementation of our paper "Rethinking Deep Active Learning for Medical Image Segmentation: A Diffusion and Angle-based Framework" | [
"computer-vision",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 0 | 789,653,092 | null | Python | null | [
"diffusion",
"segmentation"
] | miccaiif/DifABAL | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 3 | 2026-10-23T15:59:01Z | [] | 2024-04-27T08:08:48Z | [
"vision.segmentation"
] | 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 | [
"active-learning",
"active-sampling",
"angle-based-outlier-detection",
"data-mining",
"medical-image-segmentation"
] | 2025-02-19T03:12:58Z | https://github.com/miccaiif/DifABAL | null |
[
"accessibility",
"computer-vision",
"speech-and-language"
] | [
"sequence-modeling",
"sign-language-recognition",
"sign-language-translation"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"recall26.sign-language"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-22T06:59:53Z | [ICASSP 2024] Official code for Slowfast Network for Continuous Sign Language Recognition | [
"accessibility",
"computer-vision",
"speech-and-language"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 17 | 790,045,541 | null | Python | null | [
"sequence-modeling",
"sign-language-recognition",
"sign-language-translation"
] | kaistmm/SlowFastSign | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 4 | 2026-10-22T16:09:07Z | [] | 2025-07-04T07:13:57Z | [
"recall26.sign-language"
] | 9b0f14a8fe75d924cf4d897f6ca19112bc5bce6f | 2026-10-01T19:11:32Z | gh-ml-readme-evidence-v2 | "9b0f14a8fe75d924cf4d897f6ca19112bc5bce6f" | [
"abstract",
"citation",
"other"
] | [
"ml-method-context",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-10-01T19:11:32Z | kaistmm/SlowFastSign | readme-supported-paper-method-implementation | [
"ml-method-context",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 67 | [
"continuous-sign-language",
"sign-language-recognition"
] | 2026-08-31T12:15:41Z | https://github.com/kaistmm/SlowFastSign | null |
[
"3d-vision",
"computer-vision",
"graphics"
] | [
"3d-representation-learning",
"occupancy-network"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"cross26.graphics-occupancy-network"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-22T13:08:13Z | Official implementation for 'SparseOcc: Rethinking Sparse Latent Representation for Vision-Based Semantic Occupancy Prediction' (CVPR 2024) | [
"3d-vision",
"computer-vision",
"graphics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T22:51:24Z | false | 5 | 790,206,471 | null | Python | Apache-2.0 | [
"3d-representation-learning",
"occupancy-network"
] | VISION-SJTU/SparseOcc | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-09-26T22:51:24Z | [] | 2024-08-05T09:30:30Z | [
"cross26.graphics-occupancy-network"
] | bdce7f0c3f7cd7c46f5ac134da9af5069352194b | 2026-10-01T19:11:32Z | gh-ml-readme-evidence-v2 | "bdce7f0c3f7cd7c46f5ac134da9af5069352194b" | [
"abstract",
"citation",
"dataset",
"other",
"usage"
] | [
"method-contribution",
"ml-method-context",
"paper-reference"
] | ok | 2026-10-01T19:11:32Z | VISION-SJTU/SparseOcc | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-method-context",
"paper-reference"
] | include | ml-contribution-v5 | 74 | [] | 2026-07-17T07:01:55Z | https://github.com/VISION-SJTU/SparseOcc | null |
[
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"forecasting",
"neural-network",
"sequence-modeling"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"timeseries.forecasting",
"timeseries.topic"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-22T13:48:06Z | Official implementation of "Time Evidence Fusion Network: Multi-source View in Long-Term Time Series Forecasting" (https://arxiv.org/abs/2405.06419) | [
"time-series",
"time-series-and-forecasting"
] | [
"deep-learning",
"machine-learning",
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T16:30:43Z | false | 5 | 790,225,590 | https://ztxtech.github.io/Time-Evidence-Fusion-Network/ | Python | MIT | [
"neural-network",
"sequence-modeling"
] | ztxtech/Time-Evidence-Fusion-Network | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 5 | 2026-10-19T17:46:36Z | [] | 2025-04-22T12:59:43Z | [
"timeseries.topic"
] | 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 | 105 | [
"cuda",
"deep-learning",
"machine-learning",
"macos",
"neural-network",
"neural-networks",
"pytorch",
"time-series",
"time-series-analysis",
"time-series-forecasting",
"time-series-prediction",
"uestc"
] | 2026-08-18T08:29:35Z | https://github.com/ztxtech/Time-Evidence-Fusion-Network | null |
[
"3d",
"computer-vision",
"robotics"
] | [
"3d-perception",
"point-cloud-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.point-cloud"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-22T17:25:14Z | Official implementation of "Spherical Mask: Coarse-to-Fine 3D Point Cloud Instance Segmentation with Spherical Representation" | [
"3d",
"computer-vision",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-01T17:35:19Z | false | 7 | 790,330,562 | null | Python | Apache-2.0 | [
"3d-perception",
"point-cloud-learning"
] | yunshin/SphericalMask | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-23T15:59:01Z | [] | 2024-04-28T22:42:30Z | [
"multimodal.point-cloud"
] | c5a9b92d96d82f47546f1191c043f6027bb0ecfa | 2026-10-01T19:11:32Z | gh-ml-readme-evidence-v2 | "c5a9b92d96d82f47546f1191c043f6027bb0ecfa" | [
"other",
"usage"
] | [
"method-contribution",
"ml-method-context",
"paper-reference"
] | ok | 2026-10-01T19:11:32Z | yunshin/SphericalMask | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-method-context",
"paper-reference"
] | include | ml-contribution-v5 | 94 | [] | 2026-08-15T02:21:54Z | https://github.com/yunshin/SphericalMask | 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 | [] | 2024-04-24T06:31:51Z | This is source code for paper "CogCol: Code Graph Based Contrastive Learning Model for Code Summarization" | [
"deep-learning",
"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-31T16:49:26Z | false | 0 | 791,127,559 | null | null | null | [
"contrastive-learning",
"self-supervised-learning"
] | ycshi94/cogcol | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-31T16:49:26Z | [] | 2024-04-24T06:33:43Z | [
"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 | [] | 2024-12-04T13:04:47Z | https://github.com/ycshi94/cogcol | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-24T21:46:54Z | Exploring new techniques to give multiple captions for a diffusion model | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-13T18:05:26Z | false | 0 | 791,524,391 | null | Python | null | [
"diffusion"
] | seik-oh/Multi-caption-Diffusion | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-13T18:05:26Z | [] | 2024-05-10T15:24:49Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [] | 2024-05-10T15:24:59Z | https://github.com/seik-oh/Multi-caption-Diffusion | 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 | [] | 2024-04-25T03:46:54Z | The code for paper "Federated learning using a memristor compute-in-memory chip with in-situ PUF and TRNG" | [
"distributed-ml",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-11T16:48:52Z | false | 1 | 791,619,979 | null | Python | null | [
"collaborative-learning",
"federated-learning"
] | Tsinghua-LEMON-Lab/Memeristor_based_Federated_Learning | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-11T16:48:52Z | [] | 2025-03-13T15:12:31Z | [
"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 | [] | 2025-08-18T15:14:15Z | https://github.com/Tsinghua-LEMON-Lab/Memeristor_based_Federated_Learning | 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 | [] | 2024-04-25T05:47:37Z | This repository is for the new admm weight pruning methods introduced in the Neurocomputing paper: A systematic DNN weight pruning framework based on symmetric accelerated stochastic ADMM | [
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-21T15:48:06Z | false | 0 | 791,654,476 | null | Python | null | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | RockonLockon/SAS-ADMM-weight-pruning | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-21T15:48:06Z | [] | 2024-04-25T06:28:32Z | [
"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 | 1 | [] | 2025-09-18T02:00:02Z | https://github.com/RockonLockon/SAS-ADMM-weight-pruning | null |
[
"computer-vision",
"sequence-modeling",
"time-series"
] | [
"image-classification",
"sequence-modeling",
"state-space-model"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"timeseries.state-space-models",
"vision.image-classification"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-25T10:31:48Z | The official code for the paper "S2Mamba: A Spatial-spectral State Space Model for Hyperspectral Image Classification" The code will be available soon. | [
"computer-vision",
"sequence-modeling",
"time-series"
] | [
"classifier"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 2 | 791,768,391 | null | Python | null | [
"sequence-modeling",
"state-space-model"
] | PURE-melo/S2Mamba | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-10-01T17:35:19Z | [] | 2025-08-01T08:33:06Z | [
"timeseries.state-space-models"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 64 | [] | 2026-08-15T14:11:28Z | https://github.com/PURE-melo/S2Mamba | null |
[
"earth-observation",
"earth-science",
"geospatial",
"geospatial-science",
"remote-sensing",
"science-and-engineering",
"time-series-and-forecasting",
"urban-computing"
] | [
"building-detection",
"deep-learning",
"foundation-model",
"geospatial-learning",
"machine-learning",
"object-detection",
"remote-sensing",
"semantic-segmentation"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"geo.building-footprints",
"geo.topic-remote-sensing",
"science.remote-sensing"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-25T18:27:28Z | Official implementation of the WACV 2025 paper "Pix2Poly: A Sequence Prediction Method for End-to-end Polygonal Building Footprint Extraction from Remote Sensing Imagery". | [
"earth-science",
"geospatial-science",
"remote-sensing",
"science-and-engineering",
"time-series-and-forecasting"
] | [
"deep-learning",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T15:29:21Z | false | 16 | 791,977,172 | null | Python | MIT | [
"foundation-model",
"geospatial-learning",
"remote-sensing"
] | yeshwanth95/Pix2Poly | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 9 | 2026-10-15T15:20:52Z | [] | 2026-06-01T04:34:41Z | [
"science.remote-sensing"
] | 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 | 132 | [
"building-footprint-segmentation",
"deep-learning",
"pytorch",
"remote-sensing",
"road-network-extraction",
"transformers"
] | 2026-09-22T09:42:33Z | https://github.com/yeshwanth95/Pix2Poly | null |
[
"generative-ai",
"language",
"multimodal-learning",
"natural-language-processing"
] | [
"fine-tuning",
"instruction-tuning",
"language-model",
"pretraining"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.instruction-tuning",
"llm.large-language-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-26T00:23:37Z | Official Implementation of ICLR'24: Kosmos-G: Generating Images in Context with Multimodal Large Language Models | [
"generative-ai",
"language",
"multimodal-learning",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-10T16:53:26Z | false | 4 | 792,085,961 | null | Python | null | [
"language-model",
"pretraining"
] | xichenpan/Kosmos-G | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-23T15:59:01Z | [] | 2024-05-25T23:50:00Z | [
"llm.large-language-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 74 | [] | 2026-09-01T12:08:14Z | https://github.com/xichenpan/Kosmos-G | 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 | [] | 2024-04-26T02:52:41Z | Official implementation of the paper "From Optimization to Generalization: Fair Federated Learning against Quality Shift via Inter-Client Sharpness Matching" | [
"distributed-ml",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-01T17:35:19Z | false | 3 | 792,122,738 | null | Jupyter Notebook | null | [
"collaborative-learning",
"federated-learning"
] | wnn2000/FFL4MIA | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-10T16:53:26Z | [] | 2025-03-13T04:53:23Z | [
"trust.federated-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 11 | [] | 2026-09-07T14:11:41Z | https://github.com/wnn2000/FFL4MIA | null |
[
"generative-ai",
"graph-learning"
] | [
"generative-modeling",
"graph-generation",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.graph-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-26T08:46:20Z | Official Implementation of TMI24 paper "S2Former-OR: Single-Stage Bimodal Transformer for Scene Graph Generation in OR" | [
"generative-ai",
"graph-learning"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-15T15:20:52Z | false | 1 | 792,233,835 | null | Python | MIT | [
"generative-modeling",
"graph-generation",
"transformer"
] | PJLallen/S2Former-OR | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-15T15:20:52Z | [] | 2024-11-29T06:13:38Z | [
"graph.graph-generation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 4 | [] | 2024-11-29T06:13:42Z | https://github.com/PJLallen/S2Former-OR | null |
[
"reinforcement-learning"
] | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-26T14:01:54Z | code for paper "Robust Decision-Making for Off-Ramp in Prediction-based Deep Reinforcement Learning" | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-24T14:31:13Z | false | 0 | 792,358,300 | null | null | null | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | ColinFanghz/off-ramp-dir-DRL | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-24T14:31:13Z | [] | 2024-06-03T04:31:36Z | [
"rl.deep"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2024-06-03T03:54:30Z | https://github.com/ColinFanghz/off-ramp-dir-DRL | null |
[
"efficient-ml",
"generative-ai",
"generative-modeling",
"machine-learning-systems",
"model-compression",
"natural-language-processing"
] | [
"diffusion",
"neural-network-pruning",
"pruning",
"structured-pruning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning",
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-27T05:41:54Z | [COLING 2024] Official code for paper "Few-shot Temporal Pruning Accelerates Diffusion Models for Text Generation". | [
"efficient-ml",
"generative-modeling",
"machine-learning-systems",
"model-compression",
"natural-language-processing"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-12T20:31:19Z | false | 0 | 792,630,268 | null | Python | null | [
"diffusion",
"neural-network-pruning",
"pruning",
"structured-pruning"
] | bc-li/temporal-pruning | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-20T15:59:06Z | [] | 2024-07-27T16:00:22Z | [
"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 | 4 | [] | 2025-12-04T02:22:30Z | https://github.com/bc-li/temporal-pruning | null |
[
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"repository-metadata"
] | [] | false | unknown | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-27T11:51:21Z | Official implementation of FIFO-Diffusion: Generating Infinite Videos from Text without Training (NeurIPS 2024) | [
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-24T15:45:51Z | false | 36 | 792,724,435 | https://jjihwan.github.io | Python | null | [
"diffusion"
] | jjihwan/FIFO-Diffusion_public | [
"description",
"repository-metadata"
] | 1 | 2026-10-24T15:45:51Z | [
"2405.11473"
] | 2024-10-18T05:08:49Z | [] | 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 | 486 | [] | 2026-08-26T05:46:25Z | https://github.com/jjihwan/FIFO-Diffusion_public | {"discovery_source":"hf_daily_papers","paper_evidence":"unverified","queryless":true} |
[
"efficient-ml",
"model-compression"
] | [
"distillation",
"knowledge-distillation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-28T11:41:49Z | Code for our Paper "Enhancement and Evaluation for deep learning-based classification of volumetric neuroimaging with 3D-to-2D Knowledge Distillation" | [
"efficient-ml",
"model-compression"
] | [
"deep-learning",
"classifier"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 0 | 793,086,501 | null | Python | null | [
"distillation",
"knowledge-distillation"
] | LeaYoon/PPMI_3Dto2D_KD | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-02T16:59:40Z | [] | 2024-09-19T06:35:43Z | [
"efficiency.knowledge-distillation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2024-09-19T06:35:46Z | https://github.com/LeaYoon/PPMI_3Dto2D_KD | null |
[
"audio",
"generative-ai",
"language",
"natural-language-processing",
"speech"
] | [
"generative-modeling",
"inference",
"multimodal-learning",
"speech-generation",
"speech-language-model",
"text-to-speech"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"audio-voice-conversion",
"llm-text-to-speech",
"nlp-speech-language-modeling"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-28T15:58:00Z | This repository is the official implementation of SpeechGPT-gen perceptual model presented in SpeechGPT-Gen: Scaling Chain-of-Information Speech Generation. | [
"audio",
"generative-ai",
"language",
"speech"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 1 | 793,164,220 | https://github.com/0nutation/SpeechGPT | Python | MIT | [
"speech-generation",
"text-to-speech"
] | ZhangXInFD/SpeechGPT-Gen-Perceptual-Model | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-10T16:53:26Z | [] | 2024-06-17T15:59:40Z | [
"llm-text-to-speech"
] | c6cd9e60f09ee06063932f09faefae8dd21bfb1a | 2026-10-01T19:11:32Z | gh-ml-readme-evidence-v2 | "c6cd9e60f09ee06063932f09faefae8dd21bfb1a" | [
"citation",
"installation",
"method",
"other",
"usage"
] | [
"method-contribution",
"ml-method-context",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-10-01T19:11:32Z | ZhangXInFD/SpeechGPT-Gen-Perceptual-Model | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-method-context",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 7 | [] | 2025-04-15T10:03:30Z | https://github.com/ZhangXInFD/SpeechGPT-Gen-Perceptual-Model | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-29T02:08:50Z | This repository contains the official implementation of the paper titled "Probing Unlearned Diffusion Models: A Transferable Adversarial Attack Perspective". | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-12T20:31:19Z | false | 0 | 793,323,881 | null | Jupyter Notebook | MIT | [
"diffusion"
] | hxxdtd/PUND | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-12T20:31:19Z | [] | 2024-05-01T01:41:56Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 9 | [] | 2026-06-27T09:55:30Z | https://github.com/hxxdtd/PUND | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-29T06:41:54Z | This repo contains our PyTorch implementation for the paper "Mastering Context-to-Label Representation Transformation for Event Causality Identification with Diffusion Models". | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-12T20:31:19Z | false | 1 | 793,400,306 | null | Python | null | [
"diffusion"
] | hieum98/DisfusECI | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-12T20:31:19Z | [] | 2024-07-28T16:46:38Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 8 | [] | 2025-11-04T07:16:02Z | https://github.com/hieum98/DisfusECI | null |
[
"audio",
"natural-language-processing",
"speech",
"speech-and-audio"
] | [
"multimodal-learning",
"speech-language-model"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"nlp-speech-language-modeling"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-29T10:08:39Z | The official code for the SALMon🍣 benchmark (ICASSP 2025 - Oral) | [
"audio",
"natural-language-processing",
"speech",
"speech-and-audio"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 1 | 793,487,166 | https://pages.cs.huji.ac.il/adiyoss-lab/salmon/ | Python | null | [
"multimodal-learning",
"speech-language-model"
] | slp-rl/salmon | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 2 | 2026-09-30T16:30:43Z | [] | 2025-08-15T08:55:01Z | [
"nlp-speech-language-modeling"
] | 0de8aabb528b6e14eb407bb22b90bbefb06028e0 | 2026-10-01T19:11:32Z | gh-ml-readme-evidence-v2 | "0de8aabb528b6e14eb407bb22b90bbefb06028e0" | [
"citation",
"dataset",
"installation",
"method",
"other"
] | [
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-10-01T19:11:32Z | slp-rl/salmon | readme-supported-paper-method-implementation | [
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 50 | [
"acoustic-model",
"audio-processing",
"speech-language-model"
] | 2026-05-10T08:00:34Z | https://github.com/slp-rl/salmon | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-30T05:49:21Z | Official PyTorch implementation for Maximum Likelihood Training of Implicit Nonlinear Diffusion Model (INDM) in NeurIPS 2022. | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-12T20:31:19Z | false | 0 | 793,919,174 | null | Python | Apache-2.0 | [
"diffusion"
] | aailabkaist/INDM | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-12T20:31:19Z | [] | 2024-04-30T05:52:24Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 9 | [] | 2024-08-13T07:59:09Z | https://github.com/aailabkaist/INDM | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-04-30T05:53:14Z | Official PyTorch implementation for Label-Noise Robust Diffusion Models (TDSM) in ICLR 2024. | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-12T20:31:19Z | false | 0 | 793,920,402 | null | Python | Apache-2.0 | [
"diffusion"
] | aailabkaist/tdsm | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-12T20:31:19Z | [] | 2024-04-30T05:53:54Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 12 | [] | 2026-03-09T22:57:40Z | https://github.com/aailabkaist/tdsm | null |
[
"3d-vision",
"computer-vision",
"efficient-ml",
"model-compression"
] | [
"3d-perception",
"depth-estimation",
"distillation",
"knowledge-distillation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation",
"vision.depth-estimation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-01T17:26:36Z | (CVPR 2024)Official implementation of KDBTS: Boosting Self-Supervision for Single-View Scene Completion via Knowledge Distillation | [
"3d-vision",
"computer-vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 2 | 794,660,574 | https://keonhee-han.github.io/publications/kdbts/ | Python | BSD-2-Clause | [
"3d-perception",
"depth-estimation",
"distillation",
"knowledge-distillation"
] | keonhee-han/KDBTS | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-22T16:09:07Z | [] | 2026-01-20T09:13:16Z | [
"vision.depth-estimation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 10 | [] | 2026-01-20T09:13:23Z | https://github.com/keonhee-han/KDBTS | null |
[
"sequence-modeling",
"time-series"
] | [
"sequence-modeling",
"state-space-model"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"timeseries.state-space-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-02T15:03:57Z | OverlapMamba: Novel Shift State Space Model for LiDAR-based Place Recognition | [
"sequence-modeling",
"time-series"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 5 | 795,089,156 | null | Python | GPL-3.0 | [
"sequence-modeling",
"state-space-model"
] | SCNU-RISLAB/OverlapMamba | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 7 | 2026-10-01T17:35:19Z | [] | 2024-05-21T01:34:16Z | [
"timeseries.state-space-models"
] | c6012ac5fc4cf75ff53c0cdb7e333e0ed932e688 | 2026-09-26T15:53:33Z | gh-ml-readme-evidence-v2 | "c6012ac5fc4cf75ff53c0cdb7e333e0ed932e688" | [
"dataset",
"method",
"other",
"results"
] | [
"ml-method-context",
"paper-reference"
] | ok | 2026-09-26T15:53:33Z | SCNU-RISLAB/OverlapMamba | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue",
"paper-reference"
] | include | ml-contribution-v5 | 73 | [] | 2026-03-18T13:21:24Z | https://github.com/SCNU-RISLAB/OverlapMamba | null |
[
"3d",
"computer-vision",
"robotics"
] | [
"3d-perception",
"point-cloud-learning",
"transformer"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.point-cloud"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-03T03:34:03Z | Official Implementation for the Paper "TM2B: Transformer-based Motion-to-Box Network for 3D Single Object Tracking on Point Clouds" | [
"3d",
"computer-vision",
"robotics"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-24T14:31:13Z | false | 1 | 795,328,813 | null | null | null | [
"3d-perception",
"point-cloud-learning",
"transformer"
] | haooozi/TM2B | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-24T14:31:13Z | [] | 2024-05-03T03:34:04Z | [
"multimodal.point-cloud"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 2 | [] | 2024-05-21T10:07:41Z | https://github.com/haooozi/TM2B | null |
[
"computer-vision"
] | [
"mamba",
"restoration",
"super-resolution"
] | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"vision.image-super-resolution"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-03T12:26:36Z | Official PyTorch implementation of the paper IRSRMamba: Infrared Image Super-Resolution via Mamba-based Wavelet Transform Feature Modulation Model. | [
"computer-vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-01T17:35:19Z | false | 14 | 795,502,901 | https://doi.org/10.1109/TGRS.2025.3584385 | Python | Apache-2.0 | [
"mamba",
"restoration",
"super-resolution"
] | yongsongH/IRSRMamba | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 4 | 2026-10-22T16:09:07Z | [] | 2026-02-26T16:56:32Z | [
"vision.image-super-resolution"
] | 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 | 89 | [] | 2026-08-17T03:08:37Z | https://github.com/yongsongH/IRSRMamba | null |
[
"efficient-ml",
"interpretability-and-safety",
"machine-learning-systems",
"model-compression"
] | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-03T12:39:27Z | Official implementation of the paper "Zeroth-Order Adaptive Neuron Alignment Based Pruning without Re-Training", [TMLR & Workshop (SLLM) @ ICLR 2025] | [
"efficient-ml",
"interpretability-and-safety",
"machine-learning-systems",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-20T15:59:06Z | false | 0 | 795,507,649 | null | Python | Apache-2.0 | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | eliacunegatti/NeuroAL | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-20T15:59:06Z | [] | 2025-10-27T15:03:51Z | [
"efficiency.pruning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 8 | [] | 2026-02-16T14:24:15Z | https://github.com/eliacunegatti/NeuroAL | null |
[
"natural-language-processing",
"tabular-and-structured-data"
] | [
"document-classification",
"text-classification"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"nlp.text-classification"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-04T04:48:32Z | Official Implementation of the 'When XGBoost Outperforms GPT-4 on Text Classification: A Case Study' NAACL-W 2024 paper | [
"natural-language-processing",
"tabular-and-structured-data"
] | [
"large-language-model",
"classifier"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-01T17:35:19Z | false | 2 | 795,796,020 | null | Python | null | [
"document-classification",
"text-classification"
] | maty-bohacek/xgboost-vs-gpt4 | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 3 | 2026-10-25T15:11:36Z | [] | 2024-12-16T18:27:31Z | [
"nlp.text-classification"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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] | include | ml-contribution-v5 | 16 | [
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] | 2025-05-31T13:39:59Z | https://github.com/maty-bohacek/xgboost-vs-gpt4 | null |
[
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-12T20:31:19Z | false | 1 | 795,836,514 | https://emmanuelleb985.github.io/XDiffusion/ | Jupyter Notebook | MIT | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-06T08:05:49Z | A new algorithm based on DDPG algorithm, introducing self supervised learning and priority experience replay | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 0 | 796,575,061 | null | Python | null | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-06T08:24:15Z | [MICCAI, 2024] (Oral, RU @ MedIA Best Paper Award) Official implementation of the Brain Latent Progression (BrLP) method from "Enhancing Spatiotemporal Disease Progression Models via Latent Diffusion and Prior Knowledge" | [
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] | [] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-07T16:22:31Z | [CVPR 2024 AI4CC Workshop] The official implementation of the paper titled "Temporally Consistent Object Editing in Videos using Extended Attention" accepted in IEEE/CVF CVPR 2024 Workshop on AI for Content Creation (AI4CC) | [
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] | 1 | 2026-10-01T19:00:27.900159Z | [] | 2024-10-13T06:52:47Z | [] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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[
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 2 | 797,368,839 | http://amodal-flow.cs.uni-freiburg.de/ | Python | Apache-2.0 | [
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] | 2025-12-09T03:33:52Z | https://github.com/robot-learning-freiburg/amodal-optical-flow | null |
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-07T22:20:06Z | Generative Camera Dolly: Extreme Monocular Dynamic Novel View Synthesis (ECCV 2024 Oral) - Official Implementation | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-01T19:00:27.900159Z | false | 10 | 797,465,286 | https://gcd.cs.columbia.edu/ | Python | GPL-3.0 | [
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] | 2026-10-02T20:31:57Z | https://github.com/CASE-Lab-UMD/LLM-Drop | null |
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T16:30:43Z | false | 4 | 798,416,413 | https://bmvc2024.org/proceedings/922/ | Python | MIT | [
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[
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[
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[
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"graphics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T14:50:46Z | false | 0 | 799,201,183 | https://arxiv.org/abs/2405.07857 | Python | null | [
"neural-rendering",
"radiance-fields"
] | MingyuKim87/SynergyNeRF | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | 2 | 2026-10-01T17:35:19Z | [] | 2024-09-23T16:57:00Z | [
"multimodal.nerf"
] | 49dec6a2f6177697fe7da25f564a6f8f41df241a | 2026-10-02T18:21:54Z | gh-ml-readme-evidence-v2 | "49dec6a2f6177697fe7da25f564a6f8f41df241a" | [
"dataset",
"installation",
"other"
] | [
"method-contribution",
"ml-method-context",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-10-02T18:21:54Z | MingyuKim87/SynergyNeRF | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-method-context",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 12 | [
"few-shot",
"inr",
"nerf",
"resnet",
"sparse-input"
] | 2026-04-02T05:59:46Z | https://github.com/MingyuKim87/SynergyNeRF | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-11T18:36:29Z | [ICLR 2024] Official code for the paper 'Elucidating the Exposure Bias in Diffusion Models' | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-12T20:31:19Z | false | 1 | 799,309,159 | null | Jupyter Notebook | MIT | [
"diffusion"
] | forever208/LDM-ES | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-12T20:31:19Z | [] | 2024-05-12T21:51:59Z | [
"llm.diffusion-phrase"
] | 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 | [] | 2026-07-10T02:43:19Z | https://github.com/forever208/LDM-ES | null |
[
"reinforcement-learning"
] | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-13T09:59:42Z | code for paper "Deep Reinforcement Learning with Driving Intention for on-Ramp Decision-Making" | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-24T14:31:13Z | false | 0 | 799,893,264 | null | null | null | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | ColinFanghz/on-ramp-dir-DRL | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-24T14:31:13Z | [] | 2024-05-13T10:01:02Z | [
"rl.deep"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [] | 2024-08-01T11:20:38Z | https://github.com/ColinFanghz/on-ramp-dir-DRL | null |
[
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion",
"restoration",
"super-resolution"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"vision.image-super-resolution"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-13T12:15:33Z | [MICCAI 2024] Official Pytorch implementation for "Simultaneous Tri-Modal Medical Image Fusion and Super-Resolution using Conditional Diffusion Model." | [
"computer-vision",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-12T20:31:19Z | false | 1 | 799,948,512 | null | Python | null | [
"diffusion",
"restoration",
"super-resolution"
] | XylonXu01/TFS-Diff | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-22T16:09:07Z | [] | 2024-12-10T11:29:20Z | [
"vision.image-super-resolution"
] | 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 | 32 | [] | 2026-06-11T02:52:38Z | https://github.com/XylonXu01/TFS-Diff | 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 | [] | 2024-05-13T13:34:51Z | Code for paper "Anomaly Detection in Attributed Networks via Local Multi-Order Contrastive Learning and Global Topology Awareness" (Under Review) | [
"deep-learning",
"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-31T16:49:26Z | false | 0 | 799,984,265 | null | Python | null | [
"contrastive-learning",
"self-supervised-learning"
] | LiDias-ZzG/LMGTA | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-31T16:49:26Z | [] | 2025-05-13T07:04:36Z | [
"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 | [] | 2025-09-17T07:56:41Z | https://github.com/LiDias-ZzG/LMGTA | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-13T21:35:08Z | Code for paper "FreezeAsGuard: Mitigating Illegal Adaptation of Diffusion Models via Selective Tensor Freezing" | [
"generative-ai",
"generative-modeling"
] | [
"deep-learning",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-12T20:31:19Z | false | 0 | 800,186,022 | https://arxiv.org/abs/2405.17472 | Python | MIT | [
"diffusion"
] | pittisl/FreezeAsGuard | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-10-12T20:31:19Z | [] | 2024-06-09T13:17:52Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 5 | [
"deep-learning",
"diffusion-models",
"fine-tuning",
"trustworthy-ai"
] | 2025-09-09T03:56:54Z | https://github.com/pittisl/FreezeAsGuard | 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 | [] | 2024-05-14T02:48:24Z | Official Code Repository for the paper - Modeling Inter-Intra Heterogeneity for Graph Federated Learning (AAAI 2025) | [
"distributed-ml",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-11T16:48:52Z | false | 0 | 800,271,225 | null | Python | GPL-3.0 | [
"collaborative-learning",
"federated-learning"
] | blgpb/FedIIH | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-11T16:48:52Z | [] | 2025-06-24T08:26:18Z | [
"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 | 5 | [] | 2026-09-16T10:28:50Z | https://github.com/blgpb/FedIIH | null |
[
"audio",
"generative-modeling",
"speech"
] | [
"diffusion",
"flow-matching",
"inference",
"representation"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"audio-speech-enhancement"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-14T03:50:50Z | (ICASSP 2025, official code)FlowSE: Flow Matching-based Speech Enhancement | [
"audio",
"generative-modeling",
"speech"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T20:57:28.489717Z | false | 5 | 800,287,925 | https://seongqjini.com/ | Python | null | [
"diffusion",
"flow-matching",
"inference",
"representation"
] | seongq/flowmse | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 6 | 2026-10-06T17:10:23Z | [] | 2025-07-23T05:21:27Z | [
"audio-speech-enhancement"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 110 | [
"diffusion",
"flow-matching",
"flowmatching",
"speech-enhancement",
"speechenhancement"
] | 2026-09-23T08:09:13Z | https://github.com/seongq/flowmse | 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 | [] | 2024-05-14T19:37:20Z | This is the source code for paper "Enhancing Privacy in Federated Learning via Early Exit" | [
"distributed-ml",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-12T20:31:19Z | false | 0 | 800,678,381 | null | Python | null | [
"collaborative-learning",
"federated-learning"
] | ywuwuwu/Early-Exit-Privacy | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-12T20:31:19Z | [] | 2024-05-14T20:04:51Z | [
"trust.federated-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2024-05-14T20:04:55Z | https://github.com/ywuwuwu/Early-Exit-Privacy | null |
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