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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
[
"computer-vision",
"generative-ai",
"language",
"machine-learning",
"multimodal",
"multimodal-learning",
"natural-language-processing"
] | [
"fine-tuning",
"instruction-tuning",
"language-model",
"large-language-model",
"model-adaptation",
"multimodal-learning",
"pretraining",
"video-understanding"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.instruction-tuning",
"llm.large-language-model",
"probable.adaptation-finetuning-readme",
"recall.name.large-language-model",
"recall.name.multimodal-model",
"vision.video-understanding"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-13T06:30:56Z | A novel Multimodal Large Language Model (MLLM) architecture, designed to structurally align visual and textual embeddings. | [
"computer-vision",
"generative-ai",
"language",
"multimodal-learning",
"natural-language-processing"
] | [
"large-language-model",
"embedding"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T18:03:22.392067Z | false | 93 | 814,502,811 | https://huggingface.co/AIDC-AI/Ovis2.5-9B | Python | Apache-2.0 | [
"language-model",
"pretraining"
] | ATH-MaaS/Ovis | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 8 | 2026-10-28T17:27:44Z | [] | 2026-07-15T14:04:16Z | [
"llm.large-language-model"
] | 6d013d407d9cb4f735298ce6deaad160e6166e0e | 2026-09-30T18:16:34Z | gh-ml-readme-evidence-v2 | "6d013d407d9cb4f735298ce6deaad160e6166e0e" | [
"citation",
"dataset",
"installation",
"method",
"other",
"overview",
"results"
] | [
"method-contribution",
"ml-method-context",
"model-training-artifact",
"paper-reference"
] | ok | 2026-09-30T18:16:34Z | ATH-MaaS/Ovis | specific-method-with-novelty-claim | [
"method-contribution",
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue",
"model-training-artifact",
"paper-reference"
] | include | ml-contribution-v5 | 1,537 | [
"chatbot",
"llama3",
"multimodal",
"multimodal-large-language-models",
"multimodality",
"qwen",
"vision-language-learning",
"vision-language-model"
] | 2026-10-28T04:56:42Z | https://github.com/ATH-MaaS/Ovis | null |
[
"3d",
"computer-vision",
"deep-learning",
"representation-learning",
"robotics"
] | [
"3d-perception",
"multi-task-learning",
"scene-understanding",
"transfer-learning",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.multi-task-learning",
"multimodal.scene-understanding"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-13T06:32:26Z | [AAAI 2025] Official data and code for "TB-HSU: Hierarchical 3D Scene Understanding with Contextual Affordances" | [
"deep-learning",
"representation-learning"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 0 | 814,503,314 | null | Python | MIT | [
"multi-task-learning",
"transfer-learning",
"transformer"
] | WentingXu3o3/TB-HSU | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-25T15:11:36Z | [] | 2025-09-11T12:59:09Z | [
"general.multi-task-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 | 15 | [
"3d-scene-understanding",
"multi-task-learning",
"transformer"
] | 2026-03-11T07:10:01Z | https://github.com/WentingXu3o3/TB-HSU | null |
[] | [] | [
"description",
"github-topics",
"license-metadata",
"repository-metadata"
] | [] | false | unknown | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-13T08:13:54Z | Official code for NeurIPS 2024 paper LRM-Zero: Training Large Reconstruction Models with Synthesized Data | [] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-25T16:47:45Z | false | 11 | 814,546,862 | https://desaixie.github.io/lrm-zero/ | Python | NOASSERTION | [] | desaixie/zeroverse | [
"description",
"github-topics",
"license-metadata",
"repository-metadata"
] | 1 | 2026-10-25T16:47:45Z | [
"2406.09371"
] | 2024-10-07T05:54:38Z | [] | 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 | 154 | [
"3d-reconstruction",
"pre-training",
"procedural-generation",
"sparse-view",
"synthetic-data",
"transformers"
] | 2026-07-31T13:01:57Z | https://github.com/desaixie/zeroverse | {"discovery_source":"hf_daily_papers","paper_evidence":"unverified","queryless":true} |
[
"efficient-ml",
"health-and-biomedicine",
"machine-learning-systems",
"model-compression",
"neuroscience",
"signal-processing"
] | [
"neural-decoding",
"neural-network-pruning",
"pruning",
"representation-learning",
"signal-classification",
"structured-pruning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning",
"medical.eeg"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-13T17:03:21Z | implementation of our paper: "Knowledge-guided Network Pruning for EEG-based Emotion Recognition" | [
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-12T20:31:19Z | false | 1 | 814,769,305 | null | Python | NOASSERTION | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | jackyrwj/Knowledge-guided-Network-Pruning | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-24T14:31:13Z | [] | 2024-06-13T17:15:32Z | [
"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 | 0 | [] | 2024-06-13T17:15:35Z | https://github.com/jackyrwj/Knowledge-guided-Network-Pruning | null |
[
"generative-ai",
"generative-modeling",
"graph-learning"
] | [
"diffusion",
"generative-modeling",
"graph-generation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.graph-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-13T17:28:03Z | Official Release of the Implementation Of Paper "Beta Diffusion For Graph Generation" | [
"generative-ai",
"generative-modeling",
"graph-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-19T17:46:36Z | false | 1 | 814,778,992 | null | Python | null | [
"diffusion",
"generative-modeling",
"graph-generation"
] | YH-UtMSB/Graph_Beta_Diffusion | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-19T17:46:36Z | [] | 2024-07-04T22:41:25Z | [
"graph.graph-generation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 4 | [] | 2025-02-22T21:01:52Z | https://github.com/YH-UtMSB/Graph_Beta_Diffusion | null |
[
"general-ml",
"reinforcement-learning"
] | [
"novel-method",
"reinforcement-learning"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"general.novel-method"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-13T19:52:39Z | Adaptive Confidence-Weighted Policy Aggregation: A Novel Method for Federated Reinforcement Learning | [
"general-ml",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 0 | 814,831,890 | https://github.com/nabazar/PolicyAggregation.git | Jupyter Notebook | null | [
"novel-method",
"reinforcement-learning"
] | nabazar/PolicyAggregation | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 2 | 2026-10-02T16:44:32Z | [] | 2025-05-24T14:19:08Z | [
"general.novel-method"
] | 97d42529811a8e869d3cf16023b5aeaf9969473c | 2026-09-27T17:16:20Z | gh-ml-readme-evidence-v2 | "97d42529811a8e869d3cf16023b5aeaf9969473c" | [] | [] | ok | 2026-09-27T17:16:20Z | nabazar/PolicyAggregation | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [
"federated-reinforcement-learning"
] | 2025-08-20T02:35:58Z | https://github.com/nabazar/PolicyAggregation | null |
[
"generative-ai",
"language",
"natural-language-processing",
"reinforcement-learning"
] | [
"language-model",
"pretraining",
"reinforcement-learning"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"llm.language-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-14T06:55:12Z | [ICLR'24] Official code for "Language Model Self-improvement by Reinforcement Learning Contemplation". | [
"generative-ai",
"language",
"natural-language-processing",
"reinforcement-learning"
] | [
"large-language-model",
"machine-learning",
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-12T20:31:19Z | false | 0 | 815,010,461 | https://openreview.net/forum?id=38E4yUbrgr | Jupyter Notebook | null | [
"language-model",
"pretraining",
"reinforcement-learning"
] | lafmdp/RLC | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 1 | 2026-10-12T20:31:19Z | [] | 2025-05-17T14:40:51Z | [
"llm.language-models"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 7 | [
"artificial-intelligence",
"large-language-models",
"machine-learning",
"reinforcement-learning",
"self-improvement"
] | 2025-05-17T14:40:55Z | https://github.com/lafmdp/RLC | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-15T10:49:03Z | The source code of our paper "Diffusion Model-based Mobile Traffic Generation with Open Data for Network Planning and Optimization", | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-16T15:36:56Z | false | 0 | 815,500,709 | null | Python | MIT | [
"diffusion"
] | impchai/OpenDiff-diffusion-model-with-open-data | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-16T15:36:56Z | [] | 2024-09-15T09:36:34Z | [
"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 | [] | 2026-07-17T07:58:22Z | https://github.com/impchai/OpenDiff-diffusion-model-with-open-data | null |
[
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion",
"generative-modeling",
"video-generation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"vision.video-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-16T14:06:18Z | Official implementation of our paper: "Ca2-VDM: Efficient Autoregressive Video Diffusion Model with Causal Generation and Cache Sharing" (ICML 2025) | [
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-15T15:20:52Z | false | 1 | 815,912,862 | null | Python | Apache-2.0 | [
"diffusion",
"generative-modeling",
"video-generation"
] | Dawn-LX/CausalCache-VDM | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-20T15:59:06Z | [] | 2025-05-22T02:29:03Z | [
"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 | 84 | [] | 2026-10-20T02:33:34Z | https://github.com/Dawn-LX/CausalCache-VDM | null |
[
"generative-ai",
"language",
"multimodal",
"multimodal-learning",
"safety",
"video"
] | [
"alignment",
"preference-optimization",
"video-question-answering",
"video-understanding"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.preference-optimization",
"multimodal.video-question-answering"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-17T06:03:39Z | Official Implementation of ISR-DPO:Aligning Large Multimodal Models for Videos by Iterative Self-Retrospective DPO (AAAI'25) | [
"language",
"multimodal",
"multimodal-learning",
"video"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 1 | 816,145,037 | null | Python | null | [
"video-question-answering",
"video-understanding"
] | snumprlab/isr-dpo | [
"description",
"query-match",
"repository-metadata"
] | 4 | 2026-10-02T16:44:32Z | [] | 2025-11-25T10:28:16Z | [
"multimodal.video-question-answering"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 23 | [] | 2025-11-25T10:28:21Z | https://github.com/snumprlab/isr-dpo | null |
[
"computer-vision",
"generative-ai",
"language",
"natural-language-processing"
] | [
"fine-tuning",
"instruction-tuning",
"language-model",
"pretraining"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.instruction-tuning",
"llm.large-language-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-17T10:32:51Z | [CVPR'2025] VoCo-LLaMA: This repo is the official implementation of "VoCo-LLaMA: Towards Vision Compression with Large Language Models". | [
"computer-vision",
"generative-ai",
"language",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-01T17:35:19Z | false | 11 | 816,241,048 | https://yxxxb.github.io/VoCo-LLaMA-page/ | Python | Apache-2.0 | [
"language-model",
"pretraining"
] | Yxxxb/VoCo-LLaMA | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-28T17:27:44Z | [
"2406.12275"
] | 2025-06-18T03:29:22Z | [
"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 | 208 | [
"image-compression",
"llama",
"llava"
] | 2026-10-27T14:57:24Z | https://github.com/Yxxxb/VoCo-LLaMA | null |
[
"generative-ai",
"language",
"natural-language-processing"
] | [
"efficient-inference",
"language-model",
"pretraining",
"speculative-decoding"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.large-language-model",
"llm.speculative-decoding"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-18T01:06:41Z | [EMNLP 24] Official Implementation of CLEANGEN: Mitigating Backdoor Attacks for Generation Tasks in Large Language Models | [
"generative-ai",
"language",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 2 | 816,546,694 | null | Python | null | [
"language-model",
"pretraining"
] | uw-nsl/CleanGen | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-28T17:27:44Z | [] | 2025-03-09T12:13:21Z | [
"llm.large-language-model"
] | 1f796d5e8d526fd565e4a9a4e86f15486e18036e | 2026-10-03T17:01:43Z | gh-ml-readme-evidence-v2 | "1f796d5e8d526fd565e4a9a4e86f15486e18036e" | [
"abstract",
"citation",
"other",
"results"
] | [
"method-contribution",
"ml-method-context",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-10-03T17:01:43Z | uw-nsl/CleanGen | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-method-context",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 19 | [] | 2026-02-18T09:15:32Z | https://github.com/uw-nsl/CleanGen | null |
[
"computer-vision",
"distributed-ml",
"privacy-and-federated-learning"
] | [
"collaborative-learning",
"federated-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-18T06:30:09Z | FedHLC: A novel federated learning algorithm targeting heterogeneous and long-tailed data for efficient image classification in consumer electronics. | [
"computer-vision",
"distributed-ml",
"privacy-and-federated-learning"
] | [
"classifier"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-16T15:36:56Z | false | 0 | 816,645,351 | null | null | null | [
"collaborative-learning",
"federated-learning"
] | Kiritoliang/FedHLC | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-16T15:36:56Z | [] | 2024-06-18T06:30:09Z | [
"trust.federated-learning"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 1 | [] | 2024-10-21T13:25:35Z | https://github.com/Kiritoliang/FedHLC | null |
[
"computer-vision"
] | [
"restoration",
"super-resolution"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.image-super-resolution"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-18T08:52:11Z | [CVPR 2024] Official implementation of SODA-SR. | [
"computer-vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-01T17:35:19Z | false | 2 | 816,707,040 | https://shallowdream204.github.io/soda-sr-project/ | Python | Apache-2.0 | [
"restoration",
"super-resolution"
] | shallowdream204/SODA-SR | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-25T15:11:36Z | [] | 2024-10-24T08:39:32Z | [
"vision.image-super-resolution"
] | 8318a7e2ba66eb6b02901c0c93a0e333f1ac4bcf | 2026-10-03T17:01:43Z | gh-ml-readme-evidence-v2 | "8318a7e2ba66eb6b02901c0c93a0e333f1ac4bcf" | [
"abstract",
"citation",
"other",
"results"
] | [
"method-contribution",
"ml-method-context",
"model-training-artifact",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-10-03T17:01:43Z | shallowdream204/SODA-SR | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-method-context",
"model-training-artifact",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 34 | [
"cvpr2024",
"low-level-vision",
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] | 2026-07-14T13:20:04Z | https://github.com/shallowdream204/SODA-SR | null |
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-18T13:07:42Z | An official implementation of "FedBiOT: LLM Local Fine-tuning in Federated Learning without Full Model", which has been accepted by KDD'24. | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-01T17:35:19Z | false | 17 | 816,816,844 | null | Python | Apache-2.0 | [
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] | include | ml-contribution-v5 | 64 | [] | 2026-10-08T05:26:58Z | https://github.com/HarliWu/FedBiOT | null |
[
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 0 | 816,847,525 | null | null | null | [
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] | 1 | 2026-09-30T16:47:43Z | [] | 2024-06-12T09:15:56Z | [
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] | include | ml-contribution-v5 | 2 | [] | 2025-02-14T10:07:30Z | https://github.com/wwhappylife/MoFME-Pytorch | null |
[
"computer-vision",
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-03T15:45:52Z | false | 0 | 816,942,849 | https://guyyariv.github.io/LaMI/ | Python | MIT | [
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] | guyyariv/LaMI | [
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] | 3 | 2026-10-28T17:27:44Z | [
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] | ok | 2026-10-03T17:01:43Z | guyyariv/LaMI | readme-supported-paper-method-implementation | [
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] | include | ml-contribution-v5 | 20 | [
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] | 2026-08-14T07:53:22Z | https://github.com/guyyariv/LaMI | null |
[
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] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-19T13:37:13Z | Official implementation of FedIGW algorithm for the paper "Harnessing the Power of Federated Learning in Federated Contextual Bandits" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-16T15:36:56Z | false | 1 | 817,332,044 | null | Python | null | [
"collaborative-learning",
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] | ShenGroup/FedIGW | [
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] | 1 | 2026-10-16T15:36:56Z | [] | 2024-07-03T23:12:43Z | [
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] | include | ml-contribution-v5 | 0 | [] | 2024-07-03T23:12:47Z | https://github.com/ShenGroup/FedIGW | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-19T15:03:47Z | Official PyTorch implementation of the paper "Self-supervised Vision Transformer are Scalable Generative Models for Domain Generalization" @ MICCAI2024 | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 0 | 817,371,309 | null | Python | Apache-2.0 | [
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] | sdoerrich97/vits-are-generative-models | [
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] | 7 | 2026-10-17T13:17:44Z | [] | 2025-02-23T14:09:01Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 7 | [] | 2025-06-17T10:15:45Z | https://github.com/sdoerrich97/vits-are-generative-models | null |
[
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] | [
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] | [
"multimodal.video-question-answering"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-20T01:36:46Z | This is the official implementation of ICCV 2025 "Flash-VStream: Efficient Real-Time Understanding for Long Video Streams" | [
"language",
"multimodal",
"video"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T15:29:21Z | false | 22 | 817,558,389 | https://zhang9302002.github.io/vstream-iccv-page/ | Python | Apache-2.0 | [
"video-question-answering",
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] | IVGSZ/Flash-VStream | [
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] | 6 | 2026-10-02T16:44:32Z | [] | 2025-10-15T15:40:18Z | [
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] | 87e04f474701524a349e197db70d5d157a1803df | 2026-10-03T17:01:43Z | gh-ml-readme-evidence-v2 | "87e04f474701524a349e197db70d5d157a1803df" | [
"citation",
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] | [
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] | ok | 2026-10-03T17:01:43Z | IVGSZ/Flash-VStream | readme-supported-paper-method-implementation | [
"contribution-language",
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] | include | ml-contribution-v5 | 288 | [] | 2026-09-24T04:26:34Z | https://github.com/IVGSZ/Flash-VStream | null |
[
"language",
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-20T14:21:50Z | Official Implementation of "Chrono: A Simple Blueprint for Representing Time in MLLMs" | [
"language",
"multimodal",
"video"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 3 | 817,828,890 | null | Python | BSD-3-Clause | [
"video-question-answering",
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] | sudo-Boris/mr-Blip | [
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] | 8 | 2026-10-02T16:44:32Z | [] | 2025-03-09T22:08:47Z | [
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] | 45c72d2b62edf586a49325b08e054f3668f7fc59 | 2026-10-03T17:01:43Z | gh-ml-readme-evidence-v2 | "45c72d2b62edf586a49325b08e054f3668f7fc59" | [
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] | [
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] | ok | 2026-10-03T17:01:43Z | sudo-Boris/mr-Blip | readme-supported-paper-method-implementation | [
"contribution-language",
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] | include | ml-contribution-v5 | 96 | [] | 2026-08-06T07:43:13Z | https://github.com/sudo-Boris/mr-Blip | null |
[
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] | [
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"license-metadata",
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"repository-metadata"
] | [
"nlp.rag"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-21T10:24:45Z | [Official Code] Adaptive Retrieval-Augmented Generation for Conversational Systems -- NAACL 2025 Findings | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 0 | 818,205,440 | null | Jupyter Notebook | MIT | [
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] | wangxieric/RAGate | [
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] | 1 | 2026-10-08T18:15:14Z | [] | 2026-04-08T08:29:30Z | [
"nlp.rag"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 8 | [] | 2026-04-09T13:36:30Z | https://github.com/wangxieric/RAGate | null |
[
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-21T14:03:02Z | A novel graph neural network approach for druggable gene discovery. | [
"deep-learning",
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-16T15:36:56Z | false | 0 | 818,287,367 | null | Python | null | [
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] | luckylice/MDMNI-DGD | [
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] | 1 | 2026-10-16T15:36:56Z | [] | 2024-12-24T02:21:27Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
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"ml-method-cue"
] | include | ml-contribution-v5 | 1 | [] | 2024-12-24T02:21:31Z | https://github.com/luckylice/MDMNI-DGD | null |
[
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] | [
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-22T20:56:19Z | [NeurIPS 2024] Official implementation of "ClavaDDPM:Multi-relational Data Synthesis with Cluster-guided Diffusion Models" | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-15T15:20:52Z | false | 2 | 818,778,924 | null | Python | null | [
"diffusion"
] | weipang142857/ClavaDDPM | [
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] | 1 | 2026-10-15T15:20:52Z | [] | 2024-10-27T02:42:55Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 21 | [] | 2026-06-26T18:10:42Z | https://github.com/weipang142857/ClavaDDPM | null |
[
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] | [
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] | [
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"repository-metadata"
] | [
"llm.diffusion-phrase",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-23T07:40:41Z | Official implementation for "Identifying and Solving Conditional Image Leakage in Image-to-Video Diffusion Model" (NeurIPS 2024) | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-01T17:35:19Z | false | 22 | 818,899,859 | null | null | Apache-2.0 | [
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] | thu-ml/cond-image-leakage | [
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"repository-metadata"
] | 5 | 2026-10-20T15:59:06Z | [] | 2026-03-13T03:32:51Z | [
"vision.video-generation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 258 | [] | 2026-09-28T02:54:08Z | https://github.com/thu-ml/cond-image-leakage | null |
[
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] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-23T18:01:14Z | Official Pytorch implementation for paper LaMoD: Latent Motion Diffusion Model For Myocardial Strain Generation | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-16T15:36:56Z | false | 0 | 819,078,298 | null | Jupyter Notebook | null | [
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] | jr-xing/LaMoD | [
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] | 1 | 2026-10-16T15:36:56Z | [] | 2025-11-11T18:45:42Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 8 | [] | 2026-05-01T13:23:21Z | https://github.com/jr-xing/LaMoD | null |
[
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] | [
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] | [
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"query-match",
"repository-metadata"
] | [
"graph.gnn-description",
"timeseries.topic"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-24T04:01:16Z | [IJCAI'24] Official code for our paper "Make Graph Neural Networks Great Again: A Generic Integration Paradigm of Topology-Free Patterns for Traffic Speed Prediction". | [
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"time-series-and-forecasting"
] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-15T15:20:52Z | false | 2 | 819,209,518 | null | Python | null | [
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] | ibizatomorrow/DCST | [
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"repository-metadata"
] | 2 | 2026-10-23T15:59:01Z | [] | 2025-07-03T06:10:31Z | [
"timeseries.topic"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 15 | [
"finished",
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"time-series",
"um-papers"
] | 2026-07-22T06:48:26Z | https://github.com/ibizatomorrow/DCST | null |
[
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] | [
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] | [
"description",
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"repository-metadata"
] | [
"rl.multiagent"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-24T06:44:10Z | Code for paper, named "Hierarchical Task Network-Enhanced Multi-Agent Reinforcement Learning: Toward Efficient Cooperative Strategies". | [
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"reinforcement-learning"
] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-01T17:35:19Z | false | 1 | 819,257,783 | null | Python | null | [
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] | Mxc666/HS-MARL | [
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] | 3 | 2026-10-02T16:59:40Z | [] | 2024-06-24T06:54:40Z | [
"rl.multiagent"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 11 | [] | 2026-08-29T05:39:57Z | https://github.com/Mxc666/HS-MARL | null |
[
"distributed-ml",
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] | [
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] | [
"description",
"query-match",
"repository-metadata"
] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-24T09:18:08Z | A new personalized federated learning method | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-14T16:10:46Z | false | 0 | 819,321,065 | null | Python | null | [
"collaborative-learning",
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] | buaaYYC/FedGAC | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-14T16:10:46Z | [] | 2024-06-27T07:38:20Z | [
"trust.federated-learning"
] | 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 | [] | 2026-09-07T14:41:30Z | https://github.com/buaaYYC/FedGAC | null |
[
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] | [
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"license-metadata",
"query-match",
"repository-metadata"
] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-24T16:32:04Z | Official Implementation of paper "Multimodal Federated Learning with Missing Modality via Prototype Mask and Contrast" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-01T17:35:19Z | false | 1 | 819,505,804 | null | Python | MIT | [
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] | Noirebao/Multimodal_Federated | [
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] | 3 | 2026-10-14T16:10:46Z | [] | 2026-03-24T04:45:47Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 27 | [] | 2026-04-01T11:04:03Z | https://github.com/Noirebao/Multimodal_Federated | null |
[
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-25T07:22:56Z | [MedIA 2026] Official implementation of TTGA: Test-Time Generative Augmentation for Medical Image Segmentation. | [
"computer-vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-01T17:35:19Z | false | 0 | 819,777,162 | https://doi.org/10.1016/j.media.2025.103902 | Jupyter Notebook | null | [
"segmentation"
] | maxiao0234/TTGA | [
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"paper-reference",
"query-match",
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] | 3 | 2026-10-26T17:25:59Z | [] | 2026-01-05T17:30:19Z | [
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] | [
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] | ok | 2026-10-03T17:01:43Z | maxiao0234/TTGA | readme-supported-paper-method-implementation | [
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] | include | ml-contribution-v5 | 17 | [] | 2026-09-28T08:52:00Z | https://github.com/maxiao0234/TTGA | null |
[
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-25T10:01:23Z | Official code by Applied AI (Spindox) for ICML 2024 CHALLENGES ON AUTOMATED MATH REASONING - TRACK 3: AUTOMATED OPTIMIZATION PROBLEM-SOLVING WITH CODE | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-11T16:48:52Z | false | 1 | 819,847,745 | null | Jupyter Notebook | Apache-2.0 | [
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] | accelerate-spindox/icml24-automathres-t3 | [
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] | 1 | 2026-10-11T16:48:52Z | [] | 2024-06-28T15:58:18Z | [
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] | include | ml-contribution-v5 | 3 | [] | 2025-03-07T12:35:37Z | https://github.com/accelerate-spindox/icml24-automathres-t3 | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-25T15:28:08Z | Code for paper "Wearable Data From Subjects Playing Super Mario, Taking University Exams, or Performing Physical Exercise Help Detect Acute Mood Disorder Episodes via Self-Supervised Learning: Prospective, Exploratory, Observational Study" | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T16:44:32Z | false | 3 | 819,993,139 | https://mhealth.jmir.org/2024/1/e55094 | Python | MIT | [
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"transformer"
] | april-tools/e4selflearning | [
"description",
"github-topics",
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] | 3 | 2026-10-23T15:59:01Z | [] | 2024-07-18T15:52:25Z | [
"timeseries.topic"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 3 | [
"intrepibd",
"mood-disorders",
"personal-sensing",
"pytorch",
"time-series",
"timebase",
"transformer",
"wearables"
] | 2026-07-17T15:55:58Z | https://github.com/april-tools/e4selflearning | null |
[
"generative-ai",
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] | [
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-26T05:38:21Z | SEIKO is a novel reinforcement learning method to efficiently fine-tune diffusion models in an online setting. Our methods outperform all baselines (PPO, classifier-based guidance, direct reward backpropagation) for fine-tuning Stable Diffusion. | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-16T15:36:56Z | false | 0 | 820,253,411 | https://arxiv.org/abs/2402.16359 | Python | MIT | [
"diffusion",
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] | zhaoyl18/SEIKO | [
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] | 1 | 2026-10-16T15:36:56Z | [] | 2024-07-18T23:18:45Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
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] | include | ml-contribution-v5 | 31 | [
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"online-learning",
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"stable-diffusion",
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] | 2026-09-17T03:24:09Z | https://github.com/zhaoyl18/SEIKO | null |
[
"efficient-ml",
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-26T05:43:57Z | [EMNLP 2024 Findings🔥] Official implementation of ": LOOK-M: Look-Once Optimization in KV Cache for Efficient Multimodal Long-Context Inference" | [
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"language",
"multimodal-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 8 | 820,255,233 | null | Python | MIT | [
"language-model",
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] | SUSTechBruce/LOOK-M | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 9 | 2026-10-03T15:45:52Z | [] | 2024-11-09T23:03:30Z | [
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] | 1a11165f396f15f8d9b7efe2e0c6c72286fbbc8b | 2026-10-03T17:01:43Z | gh-ml-readme-evidence-v2 | "1a11165f396f15f8d9b7efe2e0c6c72286fbbc8b" | [
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] | [
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] | ok | 2026-10-03T17:01:43Z | SUSTechBruce/LOOK-M | readme-supported-paper-method-implementation | [
"contribution-language",
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] | include | ml-contribution-v5 | 103 | [] | 2026-04-27T03:05:50Z | https://github.com/SUSTechBruce/LOOK-M | null |
[
"generative-ai",
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] | [
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] | [
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] | [
"graph.graph-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-26T08:59:05Z | Official code for the MICCAI 2024 paper "3D Vessel Graph Generation Using Denoising Diffusion" | [
"generative-ai",
"generative-modeling",
"graph-learning"
] | [
"deep-learning",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-01T17:35:19Z | false | 3 | 820,330,383 | null | Python | null | [
"diffusion",
"generative-modeling",
"graph-generation"
] | chinmay5/vessel_diffuse | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 3 | 2026-10-19T17:46:36Z | [] | 2024-06-26T15:08:26Z | [
"graph.graph-generation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 25 | [
"diffusion-models",
"geometric-deep-learning",
"spatial-graphs",
"vascular"
] | 2026-03-30T01:13:03Z | https://github.com/chinmay5/vessel_diffuse | null |
[
"distributed-ml",
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] | [
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] | [
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] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-26T10:27:58Z | This repository contains the source code for our MICCAI 2024 paper titled 'CAR-MFL: Cross-Modal Augmentation by Retrieval for Multimodal Federated Learning with Missing Modalities' | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-01T17:35:19Z | false | 4 | 820,367,720 | https://arxiv.org/abs/2407.08648 | Python | MIT | [
"collaborative-learning",
"federated-learning"
] | bhattarailab/CAR-MFL | [
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] | 3 | 2026-10-14T16:10:46Z | [] | 2024-10-01T19:51:43Z | [
"trust.federated-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 29 | [] | 2026-09-03T11:39:34Z | https://github.com/bhattarailab/CAR-MFL | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
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"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-26T12:34:16Z | source code for paper: Vital node identification in complex networks based on autoencoder and graph neural network | [
"deep-learning",
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T16:30:43Z | false | 3 | 820,418,591 | null | Jupyter Notebook | null | [
"graph-neural-network",
"message-passing",
"neural-network"
] | hbjix/AGNN | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-10-15T15:20:52Z | [] | 2025-06-26T03:07:32Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 10 | [] | 2026-06-19T03:30:57Z | https://github.com/hbjix/AGNN | null |
[
"language",
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] | [
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] | [
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"repository-metadata"
] | [
"multimodal.video-language-model",
"multimodal.video-understanding"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-27T10:38:50Z | This repository is the official implementation of HazardVLM: A Video Language Model for Real-Time Hazard Description in Automated Driving Systems. | [
"language",
"multimodal",
"natural-language-processing",
"video"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 1 | 820,867,653 | null | Python | NOASSERTION | [
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] | dannierxiao/HazardVLM | [
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"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-10-02T16:44:32Z | [] | 2024-08-17T15:58:01Z | [
"multimodal.video-understanding"
] | b2c1dae5b4a3d93fbaad42684f0694867675ab53 | 2026-10-03T17:01:43Z | gh-ml-readme-evidence-v2 | "b2c1dae5b4a3d93fbaad42684f0694867675ab53" | [
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] | [
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] | ok | 2026-10-03T17:01:43Z | dannierxiao/HazardVLM | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
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] | include | ml-contribution-v5 | 9 | [] | 2026-06-04T11:37:34Z | https://github.com/dannierxiao/HazardVLM | null |
[
"generative-ai",
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] | [
"diffusion"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-27T12:44:12Z | [MICCAI 2024 Early Acceptance] Official Pytorch Code for Slice-Consistent 3D Volumetric Brain CT-to-MRI Translation with 2D Brownian Bridge Diffusion Model | [
"generative-ai",
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-15T15:20:52Z | false | 8 | 820,918,338 | null | Python | MIT | [
"diffusion"
] | MICV-yonsei/CT2MRI | [
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"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-15T15:20:52Z | [] | 2025-01-07T06:56:27Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 67 | [] | 2026-10-14T12:13:32Z | https://github.com/MICV-yonsei/CT2MRI | null |
[
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] | [
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] | [
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"repository-metadata"
] | [
"efficiency.mixture-of-experts"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-28T04:37:15Z | Official implementation for the paper Lancet: Accelerating Mixture-of-Experts Training via Whole Graph Computation-Communication Overlapping, published in MLSys'24. | [
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"language"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 4 | 821,221,170 | https://arxiv.org/abs/2404.19429 | C++ | Apache-2.0 | [
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] | awslabs/Lancet-Accelerating-MoE-Training-via-Whole-Graph-Computation-Communication-Overlapping | [
"description",
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"repository-metadata"
] | 3 | 2026-10-01T17:35:19Z | [] | 2026-05-20T00:28:36Z | [
"efficiency.mixture-of-experts"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 15 | [] | 2026-08-29T06:58:33Z | https://github.com/awslabs/Lancet-Accelerating-MoE-Training-via-Whole-Graph-Computation-Communication-Overlapping | null |
[
"computer-vision",
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] | [
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] | [
"description",
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"license-metadata",
"query-match",
"repository-metadata"
] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-28T04:56:05Z | (CVPR 2024) Official Implementation of "FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated Learning" | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-14T16:10:46Z | false | 1 | 821,225,786 | null | Python | MIT | [
"collaborative-learning",
"federated-learning"
] | Lee-Gihun/FedSOL | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-14T16:10:46Z | [] | 2024-06-28T11:03:27Z | [
"trust.federated-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 17 | [
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[
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] | [
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-28T08:53:18Z | Code for Paper 'Adversarial Scenario Generation Integrated Distributionally Robust Deep Reinforcement Learning for Survival of Critical Loads'. | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-26T17:25:59Z | false | 2 | 821,308,713 | https://ieeexplore.ieee.org/abstract/document/11011524 | Python | null | [
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] | RanZhu1989/ASGAN_DRRL_TopReconfig | [
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] | 1 | 2026-10-26T17:25:59Z | [] | 2025-06-05T03:12:02Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 3 | [] | 2026-10-08T03:24:48Z | https://github.com/RanZhu1989/ASGAN_DRRL_TopReconfig | null |
[
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] | [
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] | [
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"query-match",
"repository-metadata"
] | [
"llm.large-language-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-28T15:52:27Z | Official Implementation of "CLEFT: Language-Image Contrastive Learning with Efficient Large Language Model and Prompt Fine-Tuning" on MICCAI 2024. | [
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"language",
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] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-30T17:16:29Z | false | 1 | 821,468,112 | null | Python | Apache-2.0 | [
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"language-model",
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] | XYPB/CLEFT | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-30T17:16:29Z | [] | 2025-02-12T16:02:22Z | [
"llm.large-language-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 18 | [] | 2026-02-23T16:37:40Z | https://github.com/XYPB/CLEFT | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-29T10:26:22Z | [ACL 24] The official implementation of MapGPT: Map-Guided Prompting with Adaptive Path Planning for Vision-and-Language Navigation. | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-30T16:30:43Z | false | 11 | 821,749,841 | https://chen-judge.github.io/MapGPT/ | Python | null | [
"language-modeling"
] | chen-judge/MapGPT | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-09-30T16:30:43Z | [] | 2025-05-03T10:07:53Z | [
"recall.computational-linguistics"
] | 95f72ca13e73075865c4ca5c848d51e2f83a6027 | 2026-10-03T17:01:43Z | gh-ml-readme-evidence-v2 | "95f72ca13e73075865c4ca5c848d51e2f83a6027" | [
"citation",
"installation",
"other"
] | [
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-10-03T17:01:43Z | chen-judge/MapGPT | readme-supported-paper-method-implementation | [
"contribution-language",
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 138 | [] | 2026-09-18T05:24:47Z | https://github.com/chen-judge/MapGPT | null |
[
"distributed-ml",
"privacy-and-federated-learning",
"reinforcement-learning"
] | [
"collaborative-learning",
"federated-learning",
"reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-30T16:52:22Z | The code of our paper "Reinforcement Learning-based Layer-wise Aggregation for Personalized Federated Learning" | [
"distributed-ml",
"privacy-and-federated-learning",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-14T16:10:46Z | false | 0 | 822,234,016 | null | Python | null | [
"collaborative-learning",
"federated-learning",
"reinforcement-learning"
] | fdksd/pFedRLLA | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-14T16:10:46Z | [] | 2024-06-30T16:53:57Z | [
"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 | 10 | [] | 2026-02-21T04:43:23Z | https://github.com/fdksd/pFedRLLA | null |
[
"information-retrieval",
"natural-language-processing"
] | [
"dense-retrieval",
"distillation",
"knowledge-distillation"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"nlp.dense-retrieval"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-01T07:26:11Z | Source code of our paper "PairDistill: Pairwise Relevance Distillation for Dense Retrieval", EMNLP 2024 Main. | [
"information-retrieval",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 2 | 822,454,029 | null | Jupyter Notebook | null | [
"dense-retrieval",
"distillation",
"knowledge-distillation"
] | MiuLab/PairDistill | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 8 | 2026-10-02T16:59:40Z | [] | 2024-11-28T08:32:37Z | [
"nlp.dense-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 | 22 | [
"dense-retrieval",
"emnlp2024",
"information-retrieval",
"knowledge-distillation"
] | 2025-03-21T07:18:25Z | https://github.com/MiuLab/PairDistill | null |
[
"efficient-ml",
"model-compression"
] | [
"distillation",
"knowledge-distillation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-01T09:01:02Z | This project is the official implementation of the paper "Less-supervised learning with knowledge distillation for sperm morphology analysis". | [
"efficient-ml",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 1 | 822,493,486 | null | Python | null | [
"distillation",
"knowledge-distillation"
] | eyenpi/sperm-morphology-kd | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-04T15:58:58Z | [] | 2024-07-01T11:15:10Z | [
"efficiency.knowledge-distillation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 5 | [] | 2026-06-23T21:44:28Z | https://github.com/eyenpi/sperm-morphology-kd | null |
[
"generative-ai",
"language",
"natural-language-processing"
] | [
"language-model",
"pretraining"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.large-language-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-01T16:57:43Z | This Repo explain how to fine-tune Llama-2 using new techniques to overcome memory and computing limitations to make open-source large language models more accessible. | [
"generative-ai",
"language",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-31T16:49:26Z | false | 0 | 822,711,537 | null | Jupyter Notebook | null | [
"language-model",
"pretraining"
] | Balaji-th/Llama2_Fine_Tuning | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-31T16:49:26Z | [] | 2024-07-01T17:06:29Z | [
"llm.large-language-model"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
"contribution-language",
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [] | 2024-07-01T17:06:32Z | https://github.com/Balaji-th/Llama2_Fine_Tuning | null |
[
"computer-vision",
"deep-learning",
"language",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"representation-learning",
"vision"
] | [
"continual-learning",
"contrastive-learning",
"image-text",
"lifelong-learning",
"multimodal-learning",
"vision-language",
"vision-language-model"
] | [
"description",
"github-topics",
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"query-match",
"repository-metadata"
] | [
"general.continual-learning",
"multimodal.vision-language",
"multimodal.vlm",
"vision.vision-language"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-01T20:02:49Z | This is an official implementation of our work, Select and Distill: Selective Dual-Teacher Knowledge Transfer for Continual Learning on Vision-Language Models, accepted to ECCV'24 | [
"computer-vision",
"deep-learning",
"multimodal-learning",
"natural-language-processing",
"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 1 | 822,779,387 | https://yuchuyu.org/research/snd | Python | MIT | [
"continual-learning",
"lifelong-learning"
] | chu0802/SnD | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-18T14:44:29Z | [] | 2025-09-24T17:25:04Z | [
"general.continual-learning"
] | 87bb5db0d1bdd4bf2025ade9b4eb557cf9c0fb0d | 2026-10-04T17:18:56Z | gh-ml-readme-evidence-v2 | "87bb5db0d1bdd4bf2025ade9b4eb557cf9c0fb0d" | [
"citation",
"dataset",
"installation",
"method",
"other"
] | [
"ml-method-context",
"official-implementation-claim",
"paper-reference"
] | ok | 2026-10-04T17:18:56Z | chu0802/SnD | readme-supported-paper-method-implementation | [
"contribution-language",
"ml-method-context",
"official-implementation-claim",
"paper-reference"
] | include | ml-contribution-v5 | 18 | [
"continual-learning",
"eccv",
"eccv2024",
"vision-language-models"
] | 2026-10-15T12:22:21Z | https://github.com/chu0802/SnD | null |
[
"embodied-ai",
"generative-modeling",
"robotics"
] | [
"diffusion",
"diffusion-policy",
"imitation-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"robotics.diffusion-policy"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-02T22:50:02Z | Official implementation for paper "EquiBot: SIM(3)-Equivariant Diffusion Policy for Generalizable and Data Efficient Learning". | [
"embodied-ai",
"generative-modeling",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 21 | 823,367,216 | https://equi-bot.github.io | Python | MIT | [
"diffusion",
"diffusion-policy",
"imitation-learning"
] | yjy0625/equibot | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-09-30T16:30:43Z | [] | 2024-07-02T23:08:39Z | [
"robotics.diffusion-policy"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 174 | [] | 2026-09-29T08:24:42Z | https://github.com/yjy0625/equibot | null |
[
"3d",
"computer-vision",
"generative-ai",
"graphics",
"multimodal"
] | [
"3d-generation",
"distillation",
"generative-modeling",
"text-to-3d"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.3d-generation",
"multimodal.text-to-3d"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-03T08:53:10Z | Official implementation of "Connecting Consistency Distillation to Score Distillation for Text-to-3D Generation", ECCV2024 | [
"3d",
"computer-vision",
"generative-ai",
"graphics",
"multimodal"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-01T17:35:19Z | false | 1 | 823,554,837 | https://zongrui.page/ECCV2024-GCS-BEG/ | Python | MIT | [
"3d-generation",
"distillation",
"generative-modeling",
"text-to-3d"
] | LMozart/ECCV2024-GCS-BEG | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-01T17:50:46Z | [] | 2024-11-12T08:56:12Z | [
"multimodal.3d-generation",
"multimodal.text-to-3d"
] | 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 | 20 | [
"text-to-3d"
] | 2026-08-22T11:43:55Z | https://github.com/LMozart/ECCV2024-GCS-BEG | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-04T03:22:39Z | [ECCV 2024🔥] The official code for the paper DiffFAS: Face Anti-Spoofing via Generative Diffusion Models. | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-15T15:20:52Z | false | 3 | 823,932,554 | null | Python | null | [
"diffusion"
] | murphytju/DiffFAS | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-15T15:20:52Z | [] | 2024-09-23T11:57:47Z | [
"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 | 44 | [] | 2026-08-05T09:04:42Z | https://github.com/murphytju/DiffFAS | null |
[
"3d-vision",
"graphics"
] | [
"implicit-representation",
"neural-rendering"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"cross26.graphics-implicit-neural-representation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-04T07:24:46Z | Released code for paper: Attention Beats Linear for Fast Implicit Neural Representation Generation. Attention-based localized implicit Neural Representation (ANR) | [
"3d-vision",
"graphics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T22:51:24Z | false | 1 | 824,008,446 | null | Python | null | [
"implicit-representation",
"neural-rendering"
] | Roninton/ANR | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-09-26T22:51:24Z | [] | 2024-08-27T04:43:24Z | [
"cross26.graphics-implicit-neural-representation"
] | 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 | 16 | [] | 2025-01-21T23:51:33Z | https://github.com/Roninton/ANR | 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 | [] | 2024-07-04T07:32:17Z | official implementation of ECCV'24 paper "LPViT: Low-Power Semi-structured Pruning for Vision Transformers" | [
"computer-vision",
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 0 | 824,011,581 | null | Python | null | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | Akimoto-Cris/LPViT | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-23T15:59:01Z | [] | 2024-09-17T08:31:31Z | [
"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 | 7 | [] | 2025-12-03T06:16:26Z | https://github.com/Akimoto-Cris/LPViT | 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 | [] | 2024-07-04T12:03:44Z | Official implementation of the ECCV 2024 paper: TransFusion -- A Transparency-Based Diffusion Model for Anomaly Detection | [
"generative-ai",
"generative-modeling"
] | [
"deep-learning",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-15T15:20:52Z | false | 4 | 824,121,207 | null | Python | MIT | [
"diffusion"
] | MaticFuc/ECCV_TransFusion | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-15T15:20:52Z | [] | 2025-04-07T16:30:08Z | [
"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 | 53 | [
"anomaly-detection",
"deep-learning",
"eccv2024",
"mvtec-3d-ad",
"mvtec-ad",
"unsupervised",
"visa"
] | 2026-10-14T01:36:35Z | https://github.com/MaticFuc/ECCV_TransFusion | null |
[
"classical-ml",
"tabular-and-structured-data",
"tabular-ml"
] | [
"ensemble-learning",
"gradient-boosting"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.gradient-boosting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-05T00:47:31Z | Accompanying code for our paper - "Enhancing Prediction and Inference of Daily In-stream Nutrient and Sediment Concentrations using an Extreme Gradient Boosting based Water Quality Estimation Tool - XGBest" | [
"classical-ml",
"tabular-and-structured-data",
"tabular-ml"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-15T15:20:52Z | false | 0 | 824,357,799 | null | R | null | [
"ensemble-learning",
"gradient-boosting"
] | GEM-TAMU/XGBest-WQ-Prediction | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-15T15:20:52Z | [] | 2025-02-17T21:51:25Z | [
"general.gradient-boosting"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 2 | [] | 2026-05-06T15:21:27Z | https://github.com/GEM-TAMU/XGBest-WQ-Prediction | null |
[
"computer-vision",
"generative-ai",
"language",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"vision"
] | [
"contrastive-learning",
"fine-tuning",
"image-text",
"instruction-tuning",
"multimodal-learning",
"vision-language",
"vision-language-model"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.instruction-tuning",
"multimodal.vision-language",
"multimodal.vlm",
"vision.vision-language"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-05T04:51:15Z | [ECCV 2024] Official implementation of NavGPT-2: Unleashing Navigational Reasoning Capability for Large Vision-Language Models | [
"computer-vision",
"multimodal",
"multimodal-learning",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 17 | 824,411,768 | null | Python | MIT | [
"multimodal-learning",
"vision-language"
] | GengzeZhou/NavGPT-2 | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 15 | 2026-10-15T15:20:52Z | [
"2407.12366"
] | 2026-04-03T11:58:14Z | [
"vision.vision-language"
] | 3420731d1e37ff2923303b205c7102468490c060 | 2026-10-04T17:18:56Z | gh-ml-readme-evidence-v2 | "3420731d1e37ff2923303b205c7102468490c060" | [
"abstract",
"citation",
"installation",
"method",
"other"
] | [
"method-contribution",
"ml-method-context",
"paper-reference"
] | ok | 2026-10-04T17:18:56Z | GengzeZhou/NavGPT-2 | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-method-context",
"paper-reference"
] | include | ml-contribution-v5 | 255 | [] | 2026-10-13T05:11:53Z | https://github.com/GengzeZhou/NavGPT-2 | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"multimodal",
"vision"
] | [
"diffusion",
"generative-modeling",
"image-generation",
"text-to-image"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"multimodal.text-to-image",
"vision.image-diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-06T04:04:29Z | [ECCV 2024] "Receler: Reliable Concept Erasing of Text-to-Image Diffusion Models via Lightweight Erasers" (Official Implementation) | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-30T16:30:43Z | false | 2 | 824,826,738 | https://jasper0314-huang.github.io/receler-concept-erasing/ | Python | MIT | [
"diffusion"
] | jasper0314-huang/Receler | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-15T15:20:52Z | [] | 2025-03-02T03:25:11Z | [
"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 | 44 | [] | 2026-01-02T11:21:00Z | https://github.com/jasper0314-huang/Receler | null |
[
"computer-vision"
] | [
"distillation",
"restoration",
"super-resolution"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.image-super-resolution"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-06T04:17:30Z | [ECCV 2024] The Official Code for "Pairwise Distance Distillation for Unsupervised Real-World Image Super-Resolution" | [
"computer-vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-25T15:11:36Z | false | 3 | 824,829,438 | null | Python | MIT | [
"distillation",
"restoration",
"super-resolution"
] | Yuehan717/PDD | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-25T15:11:36Z | [] | 2024-07-25T07:47:13Z | [
"vision.image-super-resolution"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 39 | [] | 2026-01-22T12:15:59Z | https://github.com/Yuehan717/PDD | null |
[
"efficient-ml",
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] | [
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"repository-metadata"
] | [
"efficiency.pruning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-06T19:24:55Z | Implementation details of our paper "Layer Pruning with Consensus: A Triple-Win Solution" | [
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-24T14:31:13Z | false | 0 | 825,102,093 | null | Python | MIT | [
"neural-network-pruning",
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] | CarolinaTavaresDuarte/Consensus-Layer-Pruning | [
"description",
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"query-match",
"repository-metadata"
] | 1 | 2026-10-24T14:31:13Z | [] | 2025-08-17T02:33:58Z | [
"efficiency.pruning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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] | include | ml-contribution-v5 | 1 | [] | 2024-12-23T10:11:39Z | https://github.com/CarolinaTavaresDuarte/Consensus-Layer-Pruning | null |
[
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] | [
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] | [
"description",
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"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-07T02:46:39Z | Official code for the ECCV 2024 paper "Unmasking Bias in Diffusion Model Training" | [
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] | [
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"model-training"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-16T15:36:56Z | false | 0 | 825,175,744 | null | Python | MIT | [
"diffusion"
] | yuhuUSTC/Debias | [
"description",
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"query-match",
"repository-metadata"
] | 1 | 2026-10-16T15:36:56Z | [] | 2024-07-08T02:21:47Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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] | include | ml-contribution-v5 | 8 | [] | 2025-04-13T07:17:37Z | https://github.com/yuhuUSTC/Debias | null |
[
"information-retrieval",
"natural-language-processing"
] | [
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"document-retrieval",
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] | [
"description",
"query-match",
"repository-metadata"
] | [
"nlp-document-retrieval"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-07T08:56:38Z | This is the official implementation of the paper "Generative Retrieval with Semantic Tree-Structured Item Identifiers via Contrastive Learning" | [
"information-retrieval",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-13T18:05:26Z | false | 2 | 825,251,843 | null | Python | null | [
"contrastive-learning",
"document-retrieval",
"information-retrieval"
] | Ethan00Si/SEATER_Generative_Retrieval | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-14T16:10:46Z | [] | 2024-12-12T06:45:30Z | [
"nlp-document-retrieval"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 26 | [] | 2026-04-02T08:46:02Z | https://github.com/Ethan00Si/SEATER_Generative_Retrieval | null |
[
"machine-learning"
] | [
"adaptation",
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] | [
"description",
"query-match",
"repository-metadata"
] | [
"specialized.test-time-adaptation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-08T07:16:39Z | [ECCV 2024] Official Code for our paper "Reshaping the Online Data Buffering and Organizing Mechanism for Continual Test-Time Adaptation". | [
"machine-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-01T17:35:19Z | false | 0 | 825,611,080 | null | Python | null | [
"adaptation",
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] | z1358/OBAO | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-02T16:59:40Z | [] | 2025-03-24T09:39:46Z | [
"specialized.test-time-adaptation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 15 | [] | 2026-08-03T12:53:02Z | https://github.com/z1358/OBAO | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-08T07:32:39Z | This is the official Pytorch code for MICCAI2024 paper "Iterative Online Image Synthesis via Diffusion Model for Imbalanced Classification" | [
"generative-ai",
"generative-modeling"
] | [
"classifier",
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-16T15:36:56Z | false | 0 | 825,617,582 | null | Python | null | [
"diffusion"
] | ustlsh/IOIS_imbalance | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-16T15:36:56Z | [] | 2024-10-31T09:42:29Z | [
"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 | 4 | [] | 2025-12-23T14:52:16Z | https://github.com/ustlsh/IOIS_imbalance | null |
[
"anomaly-detection",
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] | [
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] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.vision-language",
"vision.visual-anomaly-detection"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-08T08:15:14Z | [ECCV2024] The Official Implementation for ''AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection'' | [
"computer-vision",
"multimodal"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T15:29:21Z | false | 19 | 825,639,715 | null | Python | MIT | [
"multimodal-learning",
"vision-language"
] | caoyunkang/AdaCLIP | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-15T15:20:52Z | [] | 2025-07-07T23:20:51Z | [
"vision.vision-language"
] | d38bbb536a7b2e0aa8f76a123264e36f4e0d8221 | 2026-10-04T17:18:56Z | gh-ml-readme-evidence-v2 | "d38bbb536a7b2e0aa8f76a123264e36f4e0d8221" | [
"citation",
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"other",
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] | [
"method-contribution",
"ml-method-context",
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] | ok | 2026-10-04T17:18:56Z | caoyunkang/AdaCLIP | readme-supported-paper-method-implementation | [
"contribution-language",
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"ml-method-context",
"paper-reference"
] | include | ml-contribution-v5 | 312 | [] | 2026-10-15T13:40:07Z | https://github.com/caoyunkang/AdaCLIP | null |
[
"computer-vision",
"efficient-ml",
"generative-ai",
"language",
"multimodal",
"multimodal-learning",
"vision"
] | [
"adapter-tuning",
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"fine-tuning",
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] | [
"description",
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"repository-metadata"
] | [
"efficiency.parameter-efficient-finetuning",
"llm.parameter-efficient-finetuning",
"multimodal.vision-language"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-08T20:26:36Z | Official code for Introducing Routing Functions to Vision-Language Parameter-Efficient Fine-Tuning with Low-Rank Bottlenecks (ECCV2024) | [
"computer-vision",
"generative-ai",
"language",
"multimodal-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-25T16:01:25Z | false | 0 | 825,951,311 | null | Python | null | [
"fine-tuning",
"parameter-efficient-fine-tuning"
] | tingyu215/Routing_VLPEFT | [
"description",
"query-match",
"repository-metadata"
] | 4 | 2026-10-08T18:15:14Z | [] | 2024-07-24T12:27:50Z | [
"llm.parameter-efficient-finetuning"
] | b64e8dde7cefa54ab2e0e5fb8a19bc94d0625473 | 2026-10-04T17:18:56Z | gh-ml-readme-evidence-v2 | "b64e8dde7cefa54ab2e0e5fb8a19bc94d0625473" | [
"citation",
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"other",
"results"
] | [
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-10-04T17:18:56Z | tingyu215/Routing_VLPEFT | readme-supported-paper-method-implementation | [
"contribution-language",
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 5 | [] | 2025-07-06T13:02:25Z | https://github.com/tingyu215/Routing_VLPEFT | null |
[
"information-retrieval",
"natural-language-processing"
] | [
"bm25",
"dense-retrieval",
"lexical-retrieval",
"question-answering",
"rag",
"reading-comprehension"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"nlp-bm25-retrieval",
"nlp.dense-retrieval",
"nlp.question-answering"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-08T21:22:37Z | Code for paper "W-RAG: Weakly Supervised Dense Retrieval in RAG for Open-domain Question Answering" | [
"information-retrieval",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-30T16:30:43Z | false | 3 | 825,968,695 | null | Jupyter Notebook | null | [
"question-answering",
"rag",
"reading-comprehension"
] | jmnian/WRAG | [
"description",
"query-match",
"repository-metadata"
] | 7 | 2026-11-01T17:09:41Z | [] | 2025-10-02T17:46:00Z | [
"nlp.question-answering"
] | 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 | [] | 2026-05-27T15:52:13Z | https://github.com/jmnian/WRAG | null |
[
"deep-learning",
"representation-learning",
"time-series-and-forecasting"
] | [
"representation-learning",
"self-supervised-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.self-supervised-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-09T03:01:57Z | Code for paper: Self-Supervised Learning on Real-World Data for Accurate Forecasting of Electric Vehicle Battery Capacity and Predictive Battery Recycle Strategy | [
"deep-learning",
"representation-learning",
"time-series-and-forecasting"
] | [
"self-supervised-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 826,054,066 | null | Jupyter Notebook | null | [
"representation-learning",
"self-supervised-learning"
] | Pandalin98/Self-supervised-EV | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-04T15:58:58Z | [] | 2024-07-09T03:03:04Z | [
"general.self-supervised-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 4 | [] | 2025-06-02T17:01:21Z | https://github.com/Pandalin98/Self-supervised-EV | null |
[
"deep-learning",
"representation-learning"
] | [
"contrastive-learning",
"self-supervised-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-09T14:53:42Z | The official implementation of "Efficient Frequency-Domain Image Deraining with Contrastive Regularization" 【ECCV2024】 | [
"deep-learning",
"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-30T16:30:43Z | false | 7 | 826,357,879 | null | Python | null | [
"contrastive-learning",
"self-supervised-learning"
] | deng-ai-lab/FADformer | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-11-01T17:09:41Z | [] | 2025-06-19T03:58:33Z | [
"general.contrastive-learning"
] | 74cdd170d59358ae2a56c91def9ef7f8166fa303 | 2026-10-04T17:18:56Z | gh-ml-readme-evidence-v2 | "74cdd170d59358ae2a56c91def9ef7f8166fa303" | [
"abstract",
"method",
"other",
"references"
] | [
"method-contribution",
"ml-method-context",
"paper-reference"
] | ok | 2026-10-04T17:18:56Z | deng-ai-lab/FADformer | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-context-only",
"ml-method-context",
"paper-reference"
] | include | ml-contribution-v5 | 160 | [] | 2026-10-12T05:45:55Z | https://github.com/deng-ai-lab/FADformer | null |
[
"generative-ai",
"multimodal",
"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 | [] | 2024-07-10T04:51:34Z | Official implementation of MARS: Mixture of Auto-Regressive Models for Fine-grained Text-to-image Synthesis | [
"generative-ai",
"multimodal",
"vision"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-01T17:35:19Z | false | 3 | 826,632,834 | null | null | null | [
"image-generation",
"text-to-image"
] | fusiming3/MARS | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-10-07T18:07:52Z | [] | 2024-07-16T02:45:27Z | [
"multimodal.text-to-image"
] | cabe604e29af46106e72b6993a2116369171b6df | 2026-10-04T17:18:56Z | gh-ml-readme-evidence-v2 | "cabe604e29af46106e72b6993a2116369171b6df" | [
"installation",
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"overview",
"usage"
] | [
"method-contribution",
"ml-method-context",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-10-04T17:18:56Z | fusiming3/MARS | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-method-context",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 86 | [] | 2025-09-30T09:40:27Z | https://github.com/fusiming3/MARS | null |
[
"generative-ai",
"generative-modeling",
"graph-learning"
] | [
"diffusion",
"generative-modeling",
"graph-generation"
] | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"graph.graph-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-10T07:36:49Z | Official Implementation of our Paper "Lift Your Molecules: Molecular Graph Generation in Latent Euclidean Space" (ICLR 2025) | [
"generative-ai",
"generative-modeling",
"graph-learning"
] | [
"machine-learning",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-18T14:44:29Z | false | 0 | 826,691,866 | https://arxiv.org/abs/2406.10513v1 | null | null | [
"diffusion",
"generative-modeling",
"graph-generation"
] | ketatam/SyCo | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | 2 | 2026-10-19T17:46:36Z | [] | 2025-02-28T11:59:46Z | [
"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 | 8 | [
"computational-biology",
"diffusion-models",
"machine-learning",
"molecule-generation"
] | 2026-01-04T07:19:08Z | https://github.com/ketatam/SyCo | null |
[
"classical-ml",
"generative-modeling",
"natural-language-processing",
"representation-learning"
] | [
"diffusion",
"metric-learning",
"representation-learning",
"text-generation"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.metric-learning",
"nlp.text-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-10T12:35:08Z | Official PyTorch Implementation of "DiffusionPen: Towards Controlling the Style of Handwritten Text Generation" - ECCV 2024 | [
"classical-ml",
"generative-modeling",
"natural-language-processing",
"representation-learning"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-01T17:35:19Z | false | 24 | 826,814,445 | null | Python | MIT | [
"diffusion",
"metric-learning",
"representation-learning"
] | koninik/DiffusionPen | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-18T14:44:29Z | [] | 2024-10-24T07:59:41Z | [
"general.metric-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 | 109 | [
"diffusion-models",
"eccv2024",
"handwriting-recognition",
"handwritten-text-generation",
"pytorch",
"stable-diffusion"
] | 2026-10-18T11:32:54Z | https://github.com/koninik/DiffusionPen | null |
[
"efficient-ml",
"machine-learning-systems",
"model-compression",
"natural-language-processing"
] | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-10T18:04:33Z | Official implementation of the paper: "A deeper look at depth pruning of LLMs" | [
"efficient-ml",
"machine-learning-systems",
"model-compression",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T16:44:32Z | false | 1 | 826,955,474 | null | Python | null | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | shoaibahmed/llm_depth_pruning | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-10-23T15:59:01Z | [] | 2024-07-24T11:29:28Z | [
"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 | 15 | [] | 2025-04-23T08:48:51Z | https://github.com/shoaibahmed/llm_depth_pruning | null |
[
"explainable-ai",
"trustworthy-ml"
] | [
"contrastive-learning",
"counterfactual-explanations",
"interpretability"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"trust.counterfactual-explanations"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-10T20:06:53Z | Official Code for Contrastive Learning with Counterfactual Explanations for Radiology Report Generation (ECCV 2024) | [
"explainable-ai",
"trustworthy-ml"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 0 | 826,997,639 | null | Python | MIT | [
"contrastive-learning",
"counterfactual-explanations",
"interpretability"
] | mlii0117/CoFE | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-01T17:35:19Z | [] | 2025-04-03T04:00:50Z | [
"trust.counterfactual-explanations"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 18 | [] | 2026-05-06T03:32:25Z | https://github.com/mlii0117/CoFE | null |
[
"generative-ai",
"language",
"natural-language-processing"
] | [
"distillation",
"language-model",
"pretraining"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.large-language-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-11T01:48:39Z | code for paper "BiLD: Bi-directional Logits Difference Loss for Large Language Model Distillation" | [
"generative-ai",
"language",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-30T17:16:29Z | false | 1 | 827,081,300 | null | Python | null | [
"distillation",
"language-model",
"pretraining"
] | fpcsong/BiLD | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-30T17:16:29Z | [] | 2024-12-17T06:31:42Z | [
"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 | 11 | [] | 2026-03-18T02:26:40Z | https://github.com/fpcsong/BiLD | 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 | [] | 2024-07-11T09:14:44Z | Official Code for "Kinetic Typography Diffusion Model (ECCV 2024)" | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-01T19:00:27.900159Z | false | 3 | 827,243,395 | https://seonmip.github.io/kinety/ | Python | NOASSERTION | [
"diffusion"
] | SeonmiP/KineTy | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-15T15:20:52Z | [] | 2025-01-11T22:49:27Z | [
"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 | 59 | [
"eccv2024",
"kinetic-typography",
"video-generation"
] | 2026-05-21T04:47:25Z | https://github.com/SeonmiP/KineTy | null |
[
"3d",
"computer-vision",
"generative-modeling",
"robotics"
] | [
"3d-perception",
"diffusion",
"point-cloud-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.point-cloud"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-11T09:59:50Z | Official implementation of the ECCV 2024 paper Diffusion Bridges for 3D Point Cloud Denoising. | [
"3d",
"computer-vision",
"generative-modeling",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-01T17:35:19Z | false | 11 | 827,262,479 | null | Python | null | [
"3d-perception",
"diffusion",
"point-cloud-learning"
] | matvogel/P2P-Bridge | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-25T15:11:36Z | [] | 2024-09-30T15:31:39Z | [
"multimodal.point-cloud"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 111 | [] | 2026-10-09T17:23:10Z | https://github.com/matvogel/P2P-Bridge | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"interpretability-and-safety",
"multimodal",
"vision"
] | [
"diffusion",
"generative-modeling",
"image-generation",
"text-to-image"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"multimodal.text-to-image",
"vision.image-diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-11T15:18:50Z | [ECCV 2024 - Oral] Official PyTorch Implementation of "Adversarial Robustification via Text-to-Image Diffusion Models" | [
"generative-ai",
"generative-modeling",
"interpretability-and-safety"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-07T18:07:52Z | false | 0 | 827,396,385 | null | Python | MIT | [
"diffusion"
] | ChoiDae1/robustify-T2I | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-16T15:36:56Z | [] | 2024-08-17T15:58:55Z | [
"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 | 13 | [
"adversarial-robustness",
"provable-security",
"text-to-image-diffusion",
"zero-shot"
] | 2025-04-28T13:22:18Z | https://github.com/ChoiDae1/robustify-T2I | null |
[
"computer-vision",
"generative-ai"
] | [
"generative-modeling",
"video-generation"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"vision.video-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-12T05:34:49Z | [ICCV 2025] Official Implementation of Puppet-Master: Scaling Interactive Video Generation as a Motion Prior for Part-Level Dynamics | [
"computer-vision",
"generative-ai"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-01T19:00:27.900159Z | false | 2 | 827,656,763 | https://vgg-puppetmaster.github.io/ | Python | null | [
"generative-modeling",
"video-generation"
] | RuiningLi/puppet-master | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 2 | 2026-10-20T15:59:06Z | [] | 2025-01-16T10:23:24Z | [
"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 | 114 | [
"video-generation"
] | 2026-05-24T21:25:08Z | https://github.com/RuiningLi/puppet-master | null |
[
"computer-vision",
"language",
"machine-learning",
"multimodal",
"multimodal-learning",
"reliable-ml",
"robust-ml",
"trustworthy-ml",
"vision"
] | [
"contrastive-learning",
"multimodal-learning",
"out-of-distribution-detection",
"uncertainty-estimation",
"vision-language",
"vision-language-model"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.vision-language",
"probable.evaluation-ood-readme",
"trust.out-of-distribution-detection",
"vision.vision-language"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-12T06:54:06Z | This is the official code repo for "Vision-Language Dual-Pattern Matching for Out-of-Distribution Detection, in ECCV 2024" | [
"computer-vision",
"multimodal",
"multimodal-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 0 | 827,683,964 | null | Python | null | [
"multimodal-learning",
"vision-language"
] | HAIV-Lab/DPM | [
"description",
"query-match",
"repository-metadata"
] | 7 | 2026-10-16T15:36:56Z | [] | 2025-08-10T08:12:55Z | [
"vision.vision-language"
] | ccc74ad9e99752430e2780539287d0a6cb30f933 | 2026-10-04T17:18:56Z | gh-ml-readme-evidence-v2 | "ccc74ad9e99752430e2780539287d0a6cb30f933" | [
"abstract",
"citation",
"installation",
"other"
] | [
"method-contribution",
"ml-method-context",
"paper-reference"
] | ok | 2026-10-04T17:18:56Z | HAIV-Lab/DPM | readme-supported-paper-method-implementation | [
"method-contribution",
"ml-method-context",
"paper-reference"
] | include | ml-contribution-v5 | 9 | [] | 2026-04-12T16:30:23Z | https://github.com/HAIV-Lab/DPM | null |
[
"generative-ai",
"generative-modeling",
"tabular-and-structured-data",
"tabular-ml"
] | [
"diffusion",
"tabular-generation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"growth26.tabular-diffusion",
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-12T20:11:00Z | [TMLR 2025] The official implementation of the paper "Balanced Mixed-Type Tabular Data Synthesis with Diffusion Models" | [
"generative-ai",
"generative-modeling",
"tabular-and-structured-data"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-26T22:51:24Z | false | 0 | 827,983,017 | null | Python | GPL-3.0 | [
"diffusion"
] | comp-well-org/fair-tab-diffusion | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-16T15:36:56Z | [] | 2025-11-06T21:29:53Z | [
"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 | [] | 2026-07-23T18:55:50Z | https://github.com/comp-well-org/fair-tab-diffusion | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-13T05:07:47Z | Official implementation of Paper "Rethinking Video Deblurring with Wavelet-Aware Dynamic Transformer and Diffusion Model" (ECCV 2024) | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model",
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-15T15:20:52Z | false | 3 | 828,089,097 | null | Python | MIT | [
"diffusion",
"transformer"
] | Chen-Rao/VD-Diff | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-15T15:20:52Z | [] | 2025-01-06T13:28:49Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 27 | [] | 2026-08-30T05:50:42Z | https://github.com/Chen-Rao/VD-Diff | null |
[
"computer-vision"
] | [
"detection",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.object-detection"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-14T08:57:56Z | [ECCV2024 Oral] Official implementation of the paper "Relation DETR: Exploring Explicit Position Relation Prior for Object Detection" | [
"computer-vision"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-01T17:35:19Z | false | 18 | 828,472,384 | null | Python | Apache-2.0 | [
"detection",
"transformer"
] | xiuqhou/Relation-DETR | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-01T17:35:19Z | [] | 2024-11-24T05:22:07Z | [
"vision.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 | 266 | [
"attention",
"detection",
"detr",
"eccv2024",
"object-detection",
"relation-detr",
"transformer",
"transformers"
] | 2026-09-29T15:57:02Z | https://github.com/xiuqhou/Relation-DETR | null |
[
"computer-vision",
"natural-language-processing"
] | [
"detection",
"open-vocabulary-detection"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.open-vocabulary-detection"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-14T11:08:48Z | [ECCV 2024] Official implementation of "LaMI-DETR: Open-Vocabulary Detection with Language Model Instruction" | [
"computer-vision",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 5 | 828,506,065 | null | Python | Apache-2.0 | [
"detection",
"open-vocabulary-detection"
] | eternaldolphin/LaMI-DETR | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 7 | 2026-10-01T17:35:19Z | [] | 2025-12-23T13:03:53Z | [
"vision.open-vocabulary-detection"
] | 203e441dc6eba12391a8f3c8a43653f249d9232f | 2026-10-04T17:18:56Z | gh-ml-readme-evidence-v2 | "203e441dc6eba12391a8f3c8a43653f249d9232f" | [
"citation",
"dataset",
"installation",
"other",
"overview"
] | [
"ml-method-context",
"model-training-artifact",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-10-04T17:18:56Z | eternaldolphin/LaMI-DETR | readme-supported-paper-method-implementation | [
"contribution-language",
"ml-method-context",
"model-training-artifact",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 89 | [] | 2026-09-03T13:28:35Z | https://github.com/eternaldolphin/LaMI-DETR | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"multimodal",
"vision"
] | [
"diffusion",
"generative-modeling",
"image-generation",
"text-to-image"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"multimodal.text-to-image",
"vision.image-diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-15T00:41:59Z | Official implementation of "Safeguard Text-to-Image Diffusion Models with Human Feedback Inversion" (ECCV 2024) | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-07T18:07:52Z | false | 0 | 828,709,329 | https://nannullna.github.io/safeguard-hfi/ | null | MIT | [
"diffusion"
] | nannullna/safeguard-hfi | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-16T15:36:56Z | [] | 2024-09-18T23:06:09Z | [
"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 | [] | 2025-06-27T15:57:54Z | https://github.com/nannullna/safeguard-hfi | null |
[
"computer-vision",
"general-ml",
"generative-modeling",
"natural-language-processing"
] | [
"computer-vision",
"deep-learning",
"diffusion",
"text-generation"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.topic-deep-learning",
"nlp.text-generation",
"vision.topic-computer-vision"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-15T02:06:50Z | Official Code for ECCV 2024 paper — One-Shot Diffusion Mimicker for Handwritten Text Generation | [
"computer-vision",
"generative-modeling",
"natural-language-processing"
] | [
"deep-learning",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 58 | 828,725,594 | null | Python | MIT | [
"diffusion",
"text-generation"
] | dailenson/One-DM | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 7 | 2026-10-31T16:49:26Z | [] | 2025-10-15T03:32:59Z | [
"nlp.text-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 | 577 | [
"computer-vision",
"deep-learning",
"diffusion-models",
"handwriting-imitator",
"handwritten-text-generation",
"image-generation",
"latent-diffusion",
"pytorch-implementation"
] | 2026-10-30T01:54:40Z | https://github.com/dailenson/One-DM | null |
[
"chemistry",
"drug-discovery"
] | [
"generative-modeling",
"molecular-design",
"property-prediction",
"qsar",
"supervised-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"chem-molecular-generation",
"chem-property-prediction"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-15T08:11:15Z | Official implementation of SketchMol. | [
"chemistry",
"drug-discovery"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-29T17:12:21Z | false | 3 | 828,832,866 | null | Python | MIT | [
"property-prediction",
"qsar",
"supervised-learning"
] | WangZiXubiubiu/SketchMol-v1 | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-02T16:59:40Z | [] | 2025-02-14T13:29:41Z | [
"chem-property-prediction"
] | 6cb0639274728c5bf82d2af6e290e19ec556f2e4 | 2026-10-04T17:18:56Z | gh-ml-readme-evidence-v2 | "6cb0639274728c5bf82d2af6e290e19ec556f2e4" | [
"dataset",
"installation",
"method",
"other",
"usage"
] | [
"method-contribution",
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-10-04T17:18:56Z | WangZiXubiubiu/SketchMol-v1 | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 34 | [] | 2026-07-06T13:55:59Z | https://github.com/WangZiXubiubiu/SketchMol-v1 | null |
[
"3d",
"computer-vision",
"robotics"
] | [
"3d-perception",
"point-cloud-learning",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.point-cloud"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-15T08:51:44Z | This is a PyTorch implementation of GPSFormer proposed by our paper "GPSFormer: A Global Perception and Local Structure Fitting-based Transformer for Point Cloud Understanding"(ECCV2024) | [
"3d",
"computer-vision",
"robotics"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-26T17:25:59Z | false | 2 | 828,849,562 | null | null | Apache-2.0 | [
"3d-perception",
"point-cloud-learning",
"transformer"
] | changshuowang/GPSFormer | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-26T17:25:59Z | [] | 2024-07-19T17:52:54Z | [
"multimodal.point-cloud"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 27 | [] | 2026-04-04T13:28:08Z | https://github.com/changshuowang/GPSFormer | null |
[
"distributed-ml",
"privacy-and-federated-learning"
] | [
"collaborative-learning",
"federated-learning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-16T02:47:12Z | We propose a novel PFL method called Federated Learning with Aggregated Head (FedAH), which initializes the head with an Aggregated Head at each iteration. | [
"distributed-ml",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-14T16:10:46Z | false | 1 | 829,227,632 | null | Python | GPL-2.0 | [
"collaborative-learning",
"federated-learning"
] | heyuepeng/FedAH | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-14T16:10:46Z | [] | 2024-07-16T03:11:58Z | [
"trust.federated-learning"
] | 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 | 6 | [
"federated-learning"
] | 2026-04-14T08:18:10Z | https://github.com/heyuepeng/FedAH | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-16T19:59:22Z | Official Implementation for ACM MM2024 paper "DreamVTON: Customizing 3D Virtual Try-on with Personalized Diffusion Models" | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-17T13:17:44Z | false | 0 | 829,620,989 | null | null | null | [
"diffusion"
] | xiezhy6/DreamVTON | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-17T13:17:44Z | [] | 2024-07-16T19:59:22Z | [
"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 | 0 | [] | 2026-02-26T05:07:34Z | https://github.com/xiezhy6/DreamVTON | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-17T15:19:11Z | The official implementation of Iterative Ensemble Training with Anti-Gradient Control for Mitigating Memorization in Diffusion Models (ECCV2024)) | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-16T15:36:56Z | false | 0 | 830,094,211 | null | Python | WTFPL | [
"diffusion"
] | liuxiao-guan/IET_AGC | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-16T15:36:56Z | [] | 2024-12-16T08:05:10Z | [
"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 | 5 | [] | 2026-02-19T15:00:00Z | https://github.com/liuxiao-guan/IET_AGC | null |
[
"computer-vision",
"health-and-biomedicine",
"medical-imaging"
] | [
"segmentation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"medical.medical-image-segmentation",
"vision.segmentation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-18T07:10:10Z | (Medical Image Analysis)Official implementation of "Dual domain distribution disruption with semantics preservation: Unsupervised domain adaptation for medical image segmentation" | [
"computer-vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-01T17:35:19Z | false | 4 | 830,385,574 | null | Python | null | [
"segmentation"
] | MIXAILAB/DDSPSeg | [
"description",
"query-match",
"repository-metadata"
] | 6 | 2026-10-26T17:25:59Z | [] | 2025-02-25T07:18:52Z | [
"vision.segmentation"
] | 4b6fcb32ec6757bb357179e65f528462d296ffce | 2026-10-04T17:18:56Z | gh-ml-readme-evidence-v2 | "4b6fcb32ec6757bb357179e65f528462d296ffce" | [
"citation",
"dataset",
"other"
] | [
"method-contribution",
"ml-method-context",
"paper-reference"
] | ok | 2026-10-04T17:18:56Z | MIXAILAB/DDSPSeg | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-method-context",
"paper-reference"
] | include | ml-contribution-v5 | 35 | [] | 2026-04-07T00:45:56Z | https://github.com/MIXAILAB/DDSPSeg | null |
[
"computer-vision",
"efficient-ml",
"language",
"machine-learning-systems",
"model-compression",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"vision"
] | [
"contrastive-learning",
"image-text",
"multimodal-learning",
"neural-network-pruning",
"pruning",
"structured-pruning",
"vision-language",
"vision-language-model"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning",
"multimodal.vision-language",
"multimodal.vlm",
"vision.vision-language"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-18T08:45:09Z | Official implementation of the paper: Structural Pruning of Large Vision Language Models: A Comprehensive Study on Pruning Dynamics, Recovery, and Data Efficiency, accepted at IJCV 2026 (Extended Version) and GCPR 2025 (Oral Presentation). | [
"computer-vision",
"efficient-ml",
"machine-learning-systems",
"model-compression",
"multimodal-learning",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 0 | 830,423,888 | null | Python | Apache-2.0 | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | YiranHuangIrene/VLMCompression | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-23T15:59:01Z | [] | 2026-05-06T17:13:08Z | [
"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 | 5 | [] | 2026-09-09T11:12:30Z | https://github.com/YiranHuangIrene/VLMCompression | null |
[
"computational-physics",
"physics",
"science-and-engineering"
] | [
"neural-network",
"physics-informed-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"science.physics-informed-neural-networks"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-19T05:59:14Z | The official codebase for the paper "Physics-Informed Neural Network for Platoon Trajectory Estimation: a Hybrid Paradigm Informed by Car-following Models" | [
"computational-physics",
"physics",
"science-and-engineering"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-03T15:45:52Z | false | 0 | 830,845,724 | null | null | null | [
"neural-network",
"physics-informed-learning"
] | XinkaiJi/PINN-PTR | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-03T15:45:52Z | [] | 2026-05-11T04:03:28Z | [
"science.physics-informed-neural-networks"
] | e71a9e0b2af2027ab7057b7f9b4254977fd25d3e | 2026-10-04T17:18:56Z | gh-ml-readme-evidence-v2 | "e71a9e0b2af2027ab7057b7f9b4254977fd25d3e" | [
"citation",
"dataset",
"other"
] | [
"ml-method-context",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-10-04T17:18:56Z | XinkaiJi/PINN-PTR | readme-supported-paper-method-implementation | [
"ml-context-only",
"ml-method-context",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 0 | [] | 2026-05-11T04:03:34Z | https://github.com/XinkaiJi/PINN-PTR | null |
[
"computer-vision",
"generative-modeling",
"image-processing",
"low-level-vision",
"multimodal-learning"
] | [
"diffusion",
"image-enhancement",
"image-restoration",
"low-light-enhancement",
"restoration"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"recall26.low-light-enhancement",
"vision.image-restoration"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-19T09:46:06Z | This is official Pytorch implementation of "DRMF: Degradation-Robust Multi-Modal Image Fusion via Composable Diffusion Prior" (ACM MM 24)) | [
"computer-vision",
"generative-modeling",
"multimodal-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 830,931,512 | null | Python | null | [
"diffusion",
"restoration"
] | Linfeng-Tang/DRMF | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-19T17:46:36Z | [] | 2025-04-25T10:37:32Z | [
"vision.image-restoration"
] | 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 | 54 | [] | 2026-04-21T11:58:41Z | https://github.com/Linfeng-Tang/DRMF | null |
[
"generative-ai",
"generative-modeling",
"language",
"natural-language-processing"
] | [
"diffusion",
"language-model",
"pretraining"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"llm.large-language-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-21T11:58:20Z | (ICLR 2025 Spotlight) DEEM: Official implementation of Diffusion models serve as the eyes of large language models for image perception. | [
"generative-ai",
"generative-modeling",
"language",
"natural-language-processing"
] | [
"large-language-model",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-15T15:20:52Z | false | 6 | 831,710,441 | null | Python | Apache-2.0 | [
"diffusion",
"language-model",
"pretraining"
] | RainBowLuoCS/DEEM | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-31T16:49:26Z | [] | 2025-07-01T00:43:23Z | [
"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 | 52 | [] | 2026-09-27T10:44:22Z | https://github.com/RainBowLuoCS/DEEM | null |
[
"deep-learning",
"representation-learning"
] | [
"representation-learning",
"self-supervised-learning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.self-supervised-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-22T08:15:22Z | Official Code of Regularizing with Pseudo-Negatives for Continual Self-Supervised Learning (ICML 2024) | [
"deep-learning",
"representation-learning"
] | [
"representation-learning",
"self-supervised-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T16:44:32Z | false | 0 | 832,034,408 | null | Python | MIT | [
"representation-learning",
"self-supervised-learning"
] | csm9493/PNR | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-03T15:45:52Z | [] | 2024-11-07T17:13:21Z | [
"general.self-supervised-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 | [
"continual-learning",
"continual-self-supervised-learning",
"representation-learning",
"self-supervised-learning"
] | 2025-03-17T12:30:35Z | https://github.com/csm9493/PNR | null |
[
"computer-vision",
"multimodal",
"speech-and-audio"
] | [
"multimodal-learning",
"transformer",
"vision-language"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"vision.vision-language"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-23T09:38:47Z | Official Implementation of "Open-Vocabulary Audio-Visual Semantic Segmentation" [ACM MM 2024 Oral]. | [
"computer-vision",
"multimodal",
"speech-and-audio"
] | [
"deep-learning",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-18T14:44:29Z | false | 3 | 832,570,889 | null | Python | null | [
"multimodal-learning",
"transformer",
"vision-language"
] | ruohaoguo/ovavss | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 2 | 2026-10-18T14:44:29Z | [
"2407.21721"
] | 2024-11-02T08:02:59Z | [
"vision.vision-language"
] | 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 | 37 | [
"audio-visual",
"deep-learning",
"open-vocabulary",
"semantic-segmentation",
"sound-localization",
"transformer",
"video-processing"
] | 2026-03-27T09:01:00Z | https://github.com/ruohaoguo/ovavss | null |
[
"applied-mathematics",
"applied-physics",
"computational-science",
"computer-vision",
"generative-modeling"
] | [
"data-assimilation",
"diffusion",
"inverse-problems",
"reconstruction",
"restoration"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"science.inverse-problems",
"vision.image-restoration"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-07-23T12:35:28Z | Official Implementation (Pytorch) of "DAVI: Diffusion Prior-Based Amortized Variational Inference for Noisy Inverse Problems", ECCV 2024 Oral paper | [
"computer-vision",
"generative-modeling"
] | [
"generative-model",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 4 | 832,642,047 | null | Python | MIT | [
"diffusion",
"restoration"
] | mlvlab/DAVI | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 7 | 2026-10-19T17:46:36Z | [] | 2024-08-16T10:46:24Z | [
"vision.image-restoration"
] | 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 | 76 | [
"diffusion-models",
"eccv2024",
"generative-model",
"inverse-problems"
] | 2026-09-04T01:13:42Z | https://github.com/mlvlab/DAVI | null |
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