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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
[
"generative-ai",
"generative-modeling",
"multimodal-learning"
] | [
"diffusion"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-09-27T05:04:38Z | Official Code for IROS 2025 Paper "GDTS: Goal-Guided Diffusion Model with Tree Sampling for Multi-Modal Pedestrian Trajectory Prediction" | [
"generative-ai",
"generative-modeling",
"multimodal-learning"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 1 | 697,117,174 | null | Python | MIT | [
"diffusion"
] | Winderting/GDTS | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-08T18:15:14Z | [] | 2026-03-25T13:36:08Z | [
"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 | 1 | [] | 2026-03-25T13:36:12Z | https://github.com/Winderting/GDTS | null |
[
"machine-learning"
] | [
"adaptation",
"test-time-adaptation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"specialized.test-time-adaptation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-09-27T08:28:20Z | [ICCV23] Official Implementation of DARTH: Holistic Test-time Adaptation for Multiple Object Tracking | [
"machine-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 0 | 697,188,935 | null | Python | Apache-2.0 | [
"adaptation",
"test-time-adaptation"
] | mattiasegu/darth | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-04T15:44:14Z | [] | 2023-12-28T08:47:41Z | [
"specialized.test-time-adaptation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 24 | [] | 2026-07-07T05:16:29Z | https://github.com/mattiasegu/darth | 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 | [] | 2023-09-27T08:50:15Z | This is the official PyTorch Implementation of "SoTTA: Robust Test-Time Adaptation on Noisy Data Streams (NeurIPS '23)" by Taesik Gong*, Yewon Kim*, Taeckyung Lee*, Sorn Chottananurak, and Sung-Ju Lee (* Equal contribution). | [
"machine-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 2 | 697,197,445 | https://nmsl.kaist.ac.kr/projects/sotta | Python | MIT | [
"adaptation",
"test-time-adaptation"
] | taeckyung/SoTTA | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-18T13:47:34Z | [] | 2026-10-01T09:51:34Z | [
"specialized.test-time-adaptation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 23 | [
"test-time-adaptation"
] | 2026-10-01T09:51:38Z | https://github.com/taeckyung/SoTTA | null |
[
"deep-learning",
"representation-learning"
] | [
"contrastive-learning",
"self-supervised-learning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-09-27T22:01:48Z | [ICCV 2023] Official code of Contrastive Learning Relies More on Spatial Inductive Bias Than Supervised Learning: An Empirical Study | [
"deep-learning",
"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-01T16:56:07Z | false | 2 | 697,509,678 | https://haorantang.github.io/cl_spatial_inductive_bias/ | Python | MIT | [
"contrastive-learning",
"self-supervised-learning"
] | HaoranTang/cl_spatial_inductive_bias | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-22T16:09:07Z | [] | 2026-09-29T14:52:46Z | [
"general.contrastive-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 4 | [
"contrastive-learning"
] | 2026-10-01T01:12:54Z | https://github.com/HaoranTang/cl_spatial_inductive_bias | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"multimodal",
"speech-and-audio",
"video"
] | [
"diffusion",
"generative-modeling",
"text-to-video",
"video-generation"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.text-to-video",
"vision.video-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-09-28T10:25:03Z | [AAAI 2024] The official PyTorch implementation of "Diverse and Aligned Audio-to-Video Generation via Text-to-Video Model Adaptation" | [
"computer-vision",
"generative-ai",
"generative-modeling",
"speech-and-audio"
] | [
"deep-learning",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 14 | 697,715,713 | https://pages.cs.huji.ac.il/adiyoss-lab/TempoTokens/ | Python | MIT | [
"diffusion",
"generative-modeling",
"video-generation"
] | guyyariv/TempoTokens | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 11 | 2026-10-11T16:48:52Z | [
"2309.16429"
] | 2026-05-18T20:25:33Z | [
"vision.video-generation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 130 | [
"ai-art",
"audio-to-video",
"audio-visual",
"deep-learning",
"diffusion-models",
"generative-ai",
"modelscope",
"pytorch",
"video-synthesis"
] | 2026-09-18T12:50:21Z | https://github.com/guyyariv/TempoTokens | null |
[
"reinforcement-learning"
] | [
"batch-reinforcement-learning",
"offline-reinforcement-learning",
"reinforcement-learning"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"rl.offline"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-09-29T06:02:32Z | [ICLR 2024 Spotlight] Code for ICLR 2024 paper "Towards Robust Offline Reinforcement Learning under Diverse Data Corruption" | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-25T16:01:25Z | false | 1 | 698,084,827 | null | Python | null | [
"batch-reinforcement-learning",
"offline-reinforcement-learning",
"reinforcement-learning"
] | YangRui2015/RIQL | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 2 | 2026-10-04T15:44:14Z | [] | 2024-11-25T06:09:26Z | [
"rl.offline"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 22 | [
"offlinerl",
"reinforcement-learning",
"robust"
] | 2026-04-21T16:03:09Z | https://github.com/YangRui2015/RIQL | null |
[
"3d",
"computer-vision",
"generative-modeling",
"human-motion"
] | [
"3d-pose-estimation",
"diffusion",
"keypoint-detection",
"pose-estimation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.3d-human-pose",
"vision.pose-estimation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-09-29T08:46:48Z | Official implementation of the IROS2023 paper "DiffuPose: Monocular 3D Human Pose Estimation via Denoising Diffusion Probabilistic Model" | [
"computer-vision",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:22:14Z | false | 1 | 698,140,126 | null | HTML | null | [
"diffusion",
"keypoint-detection",
"pose-estimation"
] | lojol2327/DiffuPose | [
"description",
"query-match",
"repository-metadata"
] | 4 | 2026-10-15T15:20:52Z | [] | 2023-10-08T18:44:12Z | [
"vision.pose-estimation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 7 | [] | 2026-04-29T07:48:18Z | https://github.com/lojol2327/DiffuPose | null |
[
"3d",
"computer-vision",
"generative-modeling",
"human-motion"
] | [
"3d-pose-estimation",
"diffusion",
"keypoint-detection",
"pose-estimation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.3d-human-pose",
"vision.pose-estimation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-09-29T12:01:17Z | About Official implementation of the IROS2023 paper "DiffuPose: Monocular 3D Human Pose Estimation via Denoising Diffusion Probabilistic Model" | [
"computer-vision",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-03T15:32:04Z | false | 1 | 698,208,424 | null | Python | null | [
"diffusion",
"keypoint-detection",
"pose-estimation"
] | snu-larr/DiffuPose_official | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-15T15:20:52Z | [] | 2023-09-29T12:03:25Z | [
"vision.pose-estimation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 2 | [] | 2026-03-04T10:15:15Z | https://github.com/snu-larr/DiffuPose_official | null |
[
"multi-agent-systems",
"reinforcement-learning"
] | [
"centralized-training",
"multi-agent-reinforcement-learning",
"reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"rl.multiagent"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-09-29T12:31:35Z | Official code for the paper "Multi-Task Multi-Agent Reinforcement Learning with Interaction and Task Representations" submitted to TNNLS. | [
"multi-agent-systems",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 0 | 698,219,387 | null | Python | null | [
"centralized-training",
"multi-agent-reinforcement-learning",
"reinforcement-learning"
] | chaobiubiu/Representing-Interactions-and-Tasks | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-04T15:44:14Z | [] | 2023-09-29T12:40:29Z | [
"rl.multiagent"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 8 | [] | 2025-06-04T06:21:33Z | https://github.com/chaobiubiu/Representing-Interactions-and-Tasks | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-09-30T08:32:16Z | [Neurips 2023] Official pytorch implementation of "Addressing Negative Transfer in Diffusion Models" | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 1 | 698,546,516 | https://gohyojun15.github.io/ANT_diffusion/ | Python | MIT | [
"diffusion"
] | gohyojun15/ANT_diffusion | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2024-07-04T13:49:16Z | [
"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 | 25 | [
"diffusion-models",
"neurips-2023"
] | 2026-07-21T15:15:42Z | https://github.com/gohyojun15/ANT_diffusion | null |
[
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"forecasting",
"sequence-modeling"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"timeseries.forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-09-30T10:19:33Z | Official implementation of "WFTNet: Exploiting Global and Local Periodicity in Long-term Time Series Forecasting" (ICASSP 2024) | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-03T15:32:04Z | false | 10 | 698,574,610 | null | Python | Apache-2.0 | [
"forecasting",
"sequence-modeling"
] | Hank0626/WFTNet | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-14T16:10:46Z | [] | 2024-03-13T02:07:18Z | [
"timeseries.forecasting"
] | 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 | 56 | [
"fourier-transform",
"time-series-forecasting",
"wavelet-transform"
] | 2026-09-21T02:20:21Z | https://github.com/Hank0626/WFTNet | null |
[
"time-series",
"time-series-and-forecasting"
] | [
"sequence-modeling",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"timeseries.topic"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-09-30T10:22:30Z | Official implementation of "Periodicity Decoupling Framework for Long-term Series Forecasting" (ICLR 2024) | [
"time-series",
"time-series-and-forecasting"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-05T19:54:32Z | false | 13 | 698,575,357 | null | Python | AGPL-3.0 | [
"sequence-modeling",
"transformer"
] | Hank0626/PDF | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-20T15:59:06Z | [] | 2024-05-29T13:00:58Z | [
"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 | 151 | [
"decouple",
"fourier-transform",
"periodicity",
"time-series",
"time-series-forecasting",
"transformer"
] | 2026-09-11T09:14:33Z | https://github.com/Hank0626/PDF | 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 | [] | 2023-09-30T15:31:20Z | Official code for Alleviating Exposure Bias in Diffusion Models through Sampling with Shifted Time Steps (ICLR24), ADM ver. | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-06T17:10:23Z | false | 0 | 698,662,725 | null | Python | MIT | [
"diffusion"
] | tingyu215/TS-DPM-ADM | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-06T17:10:23Z | [] | 2024-01-17T11:21:44Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 4 | [] | 2024-12-24T17:51:04Z | https://github.com/tingyu215/TS-DPM-ADM | null |
[
"computer-vision",
"generative-modeling",
"multimodal",
"multimodal-learning",
"natural-language-processing"
] | [
"diffusion",
"multimodal-learning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"recall.name.multimodal-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-02T03:48:04Z | Official implementation of paper "MiniGPT-5: Interleaved Vision-and-Language Generation via Generative Vokens" | [
"computer-vision",
"generative-modeling",
"multimodal",
"multimodal-learning",
"natural-language-processing"
] | [
"large-language-model",
"diffusion-model",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-05T19:54:32Z | false | 52 | 699,159,727 | https://eric-ai-lab.github.io/minigpt-5.github.io/ | Python | Apache-2.0 | [
"diffusion",
"multimodal-learning"
] | UCSB-AI/MiniGPT-5 | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T19:54:32Z | [] | 2025-05-08T02:03:21Z | [
"recall.name.multimodal-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 869 | [
"diffusion-models",
"multimodal-generation",
"multimodal-llm",
"transformers"
] | 2026-08-24T14:48:25Z | https://github.com/UCSB-AI/MiniGPT-5 | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"language",
"multimodal-learning",
"natural-language-processing"
] | [
"diffusion",
"generative-modeling",
"language-model",
"pretraining",
"video-generation"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"llm.language-models",
"vision.image-diffusion",
"vision.video-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-02T17:15:55Z | [ICLR 2024] LLM-grounded Video Diffusion Models (LVD): official implementation for the LVD paper | [
"computer-vision",
"generative-ai",
"generative-modeling",
"multimodal-learning",
"natural-language-processing"
] | [
"large-language-model",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T18:03:22.392067Z | false | 8 | 699,459,449 | https://llm-grounded-video-diffusion.github.io/ | Python | null | [
"diffusion",
"generative-modeling",
"video-generation"
] | TonyLianLong/LLM-groundedVideoDiffusion | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 7 | 2026-10-11T16:48:52Z | [] | 2024-05-07T02:55:47Z | [
"vision.video-generation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 173 | [
"diffusion",
"diffusion-models",
"large-language-models",
"llm",
"text-to-image",
"text-to-video",
"text-to-video-generation",
"video-generation"
] | 2026-09-27T06:38:29Z | https://github.com/TonyLianLong/LLM-groundedVideoDiffusion | null |
[
"audio",
"generative-ai",
"generative-modeling",
"speech"
] | [
"diffusion",
"inference",
"representation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"audio-speech-enhancement",
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-03T07:29:49Z | This is official repository of new SOTA diffusion models based method for speech enhancement | [
"audio",
"generative-modeling",
"speech"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-02T16:44:32Z | false | 8 | 699,712,935 | null | Python | GPL-3.0 | [
"diffusion",
"inference",
"representation"
] | zelokuo/VPIDM | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-06T17:10:23Z | [] | 2024-07-31T09:20:47Z | [
"audio-speech-enhancement"
] | ea26bdb0fa1da772adf9ab2264c868eda36fd0ae | 2026-10-02T18:21:54Z | gh-ml-readme-evidence-v2 | "ea26bdb0fa1da772adf9ab2264c868eda36fd0ae" | [
"method",
"other",
"overview",
"usage"
] | [
"ml-method-context",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-10-02T18:21:54Z | zelokuo/VPIDM | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 44 | [] | 2026-09-09T10:25:42Z | https://github.com/zelokuo/VPIDM | null |
[
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-03T07:51:38Z | Pytorch implementation of our paper SUBP: Soft Uniform Block Pruning for 1xN Sparse CNNs Multithreading Acceleration accepted by NeurIPS 2023. | [
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-05T19:54:32Z | false | 0 | 699,720,629 | https://arxiv.org/abs/2310.06218 | Python | null | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | JingyangXiang/SUBP | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | 2 | 2026-10-18T14:44:29Z | [] | 2024-03-12T08:07:53Z | [
"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 | 22 | [] | 2024-05-24T12:15:25Z | https://github.com/JingyangXiang/SUBP | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-03T08:40:17Z | Official code repository for the paper: Removing Structured Noise using Diffusion Models | [
"generative-ai",
"generative-modeling"
] | [
"deep-learning",
"generative-model",
"machine-learning",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 4 | 699,739,420 | https://arxiv.org/abs/2302.05290 | Python | null | [
"diffusion"
] | tristan-deep/joint-diffusion | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2025-03-23T17:45:45Z | [
"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 | 26 | [
"deep-generative-model",
"deep-learning",
"diffusion-models",
"image-processing",
"inverse-problems",
"machine-learning"
] | 2026-07-21T08:15:23Z | https://github.com/tristan-deep/joint-diffusion | null |
[
"computational-science",
"physics"
] | [
"neural-operator",
"neural-operators",
"surrogate-modeling"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"science.neural-operators"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-03T12:05:30Z | Code for training and using the Born Fourier Neural Operator (BornFNO) model proposed in our ICASSP 2023 paper | [
"computational-science",
"physics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 4 | 699,819,062 | null | Jupyter Notebook | NOASSERTION | [
"neural-operator",
"neural-operators",
"surrogate-modeling"
] | merlresearch/DeepBornFNO | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-04T15:44:14Z | [] | 2023-10-03T12:05:32Z | [
"science.neural-operators"
] | 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 | 12 | [] | 2026-03-20T08:20:00Z | https://github.com/merlresearch/DeepBornFNO | null |
[
"generative-ai",
"language",
"natural-language-processing"
] | [
"language-model",
"pretraining"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.language-models",
"llm.large-language-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-04T00:52:53Z | Official implementation of "Impact of Co-occurrence on Factual Knowledge of Large Language Models" (EMNLP 2023 Findings). | [
"generative-ai",
"language",
"natural-language-processing"
] | [
"large-language-model",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 0 | 700,109,792 | https://aclanthology.org/2023.findings-emnlp.518.pdf | Jupyter Notebook | MIT | [
"language-model",
"pretraining"
] | CheongWoong/impact_of_cooccurrence | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-10T16:53:26Z | [] | 2024-12-13T05:22:24Z | [
"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 | 10 | [
"bert",
"co-occurrence",
"commonsense",
"gpt",
"knowledge-probing",
"large-language-models",
"llm",
"memorization",
"transformers"
] | 2026-09-03T15:50:04Z | https://github.com/CheongWoong/impact_of_cooccurrence | null |
[
"computer-vision",
"language",
"multimodal",
"video"
] | [
"multimodal-learning",
"video-language-model",
"video-understanding",
"vision-language"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.video-language-model",
"vision.vision-language"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-04T09:11:37Z | Official implementation of "HowToCaption: Prompting LLMs to Transform Video Annotations at Scale." ECCV 2024 | [
"computer-vision",
"multimodal"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 0 | 700,265,646 | null | Python | NOASSERTION | [
"multimodal-learning",
"vision-language"
] | ninatu/howtocaption | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-06T17:10:23Z | [] | 2025-08-19T10:42:57Z | [
"vision.vision-language"
] | e2ae71ce9ac75a00b6ee037a5c920e43efe01cf8 | 2026-09-28T20:05:39Z | gh-ml-readme-evidence-v2 | "e2ae71ce9ac75a00b6ee037a5c920e43efe01cf8" | [
"citation",
"dataset",
"other",
"results"
] | [
"method-contribution",
"ml-method-context",
"model-training-artifact",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-09-28T20:05:39Z | ninatu/howtocaption | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-context-only",
"ml-method-context",
"model-training-artifact",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 60 | [] | 2026-09-07T06:45:09Z | https://github.com/ninatu/howtocaption | null |
[
"3d",
"computer-vision",
"interpretability-and-safety",
"language",
"multimodal-learning",
"robotics",
"safety"
] | [
"3d-object-detection",
"3d-perception",
"alignment",
"reinforcement-learning",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.alignment",
"multimodal.3d-object-detection"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-05T01:42:52Z | Official code for NeurIPS2023 paper CoDA: Collaborative Novel Box Discovery and Cross-modal Alignment for Open-vocabulary 3D Object Detection and TPAMI 2025 paper CoDAv2 | [
"3d",
"computer-vision",
"interpretability-and-safety",
"multimodal-learning",
"robotics"
] | [
"deep-learning",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T14:22:14Z | false | 16 | 700,636,892 | https://yangcaoai.github.io/publications/CoDA.html | Jupyter Notebook | MIT | [
"3d-object-detection",
"3d-perception",
"transformer"
] | yangcaoai/CoDA_NeurIPS2023 | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-13T18:05:26Z | [] | 2026-05-28T09:26:19Z | [
"multimodal.3d-object-detection"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 223 | [
"3d-detection",
"3d-vision",
"artificial-intelligence",
"deep-learning",
"detection",
"multi-modality",
"open-vocabulary",
"transformer"
] | 2026-10-12T14:08:29Z | https://github.com/yangcaoai/CoDA_NeurIPS2023 | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-06T16:33:56Z | Official Code for the ICML 2024 paper: Generalization Error of Graph Neural Networks in the Mean-field Regime | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-11T16:48:52Z | false | 0 | 701,442,701 | null | Python | MIT | [
"graph-neural-network",
"message-passing",
"neural-network"
] | SherylHYX/GNN_MF_GE | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-11T16:48:52Z | [] | 2024-07-27T21:45:20Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2024-07-27T21:45:24Z | https://github.com/SherylHYX/GNN_MF_GE | null |
[
"audio",
"language",
"natural-language-processing",
"speech"
] | [
"machine-translation",
"speech-translation",
"transformer"
] | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"audio-speech-translation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-07T14:29:16Z | Code for NeurIPS 2023 paper "DASpeech: Directed Acyclic Transformer for Fast and High-quality Speech-to-Speech Translation". | [
"audio",
"language",
"natural-language-processing",
"speech"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-02T16:44:32Z | false | 5 | 701,778,627 | https://arxiv.org/abs/2310.07403 | Python | null | [
"machine-translation",
"speech-translation",
"transformer"
] | ictnlp/DASpeech | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | 2 | 2026-10-04T15:58:58Z | [] | 2024-07-22T02:48:47Z | [
"audio-speech-translation"
] | 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 | 63 | [
"machine-translation",
"speech-to-speech",
"speech-to-speech-translation",
"speech-translation"
] | 2025-07-08T11:07:03Z | https://github.com/ictnlp/DASpeech | null |
[
"3d",
"computer-vision",
"robotics"
] | [
"3d-object-detection",
"3d-perception",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.3d-object-detection"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-08T08:59:34Z | Code release for our NeurIPS 2023 paper "Uni3DETR: Unified 3D Detection Transformer", our ECCV 2024 paper "OV-Uni3DETR: Towards Unified Open-Vocabulary 3D Object Detection via Cycle-Modality Propagation" | [
"3d",
"computer-vision",
"robotics"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T14:22:14Z | false | 4 | 702,018,376 | null | Python | Apache-2.0 | [
"3d-object-detection",
"3d-perception",
"transformer"
] | zhenyuw16/Uni3DETR | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-13T18:05:26Z | [] | 2024-07-29T03:02:53Z | [
"multimodal.3d-object-detection"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 122 | [] | 2026-09-21T14:17:35Z | https://github.com/zhenyuw16/Uni3DETR | null |
[
"generative-ai",
"generative-modeling",
"multimodal-learning"
] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-08T10:08:40Z | The official code for paper "EasyGen: Easing Multimodal Generation with a Bidirectional Conditional Diffusion Model and LLMs" | [
"generative-ai",
"generative-modeling",
"multimodal-learning"
] | [
"large-language-model",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 1 | 702,036,252 | null | Python | null | [
"diffusion"
] | xiangyu-mm/EasyGen | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2024-11-21T03:56:43Z | [
"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 | 74 | [] | 2026-09-30T17:18:40Z | https://github.com/xiangyu-mm/EasyGen | null |
[
"generative-ai",
"generative-modeling",
"multimodal-learning"
] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-08T10:32:11Z | The official code for paper "Making Multimodal Generation Easier: When Diffusion Models Meet LLMs" | [
"generative-ai",
"generative-modeling",
"multimodal-learning"
] | [
"large-language-model",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-07T18:07:52Z | false | 0 | 702,042,687 | null | null | null | [
"diffusion"
] | multi-modal-gen/EasyGen | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-07T18:07:52Z | [] | 2023-10-08T10:35:09Z | [
"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 | [] | 2023-10-08T10:32:12Z | https://github.com/multi-modal-gen/EasyGen | null |
[
"reinforcement-learning"
] | [
"reinforcement-learning"
] | [
"description",
"github-topics",
"license-metadata",
"repository-metadata"
] | [] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-08T13:41:43Z | Official implementation of "Direct Preference-based Policy Optimization without Reward Modeling" (NeurIPS 2023) | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T20:57:28.489717Z | false | 1 | 702,096,899 | null | Python | MIT | [
"reinforcement-learning"
] | snu-mllab/DPPO | [
"description",
"github-topics",
"license-metadata",
"repository-metadata"
] | 4 | 2026-10-02T18:08:49.004055Z | [] | 2024-07-20T11:11:14Z | [] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 43 | [
"offline-reinforcement-learning"
] | 2026-08-21T18:52:44Z | https://github.com/snu-mllab/DPPO | {"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["offline-reinforcement-learning"]} |
[
"classical-ml",
"multimodal-learning",
"probabilistic-ml",
"reinforcement-learning",
"unsupervised-learning"
] | [
"batch-reinforcement-learning",
"clustering",
"gaussian-mixture-models",
"offline-reinforcement-learning",
"reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.gaussian-mixture-models",
"rl.offline"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-08T13:46:30Z | Code for ICLR 2025 paper "Learning on One Mode: Addressing Multi-modality in Offline Reinforcement Learning" | [
"classical-ml",
"multimodal-learning",
"probabilistic-ml",
"reinforcement-learning",
"unsupervised-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 0 | 702,098,360 | null | Python | null | [
"clustering",
"gaussian-mixture-models",
"reinforcement-learning"
] | MianchuWang/LOM | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-10-22T16:09:07Z | [] | 2025-02-23T17:15:10Z | [
"general.gaussian-mixture-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 | 7 | [] | 2026-05-09T05:30:22Z | https://github.com/MianchuWang/LOM | null |
[
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"forecasting",
"sequence-modeling"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"timeseries.forecasting",
"timeseries.topic"
] | true | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-09T07:53:49Z | Official code repository for the paper "Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain" | [
"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-03T15:32:04Z | false | 4 | 702,377,257 | null | Python | Apache-2.0 | [
"sequence-modeling"
] | SalesforceAIResearch/pretrain-time-series-cloudops | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-20T15:59:06Z | [] | 2025-09-09T17:45:25Z | [
"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 | 26 | [
"cloudops",
"forecasting",
"pretrain",
"time-series"
] | 2026-07-07T14:59:23Z | https://github.com/SalesforceAIResearch/pretrain-time-series-cloudops | null |
[
"information-retrieval"
] | [
"bm25",
"lexical-retrieval",
"rag"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"nlp-bm25-retrieval"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-09T12:52:26Z | Code for paper "Domain Adaptation for Conversational Query Production with the RAG Model Feedback" (EMNLP23 Findings)) | [
"information-retrieval"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-09T17:31:36Z | false | 0 | 702,504,609 | null | Python | null | [
"bm25",
"lexical-retrieval",
"rag"
] | Soistesimmer/DAMF | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-09T17:31:36Z | [] | 2023-10-10T09:12:34Z | [
"nlp-bm25-retrieval"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2023-10-10T09:11:10Z | https://github.com/Soistesimmer/DAMF | 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 | [] | 2023-10-09T22:05:44Z | Implementation of our paper Re-Weighted Softmax Cross-Entropy to Control Forgetting in Federated Learning | [
"distributed-ml",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-10T16:53:26Z | false | 0 | 702,723,586 | null | Python | MIT | [
"collaborative-learning",
"federated-learning"
] | GwenLegate/Re-WeightedSoftmaxCross-EntropyForFL | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-10T16:53:26Z | [] | 2023-10-10T14:17:12Z | [
"trust.federated-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2023-10-10T14:00:06Z | https://github.com/GwenLegate/Re-WeightedSoftmaxCross-EntropyForFL | null |
[
"computer-vision",
"generative-ai",
"multimodal"
] | [
"generative-modeling",
"motion-generation",
"self-attention",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.motion-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-10T04:02:41Z | Official Pytorch implementation of the paper "PCMG:3D point cloud human motion generation based on self-attention and transformer". | [
"computer-vision",
"generative-ai",
"multimodal"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 0 | 702,808,542 | null | Python | Apache-2.0 | [
"generative-modeling",
"motion-generation",
"self-attention",
"transformer"
] | maweizhao/PCMG | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-04T15:44:14Z | [] | 2025-02-01T03:11:44Z | [
"multimodal.motion-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 | 2 | [] | 2025-02-01T03:11:47Z | https://github.com/maweizhao/PCMG | null |
[
"deep-learning",
"representation-learning"
] | [
"representation-learning",
"self-supervised-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.self-supervised-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-10T09:11:10Z | This is the official implementation of the paper titled "Multi-Task Collaborative Network: Bridge the Supervised and Self-Supervised Learning for EEG Classification in RSVP tasks". | [
"deep-learning",
"representation-learning"
] | [
"self-supervised-learning",
"classifier"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 2 | 702,924,340 | null | Python | null | [
"representation-learning",
"self-supervised-learning"
] | Tammie-Li/MTCN | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-09-30T16:47:43Z | [] | 2023-10-10T09:20:37Z | [
"general.self-supervised-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 6 | [] | 2025-04-15T13:54:18Z | https://github.com/Tammie-Li/MTCN | null |
[
"information-retrieval"
] | [
"bm25",
"lexical-retrieval",
"rag"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"nlp-bm25-retrieval"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-10T09:14:47Z | Code for paper "Domain Adaptation for Conversational Query Production with the RAG Model Feedback" (EMNLP23 Findings) | [
"information-retrieval"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-09T17:31:36Z | false | 0 | 702,925,996 | null | Python | null | [
"bm25",
"lexical-retrieval",
"rag"
] | XMUDeepLIT/DAMF | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-09T17:31:36Z | [] | 2023-10-10T09:14:59Z | [
"nlp-bm25-retrieval"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [] | 2024-05-20T02:25:16Z | https://github.com/XMUDeepLIT/DAMF | null |
[
"3d-vision",
"computer-vision",
"generative-modeling"
] | [
"3d-perception",
"depth-estimation",
"diffusion"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.depth-estimation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-10T12:46:10Z | Official implementation of the paper "RGB-D-Fusion: Image Conditioned Depth Diffusion of Humanoid Subjects" | [
"3d-vision",
"computer-vision",
"generative-modeling"
] | [
"deep-learning",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-17T13:17:44Z | false | 3 | 703,017,355 | https://ieeexplore.ieee.org/document/10239167 | Python | Apache-2.0 | [
"3d-perception",
"depth-estimation",
"diffusion"
] | sascha-kirch/rgb-d-fusion | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-17T13:17:44Z | [] | 2025-10-29T14:28:11Z | [
"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",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 10 | [
"deep-learning",
"depth-estimation",
"diffusion-models",
"generative-ai"
] | 2026-06-19T12:17:48Z | https://github.com/sascha-kirch/rgb-d-fusion | null |
[
"3d",
"computer-vision",
"general-ml",
"generative-ai",
"generative-modeling",
"robotics"
] | [
"3d-object-detection",
"3d-perception",
"deep-learning",
"diffusion",
"generative-modeling",
"video-generation"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.topic-deep-learning",
"multimodal.3d-object-detection",
"vision.video-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-11T08:33:23Z | [ICLR24] Official implementation of the paper “MagicDrive: Street View Generation with Diverse 3D Geometry Control” | [
"3d",
"computer-vision",
"generative-modeling",
"robotics"
] | [
"deep-learning",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T14:22:14Z | false | 52 | 703,433,265 | https://gaoruiyuan.com/magicdrive/ | Python | Apache-2.0 | [
"3d-object-detection",
"3d-perception",
"diffusion"
] | cure-lab/MagicDrive | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 9 | 2026-10-13T18:05:26Z | [] | 2025-04-21T10:20:04Z | [
"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 | 1,165 | [
"autonomous-vehicles",
"deep-learning",
"diffusion-models",
"image-generation",
"pytorch",
"video-generation"
] | 2026-10-09T17:45:35Z | https://github.com/cure-lab/MagicDrive | null |
[
"generative-ai",
"language"
] | [
"language-model",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.transformers"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-11T11:26:48Z | fill the GRACE gap using novel Transformer method | [
"generative-ai",
"language"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-23T14:45:14Z | false | 1 | 703,508,384 | null | Jupyter Notebook | MIT | [
"language-model",
"transformer"
] | GISWLH/Transformer_GRACE | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-25T14:09:27Z | [] | 2026-10-17T17:44:13Z | [
"llm.transformers"
] | 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 | 18 | [] | 2026-10-17T17:44:17Z | https://github.com/GISWLH/Transformer_GRACE | 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 | [] | 2023-10-12T00:01:46Z | Official implementation for paper "FedGame: A Game-Theoretic Defense against Backdoor Attacks in Federated Learning" (NeurIPS 2023). | [
"distributed-ml",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 0 | 703,795,084 | null | Python | MIT | [
"collaborative-learning",
"federated-learning"
] | AI-secure/FedGame | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-08T18:15:14Z | [] | 2024-10-25T20:15:35Z | [
"trust.federated-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 13 | [] | 2025-12-17T13:36:22Z | https://github.com/AI-secure/FedGame | null |
[
"generative-ai",
"generative-modeling",
"graph-learning"
] | [
"diffusion",
"generative-modeling",
"graph-generation"
] | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"graph.graph-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-12T08:50:18Z | Official Code Repository for the paper "Graph Generation with Diffusion Mixture" (ICML 2024). | [
"generative-ai",
"generative-modeling",
"graph-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-03T15:32:04Z | false | 4 | 703,957,297 | https://arxiv.org/abs/2302.03596 | Python | null | [
"diffusion",
"generative-modeling",
"graph-generation"
] | harryjo97/GruM | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | 3 | 2026-10-13T18:05:26Z | [] | 2024-05-20T06:15:15Z | [
"graph.graph-generation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 36 | [] | 2026-08-28T11:47:46Z | https://github.com/harryjo97/GruM | null |
[
"deep-learning",
"representation-learning"
] | [
"contrastive-learning",
"self-supervised-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-12T10:50:43Z | The official code of EMNLP 2023 paper HierarchicalContrast: A Coarse-to-Fine Contrastive Learning Framework for Cross-Domain Zero-Shot Slot Filling | [
"deep-learning",
"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-22T16:09:07Z | false | 0 | 704,007,148 | null | Python | MIT | [
"contrastive-learning",
"self-supervised-learning"
] | ai-agi/HiCL | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-22T16:09:07Z | [] | 2024-09-12T02:30:47Z | [
"general.contrastive-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 5 | [] | 2024-10-16T12:25:14Z | https://github.com/ai-agi/HiCL | null |
[
"applied-mathematics",
"applied-physics",
"computational-science",
"generative-modeling"
] | [
"data-assimilation",
"diffusion",
"inverse-problems",
"reconstruction"
] | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"science.inverse-problems"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-13T04:52:34Z | Official PyTorch implementation of [Direct Diffusion Bridge using Data Consistency for Inverse Problems](https://arxiv.org/abs/2305.19809) | [
"applied-mathematics",
"applied-physics",
"computational-science",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 1 | 704,353,421 | null | Python | null | [
"data-assimilation",
"diffusion",
"inverse-problems",
"reconstruction"
] | hyungjin-chung/CDDB | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | 2 | 2026-10-04T15:44:14Z | [] | 2023-10-13T04:53:13Z | [
"science.inverse-problems"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 62 | [] | 2026-08-30T05:20:49Z | https://github.com/hyungjin-chung/CDDB | null |
[
"multi-agent-systems",
"reinforcement-learning"
] | [
"centralized-training",
"multi-agent-reinforcement-learning",
"reinforcement-learning"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"rl.multiagent"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-13T09:41:44Z | [NeurIPS 2023] The official implementation of "Offline Multi-Agent Reinforcement Learning with Implicit Global-to-Local Value Regularization" | [
"multi-agent-systems",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T20:57:28.489717Z | false | 4 | 704,454,508 | null | Python | null | [
"centralized-training",
"multi-agent-reinforcement-learning",
"reinforcement-learning"
] | ZhengYinan-AIR/OMIGA | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 6 | 2026-10-04T15:44:14Z | [] | 2024-03-03T06:28:58Z | [
"rl.multiagent"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 44 | [
"mujoco",
"multi-agent-reinforcement-learning",
"offline-reinforcement-learning",
"pytorch",
"reinforcement-learning"
] | 2026-06-07T03:12:47Z | https://github.com/ZhengYinan-AIR/OMIGA | null |
[
"earth-science",
"geospatial-science",
"information-retrieval",
"language",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"remote-sensing",
"science-and-engineering",
"vision"
] | [
"foundation-model",
"geospatial-learning",
"question-answering",
"reading-comprehension",
"remote-sensing",
"vision-language-model",
"visual-question-answering"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.visual-question-answering",
"nlp.question-answering",
"science.remote-sensing"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-14T15:56:15Z | [TGRS 2024] The official PyTorch implementation of the paper "RSAdapter: Adapting Multimodal Models for Remote Sensing Visual Question Answering". | [
"information-retrieval",
"multimodal-learning",
"natural-language-processing",
"science-and-engineering"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 4 | 704,953,167 | null | Python | null | [
"question-answering",
"reading-comprehension"
] | AI4RS/RSAdapter | [
"description",
"query-match",
"repository-metadata"
] | 6 | 2026-10-26T17:25:59Z | [] | 2024-10-25T00:17:39Z | [
"nlp.question-answering"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 12 | [] | 2025-12-01T12:28:48Z | https://github.com/AI4RS/RSAdapter | null |
[
"computer-vision",
"generative-ai",
"multimodal",
"natural-language-processing",
"vision"
] | [
"image-generation",
"text-to-image"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.text-to-image"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-14T23:00:47Z | [ICLR 2024] Official code for the paper "LLM Blueprint: Enabling Text-to-Image Generation with Complex and Detailed Prompts" | [
"computer-vision",
"generative-ai",
"multimodal",
"natural-language-processing",
"vision"
] | [
"large-language-model",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 4 | 705,056,099 | null | Jupyter Notebook | null | [
"image-generation",
"text-to-image"
] | hananshafi/llmblueprint | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-04T15:44:14Z | [] | 2024-05-18T12:56:19Z | [
"multimodal.text-to-image"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-context-only",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 85 | [] | 2026-05-05T08:14:20Z | https://github.com/hananshafi/llmblueprint | null |
[
"natural-language-processing"
] | [
"distillation",
"machine-translation",
"neural-machine-translation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"nlp.machine-translation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-15T13:18:19Z | This is the official code for our paper "Datastore Distillation for Nearest Neighbor Machine Translation" | [
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-16T15:36:56Z | false | 0 | 705,242,352 | null | Python | null | [
"distillation",
"machine-translation",
"neural-machine-translation"
] | dirkiedai/datastore_distillation | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-16T15:36:56Z | [] | 2024-08-21T17:15:56Z | [
"nlp.machine-translation"
] | 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 | [] | 2024-08-21T17:15:59Z | https://github.com/dirkiedai/datastore_distillation | null |
[
"general-ml",
"reinforcement-learning"
] | [
"novel-method",
"reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.novel-method"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-15T17:18:53Z | Novel Methods for Generalization in Reinforcement Learning | [
"general-ml",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 0 | 705,316,963 | null | Jupyter Notebook | null | [
"novel-method",
"reinforcement-learning"
] | felixk29/thesis | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-05T19:54:32Z | [] | 2024-07-24T13:46:04Z | [
"general.novel-method"
] | 20fefda1afc1c77b003d88340839777fba090b4e | 2026-09-26T15:53:33Z | gh-ml-readme-evidence-v2 | "20fefda1afc1c77b003d88340839777fba090b4e" | [
"other"
] | [] | ok | 2026-09-26T15:53:33Z | felixk29/thesis | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [] | 2024-07-24T13:46:08Z | https://github.com/felixk29/thesis | null |
[
"deep-learning",
"graph-learning",
"multimodal-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-16T17:15:52Z | Code for paper, A Multimodal Graph Neural Network Framework for Cancer Molecular Subtype Classification | [
"deep-learning",
"graph-learning",
"multimodal-learning"
] | [
"neural-network",
"classifier"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-10T16:53:26Z | false | 2 | 705,780,706 | null | Python | MIT | [
"graph-neural-network",
"message-passing",
"neural-network"
] | NabaviLab/Multimodal-GNN-for-Cancer-Subtype-Clasification | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-10T16:53:26Z | [] | 2025-04-14T18:40:48Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 16 | [] | 2026-06-18T09:06:31Z | https://github.com/NabaviLab/Multimodal-GNN-for-Cancer-Subtype-Clasification | null |
[
"generative-ai",
"language",
"natural-language-processing",
"reinforcement-learning"
] | [
"language-model",
"policy-gradient",
"policy-learning",
"pretraining",
"reinforcement-learning"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"llm.language-models",
"llm.large-language-model",
"rl.core"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-17T05:25:36Z | Code for Paper (ReMax: A Simple, Efficient and Effective Reinforcement Learning Method for Aligning Large Language Models) | [
"generative-ai",
"language",
"natural-language-processing",
"reinforcement-learning"
] | [
"large-language-model",
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 16 | 705,994,984 | null | Python | null | [
"language-model",
"policy-gradient",
"pretraining",
"reinforcement-learning"
] | liziniu/ReMax | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 3 | 2026-10-10T16:53:26Z | [] | 2023-12-16T12:38:36Z | [
"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 | 200 | [
"large-language-models",
"policy-gradient",
"reinforcement-learning",
"rlhf"
] | 2026-09-26T20:36:13Z | https://github.com/liziniu/ReMax | null |
[
"3d-vision",
"generative-ai",
"generative-modeling",
"graphics",
"robotics"
] | [
"6d-perception",
"diffusion",
"pose-estimation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"cross26.graphics-sixd-pose",
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-17T13:25:13Z | code for paper: Simultaneous Image to Zero and Zero to Noise: Diffusion Models with Analytical Image Attenuation | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-26T22:51:24Z | false | 12 | 706,189,472 | null | Python | null | [
"diffusion"
] | GuHuangAI/ADM-Public | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-04T15:58:58Z | [] | 2026-05-07T11:42:09Z | [
"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 | 64 | [] | 2026-07-27T11:56:45Z | https://github.com/GuHuangAI/ADM-Public | null |
[
"generative-ai",
"generative-modeling",
"machine-learning"
] | [
"diffusion",
"machine-unlearning",
"privacy"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"specialized.machine-unlearning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-17T13:54:37Z | The official implementation of ECCV'24 paper "To Generate or Not? Safety-Driven Unlearned Diffusion Models Are Still Easy To Generate Unsafe Images ... For Now". This work introduces one fast and effective attack method to evaluate the harmful-content generation ability of safety-driven unlearned diffusion models. | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 6 | 706,203,772 | null | Python | MIT | [
"diffusion"
] | OPTML-Group/Diffusion-MU-Attack | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-10-05T20:10:04Z | [] | 2025-02-28T02:41:05Z | [
"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 | 89 | [
"adversarial-attacks",
"attack-unlearned-diffusion-model",
"evaluation-framework",
"robustness",
"stable-diffusion",
"unlearning"
] | 2026-07-06T00:45:50Z | https://github.com/OPTML-Group/Diffusion-MU-Attack | null |
[
"deep-learning",
"representation-learning"
] | [
"contrastive-learning",
"self-supervised-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-18T20:08:31Z | Code for paper How does Contrastive Learning Organize Images? | [
"deep-learning",
"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 0 | 706,886,376 | null | Jupyter Notebook | null | [
"contrastive-learning",
"self-supervised-learning"
] | xsgxlz/How-does-Contrastive-Learning-Organize-Images | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-23T15:59:01Z | [] | 2023-10-18T20:50:32Z | [
"general.contrastive-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [] | 2023-12-08T19:11:47Z | https://github.com/xsgxlz/How-does-Contrastive-Learning-Organize-Images | null |
[
"forecasting",
"general-ml",
"generative-ai",
"language",
"time-series",
"time-series-and-forecasting"
] | [
"forecasting",
"language-model",
"sequence-modeling",
"time-series-forecasting",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"applied.time-series-forecasting",
"llm.transformers",
"recall.name.transformer-model",
"timeseries.forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-19T03:23:15Z | Official implementation for "iTransformer: Inverted Transformers Are Effective for Time Series Forecasting" (ICLR 2024 Spotlight) | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 374 | 707,007,525 | https://arxiv.org/abs/2310.06625 | Python | MIT | [
"forecasting",
"sequence-modeling",
"transformer"
] | thuml/iTransformer | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 12 | 2026-10-14T16:10:46Z | [] | 2025-07-17T15:53:48Z | [
"timeseries.forecasting"
] | 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 | 2,231 | [
"time-series-forecasting",
"transformer"
] | 2026-10-14T07:31:51Z | https://github.com/thuml/iTransformer | null |
[
"deep-learning",
"graph-learning",
"representation-learning"
] | [
"contrastive-learning",
"graph-contrastive-learning",
"graph-neural-network",
"neural-network",
"self-supervised-learning"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning",
"graph.graph-contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-19T04:18:16Z | Official code for TNNLS paper "Affinity Uncertainty-based Hard Negative Mining in Graph Contrastive Learning" | [
"deep-learning",
"graph-learning",
"representation-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 2 | 707,021,202 | null | Python | null | [
"contrastive-learning",
"graph-neural-network",
"neural-network",
"self-supervised-learning"
] | mala-lab/AUGCL | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 5 | 2026-10-22T16:09:07Z | [] | 2025-05-22T07:58:33Z | [
"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 | 9 | [
"contrastive-learning",
"graph-contrastive-learning",
"graph-learning",
"graph-neural-networks",
"hard-negative-mining",
"uncertainty-estimation"
] | 2026-01-25T02:16:10Z | https://github.com/mala-lab/AUGCL | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion",
"distillation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-19T12:50:30Z | Official implementation for paper: "Reducing Spatial Fitting Error in Distillation of Denoising (AAAI2024) Diffusion Models". | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-06T17:10:23Z | false | 0 | 707,211,910 | null | Python | MIT | [
"diffusion",
"distillation"
] | Sainzerjj/SFERD | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-06T17:10:23Z | [] | 2023-12-19T05:50:29Z | [
"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 | 4 | [] | 2025-09-05T06:39:36Z | https://github.com/Sainzerjj/SFERD | null |
[
"chemistry",
"organic-chemistry"
] | [
"planning",
"reaction-prediction"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"chem-retrosynthesis"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-19T22:19:54Z | Official code for 'A User-Tunable Machine Learning Framework for Step-Wise Synthesis Planning' | [
"chemistry",
"organic-chemistry"
] | [
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 1 | 707,432,529 | null | Python | null | [
"planning",
"reaction-prediction"
] | MSRG/mhnpath | [
"description",
"query-match",
"repository-metadata"
] | 4 | 2026-10-02T16:44:32Z | [] | 2026-03-23T05:22:00Z | [
"chem-retrosynthesis"
] | 648fdca2ef7b97c300104dbc9a2dd9ce51dce6c6 | 2026-09-28T20:05:39Z | gh-ml-readme-evidence-v2 | "648fdca2ef7b97c300104dbc9a2dd9ce51dce6c6" | [
"citation",
"other",
"usage"
] | [
"method-contribution",
"ml-method-context",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-09-28T20:05:39Z | MSRG/mhnpath | readme-supported-paper-method-implementation | [
"method-contribution",
"ml-context-only",
"ml-method-context",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 6 | [] | 2026-03-23T05:22:06Z | https://github.com/MSRG/mhnpath | null |
[
"generative-ai",
"information-retrieval",
"language",
"natural-language-processing"
] | [
"language-model",
"pretraining",
"rag",
"retrieval-augmented-generation"
] | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"llm-retrieval-augmented-generation",
"llm.large-language-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-20T01:27:28Z | 🌲 Code for our EMNLP 2023 paper - 🎄 "Tree of Clarifications: Answering Ambiguous Questions with Retrieval-Augmented Large Language Models" | [
"information-retrieval",
"language",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-10T16:53:26Z | false | 6 | 707,472,914 | https://arxiv.org/abs/2310.14696 | Python | null | [
"rag",
"retrieval-augmented-generation"
] | gankim/tree-of-clarifications | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | 3 | 2026-10-24T14:31:13Z | [] | 2023-12-04T07:44:04Z | [
"llm-retrieval-augmented-generation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 55 | [
"ambiguous-qa",
"gpt-3",
"long-form-qa",
"question-answering",
"rag"
] | 2026-10-24T07:36:55Z | https://github.com/gankim/tree-of-clarifications | null |
[
"clinical-informatics",
"deep-learning",
"graph-learning",
"health-and-biomedicine",
"multimodal-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network",
"representation-learning",
"risk-prediction"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description",
"medical.ehr"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-20T11:20:06Z | Official implementation of paper "An objective quantitative diagnosis of depression using a local-to-global multi-modal fusion graph neural network" | [
"clinical-informatics",
"graph-learning",
"health-and-biomedicine",
"multimodal-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-10T16:53:26Z | false | 1 | 707,655,620 | null | Python | MIT | [
"graph-neural-network",
"neural-network",
"representation-learning",
"risk-prediction"
] | ATP-BME/LGMF-GNN | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-16T15:36:56Z | [] | 2025-01-13T06:35:57Z | [
"medical.ehr"
] | 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 | 14 | [] | 2026-04-29T19:15:08Z | https://github.com/ATP-BME/LGMF-GNN | 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 | [] | 2023-10-21T04:08:44Z | Official Implementation of understanding the latent space of diffusion models through the lens of riemannian geometry (NeurIPS 2023) | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 11 | 707,947,816 | null | Python | Apache-2.0 | [
"diffusion"
] | enkeejunior1/Diffusion-Pullback | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-04T15:58:58Z | [] | 2024-02-20T02:14:00Z | [
"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 | 94 | [] | 2026-08-20T21:16:34Z | https://github.com/enkeejunior1/Diffusion-Pullback | 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 | [] | 2023-10-21T12:41:37Z | Official implementation of the TMM'23 paper “End-to-End Video Scene Graph Generation with Temporal Propagation Transformer” | [
"generative-ai",
"graph-learning"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-14T16:10:46Z | false | 0 | 708,070,219 | null | Python | Apache-2.0 | [
"generative-modeling",
"graph-generation",
"transformer"
] | gitzyong812/TPT_TMM23 | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-14T16:10:46Z | [] | 2023-10-21T12:52:29Z | [
"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 | 3 | [] | 2025-06-29T04:38:55Z | https://github.com/gitzyong812/TPT_TMM23 | 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 | [] | 2023-10-22T08:58:26Z | Official implementation of NeurIPS 2023 paper "Predicting Global Label Relationship Matrix for Graph Neural Networks under Heterophily" | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-10T16:53:26Z | false | 1 | 708,336,876 | null | Python | null | [
"graph-neural-network",
"message-passing",
"neural-network"
] | Jinx-byebye/LRGNN | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-10T16:53:26Z | [] | 2023-10-23T02:36:29Z | [
"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 | 8 | [] | 2025-04-20T01:38:25Z | https://github.com/Jinx-byebye/LRGNN | null |
[
"reinforcement-learning",
"robotics-and-control"
] | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-23T03:17:35Z | This repository contains the Python implementation of our submitted paper titled "Deep Reinforcement Learning for Joint Trajectory and Communication Design in Internet of Robotic Things" . | [
"reinforcement-learning",
"robotics-and-control"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 1 | 708,621,329 | null | Python | null | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | lry-bupt/MAAC_DRL | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-23T15:59:01Z | [] | 2024-06-29T12:39:45Z | [
"rl.deep"
] | 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 | 16 | [] | 2026-07-19T12:36:21Z | https://github.com/lry-bupt/MAAC_DRL | null |
[
"3d",
"computer-vision",
"generative-ai",
"generative-modeling",
"graphics"
] | [
"3d-generation",
"diffusion",
"generative-modeling"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.3d-generation",
"recall.name.diffusion-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-23T07:40:20Z | [ICLR 2024] Official implementation of DreamCraft3D: Hierarchical 3D Generation with Bootstrapped Diffusion Prior | [
"generative-ai",
"generative-modeling"
] | [
"generative-model",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 357 | 708,693,209 | https://mrtornado24.github.io/DreamCraft3D/ | Python | MIT | [
"diffusion"
] | deepseek-ai/DreamCraft3D | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 8 | 2026-10-04T15:44:14Z | [
"2310.16818"
] | 2025-04-22T11:09:39Z | [
"recall.name.diffusion-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 | 3,031 | [
"3d-creation",
"3d-generation",
"aigc",
"diffusion-models",
"generative-model",
"image-to-3d"
] | 2026-10-03T13:46:50Z | https://github.com/deepseek-ai/DreamCraft3D | null |
[
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"forecasting",
"multivariate-forecasting",
"sequence-modeling"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"timeseries.forecasting",
"timeseries.multivariate"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-23T13:14:45Z | Official implementation of the paper "FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective" | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 25 | 708,823,034 | null | Python | MIT | [
"forecasting",
"sequence-modeling"
] | aikunyi/FourierGNN | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 12 | 2026-10-17T13:17:44Z | [] | 2024-02-22T03:43:43Z | [
"timeseries.forecasting"
] | 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 | 250 | [] | 2026-08-30T14:39:53Z | https://github.com/aikunyi/FourierGNN | 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 | [] | 2023-10-23T13:15:14Z | Official implementation of the paper "Frequency-domain MLPs are More Effective Learners in Time Series Forecasting" | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-03T15:32:04Z | false | 29 | 708,823,265 | null | Python | Apache-2.0 | [
"forecasting",
"sequence-modeling"
] | aikunyi/FreTS | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-17T13:17:44Z | [] | 2024-03-26T14:18:45Z | [
"timeseries.forecasting"
] | 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 | 199 | [] | 2026-08-26T03:00:32Z | https://github.com/aikunyi/FreTS | null |
[
"computer-vision",
"earth-science",
"geospatial-science",
"remote-sensing",
"science-and-engineering"
] | [
"foundation-model",
"geospatial-learning",
"remote-sensing",
"restoration",
"super-resolution",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"science.remote-sensing",
"vision.image-super-resolution"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-24T02:47:35Z | Official Pytorch implementation of the paper "ACT-SR: Aggregation Connection Transformer for Remote Sensing Image Super-Resolution". | [
"computer-vision",
"science-and-engineering"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-16T15:36:56Z | false | 0 | 709,105,300 | null | Python | Apache-2.0 | [
"restoration",
"super-resolution",
"transformer"
] | YingdongKang/ACT_SR | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-22T16:09:07Z | [] | 2025-04-20T14:13:02Z | [
"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 | 2 | [] | 2026-07-26T14:28:11Z | https://github.com/YingdongKang/ACT_SR | null |
[
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"forecasting",
"sequence-modeling"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"timeseries.forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-24T04:39:20Z | Official implementation of the paper "Mixture-of-Linear-Experts for Long-term Time Series Forecasting" (AISTATS 2024) | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-03T15:32:04Z | false | 7 | 709,131,717 | null | Python | null | [
"forecasting",
"sequence-modeling"
] | RogerNi/MoLE | [
"description",
"query-match",
"repository-metadata"
] | 4 | 2026-10-17T13:17:44Z | [] | 2026-02-04T03:42:12Z | [
"timeseries.forecasting"
] | 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 | 43 | [] | 2026-09-17T03:32:04Z | https://github.com/RogerNi/MoLE | null |
[
"information-retrieval",
"language",
"natural-language-processing"
] | [
"question-answering",
"rag",
"reading-comprehension",
"retrieval-augmented-generation"
] | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"llm-retrieval-augmented-generation",
"nlp.question-answering",
"nlp.rag"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-25T02:15:26Z | Official Implementation of NeurIPS 2024 paper "G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering"" | [
"information-retrieval",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 94 | 709,595,545 | https://arxiv.org/abs/2402.07630 | Python | MIT | [
"question-answering",
"reading-comprehension",
"retrieval-augmented-generation"
] | XiaoxinHe/G-Retriever | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 16 | 2026-10-29T17:49:51Z | [] | 2025-03-19T11:36:50Z | [
"nlp.question-answering"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 557 | [] | 2026-10-27T06:55:46Z | https://github.com/XiaoxinHe/G-Retriever | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-25T07:23:33Z | Code for paper "On the Generalization Properties of Diffusion Models" | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-09T17:31:36Z | false | 0 | 709,680,681 | null | Jupyter Notebook | null | [
"diffusion"
] | lphLeo/Diffusion_Generalization | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-09T17:31:36Z | [] | 2024-02-12T11:01:12Z | [
"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 | 11 | [] | 2025-12-18T07:46:17Z | https://github.com/lphLeo/Diffusion_Generalization | null |
[
"deep-learning",
"graph-learning",
"representation-learning"
] | [
"contrastive-learning",
"meta-learning",
"self-supervised-learning"
] | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-25T09:49:02Z | The official source code of the paper "Unsupervised Episode Generation for Graph Meta-learning" (ICML 2024) | [
"deep-learning",
"graph-learning",
"representation-learning"
] | [
"classifier"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-25T15:11:36Z | false | 0 | 709,737,292 | https://arxiv.org/abs/2306.15217 | Python | null | [
"contrastive-learning",
"meta-learning",
"self-supervised-learning"
] | JhngJng/NaQ-PyTorch | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-10-25T15:11:36Z | [] | 2024-06-25T14:28:41Z | [
"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 | 14 | [
"few-shot-node-classification",
"gnn",
"meta-learning",
"unsupervised-learning"
] | 2025-10-08T07:41:17Z | https://github.com/JhngJng/NaQ-PyTorch | null |
[
"machine-learning"
] | [
"adaptation",
"test-time-adaptation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"specialized.test-time-adaptation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-25T20:26:56Z | Official code for the paper "pSTarC: Pseudo Source Guided Target Clustering for Fully Test-Time Adaptation" | [
"machine-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 0 | 710,009,093 | null | Python | MIT | [
"adaptation",
"test-time-adaptation"
] | manogna-s/pSTarC | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-04T15:44:14Z | [] | 2023-12-29T21:07:07Z | [
"specialized.test-time-adaptation"
] | 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 | [] | 2025-04-12T09:17:26Z | https://github.com/manogna-s/pSTarC | null |
[
"efficient-ml",
"generative-ai",
"generative-modeling",
"machine-learning-systems",
"model-compression"
] | [
"diffusion",
"quantization"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.quantization",
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-26T05:59:43Z | [NeurIPS23] Official Implementation of Temporal Dynamic Quantization of Diffusion Models | [
"efficient-ml",
"generative-modeling",
"machine-learning-systems",
"model-compression"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-10T16:53:26Z | false | 0 | 710,150,935 | null | null | null | [
"diffusion",
"quantization"
] | junhyukso/tdq | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-13T18:05:26Z | [] | 2023-10-26T05:59:43Z | [
"efficiency.quantization"
] | 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 | [] | 2025-10-14T11:47:32Z | https://github.com/junhyukso/tdq | null |
[
"deep-learning",
"representation-learning"
] | [
"contrastive-learning",
"self-supervised-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-26T10:24:05Z | Official Code for NeurIPS 2023 Paper: Identifiable Contrastive Learning with Automatic Feature Importance Discovery | [
"deep-learning",
"representation-learning"
] | [
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-25T15:11:36Z | false | 0 | 710,252,510 | null | Python | null | [
"contrastive-learning",
"self-supervised-learning"
] | PKU-ML/Tri-factor-Contrastive-Learning | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-25T15:11:36Z | [] | 2023-10-31T05:51:41Z | [
"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 | 5 | [] | 2025-05-06T16:04:47Z | https://github.com/PKU-ML/Tri-factor-Contrastive-Learning | null |
[
"deep-learning",
"graph-learning",
"representation-learning"
] | [
"contrastive-learning",
"graph-contrastive-learning",
"self-supervised-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning",
"graph.graph-contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-26T17:08:55Z | Official code for NeurIPS 2023 "Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive Learning" | [
"deep-learning",
"representation-learning"
] | [
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 1 | 710,428,500 | null | Python | MIT | [
"contrastive-learning",
"self-supervised-learning"
] | PKU-ML/ArchitectureMattersGCL | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-25T15:11:36Z | [] | 2023-11-10T07:59:20Z | [
"general.contrastive-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 9 | [] | 2025-03-25T13:16:57Z | https://github.com/PKU-ML/ArchitectureMattersGCL | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-27T06:49:43Z | Official PyTorch implementation of "Fair Sampling in Diffusion Models through Switching Mechanism", AAAI 2024. | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-09T17:31:36Z | false | 0 | 710,656,078 | https://doi.org/10.1609/aaai.v38i20.30202 | Jupyter Notebook | null | [
"diffusion"
] | uzn36/AttributeSwitching | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-10-09T17:31:36Z | [] | 2025-07-22T07:47:48Z | [
"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 | [] | 2025-07-22T07:47:52Z | https://github.com/uzn36/AttributeSwitching | null |
[
"multimodal",
"reinforcement-learning",
"robotics"
] | [
"environment-modeling",
"world-model"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.world-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-27T07:18:28Z | Open source code for paper "Learning World Models with Identifiable Factorization" | [
"multimodal",
"reinforcement-learning",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 0 | 710,666,071 | null | null | null | [
"environment-modeling",
"world-model"
] | AlexLiuyuren/Factor | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-05T19:54:32Z | [] | 2023-10-27T07:18:29Z | [
"multimodal.world-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 | 0 | [] | 2023-10-27T07:18:29Z | https://github.com/AlexLiuyuren/Factor | null |
[
"multimodal",
"reinforcement-learning",
"robotics"
] | [
"environment-modeling",
"world-model"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.world-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-27T07:18:57Z | Open source code for paper "Learning World Models with Identifiable Factorization" | [
"multimodal",
"reinforcement-learning",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 2 | 710,666,246 | null | Python | MIT | [
"environment-modeling",
"world-model"
] | AlexLiuyuren/IFactor | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-05T19:54:32Z | [] | 2024-03-04T06:30:24Z | [
"multimodal.world-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 | 13 | [] | 2025-09-12T12:24:50Z | https://github.com/AlexLiuyuren/IFactor | null |
[
"distributed-ml",
"privacy-and-federated-learning"
] | [
"collaborative-learning",
"federated-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-27T15:17:34Z | This repository contains the official PyTorch code for the paper, Federated Learning via Meta-Variational Dropout published, in NeurIPS 2023 | [
"distributed-ml",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-11T16:48:52Z | false | 0 | 710,853,672 | null | Python | null | [
"collaborative-learning",
"federated-learning"
] | insujeon/MetaVD | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-11T16:48:52Z | [] | 2024-04-22T20:22:03Z | [
"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 | [] | 2025-03-07T02:37:38Z | https://github.com/insujeon/MetaVD | null |
[
"computer-vision",
"interpretability-and-safety"
] | [
"image-classification",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.image-classification"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-27T17:57:33Z | This is an official implementation for [ICLR'24] INTR: Interpretable Transformer for Fine-grained Image Classification. | [
"computer-vision",
"interpretability-and-safety"
] | [
"classifier",
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-03T15:32:04Z | false | 6 | 710,912,668 | null | Jupyter Notebook | Apache-2.0 | [
"image-classification",
"transformer"
] | Imageomics/INTR | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-03T15:32:04Z | [] | 2024-04-15T02:54:06Z | [
"vision.image-classification"
] | 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 | 60 | [
"computer-vision",
"explainable-ai",
"fine-grained-classification",
"imageomics",
"interpretation",
"transformer"
] | 2026-07-04T00:56:34Z | https://github.com/Imageomics/INTR | null |
[
"reinforcement-learning"
] | [
"policy-gradient",
"policy-optimization"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"rl.ppo"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-28T00:52:07Z | Code for Paper "Gradient Informed Proximal Policy Optimization" (NeurIPS 2023) | [
"reinforcement-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 2 | 711,012,334 | null | Python | null | [
"policy-gradient",
"policy-optimization"
] | SonSang/gippo | [
"description",
"query-match",
"repository-metadata"
] | 6 | 2026-10-04T15:44:14Z | [] | 2023-12-18T18:37:02Z | [
"rl.ppo"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 27 | [] | 2026-01-11T07:30:27Z | https://github.com/SonSang/gippo | null |
[
"generative-ai",
"generative-modeling",
"reinforcement-learning"
] | [
"batch-reinforcement-learning",
"diffusion",
"offline-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"rl.core",
"rl.offline"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-29T15:34:30Z | [ICLR 2024] The official implementation of "Safe Offline Reinforcement Learning with Feasibility-Guided Diffusion Model" | [
"generative-ai",
"generative-modeling",
"reinforcement-learning"
] | [
"reinforcement-learning",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 10 | 711,542,626 | https://zhengyinan-air.github.io/FISOR/ | Python | null | [
"diffusion",
"reinforcement-learning"
] | ZhengYinan-AIR/FISOR | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 10 | 2026-10-08T18:15:14Z | [] | 2025-02-11T09:37:33Z | [
"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 | 132 | [
"diffusion-models",
"hamilton-jacobi-reachability",
"imitation-learning",
"jax",
"offline-reinforcement-learning",
"reinforcement-learning",
"safe-reinforcement-learning"
] | 2026-09-17T09:44:17Z | https://github.com/ZhengYinan-AIR/FISOR | null |
[
"generative-ai",
"generative-modeling",
"time-series-and-forecasting"
] | [
"diffusion"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-30T12:09:44Z | Official Code for "SingularTrajectory: Universal Trajectory Predictor using Diffusion Model (CVPR 2024)" | [
"generative-ai",
"generative-modeling",
"time-series-and-forecasting"
] | [
"deep-learning",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 13 | 711,903,713 | https://ihbae.com/publication/singulartrajectory/ | Python | NOASSERTION | [
"diffusion"
] | InhwanBae/SingularTrajectory | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-08T18:15:14Z | [] | 2025-07-16T02:15:36Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 156 | [
"autonomous-vehicles",
"cvpr2024",
"deep-learning",
"domain-adaptation",
"few-shot-learning",
"human-trajectory-prediction",
"motion-forecasting",
"multi-agent"
] | 2026-09-06T08:27:43Z | https://github.com/InhwanBae/SingularTrajectory | null |
[
"language",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"time-series-and-forecasting"
] | [
"language-model",
"multimodal"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.multimodal"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-30T12:11:45Z | Official Code for "Can Language Beat Numerical Regression? Language-Based Multimodal Trajectory Prediction (CVPR 2024)" and "Social Reasoning-Aware Trajectory Prediction via Multimodal Language Model (TPAMI)" | [
"language",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"time-series-and-forecasting"
] | [
"deep-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 11 | 711,904,523 | https://ihbae.com/publication/lmtrajectory/ | Python | NOASSERTION | [
"language-model",
"multimodal"
] | InhwanBae/LMTrajectory | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-04T15:44:14Z | [] | 2025-09-18T13:42:42Z | [
"llm.multimodal"
] | 2f480ed48114701d8181d32fd7000986a6877358 | 2026-09-28T20:05:39Z | gh-ml-readme-evidence-v2 | "2f480ed48114701d8181d32fd7000986a6877358" | [
"citation",
"installation",
"other",
"results"
] | [
"method-contribution",
"ml-method-context",
"paper-reference"
] | ok | 2026-09-28T20:05:39Z | InhwanBae/LMTrajectory | readme-supported-paper-method-implementation | [
"method-contribution",
"ml-context-only",
"ml-method-context",
"paper-reference"
] | include | ml-contribution-v5 | 171 | [
"autonomous-vehicles",
"cvpr2024",
"deep-learning",
"human-trajectory-prediction",
"language-model",
"motion-forecasting",
"multi-agent",
"question-answering",
"tpami"
] | 2026-08-11T10:26:43Z | https://github.com/InhwanBae/LMTrajectory | null |
[
"applied-mathematics",
"computational-science",
"physics",
"scientific-computing"
] | [
"differentiable-programming",
"learned-solvers",
"neural-operators",
"surrogate-modeling"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"science.neural-operators",
"science.scientific-machine-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-30T16:55:37Z | Source code for paper: "Multiscale Neural Operators for Solving Time-Independent PDEs" | [
"applied-mathematics",
"computational-science",
"scientific-computing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 2 | 712,033,873 | null | Python | MIT | [
"differentiable-programming",
"learned-solvers",
"surrogate-modeling"
] | merantix-momentum/multiscale-pde-operators | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-05T19:54:32Z | [] | 2023-11-13T09:47:30Z | [
"science.scientific-machine-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 | [] | 2026-03-12T15:20:31Z | https://github.com/merantix-momentum/multiscale-pde-operators | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-10-31T18:43:07Z | Official implementation of TRACE, the TRAjectory Diffusion Model for Controllable PEdestrians, from the CVPR 2023 paper: "Trace and Pace: Controllable Pedestrian Animation via Guided Trajectory Diffusion". | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-09T17:31:36Z | false | 12 | 712,578,555 | https://research.nvidia.com/labs/toronto-ai/trace-pace/ | Python | NOASSERTION | [
"diffusion"
] | nv-tlabs/trace | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-09T17:31:36Z | [] | 2024-10-31T23:26:00Z | [
"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 | 129 | [
"diffusion",
"human-motion",
"trajectory-generation"
] | 2026-09-29T16:34:10Z | https://github.com/nv-tlabs/trace | null |
[
"interpretability-and-safety",
"reinforcement-learning"
] | [
"batch-reinforcement-learning",
"offline-reinforcement-learning",
"reinforcement-learning"
] | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"rl.offline"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-11-02T00:21:23Z | Code for NeurIPS 2023 paper Accountability in Offline Reinforcement Learning: Explaining Decisions with a Corpus of Examples | [
"interpretability-and-safety",
"reinforcement-learning"
] | [
"machine-learning",
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 1 | 713,154,227 | https://arxiv.org/pdf/2310.07747.pdf | Python | null | [
"batch-reinforcement-learning",
"offline-reinforcement-learning",
"reinforcement-learning"
] | holarissun/Accountable-Offline-RL | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | 2 | 2026-10-04T15:44:14Z | [] | 2023-11-28T13:16:14Z | [
"rl.offline"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 5 | [
"batched-control",
"behavioral-cloning",
"deep-rl",
"interpretable-machine-learning",
"interpretable-reinforcement-learning",
"offline-control",
"offline-rl",
"offlinerl",
"reinforcement-learning",
"xai"
] | 2024-08-10T12:27:40Z | https://github.com/holarissun/Accountable-Offline-RL | null |
[
"distributed-ml",
"privacy-ml"
] | [
"distributed-training",
"split-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"growth26.privacy-split-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-11-02T03:31:22Z | Official Code for CVPR 2024 paper: Permutation Equivariance of Transformers and Its Applications. | [
"distributed-ml",
"privacy-ml"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-26T22:51:24Z | false | 3 | 713,205,140 | null | Python | null | [
"distributed-training",
"split-learning"
] | Doby-Xu/ST | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-09-26T22:51:24Z | [] | 2024-11-12T11:15:49Z | [
"growth26.privacy-split-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 | 19 | [] | 2026-08-14T01:05:44Z | https://github.com/Doby-Xu/ST | null |
[
"computer-vision",
"language",
"multimodal",
"multimodal-learning",
"vision"
] | [
"contrastive-learning",
"multimodal-learning",
"vision-language",
"vision-language-model"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.vision-language",
"vision.vision-language"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-11-02T14:49:38Z | [ECCV'24] Official Implementation of SemiVL: Semi-Supervised Semantic Segmentation with Vision-Language Guidance | [
"computer-vision",
"multimodal",
"multimodal-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 15 | 713,452,310 | null | Python | Apache-2.0 | [
"multimodal-learning",
"vision-language"
] | google-research/semivl | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-08T18:15:14Z | [] | 2026-07-23T21:30:27Z | [
"vision.vision-language"
] | caf57f9eb9c4b1c980ed529e530e19585af132fa | 2026-09-28T20:05:39Z | gh-ml-readme-evidence-v2 | "caf57f9eb9c4b1c980ed529e530e19585af132fa" | [
"other",
"overview",
"results",
"usage"
] | [
"method-contribution",
"ml-method-context",
"model-training-artifact",
"paper-reference"
] | ok | 2026-09-28T20:05:39Z | google-research/semivl | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-method-context",
"model-training-artifact",
"paper-reference"
] | include | ml-contribution-v5 | 148 | [] | 2026-10-08T09:32:00Z | https://github.com/google-research/semivl | null |
[
"3d",
"computer-vision",
"generative-ai",
"graphics",
"multimodal"
] | [
"3d-generation",
"diffusion",
"generative-modeling",
"text-to-3d"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.3d-generation",
"multimodal.text-to-3d",
"vision.image-diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-11-03T03:05:02Z | [ICLR 2024] Official Implementation of Consistent4D: Consistent 360° Dynamic Object Generation from Monocular Video | [
"computer-vision",
"generative-ai"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 8 | 713,686,110 | https://consistent4d.github.io/ | Python | Apache-2.0 | [
"diffusion",
"generative-modeling"
] | yanqinJiang/Consistent4D | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-04T15:58:58Z | [] | 2024-11-14T08:48:13Z | [
"vision.image-diffusion"
] | dbf0de88e4b989b8ca62bcdc102ad1b88df81ffb | 2026-09-28T20:05:39Z | gh-ml-readme-evidence-v2 | "dbf0de88e4b989b8ca62bcdc102ad1b88df81ffb" | [
"abstract",
"citation",
"installation",
"other",
"results"
] | [
"method-contribution",
"ml-method-context",
"paper-reference"
] | ok | 2026-09-28T20:05:39Z | yanqinJiang/Consistent4D | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-method-context",
"paper-reference"
] | include | ml-contribution-v5 | 283 | [
"3d-generation",
"4d-generation",
"aigc"
] | 2026-09-12T13:14:24Z | https://github.com/yanqinJiang/Consistent4D | null |
[
"predictive-maintenance",
"time-series"
] | [
"failure-prediction",
"neural-network",
"remaining-useful-life",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"timeseries.remaining-useful-life"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-11-03T07:26:40Z | Code for the paper "Breaking the black box barrier: predicting remaining useful life under uncertainty from raw images with interpretable neural networks". This is a novel interpretable transformer-based model for Remaining Useful Life (RUL) prediction from raw sequential images (frames) representing a composite struct... | [
"predictive-maintenance",
"time-series"
] | [
"neural-network",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 0 | 713,762,280 | null | Python | NOASSERTION | [
"failure-prediction",
"neural-network",
"remaining-useful-life",
"transformer"
] | Center-of-Excellence-AI-for-Structures/ISTRUST_MODEL | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-04T15:44:14Z | [] | 2025-02-26T13:27:38Z | [
"timeseries.remaining-useful-life"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 14 | [] | 2026-05-10T06:29:15Z | https://github.com/Center-of-Excellence-AI-for-Structures/ISTRUST_MODEL | null |
[
"computer-vision",
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-11-03T07:51:22Z | Code and pruned models for our paper: K. Gkrispanis, N. Gkalelis, V. Mezaris, "Filter-Pruning of Lightweight Face Detectors Using a Geometric Median Criterion", Proc. IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW 2024), Waikoloa, Hawaii, USA, Jan. 2024. Repository updated in April 2024. | [
"computer-vision",
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-20T15:59:06Z | false | 2 | 713,771,380 | null | Python | null | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | IDT-ITI/Lightweight-Face-Detector-Pruning | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-20T15:59:06Z | [] | 2024-05-08T10:01:05Z | [
"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 | 14 | [] | 2025-06-20T09:42:52Z | https://github.com/IDT-ITI/Lightweight-Face-Detector-Pruning | null |
[
"autonomous-driving",
"reinforcement-learning",
"robotics",
"robotics-and-control"
] | [
"perception",
"planning",
"reinforcement-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"robotics.autonomous-driving"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-11-04T02:30:49Z | The official code for paper "Human as AI Mentor: Enhanced Human-in-the-loop Reinforcement Learning for Safe and Efficient Autonomous Driving" | [
"autonomous-driving",
"reinforcement-learning",
"robotics",
"robotics-and-control"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-07T18:07:52Z | false | 5 | 714,135,191 | https://zilin-huang.github.io/HAIM-DRL-website/ | Python | MIT | [
"perception",
"planning",
"reinforcement-learning"
] | zilin-huang/HAIM-DRL | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-07T18:07:52Z | [] | 2024-03-02T03:27:38Z | [
"robotics.autonomous-driving"
] | 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 | 47 | [] | 2026-09-07T08:31:18Z | https://github.com/zilin-huang/HAIM-DRL | null |
[
"distributed-ml",
"privacy-and-federated-learning"
] | [
"collaborative-learning",
"federated-learning"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-11-04T09:23:55Z | Code for our paper "Comparison of Federated Learning Strategies on ECG Classification", ASYU 2023. | [
"distributed-ml",
"privacy-and-federated-learning"
] | [
"classifier"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-11T16:48:52Z | false | 0 | 714,224,087 | null | Jupyter Notebook | null | [
"collaborative-learning",
"federated-learning"
] | Skyress1/Comparison-of-Federated-Learning-Strategies-on-ECG-Classification | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 1 | 2026-10-11T16:48:52Z | [] | 2023-11-05T16:58:13Z | [
"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 | [
"ecg-classification",
"federated-learning"
] | 2026-02-09T13:48:30Z | https://github.com/Skyress1/Comparison-of-Federated-Learning-Strategies-on-ECG-Classification | null |
[
"automl",
"efficient-ml",
"generative-ai",
"language"
] | [
"fine-tuning",
"neural-architecture-search",
"parameter-efficient-fine-tuning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.neural-architecture-search",
"llm.finetuning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-11-06T03:25:04Z | [CVPR2024] official code for "Boosting Order-Preserving and Transferability for Neural Architecture Search: a Joint Architecture Refined Search and Fine-tuning Approach" | [
"generative-ai",
"language"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T14:50:46Z | false | 0 | 714,885,338 | null | Python | null | [
"fine-tuning",
"parameter-efficient-fine-tuning"
] | beichenzbc/Supernet-shifting | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-10-24T14:31:13Z | [] | 2024-03-23T07:51:01Z | [
"llm.finetuning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-context-only",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 8 | [] | 2025-06-17T07:15:21Z | https://github.com/beichenzbc/Supernet-shifting | null |
[
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"forecasting",
"sequence-modeling"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"timeseries.forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-11-06T23:18:58Z | A novel hybrid model with STL decomposition for time series forecasting | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-17T13:17:44Z | false | 0 | 715,327,338 | null | Jupyter Notebook | null | [
"forecasting",
"sequence-modeling"
] | edmilsondejesus/stl-arima-svr-lstm | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-17T13:17:44Z | [] | 2026-08-13T00:50:00Z | [
"timeseries.forecasting"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 2 | [] | 2026-08-13T00:50:04Z | https://github.com/edmilsondejesus/stl-arima-svr-lstm | null |
[
"generative-ai",
"language",
"natural-language-processing"
] | [
"language-model",
"pretraining"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.large-language-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-11-08T17:05:58Z | A New Tamil Large Language Model (LLM) Based on Llama 2 | [
"generative-ai",
"language",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-12T20:31:19Z | false | 56 | 716,213,509 | null | Python | GPL-3.0 | [
"language-model",
"pretraining"
] | abhinand5/tamil-llama | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-13T18:05:26Z | [] | 2024-04-05T07:02:21Z | [
"llm.large-language-model"
] | 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 | 334 | [] | 2026-10-05T01:10:00Z | https://github.com/abhinand5/tamil-llama | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-11-09T10:59:09Z | [ICLR 2024] Official pytorch implementation of "Denoising Task Routing for Diffusion Models" | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-09T17:31:36Z | false | 1 | 716,541,716 | https://byeongjun-park.github.io/DTR/ | Python | MIT | [
"diffusion"
] | byeongjun-park/DTR | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-09T17:31:36Z | [] | 2024-02-19T16:36:01Z | [
"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"
] | include | ml-contribution-v5 | 25 | [
"diffusion-models",
"multi-task-learning"
] | 2026-03-16T07:48:37Z | https://github.com/byeongjun-park/DTR | null |
[
"forecasting",
"generative-ai",
"generative-modeling",
"time-series",
"time-series-and-forecasting"
] | [
"diffusion",
"forecasting",
"sequence-modeling"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"timeseries.forecasting",
"timeseries.topic"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-11-09T14:20:48Z | Official PyTorch implementation of TSDiff models presented in the NeurIPS 2023 paper "Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting" | [
"generative-modeling",
"time-series",
"time-series-and-forecasting"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-03T15:32:04Z | false | 40 | 716,622,158 | null | Python | Apache-2.0 | [
"diffusion",
"sequence-modeling"
] | amazon-science/unconditional-time-series-diffusion | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 7 | 2026-10-19T17:46:36Z | [] | 2024-01-30T18:58:36Z | [
"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 | 260 | [
"diffusion-models",
"neurips",
"neurips-2023",
"pytorch",
"time-series",
"time-series-forecasting"
] | 2026-09-24T06:37:22Z | https://github.com/amazon-science/unconditional-time-series-diffusion | null |
[
"deep-learning",
"representation-learning"
] | [
"contrastive-learning",
"self-supervised-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-11-14T12:07:03Z | The official implementation of GCLSS (Generalized CLSS) and CLSS (NeurIPS 2023: Semi-Supervised Contrastive Learning for Deep Regression with Ordinal Rankings from Spectral Seriation) | [
"deep-learning",
"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-25T15:11:36Z | false | 2 | 718,592,733 | null | Python | null | [
"contrastive-learning",
"self-supervised-learning"
] | xmed-lab/CLSS | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-25T15:11:36Z | [] | 2026-04-11T12:28:31Z | [
"general.contrastive-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 15 | [] | 2026-06-26T18:04:10Z | https://github.com/xmed-lab/CLSS | null |
[
"reinforcement-learning"
] | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2023-11-15T11:32:26Z | A Novel Resource Allocation Algorithm in Edge Computing with Deep Reinforcement Learning | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 0 | 719,066,651 | null | TeX | MIT | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | ImanRHT/Master_Thesis | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-23T15:59:01Z | [] | 2023-11-15T12:03:48Z | [
"rl.deep"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 5 | [
"deep-reinforcement-learning",
"edge-computing",
"master-thesis",
"resource-allocation"
] | 2025-12-15T18:24:35Z | https://github.com/ImanRHT/Master_Thesis | null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.