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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
[
"general-ml"
] | [
"model-architecture",
"transformer"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.model-architecture"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-19T05:28:53Z | Exploring a novel ML model architecture that integrates transformer attention mechanisms with an FCN to classify all pixels in an image. | [
"general-ml"
] | [
"machine-learning",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-03T15:45:52Z | false | 0 | 1,004,737,541 | https://www.overleaf.com/read/shhbxgcrzzsv#9c841b | null | null | [
"model-architecture",
"transformer"
] | hannahTao/scene-segmentation | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-03T15:45:52Z | [] | 2025-05-17T02:03:02Z | [
"general.model-architecture"
] | 72ffeb8e71001d3499c9a5d61905f2c0b1034276 | 2026-10-03T17:01:43Z | gh-ml-readme-evidence-v2 | "72ffeb8e71001d3499c9a5d61905f2c0b1034276" | [
"dataset",
"installation",
"method",
"other",
"results",
"usage"
] | [
"paper-reference"
] | ok | 2026-10-03T17:01:43Z | hannahTao/scene-segmentation | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue",
"paper-reference"
] | include | ml-contribution-v5 | 0 | [] | 2025-12-18T05:56:11Z | https://github.com/hannahTao/scene-segmentation | null |
[
"deep-learning",
"forecasting",
"graph-learning",
"representation-learning",
"streaming",
"time-series",
"time-series-and-forecasting"
] | [
"dynamic-graph-learning",
"forecasting",
"multi-task-learning",
"online-learning",
"sequence-modeling",
"temporal-graph-learning",
"transfer-learning"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"general.multi-task-learning",
"graph.temporal-learning",
"timeseries.forecasting",
"timeseries.streaming"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-19T10:18:25Z | [AAAI 2026] Official implementation of "TimeMosaic: Information-Density Guided Time Series Forecasting via Adaptive Granularity Patch and Segment-wise Decoding" | [
"deep-learning",
"representation-learning",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 4 | 1,004,880,231 | null | Python | null | [
"multi-task-learning",
"transfer-learning"
] | BenchCouncil/TimeMosaic | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 18 | 2026-10-31T16:49:26Z | [] | 2026-03-24T00:12:13Z | [
"general.multi-task-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 | 81 | [
"time-series-forecasting"
] | 2026-10-21T02:58:14Z | https://github.com/BenchCouncil/TimeMosaic | null |
[
"computer-vision",
"language",
"multimodal",
"multimodal-learning",
"reinforcement-learning",
"vision"
] | [
"contrastive-learning",
"reinforcement-learning",
"restoration",
"super-resolution",
"vision-language-model"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.vision-language",
"vision.image-super-resolution"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-20T03:33:43Z | Code for paper "RealSR-R1: Reinforcement Learning for Real-World Image Super-Resolution with Vision-Language Chain-of-Thought" | [
"computer-vision",
"multimodal-learning",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 2 | 1,005,318,412 | null | Python | null | [
"reinforcement-learning",
"restoration",
"super-resolution"
] | Junboooo/RealSR-R1 | [
"description",
"query-match",
"repository-metadata"
] | 6 | 2026-10-30T17:16:29Z | [] | 2026-01-24T10:20:59Z | [
"vision.image-super-resolution"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 109 | [] | 2026-10-03T13:47:29Z | https://github.com/Junboooo/RealSR-R1 | null |
[
"generative-ai",
"language"
] | [
"language-model",
"reasoning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm-reasoning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-20T08:18:46Z | [NeurIPS 2025] Official code for "Tropical Attention: Neural Algorithmic Reasoning for Combinatorial Algorithms" | [
"generative-ai",
"language"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-13T18:05:26Z | false | 5 | 1,005,435,645 | null | Python | MIT | [
"language-model",
"reasoning"
] | Baran-phys/Tropical-Attention | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-18T14:44:29Z | [] | 2026-05-21T09:43:22Z | [
"llm-reasoning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 34 | [
"amoeba",
"attention-mechanism",
"dynamic-programming",
"length-generalization",
"neural-algorithmic-reasoning",
"noise-robustness",
"ood-generalization",
"softmax",
"transformers",
"tropical-attention",
"tropical-geometry",
"value-generation"
] | 2026-09-30T21:39:57Z | https://github.com/Baran-phys/Tropical-Attention | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-transformer",
"message-passing",
"transformer"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"graph.graph-transformer"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-20T10:02:07Z | [MICCAI 2025] Source code for paper: DHGFormer: Dynamic Hierarchical Graph Transformer for Disorder Brain Disease Diagnosis | [
"deep-learning",
"graph-learning"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 4 | 1,005,490,651 | null | Python | null | [
"graph-transformer",
"message-passing",
"transformer"
] | iMoonLab/DHGFormer | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 6 | 2026-09-28T18:33:08Z | [] | 2026-09-09T08:57:47Z | [
"graph.graph-transformer"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 17 | [
"brain-network",
"graph-transformer",
"hierarchical-models"
] | 2026-09-09T09:00:12Z | https://github.com/iMoonLab/DHGFormer | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-20T11:05:55Z | Official PyTorch implementation of "StegGNN: Learning Graphical Representation for Image Steganography". This work proposes a novel graph neural network-based, cover-agnostic framework for high-fidelity image steganography. | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-19T17:46:36Z | false | 0 | 1,005,521,964 | null | Python | Apache-2.0 | [
"graph-neural-network",
"message-passing",
"neural-network"
] | AGAMPANDEYY/StegGNN | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-19T17:46:36Z | [] | 2025-06-21T08:41:23Z | [
"graph.gnn-description"
] | 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 | 1 | [] | 2026-09-29T18:05:03Z | https://github.com/AGAMPANDEYY/StegGNN | null |
[
"multimodal",
"reinforcement-learning",
"robotics"
] | [
"environment-modeling",
"world-model"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.world-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-20T12:32:56Z | Official code for the ICLR2025 article "FOSP: Fine-tuning Offline Safe Policy through World Models" | [
"multimodal",
"reinforcement-learning",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 0 | 1,005,564,645 | null | Python | Apache-2.0 | [
"environment-modeling",
"world-model"
] | Sunlighted/FOSP | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-09-30T16:47:43Z | [] | 2026-05-05T03:17:06Z | [
"multimodal.world-models"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 8 | [] | 2026-08-06T02:00:30Z | https://github.com/Sunlighted/FOSP | null |
[
"language",
"multimodal",
"multimodal-learning",
"vision"
] | [
"image-text",
"pretraining"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.image-text"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-22T09:11:19Z | Implementation of our paper, "High Feature Distinguishability for Adaptive Image-text Matching with Dual-stream Transformers" | [
"language",
"multimodal",
"multimodal-learning",
"vision"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 0 | 1,006,436,842 | null | Python | null | [
"image-text",
"pretraining"
] | Hudjkfhdsjfhdjkg/DAIM | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2025-06-22T09:32:57Z | [
"multimodal.image-text"
] | 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 | [] | 2025-06-22T09:33:00Z | https://github.com/Hudjkfhdsjfhdjkg/DAIM | null |
[
"computer-vision",
"general-ml",
"generative-ai",
"generative-modeling",
"language",
"reinforcement-learning",
"robotics"
] | [
"flow-matching",
"language-model",
"robot-learning",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.transformers",
"recall.name.transformer-model",
"robotics.robot-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-22T19:52:51Z | Code for paper: "Look, Focus, Act: Efficient and Robust Robot Learning via Human Gaze and Foveated Vision Transformers" | [
"computer-vision",
"generative-ai",
"generative-modeling",
"language"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 1 | 1,006,688,475 | https://ian-chuang.github.io/gaze-av-aloha/ | Jupyter Notebook | MIT | [
"flow-matching",
"language-model",
"transformer"
] | Soltanilara/giava | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 7 | 2026-10-23T14:45:14Z | [] | 2026-10-12T00:15:43Z | [
"llm.transformers"
] | 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 | 37 | [
"aloha",
"av-aloha",
"bimanual",
"eye-tracking",
"flow-matching",
"gaze",
"imitation-learning",
"manipulation",
"robot-learning",
"vision-transformers"
] | 2026-10-18T16:57:25Z | https://github.com/Soltanilara/giava | null |
[
"multi-agent-systems",
"reinforcement-learning"
] | [
"centralized-training",
"multi-agent-reinforcement-learning",
"reinforcement-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.multiagent"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-23T11:50:16Z | Source code for paper "Improving Stability of Parameter Sharing in Cooperative Multi-Agent Reinforcement Learning" | [
"multi-agent-systems",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 0 | 1,007,054,829 | null | Python | Apache-2.0 | [
"centralized-training",
"multi-agent-reinforcement-learning",
"reinforcement-learning"
] | Yurui-Li/ImS | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-02T16:59:40Z | [] | 2025-06-27T01:55:03Z | [
"rl.multiagent"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2025-06-27T01:55:06Z | https://github.com/Yurui-Li/ImS | null |
[
"computer-vision",
"efficient-ml",
"machine-learning-systems",
"model-compression",
"multimodal-learning",
"natural-language-processing"
] | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-24T07:00:47Z | [ICCV 2025] Official code for paper: Beyond Text-Visual Attention: Exploiting Visual Cues for Effective Token Pruning in VLMs | [
"computer-vision",
"efficient-ml",
"machine-learning-systems",
"model-compression",
"multimodal-learning",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 8 | 1,007,546,796 | http://theia4869.com/VisPruner/ | Python | Apache-2.0 | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | Theia-4869/VisPruner | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-10-26T17:25:59Z | [] | 2025-07-01T07:00:17Z | [
"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 | 85 | [
"inference-efficiency",
"training-free-acceleration",
"vision-language-model",
"visual-token-pruning"
] | 2026-09-14T03:22:14Z | https://github.com/Theia-4869/VisPruner | null |
[
"general-ml",
"generative-ai",
"language",
"machine-learning"
] | [
"fine-tuning",
"language-model",
"model-adaptation",
"neural-network",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.transformers",
"probable.adaptation-finetuning-readme",
"recall.name.neural-network",
"recall.name.transformer-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-24T07:46:08Z | Official code repository for the paper "Visual Interpretation of Learned Representations in ResNet and Swin Transformer Architectures" (presented at the 16th ICCCNT 2025, IIT Indore). | [
"general-ml"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-26T13:50:07Z | false | 0 | 1,007,571,343 | null | Jupyter Notebook | MIT | [
"transformer"
] | deepshiksharma/visual-interpretation-resnet50-swinT | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 7 | 2026-10-13T17:04:48Z | [] | 2026-09-29T06:56:39Z | [
"recall.name.transformer-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 | 0 | [] | 2026-09-29T06:56:43Z | https://github.com/deepshiksharma/visual-interpretation-resnet50-swinT | null |
[
"biology",
"chemistry",
"computational-biology"
] | [
"molecular-dynamics",
"scientific-machine-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"bio.molecular-dynamics"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-24T09:39:54Z | This is the open source version of Jumol - a handy molecular dynamics solution tool in which new methods are developed. | [
"biology",
"chemistry",
"computational-biology"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-12T20:31:19Z | false | 0 | 1,007,631,816 | null | Julia | GPL-3.0 | [
"molecular-dynamics",
"scientific-machine-learning"
] | maximilianreihn/Jumol | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-12T20:31:19Z | [] | 2025-07-31T08:14:52Z | [
"bio.molecular-dynamics"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [] | 2025-07-31T08:14:56Z | https://github.com/maximilianreihn/Jumol | null |
[
"generative-ai",
"generative-modeling",
"graph-learning"
] | [
"diffusion",
"generative-modeling",
"graph-generation"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.graph-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-24T12:36:51Z | Official code for the MICCAI 2025 paper "Semantically Consistent Discrete Diffusion for 3D Biological Graph Generation" | [
"generative-ai",
"generative-modeling",
"graph-learning"
] | [
"deep-learning",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 0 | 1,007,723,645 | null | Python | MIT | [
"diffusion",
"generative-modeling",
"graph-generation"
] | chinmay5/semantically_consistent_graph_generation | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-28T17:27:44Z | [] | 2025-07-07T15:44:28Z | [
"graph.graph-generation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 19 | [
"diffusion-models",
"discrete-diffusion",
"geometric-deep-learning",
"spatial-graphs",
"vessel"
] | 2026-03-02T18:17:29Z | https://github.com/chinmay5/semantically_consistent_graph_generation | null |
[
"information-retrieval",
"interpretability-and-safety",
"natural-language-processing",
"time-series-and-forecasting"
] | [
"rag",
"retrieval-augmented-generation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"nlp.rag"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-25T03:19:27Z | An official code for paper submitted to IEEE TSTE "Pre-trained Large Model as Distributed PV Power Forecaster: Integrating Scenario-Consistent Retrieval-Augmented Generation and Cross-modal Semantic Alignment" | [
"information-retrieval",
"interpretability-and-safety",
"natural-language-processing",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-11-01T17:09:41Z | false | 0 | 1,008,103,423 | null | Jupyter Notebook | null | [
"rag",
"retrieval-augmented-generation"
] | beginner1997/RAG4PVF | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-11-01T17:09:41Z | [] | 2026-01-28T13:25:37Z | [
"nlp.rag"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
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] | include | ml-contribution-v5 | 4 | [] | 2026-03-25T15:03:23Z | https://github.com/beginner1997/RAG4PVF | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-25T11:36:18Z | Official implementation of MICCAI 2025 paper Equitable Federated Learning with NCA | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-17T13:17:44Z | false | 1 | 1,008,333,235 | null | Jupyter Notebook | null | [
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] | 2 | 2026-10-19T17:46:36Z | [] | 2025-10-08T05:08:27Z | [
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] | include | ml-contribution-v5 | 4 | [
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] | 2026-05-24T20:52:45Z | https://github.com/MECLabTUDA/FedNCA | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-25T19:34:13Z | Official implementation of "RL4Med-DDPO: Reinforcement Learning for Controlled Guidance Towards Diverse Medical Image Generation using Vision-Language Foundation Models", MICCAI 2025 | [
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] | 2 | 2026-10-08T18:15:14Z | [] | 2025-08-06T13:48:10Z | [
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] | include | ml-contribution-v5 | 11 | [] | 2026-10-05T14:08:34Z | https://github.com/parhamsaremi/rl4med-ddpo | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-26T02:15:43Z | Official implementation for "Boosting Generative Adversarial Transferability with Self-supervised Vision Transformer Features" (ICCV 2025) | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 0 | 1,008,725,521 | https://arxiv.org/abs/2506.21046 | null | MIT | [
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] | spencerwooo/dSVA | [
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] | 5 | 2026-09-29T17:12:21Z | [] | 2025-11-04T08:33:13Z | [
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] | include | ml-contribution-v5 | 5 | [] | 2025-11-21T00:50:18Z | https://github.com/spencerwooo/dSVA | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-26T02:16:35Z | Official Code for ICCV 2025 paper — Beyond Isolated Words: Diffusion Brush for Handwritten Text-Line Generation | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 18 | 1,008,725,811 | null | Python | MIT | [
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] | dailenson/DiffBrush | [
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] | 2 | 2026-09-28T18:33:08Z | [] | 2025-11-24T08:00:32Z | [
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] | include | ml-contribution-v5 | 189 | [] | 2026-09-26T07:10:35Z | https://github.com/dailenson/DiffBrush | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-26T03:15:47Z | Code for paper："Bond-aware Message Passing Across Structural and Heterogeneous Views for Molecular Property Prediction " | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 0 | 1,008,744,796 | null | Python | null | [
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] | zhr1Github/IMPGNN | [
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] | 1 | 2026-10-02T16:59:40Z | [] | 2025-06-26T03:16:27Z | [
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] | include | ml-contribution-v5 | 0 | [] | 2025-07-22T08:03:42Z | https://github.com/zhr1Github/IMPGNN | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-26T05:01:34Z | Official code for our ICCV2025 paper "SDMatte: Grafting Diffusion Models for Interactive Matting" | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 9 | 1,008,782,014 | null | Python | MIT | [
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] | vivoCameraResearch/SDMatte | [
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] | 4 | 2026-10-22T16:09:07Z | [] | 2025-08-21T03:57:09Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 206 | [
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] | 2026-10-22T15:18:16Z | https://github.com/vivoCameraResearch/SDMatte | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-26T06:47:11Z | The repo is the official implementation for the paper: Understanding Token-level Topological Structures in Transformer-based Time Series Forecasting. | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-29T17:49:51Z | false | 0 | 1,008,828,684 | null | Python | null | [
"forecasting",
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] | jlu-phyComputer/TEM | [
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] | 1 | 2026-10-29T17:49:51Z | [] | 2025-10-24T06:41:14Z | [
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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 | 0 | [] | 2025-10-24T06:41:18Z | https://github.com/jlu-phyComputer/TEM | null |
[
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] | [
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] | [
"description",
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"repository-metadata"
] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-26T07:20:10Z | Official implementation of paper "VMoBA: Mixture-of-Block Attention for Video Diffusion Models" | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-22T16:09:07Z | false | 4 | 1,008,845,225 | null | Python | null | [
"diffusion"
] | KlingAIResearch/VMoBA | [
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] | 1 | 2026-10-22T16:09:07Z | [] | 2025-07-01T02:30:40Z | [
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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 | 65 | [] | 2026-09-21T01:08:37Z | https://github.com/KlingAIResearch/VMoBA | null |
[
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] | [
"multimodal.world-models",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-26T13:31:27Z | Official Code for Epona: Autoregressive Diffusion World Model for Autonomous Driving (ICCV 2025) | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T16:16:53Z | false | 31 | 1,009,038,213 | https://kevin-thu.github.io/Epona/ | Python | MIT | [
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] | Kevin-thu/Epona | [
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] | 10 | 2026-10-13T18:05:26Z | [] | 2025-07-22T06:35:46Z | [
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] | include | ml-contribution-v5 | 391 | [
"autonomous-driving",
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] | 2026-10-07T07:28:24Z | https://github.com/Kevin-thu/Epona | null |
[
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] | [
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] | [
"efficiency.pruning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-27T03:15:31Z | Code for paper Adaptive Channel Pruning Based on Layer-Wise Parameter Distributions for Efficient CNNs | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-28T17:27:44Z | false | 0 | 1,009,377,043 | null | null | NOASSERTION | [
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] | 1 | 2026-10-28T17:27:44Z | [] | 2024-09-12T13:07:59Z | [
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] | include | ml-contribution-v5 | 0 | [] | 2025-06-27T03:15:31Z | https://github.com/leowang686/Adaptive-Channel-Pruning-Based-on-Layer-Wise-Parameter-Distributions-for-Efficient-CNNs | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-27T16:04:44Z | Official PyTorch Implementation of the paper "FDAL: Leveraging Feature Distillation for Efficient and Task-Aware Active Learning." | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-09T17:31:36Z | false | 0 | 1,009,731,982 | null | Python | MIT | [
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] | 1 | 2026-10-09T17:31:36Z | [] | 2026-03-18T06:07:38Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 1 | [] | 2026-03-18T06:07:42Z | https://github.com/rrgaire/FDAL | null |
[
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] | [
"general.multi-task-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-30T03:31:01Z | [CVPR2025] Official implementation of TADFormer : Task-Adaptive Dynamic Transformer for Efficient Multi-Task Learning | [
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] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 2 | 1,010,921,523 | https://arxiv.org/pdf/2501.04293 | Python | MIT | [
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] | Min100KM/TADFormer | [
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"github-topics",
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] | 6 | 2026-10-31T16:49:26Z | [] | 2025-06-30T07:20:20Z | [
"general.multi-task-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 13 | [
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] | 2026-05-22T15:51:32Z | https://github.com/Min100KM/TADFormer | null |
[
"computer-vision",
"efficient-ml",
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] | [
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] | [
"efficiency.quantization"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-30T05:47:40Z | [ICCV 2025] Official Implementation of "OuroMamba: A Data-Free Quantization Framework for Vision Mamba Models" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-18T14:44:29Z | false | 2 | 1,010,972,617 | null | Python | MIT | [
"mamba",
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] | gt-synergy-lab/ICCV-OuroMamba | [
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] | 2 | 2026-10-20T15:59:06Z | [] | 2025-10-31T02:01:34Z | [
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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 | 5 | [] | 2025-10-31T02:01:37Z | https://github.com/gt-synergy-lab/ICCV-OuroMamba | null |
[
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] | [
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"query-match",
"repository-metadata"
] | [
"robotics.manipulation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-30T07:03:49Z | Official implementation of the paper "EmbodiedMAE: A Unified 3D Multi-Modal Representation for Robot Manipulation". | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-28T18:33:08Z | false | 1 | 1,011,009,486 | null | Python | null | [
"manipulation"
] | ZibinDong/embodiedmae | [
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"repository-metadata"
] | 2 | 2026-09-30T16:47:43Z | [] | 2025-07-02T14:00:07Z | [
"robotics.manipulation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 10 | [] | 2025-12-30T05:32:08Z | https://github.com/ZibinDong/embodiedmae | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-30T08:01:13Z | Official Implementation of ICCV 25: Pruning all-rounder: Rethinking and improving inference efficiency for large vision language models | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 0 | 1,011,039,458 | null | null | MIT | [
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] | 3 | 2026-10-28T17:27:44Z | [] | 2025-07-05T05:21:47Z | [
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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 | 1 | [] | 2025-07-05T05:21:50Z | https://github.com/Ji-Ma-NWPU/Pruning-All-Rounder | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-06-30T17:34:16Z | Official code and experiments for the paper "Memetic Constrained Evolutionary Optimization of Deep Learning Models for Audio Classification" | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T16:16:53Z | false | 0 | 1,011,358,646 | null | Jupyter Notebook | MIT | [
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] | sumansamui/CMOOP_Audio_Processing | [
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] | 4 | 2026-10-19T17:46:36Z | [] | 2025-07-01T07:07:21Z | [
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[
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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 | [] | 2025-07-07T07:42:12Z | Official implementation of "MST-Distill: Mixture of Specialized Teachers for Cross-Modal Knowledge Distillation" (ACM MM 2025) | [
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[
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[
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[
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[
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] | include | ml-contribution-v5 | 0 | [] | 2026-09-15T16:35:19Z | https://github.com/Evo-Sudip/evoboost | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-09T09:04:40Z | The official implementation of AAAI 2025 paper "Vision-Based Generic Potential Function for Policy Alignment in Multi-Agent Reinforcement Learning". | [
"computer-vision",
"interpretability-and-safety",
"multi-agent-systems",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 3 | 1,016,608,840 | null | Python | NOASSERTION | [
"centralized-training",
"multi-agent-reinforcement-learning",
"reinforcement-learning"
] | Marroh/V-GEPF-official-code | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-02T16:59:40Z | [] | 2026-03-13T06:51:57Z | [
"rl.multiagent"
] | 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 | [] | 2026-03-29T13:36:36Z | https://github.com/Marroh/V-GEPF-official-code | null |
[
"automl",
"general-ml"
] | [
"few-shot-learning",
"meta-learning"
] | [
"description",
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"paper-reference",
"query-match",
"repository-metadata"
] | [
"general.meta-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-09T22:07:45Z | This repository provides the implementation of our paper A Gradient of Complementary Learning Systems Emerges Through Meta‑Learning (https://www.biorxiv.org/content/10.1101/2025.07.10.664201v1) | [
"automl",
"general-ml"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-25T15:11:36Z | false | 0 | 1,017,009,147 | null | Python | Apache-2.0 | [
"few-shot-learning",
"meta-learning"
] | schapirolab/meta-CLS | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 2 | 2026-10-26T17:25:59Z | [] | 2025-07-17T00:59:32Z | [
"general.meta-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 2 | [] | 2026-05-23T01:08:57Z | https://github.com/schapirolab/meta-CLS | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-10T00:39:13Z | Code for paper “Graph Neural Networks Powered by Encoder Embedding for Improved Node Learning” | [
"deep-learning",
"graph-learning"
] | [
"neural-network",
"embedding"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-18T14:44:29Z | false | 1 | 1,017,053,130 | https://arxiv.org/pdf/2507.11732 | Jupyter Notebook | null | [
"graph-neural-network",
"message-passing",
"neural-network"
] | chenshy202/GG | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-10-18T14:44:29Z | [] | 2026-09-08T16:29:58Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [] | 2026-09-08T16:30:57Z | https://github.com/chenshy202/GG | null |
[
"computer-vision",
"language",
"multimodal",
"multimodal-learning",
"vision"
] | [
"distillation",
"image-text",
"pretraining"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.image-text"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-10T02:59:22Z | Code for ICCV 2025 paper - Aligning Information Capacity Between Vision and Language via Dense-to-Sparse Feature Distillation for Image-Text Matching | [
"computer-vision",
"language",
"multimodal",
"multimodal-learning",
"vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 10 | 1,017,097,415 | https://d2s-vse.github.io | Python | null | [
"distillation",
"image-text",
"pretraining"
] | liuyyy111/d2s-vse | [
"description",
"query-match",
"repository-metadata"
] | 9 | 2026-10-04T15:58:58Z | [] | 2025-10-30T09:28:06Z | [
"multimodal.image-text"
] | 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 | 109 | [] | 2026-09-03T08:49:49Z | https://github.com/liuyyy111/d2s-vse | null |
[
"generative-ai",
"language",
"natural-language-processing",
"reinforcement-learning",
"software-engineering"
] | [
"code-generation",
"language-model",
"reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm-code-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-10T09:10:28Z | Code for paper "A Large Language Model-Driven Reward Design Framework via Dynamic Feedback for Reinforcement Learning" | [
"generative-ai",
"language",
"natural-language-processing",
"reinforcement-learning",
"software-engineering"
] | [
"large-language-model",
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-22T16:09:07Z | false | 2 | 1,017,261,974 | null | Jupyter Notebook | null | [
"code-generation",
"language-model",
"reinforcement-learning"
] | ShengjieSun419/CARD | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-23T15:59:01Z | [] | 2025-07-15T08:01:14Z | [
"llm-code-generation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 17 | [] | 2026-10-01T09:26:13Z | https://github.com/ShengjieSun419/CARD | null |
[
"efficient-ml",
"machine-learning",
"model-compression"
] | [
"data-efficiency",
"dataset-distillation",
"distillation",
"knowledge-distillation"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation",
"specialized.dataset-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-10T11:52:12Z | Official Implementation of Evidential Knowledge Distillation (ICCV 2025) | [
"efficient-ml",
"model-compression"
] | [
"deep-learning",
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 1 | 1,017,346,717 | null | Jupyter Notebook | null | [
"distillation",
"knowledge-distillation"
] | Liangyu-Xiang/EKD | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 5 | 2026-10-03T15:45:52Z | [] | 2025-12-01T15:27:30Z | [
"efficiency.knowledge-distillation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 7 | [
"evidential-deep-learning",
"knowledge-distillation",
"machine-learning"
] | 2026-06-24T10:41:40Z | https://github.com/Liangyu-Xiang/EKD | null |
[
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"probabilistic-forecasting",
"sequence-modeling",
"transformer",
"uncertainty-estimation"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"timeseries.probabilistic-forecasting",
"timeseries.topic"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-10T17:06:19Z | This repository contains the official source code and data for the research paper: Enhancing stock price forecasting with a modular deep learning framework incorporating plug-and-play Transformer variants. | [
"time-series",
"time-series-and-forecasting"
] | [
"deep-learning",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 3 | 1,017,525,843 | https://doi.org/10.1016/j.eswa.2026.131572 | Python | Apache-2.0 | [
"sequence-modeling",
"transformer"
] | DyeDye-diyung/Framework_for_Financial_Time-Series_Forecasting | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 2 | 2026-10-24T14:31:13Z | [] | 2026-04-16T01:47:45Z | [
"timeseries.topic"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 7 | [
"conda",
"cross-market",
"financial-time-series",
"python",
"pytorch",
"pytorch-lightning",
"replacable-transformer-variants",
"reproducible",
"stock-price-prediction",
"time-series",
"time-series-forecasting",
"transformer"
] | 2026-09-12T15:59:01Z | https://github.com/DyeDye-diyung/Framework_for_Financial_Time-Series_Forecasting | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-11T02:00:35Z | An official PyTorch implementation of "PreGIP: Watermarking the Pretraining of Graph Neural Networks for Deep Intellectual Property Protection" (KDD 2025) | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-18T14:44:29Z | false | 0 | 1,017,718,905 | null | Python | null | [
"graph-neural-network",
"message-passing",
"neural-network"
] | ventr1c/PreGIP | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-18T14:44:29Z | [] | 2025-09-12T04:59:32Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 0 | [] | 2025-07-11T02:21:07Z | https://github.com/ventr1c/PreGIP | null |
[
"computer-vision"
] | [
"keypoint-detection",
"pose-estimation",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.pose-estimation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-12T02:43:51Z | This is the official implementation of our paper "End-to-End Multi-Person Pose Estimation with Pose-Aware Video Transformer". | [
"computer-vision"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 8 | 1,018,316,294 | null | Python | Apache-2.0 | [
"keypoint-detection",
"pose-estimation",
"transformer"
] | zgspose/PAVENet | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-22T16:09:07Z | [] | 2026-05-08T02:43:42Z | [
"vision.pose-estimation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 26 | [] | 2026-09-30T14:05:10Z | https://github.com/zgspose/PAVENet | null |
[
"computer-vision",
"efficient-ml",
"language",
"machine-learning-systems",
"model-compression",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"vision"
] | [
"contrastive-learning",
"image-text",
"neural-network-pruning",
"pruning",
"structured-pruning",
"vision-language-model"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning",
"multimodal.vision-language",
"multimodal.vlm"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-14T03:02:56Z | The official code for ICCV 2025 paper: METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models | [
"computer-vision",
"efficient-ml",
"machine-learning-systems",
"model-compression",
"multimodal-learning",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 1 | 1,019,241,064 | null | null | null | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | YuchenLiu98/METEOR | [
"description",
"query-match",
"repository-metadata"
] | 4 | 2026-10-26T17:25:59Z | [] | 2025-07-14T03:05:58Z | [
"efficiency.pruning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 6 | [] | 2026-09-17T18:32:47Z | https://github.com/YuchenLiu98/METEOR | null |
[
"generative-modeling",
"multimodal",
"reinforcement-learning",
"robotics"
] | [
"diffusion",
"environment-modeling",
"world-model"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.world-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-14T11:49:26Z | [ICLR26] Official implementation of Geometry Forcing: Marrying Video Diffusion and 3D Representation for Consistent World Modeling | [
"generative-modeling",
"multimodal",
"reinforcement-learning",
"robotics"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 8 | 1,019,482,458 | https://geometryforcing.github.io/ | Python | NOASSERTION | [
"diffusion",
"environment-modeling",
"world-model"
] | CIntellifusion/GeometryForcing | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-09-29T17:30:08Z | [] | 2026-01-26T12:36:02Z | [
"multimodal.world-models"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 227 | [
"diffusion-models",
"interactive-video",
"video-generation",
"world-models"
] | 2026-09-27T11:31:45Z | https://github.com/CIntellifusion/GeometryForcing | null |
[
"deep-learning",
"interpretability-and-safety",
"representation-learning"
] | [
"representation-learning",
"self-supervised-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.self-supervised-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-14T14:05:33Z | Official code for paper "CLA: Latent Alignment for Online Continual Self-Supervised Learning" [Accepted CoLLAs 2025 oral] | [
"deep-learning",
"interpretability-and-safety",
"representation-learning"
] | [
"self-supervised-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 0 | 1,019,556,283 | null | Python | null | [
"representation-learning",
"self-supervised-learning"
] | giacomo-cgn/cla | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-10-04T15:58:58Z | [] | 2025-07-14T14:05:38Z | [
"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 | 3 | [] | 2026-09-07T17:18:48Z | https://github.com/giacomo-cgn/cla | null |
[
"computational-science",
"physics"
] | [
"neural-operator",
"neural-operators",
"surrogate-modeling"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"science.neural-operators"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-14T14:06:59Z | Code for A novel spatiotemporal Fourier neural operator for dynamic wake prediction | [
"computational-science",
"physics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 0 | 1,019,557,116 | null | null | null | [
"neural-operator",
"neural-operators",
"surrogate-modeling"
] | xiaomi4356/A-novel-spatiotemporal-Fourier-neural-operator-for-dynamic-wake-prediction | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-09-27T16:09:59Z | [] | 2025-07-14T14:15:48Z | [
"science.neural-operators"
] | f63fed8cf936e26da057f7ac88246cc1df30e32e | 2026-09-27T17:16:20Z | gh-ml-readme-evidence-v2 | "f63fed8cf936e26da057f7ac88246cc1df30e32e" | [
"citation",
"dataset",
"other",
"overview"
] | [
"ml-method-context",
"paper-reference"
] | ok | 2026-09-27T17:16:20Z | xiaomi4356/A-novel-spatiotemporal-Fourier-neural-operator-for-dynamic-wake-prediction | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue",
"paper-reference"
] | include | ml-contribution-v5 | 1 | [] | 2025-07-15T09:53:01Z | https://github.com/xiaomi4356/A-novel-spatiotemporal-Fourier-neural-operator-for-dynamic-wake-prediction | null |
[
"deep-learning",
"general-ml",
"graph-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"general.neural-network",
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-15T12:04:49Z | Code for paper "Automated Design for Hardware-aware Graph Neural Networks on Edge Devices" | [
"general-ml",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-17T13:17:44Z | false | 1 | 1,020,143,240 | https://fangvv.github.io/HWGNAS/ | Python | null | [
"graph-neural-network",
"neural-network"
] | fangvv/HWGNAS | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 3 | 2026-10-22T14:58:59Z | [] | 2026-10-08T14:52:58Z | [
"general.neural-network"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 2 | [
"edge-devices",
"efficient-ai",
"gnn",
"gnn-inference",
"graph-neural-networks",
"hardware-aware-nas",
"inference-acceleration",
"jetson-nano",
"latency-prediction",
"nas",
"neural-architecture-search",
"neural-networks",
"on-device-ai"
] | 2026-10-11T09:32:32Z | https://github.com/fangvv/HWGNAS | null |
[
"artificial-intelligence",
"interpretability-and-safety",
"machine-learning",
"natural-language-processing"
] | [
"interpretability",
"mechanistic-interpretability",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"specialized.mechanistic-interpretability"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-15T12:21:04Z | A toolkit for analyzing and visualizing attention patterns in transformer-based language models, implementing novel interpretability techniques to understand model decision-making processes. | [
"artificial-intelligence",
"interpretability-and-safety",
"machine-learning",
"natural-language-processing"
] | [
"large-language-model",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 0 | 1,020,151,902 | null | Python | MIT | [
"interpretability",
"mechanistic-interpretability",
"transformer"
] | realtobyfu/llm-insight | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2025-07-20T21:38:24Z | [
"specialized.mechanistic-interpretability"
] | 467ad51b579ed2996e3520e7151dbf8435b4fe2e | 2026-10-05T21:36:53Z | gh-ml-readme-evidence-v2 | "467ad51b579ed2996e3520e7151dbf8435b4fe2e" | [
"citation",
"installation",
"method",
"other",
"results",
"usage"
] | [
"ml-method-context"
] | ok | 2026-10-05T21:36:53Z | realtobyfu/llm-insight | specific-method-with-novelty-claim | [
"contribution-language",
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [] | 2025-07-29T17:36:08Z | https://github.com/realtobyfu/llm-insight | null |
[
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning"
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] | include | ml-contribution-v5 | 15 | [] | 2026-10-26T04:23:40Z | https://github.com/LOG-postech/safe-torch | null |
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-16T01:45:43Z | This is the code for paper A Partially Homomorphic Encryption Algorithm with Multi-Key Support and Short Ciphertexts for Federated Learning | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-20T15:59:06Z | false | 0 | 1,020,508,937 | null | null | null | [
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] | include | ml-contribution-v5 | 1 | [] | 2025-07-27T08:40:30Z | https://github.com/ignorance163/LHEFL_algorithm | null |
[
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 0 | 1,020,575,072 | https://dlfundamentals.github.io/directional-neural-collapse/ | Jupyter Notebook | null | [
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] | DLFundamentals/directional-nc | [
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] | 3 | 2026-10-03T15:45:52Z | [] | 2026-04-26T08:55:28Z | [
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] | include | ml-contribution-v5 | 12 | [
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] | 2026-06-20T15:58:56Z | https://github.com/DLFundamentals/directional-nc | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-16T09:53:01Z | Official Implementation (Pytorch) of the "Representation Shift: Unifying Token Compression with FlashAttention", ICCV 2025 | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 3 | 1,020,726,392 | null | Python | MIT | [
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] | mlvlab/Representation-Shift | [
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] | 6 | 2026-09-27T15:53:49Z | [] | 2026-02-22T11:22:11Z | [
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] | include | ml-contribution-v5 | 36 | [
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] | 2026-07-09T16:38:01Z | https://github.com/mlvlab/Representation-Shift | null |
[
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] | [
"general.self-supervised-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-16T12:45:40Z | Official implementation of the paper "Interpretable Feature Interaction via Statistical Self-supervised Learning on Tabular Data" | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 1,020,817,620 | null | null | MIT | [
"representation-learning",
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] | xiaochenzhang166/Interpretable-Feature-Interaction-via-Statistical-Self-supervised-Learning-on-Tabular-Data | [
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] | 1 | 2026-10-04T15:58:58Z | [] | 2025-07-16T12:57:44Z | [
"general.self-supervised-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2025-07-16T12:57:47Z | https://github.com/xiaochenzhang166/Interpretable-Feature-Interaction-via-Statistical-Self-supervised-Learning-on-Tabular-Data | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-16T13:40:05Z | A deep learning model for malaria parasite classification using a novel architecture that combines Vision Transformer (ViT) with Kolmogorov-Arnold Networks (KAN) and Mixture of Experts (MoE) | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 0 | 1,020,848,471 | null | Jupyter Notebook | null | [
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] | mukesh-m23/Malaria_classification | [
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] | 1 | 2026-09-30T16:47:43Z | [] | 2025-07-16T13:59:09Z | [
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] | include | ml-contribution-v5 | 0 | [] | 2025-07-16T13:59:12Z | https://github.com/mukesh-m23/Malaria_classification | null |
[
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] | [
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] | [
"efficiency.pruning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-16T17:37:23Z | [ICML 2025 Spotlight] Official JAX implementation of SAFE: Finding Sparse and Flat Minima to Improve Pruning | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 1 | 1,020,984,197 | null | Python | Apache-2.0 | [
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] | LOG-postech/safe-jax | [
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] | 4 | 2026-10-26T17:25:59Z | [] | 2026-04-03T07:20:04Z | [
"efficiency.pruning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 16 | [] | 2026-10-26T04:23:40Z | https://github.com/LOG-postech/safe-jax | null |
[
"distributed-ml",
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] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-16T18:17:49Z | This is my Final Year project. A Novel Prediction Technique for Federated Learning. | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-19T17:46:36Z | false | 0 | 1,021,005,217 | null | Python | null | [
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] | sksahebali/FinalYearProject | [
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] | 1 | 2026-10-19T17:46:36Z | [] | 2025-07-16T18:23:34Z | [
"trust.federated-learning"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
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] | include | ml-contribution-v5 | 0 | [] | 2025-07-16T18:23:37Z | https://github.com/sksahebali/FinalYearProject | null |
[
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] | [
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] | [
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"repository-metadata"
] | [
"science.neural-operators"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-17T08:12:03Z | The official code repository of the paper "An effective physics-informed neural operator framework for predicting wavefields" " . | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 0 | 1,021,318,711 | null | Jupyter Notebook | null | [
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] | DeepWave-KAUST/PICNO-pub | [
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] | 3 | 2026-09-28T18:33:08Z | [] | 2026-01-12T16:08:07Z | [
"science.neural-operators"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 7 | [
"neural-operator",
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] | 2026-06-11T19:41:22Z | https://github.com/DeepWave-KAUST/PICNO-pub | null |
[
"3d",
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] | [
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] | [
"description",
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"repository-metadata"
] | [
"multimodal.gaussian-splatting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-17T11:34:53Z | Official implementation for RoMaP :Robust 3D-Masked Part-level Editing in 3D Gaussian Splatting with Regularized Score Distillation Sampling in ICCV 2025 | [
"3d",
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"graphics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 2 | 1,021,428,590 | null | Python | null | [
"distillation",
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] | janeyeon/RoMaP-code | [
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] | 1 | 2026-10-02T16:59:40Z | [] | 2025-08-05T06:11:03Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 22 | [] | 2026-05-20T17:40:11Z | https://github.com/janeyeon/RoMaP-code | null |
[
"computer-vision",
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] | [
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] | [
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] | [
"multimodal.motion-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-17T13:23:27Z | [ACM MM 2025] Official code repository for the paper "Towards Robust and Controllable Text-to-Motion via Masked Autoregressive Diffusion". | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 2 | 1,021,488,264 | null | Jupyter Notebook | NOASSERTION | [
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] | zzysteve/MoMADiff | [
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] | 3 | 2026-09-28T18:33:08Z | [] | 2025-12-07T09:29:04Z | [
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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 | 9 | [] | 2026-06-07T14:53:46Z | https://github.com/zzysteve/MoMADiff | null |
[
"generative-ai",
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] | [
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] | [
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] | [
"multimodal.text-to-image",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-18T06:53:44Z | [CVPR 2025] Official implementation of paper: Encapsulated Composition of Text-to-Image and Text-to-Video Models for High-Quality Video Synthesis | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 0 | 1,021,921,967 | null | Python | null | [
"image-generation",
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] | Tonniia/EVS | [
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"query-match",
"repository-metadata"
] | 2 | 2026-10-10T16:53:26Z | [] | 2025-07-18T08:27:24Z | [
"multimodal.text-to-image"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 7 | [] | 2026-06-19T16:30:56Z | https://github.com/Tonniia/EVS | null |
[
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] | [
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] | [
"medical.eeg"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-19T08:08:18Z | Official PyTorch implementation of RBTransformer from our paper "A Brain Wave Encodes a Thousand Tokens: Modeling Inter-Cortical Neural Interactions for Effective EEG-based Emotion Recognition". | [
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] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-16T15:36:56Z | false | 2 | 1,022,506,094 | null | Python | null | [
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] | nnilayy/RBTransformer | [
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] | 1 | 2026-10-16T15:36:56Z | [] | 2026-04-29T07:01:02Z | [
"medical.eeg"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 11 | [
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"eeg",
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"intercortical-attention",
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"seed-dataset",
"transformer"
] | 2026-07-02T06:20:54Z | https://github.com/nnilayy/RBTransformer | null |
[
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] | [
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] | [
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] | [
"chem-molecular-property-benchmark",
"chem-property-prediction"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-20T08:03:44Z | Source code for paper "MPTF: A Multi-Level Prompt-Enhanced Transformer Framework for Unified Molecular Property and Drug-Drug Interaction event Prediction" | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 0 | 1,022,957,231 | null | Python | null | [
"benchmarking",
"property-prediction",
"qsar",
"representation-learning",
"supervised-learning",
"transformer"
] | fuhaitao95/MLPET | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-02T16:59:40Z | [] | 2025-08-06T15:43:25Z | [
"chem-molecular-property-benchmark",
"chem-property-prediction"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2025-08-06T15:43:28Z | https://github.com/fuhaitao95/MLPET | null |
[
"3d",
"computer-vision",
"robotics"
] | [
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] | [
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"query-match",
"repository-metadata"
] | [
"multimodal.point-cloud"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-20T09:31:07Z | Official implementation of "Efficient Spiking Point Mamba for Point Cloud Analysis" (ICCV2025) | [
"3d",
"computer-vision",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-30T17:16:29Z | false | 0 | 1,022,987,323 | null | Python | MIT | [
"3d-perception",
"mamba",
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] | PeppaWu/SPM | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-30T17:16:29Z | [] | 2025-12-11T04:52:07Z | [
"multimodal.point-cloud"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 13 | [] | 2026-07-01T14:06:37Z | https://github.com/PeppaWu/SPM | null |
[
"computer-vision",
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] | [
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"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.motion-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-20T20:03:01Z | Official code repository for the paper "FlowMotion: Target-Predictive Conditional Flow Matching for Jitter-Reduced Text-Driven Human Motion Generation". | [
"computer-vision",
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"generative-modeling",
"multimodal"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 1 | 1,023,233,980 | null | Python | MIT | [
"flow-matching",
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] | mccuba/FlowMotion | [
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"query-match",
"repository-metadata"
] | 3 | 2026-09-28T18:33:08Z | [] | 2026-08-17T08:19:14Z | [
"multimodal.motion-generation"
] | 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 | 1 | [] | 2026-08-17T08:20:57Z | https://github.com/mccuba/FlowMotion | null |
[
"computational-science",
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"license-metadata",
"query-match",
"repository-metadata"
] | [
"science.neural-operators"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-21T02:24:47Z | Official implementation of the ICCV'25 Paper "JPNeO : JPEG Processing Neural Operator for Backward-Compatible Coding " | [
"computational-science",
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 0 | 1,023,333,555 | null | Python | BSD-3-Clause | [
"neural-operator",
"neural-operators",
"surrogate-modeling"
] | WooKyoungHan/JPNeO | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-09-28T18:33:08Z | [] | 2025-08-01T04:07:13Z | [
"science.neural-operators"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 5 | [] | 2025-12-23T07:10:18Z | https://github.com/WooKyoungHan/JPNeO | null |
[
"multimodal-learning",
"sequence-modeling",
"time-series"
] | [
"sequence-modeling",
"state-space-model"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"timeseries.state-space-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-21T13:28:34Z | [AAAI'25] Official Implementation of 'MSAmba: Exploring Multimodal Sentiment Analysis with State Space Models' | [
"multimodal-learning",
"sequence-modeling",
"time-series"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 0 | 1,023,646,858 | null | null | null | [
"sequence-modeling",
"state-space-model"
] | C0notSilly/MSAmba | [
"description",
"query-match",
"repository-metadata"
] | 4 | 2026-10-01T17:50:46Z | [] | 2025-07-21T14:36:24Z | [
"timeseries.state-space-models"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 31 | [] | 2026-07-08T10:37:26Z | https://github.com/C0notSilly/MSAmba | null |
[
"sequence-modeling",
"time-series"
] | [
"mamba",
"sequence-modeling",
"state-space-model"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"timeseries.state-space-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-21T18:15:14Z | **MARU** (Memory-Augmented Recurrent Unit) is a novel neural architecture that combines the strengths of state-space models (Mamba) and memory-augmented recurrent networks to achieve efficient long-sequence modeling with **O(1) inference complexity**. | [
"sequence-modeling",
"time-series"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 0 | 1,023,808,176 | null | Python | MIT | [
"mamba",
"sequence-modeling",
"state-space-model"
] | Fingersoup/MARU | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-02T16:59:40Z | [] | 2025-08-08T09:04:25Z | [
"timeseries.state-space-models"
] | d6b83eba80772ded4dcc7d3f8a9da06362040f23 | 2026-10-02T18:21:54Z | gh-ml-readme-evidence-v2 | "d6b83eba80772ded4dcc7d3f8a9da06362040f23" | [
"installation",
"method",
"other",
"overview",
"usage"
] | [
"method-contribution",
"ml-method-context"
] | ok | 2026-10-02T18:21:54Z | Fingersoup/MARU | specific-method-with-novelty-claim | [
"method-contribution",
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue"
] | include | ml-contribution-v5 | 1 | [] | 2025-08-17T23:53:24Z | https://github.com/Fingersoup/MARU | null |
[
"machine-learning"
] | [
"adaptation",
"test-time-adaptation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"specialized.test-time-adaptation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-22T10:12:01Z | Official code for "DC-TTA: Divide-and-Conquer Framework for Test-Time Adaptation of Interactive Segmentation" (ICCV 2025) | [
"machine-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-03T15:45:52Z | false | 1 | 1,024,190,479 | null | Python | Apache-2.0 | [
"adaptation",
"test-time-adaptation"
] | jihun1998/DCTTA | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-03T15:45:52Z | [] | 2026-06-11T06:31:28Z | [
"specialized.test-time-adaptation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 2 | [] | 2026-09-21T10:57:37Z | https://github.com/jihun1998/DCTTA | null |
[
"computer-vision",
"deep-learning",
"efficient-ml",
"language"
] | [
"conditional-computation",
"mixture-of-experts",
"sparse-activation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.mixture-of-experts"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-22T12:01:48Z | [ICCV 2025] Official implementation of the paper: "Dynamic-DINO: Fine-Grained Mixture of Experts Tuning for Real-time Open-Vocabulary Object Detection" | [
"computer-vision",
"deep-learning",
"efficient-ml",
"language"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 0 | 1,024,246,656 | null | null | null | [
"conditional-computation",
"mixture-of-experts",
"sparse-activation"
] | wengminghe/Dynamic-DINO | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-09-29T17:30:08Z | [] | 2025-07-29T07:25:27Z | [
"efficiency.mixture-of-experts"
] | 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 | 81 | [] | 2026-08-13T06:11:56Z | https://github.com/wengminghe/Dynamic-DINO | null |
[
"audio",
"generative-modeling",
"multimodal-learning",
"natural-language-processing",
"speech-and-audio",
"speech-and-language"
] | [
"audio-language-modeling",
"audio-understanding",
"diffusion",
"representation-learning"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"recall26.audio-language-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-24T10:51:39Z | [AAAI 2026 & ACL 2026] The official implementation of the DIFFA series for dLLM-based large audio language model | [
"audio",
"generative-modeling",
"multimodal-learning",
"natural-language-processing",
"speech-and-audio",
"speech-and-language"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 5 | 1,025,500,412 | null | Python | null | [
"audio-language-modeling",
"audio-understanding",
"diffusion",
"representation-learning"
] | NKU-HLT/DIFFA | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 5 | 2026-09-29T17:30:08Z | [] | 2026-04-07T13:14:01Z | [
"recall26.audio-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 | 84 | [
"audio-understanding",
"diffusion-language-model",
"diffusion-language-models",
"large-audio-models",
"omni"
] | 2026-08-31T10:55:55Z | https://github.com/NKU-HLT/DIFFA | null |
[
"information-retrieval",
"natural-language-processing"
] | [
"rag",
"retrieval-augmented-generation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"nlp.rag"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-24T13:41:52Z | Official code for the paper Injecting External Knowledge into the Reasoning Process Enhances Retrieval-Augmented Generation. | [
"information-retrieval",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-11-01T17:09:41Z | false | 1 | 1,025,590,645 | null | Python | null | [
"rag",
"retrieval-augmented-generation"
] | Trustworthy-Information-Access/Passage-Injection | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-11-01T17:09:41Z | [] | 2025-09-16T13:41:45Z | [
"nlp.rag"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 8 | [] | 2025-12-15T02:50:11Z | https://github.com/Trustworthy-Information-Access/Passage-Injection | null |
[
"computer-vision",
"embodied-ai",
"language",
"multimodal",
"multimodal-learning",
"robotics",
"robotics-and-control",
"vision"
] | [
"contrastive-learning",
"manipulation",
"robot-learning",
"vision-language-action",
"vision-language-model"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.vision-language",
"robotics.manipulation",
"robotics.visual-language-action"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-25T03:25:43Z | Code for paper "TCoT: Trajectory Chain-of-Thoughts for Robotic Manipulation with Failure Recovery in Vision-Language-Action Model" | [
"computer-vision",
"language",
"multimodal",
"multimodal-learning",
"robotics-and-control",
"vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T14:50:46Z | false | 0 | 1,025,925,707 | null | Python | MIT | [
"contrastive-learning",
"vision-language-model"
] | Serenos/TCoT | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-11T16:48:52Z | [] | 2026-08-16T13:10:59Z | [
"multimodal.vision-language"
] | 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 | [] | 2026-09-11T14:40:42Z | https://github.com/Serenos/TCoT | null |
[
"deep-learning",
"representation-learning"
] | [
"distillation",
"representation-learning",
"self-supervised-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.self-supervised-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-25T13:49:11Z | Code for Paper "Self-supervised Learning of Echocardiographic Video Representations via Online Cluster Distillation" | [
"deep-learning",
"representation-learning"
] | [
"self-supervised-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-06T17:10:23Z | false | 0 | 1,026,211,715 | null | Python | null | [
"distillation",
"representation-learning",
"self-supervised-learning"
] | DISCOVR-ECHO/DISCOVR | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-06T17:10:23Z | [] | 2025-07-25T14:54:02Z | [
"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 | 0 | [] | 2025-07-25T14:54:06Z | https://github.com/DISCOVR-ECHO/DISCOVR | null |
[
"sequence-modeling",
"time-series"
] | [
"mamba",
"sequence-modeling",
"state-space-model"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"timeseries.state-space-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-25T16:35:03Z | Official code for "SP-Mamba: Spatial-Perception State Space Model for Unsupervised Medical Anomaly Detection" published in ACM MM 2025. | [
"sequence-modeling",
"time-series"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-03T15:45:52Z | false | 0 | 1,026,298,220 | null | Python | null | [
"mamba",
"sequence-modeling",
"state-space-model"
] | Ray-RuiPan/SP-Mamba | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-03T15:45:52Z | [] | 2025-10-27T11:18:11Z | [
"timeseries.state-space-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 | 9 | [] | 2026-07-15T17:40:36Z | https://github.com/Ray-RuiPan/SP-Mamba | null |
[
"multimodal",
"reinforcement-learning",
"robotics"
] | [
"environment-modeling",
"world-model"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.world-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-25T22:44:49Z | Official code for the CVPR 2025 paper "Navigation World Models". | [
"multimodal",
"reinforcement-learning",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-03T15:45:52Z | false | 0 | 1,026,442,910 | null | null | NOASSERTION | [
"environment-modeling",
"world-model"
] | elilaird/nwm | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-03T15:45:52Z | [] | 2025-07-10T03:04:45Z | [
"multimodal.world-models"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [] | 2026-03-14T03:37:30Z | https://github.com/elilaird/nwm | null |
[
"deep-learning",
"efficient-ml",
"generative-modeling",
"language",
"recommender-systems"
] | [
"conditional-computation",
"diffusion",
"mixture-of-experts",
"sparse-activation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.mixture-of-experts"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-27T09:19:26Z | An official source code for paper " De-collapsing User Intent: Adaptive Diffusion Augmentation with Mixture-of-Experts for Sequential Recommendation". | [
"deep-learning",
"efficient-ml",
"generative-modeling",
"language",
"recommender-systems"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 0 | 1,027,082,248 | null | Python | null | [
"conditional-computation",
"diffusion",
"mixture-of-experts",
"sparse-activation"
] | Cxx-0/ADARec | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-01T17:50:46Z | [] | 2025-07-30T14:10:54Z | [
"efficiency.mixture-of-experts"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 5 | [] | 2026-08-22T11:25:12Z | https://github.com/Cxx-0/ADARec | null |
[
"generative-ai",
"language",
"machine-learning",
"software-engineering"
] | [
"code-generation",
"curriculum-learning",
"language-model",
"training-strategy",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm-code-generation",
"specialized.curriculum-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-27T14:38:43Z | HNet-GPT: Structure-Aware Code Generation via Hierarchical Encoding and Transformer Decoding, a novel Hybrid architecture | [
"generative-ai",
"language",
"software-engineering"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-19T17:46:36Z | false | 0 | 1,027,205,601 | null | Jupyter Notebook | MIT | [
"code-generation",
"language-model",
"transformer"
] | CaraccioloFrancesco/HNet-GPT | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-26T17:25:59Z | [] | 2025-08-02T10:55:06Z | [
"llm-code-generation"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 1 | [] | 2025-08-25T21:31:17Z | https://github.com/CaraccioloFrancesco/HNet-GPT | null |
[
"computer-vision"
] | [
"mamba",
"restoration",
"super-resolution"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.image-super-resolution"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-28T04:36:41Z | Official PyTorch implementation of the paper GPSMamba: A Global Phase and Spectral Prompt-guided Mamba for Infrared Image Super-Resolution. | [
"computer-vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-11-01T17:09:41Z | false | 0 | 1,027,477,590 | null | Python | Apache-2.0 | [
"mamba",
"restoration",
"super-resolution"
] | yongsongH/GPSMamba | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-11-01T17:09:41Z | [] | 2026-02-23T09:42:27Z | [
"vision.image-super-resolution"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 13 | [] | 2026-10-10T03:52:07Z | https://github.com/yongsongH/GPSMamba | 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 | [] | 2025-07-28T12:59:05Z | Official implementation of the paper: A Conditional Generative Diffusion Model for Spatio-Temporal Data | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-24T14:31:13Z | false | 0 | 1,027,732,466 | null | Python | NOASSERTION | [
"diffusion"
] | g-loddi/DiffWave3D1D | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-24T14:31:13Z | [] | 2025-07-28T13:12:26Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [] | 2026-04-05T06:58:31Z | https://github.com/g-loddi/DiffWave3D1D | null |
[
"reinforcement-learning"
] | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-29T08:16:12Z | Source code for paper Classification with Costly Features using Deep Reinforcement Learning. | [
"reinforcement-learning"
] | [
"reinforcement-learning",
"classifier"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-31T16:49:26Z | false | 0 | 1,028,232,280 | null | TypeScript | Apache-2.0 | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | SpacesWalkers/Featurese | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-31T16:49:26Z | [] | 2025-10-29T01:04:40Z | [
"rl.deep"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2025-07-31T08:12:34Z | https://github.com/SpacesWalkers/Featurese | null |
[
"computer-vision",
"image-processing",
"low-level-vision",
"machine-learning"
] | [
"adaptation",
"image-denoising",
"image-restoration",
"self-supervised-learning",
"test-time-adaptation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"recall26.image-denoising",
"specialized.test-time-adaptation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-29T14:51:09Z | Official PyTorch implementation of "Robust Test-Time Adaptation for Single Image Denoising Using Deep Gaussian Prior" in ICCV 2025 | [
"computer-vision",
"image-processing",
"low-level-vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-27T15:53:49Z | false | 0 | 1,028,453,129 | null | Python | null | [
"image-denoising",
"image-restoration",
"self-supervised-learning",
"test-time-adaptation"
] | qingma2016/TTAD | [
"description",
"query-match",
"repository-metadata"
] | 6 | 2026-10-23T15:59:01Z | [] | 2025-12-27T10:22:48Z | [
"recall26.image-denoising"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 9 | [] | 2026-07-24T05:39:03Z | https://github.com/qingma2016/TTAD | null |
[
"computer-vision",
"generative-modeling"
] | [
"diffusion",
"restoration"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"vision.image-restoration"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-07-30T06:49:49Z | The source code of our paper "Efficient Guided Diffusion Toward Adverse Weather Image Restoration" | [
"computer-vision",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 0 | 1,028,852,705 | null | null | null | [
"diffusion",
"restoration"
] | TianhangTang/WGDiff | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-23T15:59:01Z | [] | 2025-07-30T07:04:20Z | [
"vision.image-restoration"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-02-11T05:23:40Z | https://github.com/TianhangTang/WGDiff | null |
[
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"forecasting",
"sequence-modeling"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"timeseries.forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-01T02:46:25Z | Official implementation of the paper "MAFNet: Multi-scale Active Fusion Network for Long-Term Time Series Forecasting" | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-30T17:16:29Z | false | 1 | 1,030,054,395 | https://github.com/hit636/MAFNet | Python | Apache-2.0 | [
"forecasting",
"sequence-modeling"
] | hit636/MAFNet | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-31T16:49:26Z | [] | 2026-02-04T06:05:51Z | [
"timeseries.forecasting"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 4 | [] | 2026-02-24T07:46:30Z | https://github.com/hit636/MAFNet | null |
[
"computer-vision",
"generative-modeling"
] | [
"diffusion",
"restoration"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"vision.image-restoration"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-01T04:40:34Z | [ICCV 2025] Official implementation of "Exploiting Diffusion Prior for Task-driven Image Restoration" | [
"computer-vision",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-22T16:09:07Z | false | 1 | 1,030,092,137 | null | Python | null | [
"diffusion",
"restoration"
] | JaehaKim97/EDTR | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-23T15:59:01Z | [] | 2026-08-14T01:45:04Z | [
"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 | 34 | [] | 2026-10-04T02:36:42Z | https://github.com/JaehaKim97/EDTR | null |
[
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"forecasting",
"sequence-modeling"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"timeseries.forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-01T08:20:38Z | [AAAI'26 Oral] Official Implementation of "GraFT: Infusing Pre-trained Transformers with Relational Structure for Time Series Forecasting" | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-30T17:16:29Z | false | 0 | 1,030,188,283 | null | Python | null | [
"forecasting",
"sequence-modeling"
] | yuanyumi/GraFT | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-31T16:49:26Z | [] | 2025-08-01T09:09:29Z | [
"timeseries.forecasting"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 4 | [] | 2026-07-02T02:15:57Z | https://github.com/yuanyumi/GraFT | null |
[
"efficient-ml",
"generative-modeling",
"machine-learning-systems",
"model-compression"
] | [
"flow-matching",
"quantization"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.quantization"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-08-01T11:14:37Z | GitHub repository provided as supplementary material for our paper titled "Low-Bit, High-Fidelity: Optimal Transport Quantization for Flow Matching." | [
"efficient-ml",
"generative-modeling",
"machine-learning-systems",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-24T14:31:13Z | false | 0 | 1,030,274,483 | null | Python | null | [
"flow-matching",
"quantization"
] | Quantized-FM/LowBit-HighFid | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-24T14:31:13Z | [] | 2025-11-14T15:59:43Z | [
"efficiency.quantization"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [] | 2026-02-09T02:15:48Z | https://github.com/Quantized-FM/LowBit-HighFid | null |
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