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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
[
"imitation-learning",
"reinforcement-learning"
] | [
"imitation-learning",
"inverse-reinforcement-learning",
"reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"rl.inverse"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-01T07:11:32Z | Code for paper "Analyzing sequential activity and travel decisions with interpretable deep inverse reinforcement learning" | [
"imitation-learning",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 0 | 1,087,568,922 | null | Jupyter Notebook | null | [
"imitation-learning",
"inverse-reinforcement-learning",
"reinforcement-learning"
] | yuebingliang/Interpretable-DIRL4Act | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-09-28T18:33:08Z | [] | 2025-11-01T07:21:01Z | [
"rl.inverse"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 2 | [] | 2026-07-22T10:18:41Z | https://github.com/yuebingliang/Interpretable-DIRL4Act | null |
[
"computer-vision",
"embodied-ai",
"general-ml",
"information-retrieval",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"robotics"
] | [
"deep-learning",
"image-retrieval",
"machine-learning",
"metric-learning",
"multimodal-learning",
"neural-network",
"robot-foundation-model",
"robot-learning",
"transformer",
"vision-language"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.topic-deep-learning",
"general.topic-machine-learning",
"robotics.robot-foundation-model",
"vision.image-retrieval",
"vision.vision-language"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-02T01:50:23Z | [NeurIPS 2025] Official implementation of "Instance-Level Composed Image Retrieval". | [
"computer-vision",
"general-ml",
"multimodal-learning",
"natural-language-processing"
] | [
"deep-learning",
"machine-learning",
"neural-network",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 1 | 1,087,956,873 | https://vrg.fel.cvut.cz/icir/ | Python | MIT | [
"machine-learning",
"neural-network",
"transformer"
] | billpsomas/icir | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-10-24T13:28:22Z | [] | 2026-10-05T14:44:47Z | [
"general.topic-machine-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 | 56 | [
"composed-image-retrieval",
"dataset",
"datasets",
"deep-learning",
"deep-neural-networks",
"fine-grained-visual-categorization",
"image-retrieval",
"information-retrieval",
"instance-level-retrieval",
"machine-learning",
"multi-modal-learning",
"personalized-learning",
"vision-language-mode... | 2026-10-14T11:09:39Z | https://github.com/billpsomas/icir | null |
[
"efficient-ml",
"generative-modeling",
"model-compression"
] | [
"diffusion",
"distillation",
"knowledge-distillation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-04T08:00:01Z | This repository provides the official and fully reproducible implementation of the paper: A Knowledge Distillation Approach for Diffusion-based Generative Skin Lesion Segmentation | [
"efficient-ml",
"generative-modeling",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-07T18:07:52Z | false | 1 | 1,089,340,753 | null | Python | null | [
"diffusion",
"distillation",
"knowledge-distillation"
] | hoangdv05/EFKD | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-07T18:07:52Z | [] | 2025-11-23T15:09:35Z | [
"efficiency.knowledge-distillation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 2 | [] | 2026-02-27T17:01:16Z | https://github.com/hoangdv05/EFKD | null |
[
"generative-ai",
"generative-modeling",
"planning",
"robotics"
] | [
"diffusion",
"motion-planning",
"robotics"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"robotics.robot-motion-planning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-04T14:21:19Z | Official implementation of "Simultaneous Multi-Robot Motion Planning with Projected Diffusion Models" (ICML 2025) | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 1 | 1,089,571,480 | null | Python | NOASSERTION | [
"diffusion"
] | RAISELab-atUVA/Diffusion-MRMP | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-10-23T15:59:01Z | [] | 2025-12-30T03:28:27Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 15 | [] | 2026-09-22T17:32:27Z | https://github.com/RAISELab-atUVA/Diffusion-MRMP | null |
[
"computer-vision",
"health-and-biomedicine",
"medical-imaging"
] | [
"rag",
"segmentation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"medical.medical-image-segmentation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-06T02:28:08Z | This repository is the official implementation of the paper entitled: "Morph-RAG-SAM: Few-Shot Medical Image Segmentation via Hybrid Semantic–Morphological Re-Ranking and Prompt-Guided Refinement" | [
"computer-vision",
"health-and-biomedicine",
"medical-imaging"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-09T17:31:36Z | false | 0 | 1,090,693,235 | null | Python | null | [
"rag",
"segmentation"
] | danhntd/Morph-RAG-SAM | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-09T17:31:36Z | [] | 2025-12-13T13:13:08Z | [
"medical.medical-image-segmentation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2025-12-13T13:13:11Z | https://github.com/danhntd/Morph-RAG-SAM | null |
[
"efficient-ml",
"health-and-biomedicine",
"model-compression"
] | [
"distillation",
"knowledge-distillation"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-06T02:38:26Z | Code for our paper"KD-OCT: Efficient Knowledge Distillation for Clinical-Grade Retinal OCT Classification" | [
"efficient-ml",
"health-and-biomedicine",
"model-compression"
] | [
"classifier"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-07T18:07:52Z | false | 0 | 1,090,697,222 | null | Python | MIT | [
"distillation",
"knowledge-distillation"
] | erfan-nourbakhsh/KD-OCT | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-07T18:07:52Z | [] | 2025-12-14T06:11:31Z | [
"efficiency.knowledge-distillation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [
"cnv-detection",
"knowledge-distillation",
"medical-image-analysis",
"model-compression",
"retinal-oct-classification"
] | 2026-04-24T16:26:53Z | https://github.com/erfan-nourbakhsh/KD-OCT | null |
[
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"neural-network",
"quantization"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.quantization"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-07T06:35:36Z | We introduce a Novel Neural Network Architecture that combines autoencoders with quantization techniques to compress images in a learned feature space. Experimet The proposed method also offers the flexibility to adapt to different compression levels, making it a promising solution for a wide range of applications in i... | [
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-24T14:31:13Z | false | 0 | 1,091,535,262 | null | Python | null | [
"neural-network",
"quantization"
] | gunavathy805-blip/Image-Compression-using-Neural-Network- | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-24T14:31:13Z | [] | 2025-11-09T15:44:30Z | [
"efficiency.quantization"
] | 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 | [] | 2025-11-09T15:51:32Z | https://github.com/gunavathy805-blip/Image-Compression-using-Neural-Network- | null |
[
"computer-vision",
"embodied-ai",
"language",
"multimodal",
"multimodal-learning",
"reinforcement-learning",
"robotics",
"vision"
] | [
"contrastive-learning",
"environment-modeling",
"robot-learning",
"vision-language-action",
"vision-language-model",
"world-model"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.vision-language",
"multimodal.world-models",
"robotics.visual-language-action"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-07T06:38:32Z | Official Implementation of Paper: WMPO: World Model-based Policy Optimization for Vision-Language-Action Models | [
"computer-vision",
"language",
"multimodal",
"multimodal-learning",
"vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 14 | 1,091,536,623 | https://WM-PO.github.io | Python | Apache-2.0 | [
"contrastive-learning",
"vision-language-model",
"world-model"
] | WM-PO/WMPO | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 9 | 2026-10-11T16:48:52Z | [] | 2026-01-04T13:33:56Z | [
"multimodal.vision-language"
] | 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 | 243 | [] | 2026-10-04T07:14:41Z | https://github.com/WM-PO/WMPO | null |
[
"reinforcement-learning"
] | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-08T02:40:19Z | This project is a System Implementation applying a Novel Algorithm in Reinforcement Learning (RL). We will implement the Suika Game (a physics-based fruit-merging puzzle) and train a Deep Reinforcement Learning (DRL) agent to achieve optimal play (i.e. obtaining the highest possible score). | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-11-01T17:09:41Z | false | 0 | 1,092,111,653 | null | Python | null | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | Geoffrey-Wang-UCD/ecs-170-project-group-1-suika-game-reinforcement-learning | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-11-01T17:09:41Z | [] | 2025-12-09T16:48:25Z | [
"rl.deep"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
"contribution-language",
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [] | 2025-12-11T04:23:51Z | https://github.com/Geoffrey-Wang-UCD/ecs-170-project-group-1-suika-game-reinforcement-learning | null |
[
"computer-vision",
"reinforcement-learning"
] | [
"policy-learning",
"reinforcement-learning",
"video-understanding"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.core",
"vision.video-understanding"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-08T06:08:55Z | [ICLR 2026] Official code for paper: TimeSearch-R: Adaptive Temporal Search for Long-Form Video Understanding via Self-Verification Reinforcement Learning. | [
"computer-vision",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 8 | 1,092,176,707 | null | Python | Apache-2.0 | [
"reinforcement-learning",
"video-understanding"
] | Time-Search/TimeSearch-R | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-09-29T17:12:21Z | [] | 2026-01-29T01:52:58Z | [
"vision.video-understanding"
] | 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 | 225 | [
"reinforcement-learning",
"self-verification",
"temporal-search",
"video-understanding"
] | 2026-09-17T19:09:25Z | https://github.com/Time-Search/TimeSearch-R | null |
[
"computer-vision",
"foundation-models",
"generative-ai",
"language",
"machine-learning",
"multimodal",
"multimodal-learning",
"vision"
] | [
"adaptation",
"contrastive-learning",
"multimodal",
"pretraining",
"test-time-adaptation",
"vision-language-model"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.foundation-models",
"multimodal.vision-language",
"specialized.test-time-adaptation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-08T20:33:17Z | [AAAI 2026] Official implementation of the paper "Adapt-As-You-Walk Through the Clouds: Training-Free Online Test-Time Adaptation of 3D Vision-Language Foundation Models" ( Uni-Adapter ) | [
"computer-vision",
"foundation-models",
"generative-ai",
"language",
"multimodal",
"multimodal-learning",
"vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-28T18:33:08Z | false | 3 | 1,092,545,592 | null | Python | MIT | [
"contrastive-learning",
"multimodal",
"pretraining",
"test-time-adaptation",
"vision-language-model"
] | Mehran-TAM/Uni-Adapter | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-11T16:48:52Z | [] | 2026-05-23T07:58:50Z | [
"llm.foundation-models",
"multimodal.vision-language"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 9 | [] | 2026-06-24T04:32:03Z | https://github.com/Mehran-TAM/Uni-Adapter | null |
[
"generative-ai",
"generative-modeling",
"language",
"reinforcement-learning"
] | [
"diffusion",
"language-model",
"reasoning",
"reinforcement-learning"
] | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"llm-reasoning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-09T17:55:29Z | Official PyTorch implementation of "Latent Reasoning in TRMs is Secretly a Policy Improvement Operator" (ICML 2026) | [
"generative-ai",
"generative-modeling",
"language",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-11-02T21:04:38Z | false | 2 | 1,092,996,180 | https://arxiv.org/pdf/2511.16886 | Python | null | [
"diffusion",
"language-model",
"reasoning",
"reinforcement-learning"
] | machinestein/Deep-Improvement-Supervision | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-11-02T21:04:38Z | [] | 2026-05-29T10:39:48Z | [
"llm-reasoning"
] | 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 | 26 | [
"discrete-diffusion",
"hierarchical-reasoning-model",
"latent-reasoning",
"reasoning",
"reinforcement-learning",
"tiny-recursive-model"
] | 2026-08-25T03:33:40Z | https://github.com/machinestein/Deep-Improvement-Supervision | null |
[
"anomaly-detection",
"generative-modeling",
"graph-learning"
] | [
"anomaly-detection",
"contrastive-learning",
"diffusion",
"graph-anomaly-detection"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.graph-anomaly-detection"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-10T06:18:33Z | Official PyTorch implementation for the paper FedDCL: Federated Diffusion-enhanced Contrastive Learning for Graph Anomaly Detection. | [
"anomaly-detection",
"generative-modeling",
"graph-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T14:50:46Z | false | 0 | 1,093,266,780 | null | Python | null | [
"anomaly-detection",
"contrastive-learning",
"diffusion",
"graph-anomaly-detection"
] | weihus/FedDCL | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-09-27T15:53:49Z | [] | 2025-11-10T08:16:49Z | [
"graph.graph-anomaly-detection"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [] | 2025-12-15T02:37:14Z | https://github.com/weihus/FedDCL | 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 | [] | 2025-11-10T07:22:57Z | [AAAI 2026] Official implementation of "Time Series Forecasting via Direct Per-Step Probability Distribution Modeling" | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 1 | 1,093,298,770 | null | Python | Apache-2.0 | [
"forecasting",
"sequence-modeling"
] | leonardokong486/interPDN | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-30T17:16:29Z | [] | 2025-12-10T06:53:20Z | [
"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 | 37 | [] | 2026-08-30T03:20:11Z | https://github.com/leonardokong486/interPDN | null |
[
"computer-vision",
"deep-learning",
"representation-learning"
] | [
"continual-learning",
"lifelong-learning",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.continual-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-10T07:30:12Z | [AAAI 2026] Official implementation of paper “Better Matching, Less Forgetting: A Quality-Guided Matcher for Transformer-based Incremental Object Detection”. | [
"computer-vision",
"deep-learning",
"representation-learning"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-28T17:27:44Z | false | 0 | 1,093,302,440 | null | Python | Apache-2.0 | [
"continual-learning",
"lifelong-learning",
"transformer"
] | fanrena/Q-MCMFpp | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-28T17:27:44Z | [] | 2026-08-27T05:38:36Z | [
"general.continual-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 | 4 | [] | 2026-09-29T03:59:51Z | https://github.com/fanrena/Q-MCMFpp | null |
[
"efficient-ml",
"machine-learning-systems",
"model-compression",
"natural-language-processing"
] | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-10T08:05:11Z | [ICLR 2026] Official PyTorch implementation of LSA: Layer-wise Sparsity Allocation for Large Language Model Pruning Based on Minimal Linear Reconstruction Error | [
"efficient-ml",
"machine-learning-systems",
"model-compression",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-31T16:49:26Z | false | 0 | 1,093,321,073 | https://openreview.net/forum?id=xq3lza5IjN | Python | null | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | BeiYazi0/LSA | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-31T16:49:26Z | [] | 2026-01-30T06:59:55Z | [
"efficiency.pruning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 3 | [] | 2026-09-24T08:25:37Z | https://github.com/BeiYazi0/LSA | null |
[
"audio",
"generative-ai",
"generative-modeling",
"multimodal",
"speech-and-audio"
] | [
"diffusion",
"generative-modeling",
"training"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"audio-text-to-audio",
"recall.name.diffusion-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-10T10:29:18Z | Official implementation for the paper "Anchor Watermark: Robust Attribution for Diffusion-based Text-to-Audio Model" in AAAI 2026. | [
"generative-ai",
"generative-modeling",
"speech-and-audio"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T16:16:53Z | false | 0 | 1,093,406,844 | null | Python | null | [
"diffusion"
] | DDAN-LAB/AnchorWM | [
"description",
"query-match",
"repository-metadata"
] | 6 | 2026-10-30T16:05:33Z | [] | 2026-10-15T14:30:01Z | [
"recall.name.diffusion-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 3 | [] | 2026-10-15T14:30:06Z | https://github.com/DDAN-LAB/AnchorWM | null |
[
"computer-vision",
"health-and-biomedicine",
"medical-imaging"
] | [
"distillation",
"segmentation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"medical.medical-image-segmentation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-10T12:31:33Z | This is the official PyTorch implementation of our work: **Class Incremental Medical Image Segmentation via Prototype-Guided Calibration and Dual-Aligned Distillation** published at AAAI 2026. | [
"computer-vision",
"health-and-biomedicine",
"medical-imaging"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 0 | 1,093,477,054 | null | null | null | [
"distillation",
"segmentation"
] | shengqianzhu/PCDD | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-08T18:15:14Z | [] | 2025-11-11T07:48:35Z | [
"medical.medical-image-segmentation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 2 | [] | 2026-03-22T03:05:32Z | https://github.com/shengqianzhu/PCDD | null |
[
"audio",
"speech",
"speech-and-audio"
] | [
"architecture",
"compression",
"inference",
"representation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"audio-neural-codec",
"audio-speech-enhancement"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-10T12:57:36Z | Official implementation of the paper on "Modeling strategies for speech enhancement in the latent space of a neural audio codec", exploring autoregressive and non-autoregressive strategies in both discrete and continuous representation spaces. | [
"audio",
"speech",
"speech-and-audio"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 1 | 1,093,493,184 | null | Python | MIT | [
"inference",
"representation"
] | SofieneKammoun/SE-NAC-CODE | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 8 | 2026-10-13T18:05:26Z | [] | 2026-05-12T16:53:09Z | [
"audio-speech-enhancement"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-context-only",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 17 | [] | 2026-09-29T13:40:47Z | https://github.com/SofieneKammoun/SE-NAC-CODE | null |
[
"biology",
"computational-biology",
"single-cell",
"transcriptomics"
] | [
"cell-type-annotation",
"contrastive-learning",
"gene-expression-modeling",
"representation-learning",
"spatial-modeling"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"bio.scrna-seq",
"bio.spatial-transcriptomics"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-10T14:11:24Z | Official PyTorch Implementation of scRCL (AAAI'26 Post: Refinement Contrastive Learning of Cell-Gene Associations for Unsupervised Cell Type Identification) | [
"biology",
"computational-biology",
"single-cell"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-17T13:17:44Z | false | 0 | 1,093,543,615 | null | Python | MIT | [
"cell-type-annotation",
"contrastive-learning",
"representation-learning"
] | THPengL/scRCL | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-11-02T21:04:38Z | [] | 2026-01-09T09:15:44Z | [
"bio.scrna-seq"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 5 | [] | 2026-04-27T10:35:42Z | https://github.com/THPengL/scRCL | null |
[
"computer-vision",
"generative-modeling"
] | [
"diffusion",
"restoration"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.image-restoration"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-11T04:52:12Z | This repository contains the code for our AAAI26 paper "Integrating Reweighted Least Squares with Plug-and-Play Diffusion Priors for Noisy Image Restoration" | [
"computer-vision",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-25T15:11:36Z | false | 0 | 1,093,974,523 | null | Python | GPL-3.0 | [
"diffusion",
"restoration"
] | Chilie/IRLS-PnPDP | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-25T15:11:36Z | [] | 2026-03-09T03:35:25Z | [
"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 | 3 | [] | 2026-03-12T06:17:56Z | https://github.com/Chilie/IRLS-PnPDP | null |
[
"generative-modeling",
"multimodal",
"reinforcement-learning",
"robotics"
] | [
"diffusion",
"environment-modeling",
"world-model"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.world-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-11T07:21:05Z | [NIPS'25] Official Implementation of "Revisiting Multi-Agent World Modeling from a Diffusion-Inspired Perspective" in PyTorch. | [
"generative-modeling",
"multimodal",
"reinforcement-learning",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 3 | 1,094,047,577 | null | Python | null | [
"diffusion",
"environment-modeling",
"world-model"
] | breez3young/DIMA | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-02T16:59:40Z | [] | 2025-11-11T08:08:21Z | [
"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 | 18 | [] | 2026-09-10T01:57:44Z | https://github.com/breez3young/DIMA | null |
[
"interpretability-and-safety",
"machine-learning",
"multimodal-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 | [] | 2025-11-11T16:17:14Z | This is the official PyTorch implementation for Bridging Modalities via Progressive Re-alignment for Multimodal Test-Time Adaptation (AAAI 2026 Oral). | [
"interpretability-and-safety",
"machine-learning",
"multimodal-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 0 | 1,094,375,984 | null | Python | MIT | [
"adaptation",
"test-time-adaptation"
] | Luchicken/BriMPR | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2026-03-19T08:32:32Z | [
"specialized.test-time-adaptation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 6 | [] | 2026-07-10T04:24:19Z | https://github.com/Luchicken/BriMPR | null |
[
"embodied-ai",
"generative-modeling",
"reinforcement-learning",
"robotics"
] | [
"diffusion",
"diffusion-policy",
"imitation-learning",
"reinforcement-learning"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"robotics.diffusion-policy"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-11T16:23:23Z | [ICLR 2026] The official implementation of Dichotomous Diffusion Policy Optimization (DIPOLE) in RL bench | [
"embodied-ai",
"generative-modeling",
"reinforcement-learning",
"robotics"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 0 | 1,094,380,672 | https://lrmbbj.github.io/DIPOLE/ | Python | null | [
"diffusion",
"diffusion-policy",
"imitation-learning",
"reinforcement-learning"
] | Whiterrrrr/dipole-rl | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 3 | 2026-09-27T15:53:49Z | [] | 2026-03-05T11:40:07Z | [
"robotics.diffusion-policy"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 8 | [
"diffusion-policy",
"diffusion-rl",
"reinforcement-learning"
] | 2026-08-09T10:16:35Z | https://github.com/Whiterrrrr/dipole-rl | null |
[
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"forecasting",
"multivariate-forecasting",
"transformer"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"timeseries.multivariate"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-12T03:15:49Z | A New Transformer-based Model for Multivariate Time Series Forecasting. | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-03T15:45:52Z | false | 0 | 1,094,683,050 | null | null | null | [
"forecasting",
"multivariate-forecasting",
"transformer"
] | oyhurry/TransLFF | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-03T15:45:52Z | [] | 2025-11-12T03:15:50Z | [
"timeseries.multivariate"
] | 4c8b428b4c9670c214c843795ca6453a59012218 | 2026-10-03T17:01:43Z | gh-ml-readme-evidence-v2 | "4c8b428b4c9670c214c843795ca6453a59012218" | [
"other"
] | [
"ml-method-context"
] | ok | 2026-10-03T17:01:43Z | oyhurry/TransLFF | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue"
] | include | ml-contribution-v5 | 1 | [] | 2025-11-12T14:20:33Z | https://github.com/oyhurry/TransLFF | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-12T06:03:25Z | Official Pytorch Implementation for "Mass Concept Erasure in Diffusion Models with Concept Hierarchy" (AAAI 2026) | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-28T17:27:44Z | false | 0 | 1,094,756,543 | null | null | null | [
"diffusion"
] | TtuHamg/SuPLoRA | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-28T17:27:44Z | [] | 2025-11-12T06:03:25Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 1 | [] | 2026-01-28T06:09:28Z | https://github.com/TtuHamg/SuPLoRA | null |
[
"generative-ai",
"language"
] | [
"fine-tuning",
"low-rank-adaptation",
"parameter-efficient-fine-tuning",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"llm.parameter-efficient-finetuning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-12T07:29:45Z | Official implementation of "Group Orthogonal Low-Rank Adaptation for RGB-T Tracking" (AAAI2026) | [
"generative-ai",
"language"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 2 | 1,094,801,238 | https://arxiv.org/abs/2512.05359 | Python | Apache-2.0 | [
"fine-tuning",
"low-rank-adaptation",
"parameter-efficient-fine-tuning",
"transformer"
] | MelanTech/GOLA | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 2 | 2026-11-02T21:04:38Z | [] | 2025-12-24T02:08:40Z | [
"llm.parameter-efficient-finetuning"
] | 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 | 30 | [
"tracking",
"transformer"
] | 2026-10-21T14:47:50Z | https://github.com/MelanTech/GOLA | null |
[
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-12T08:59:06Z | [AAAI 2026] Official PyTorch implementation of "Prune&Comp: Free Lunch for Layer-Pruned LLMs via Iterative Pruning with Magnitude Compensation" | [
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-31T16:49:26Z | false | 0 | 1,094,850,379 | null | Python | MIT | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | chenxinrui-tsinghua/PruneAndComp | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-31T16:49:26Z | [] | 2025-11-19T13:51:22Z | [
"efficiency.pruning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 5 | [] | 2026-09-15T14:45:49Z | https://github.com/chenxinrui-tsinghua/PruneAndComp | null |
[
"reinforcement-learning",
"robotics"
] | [
"model-based-reinforcement-learning",
"reinforcement-learning",
"world-model"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.model-based"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-12T10:57:52Z | Official implementation for "Planning in Branch-and-Bound: Model-Based Reinforcement Learning for Exact Combinatorial Optimization" @ AAAI 2026 | [
"reinforcement-learning",
"robotics"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 3 | 1,094,921,488 | null | Python | MIT | [
"model-based-reinforcement-learning",
"reinforcement-learning",
"world-model"
] | abfariah/planbb | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-09-28T18:33:08Z | [] | 2026-07-21T13:55:22Z | [
"rl.model-based"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 5 | [] | 2026-09-02T13:53:43Z | https://github.com/abfariah/planbb | null |
[
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion",
"restoration"
] | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"vision.image-restoration"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-13T03:46:05Z | [WACV 2026]Official Code of the paper “Equivariant Sampling for Improving Diffusion Model-based Image Restoration“ | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-22T16:09:07Z | false | 0 | 1,095,437,932 | https://arxiv.org/abs/2511.09965 | Python | null | [
"diffusion"
] | FouierL/EquS | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | 3 | 2026-10-26T17:25:59Z | [] | 2026-01-29T06:38:43Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 19 | [
"diffusion",
"diffusion-models",
"image-restoration",
"wacv",
"wacv2026",
"zero-shot"
] | 2026-09-02T09:51:36Z | https://github.com/FouierL/EquS | 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 | [] | 2025-11-13T04:01:00Z | A novel CNN-Mamba hybrid architecture that fuses convolutional feature extraction, CBAM attention, and Mamba State Space Models to classify 24 cancer types from medical images with surgical precision. | [
"sequence-modeling",
"time-series"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 1,095,443,941 | null | Jupyter Notebook | null | [
"mamba",
"sequence-modeling",
"state-space-model"
] | tarunmaurya13/multicancerdetection | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-04T15:58:58Z | [] | 2026-07-13T18:00:29Z | [
"timeseries.state-space-models"
] | 0aea927c8a4c1f7dee8b3ca4d6db4417bee16d46 | 2026-10-04T17:18:56Z | gh-ml-readme-evidence-v2 | "0aea927c8a4c1f7dee8b3ca4d6db4417bee16d46" | [
"dataset",
"installation",
"method",
"other",
"overview",
"results"
] | [
"ml-method-context"
] | ok | 2026-10-04T17:18:56Z | tarunmaurya13/multicancerdetection | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue"
] | include | ml-contribution-v5 | 2 | [] | 2026-08-23T19:32:50Z | https://github.com/tarunmaurya13/multicancerdetection | null |
[
"chemistry",
"computational-chemistry",
"drug-discovery",
"graph-learning",
"materials-science"
] | [
"benchmarking",
"graph-neural-network",
"neural-network",
"property-prediction",
"qsar",
"representation-learning",
"supervised-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"chem-molecular-property-benchmark",
"chem-property-prediction"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-14T08:39:56Z | This repository provides the official PyTorch implementation for CHAMP (Coupled Hierarchical Atom-Motif Predictor), a novel hierarchical Graph Neural Network framework designed to achieve state-of-the-art performance in molecular property prediction. | [
"chemistry",
"drug-discovery",
"graph-learning",
"materials-science"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T16:16:53Z | false | 0 | 1,096,311,897 | null | Python | null | [
"graph-neural-network",
"neural-network",
"property-prediction",
"qsar",
"supervised-learning"
] | xbtc-lab/CHAMP | [
"description",
"query-match",
"repository-metadata"
] | 4 | 2026-10-05T20:10:04Z | [] | 2026-07-07T07:24:24Z | [
"chem-property-prediction"
] | 65fe3cd0cfd07367bff6ae7ba9f70453dfe797e7 | 2026-09-26T15:53:33Z | gh-ml-readme-evidence-v2 | "65fe3cd0cfd07367bff6ae7ba9f70453dfe797e7" | [
"method",
"other",
"overview",
"usage"
] | [
"method-contribution",
"ml-method-context",
"official-implementation-claim",
"paper-reference"
] | ok | 2026-09-26T15:53:33Z | xbtc-lab/CHAMP | specific-method-with-novelty-claim | [
"contribution-language",
"method-contribution",
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue",
"official-implementation-claim",
"paper-reference"
] | include | ml-contribution-v5 | 4 | [] | 2026-08-14T07:11:05Z | https://github.com/xbtc-lab/CHAMP | null |
[
"anomaly-detection",
"computer-vision",
"generative-modeling",
"multimodal-learning",
"natural-language-processing"
] | [
"anomaly-detection",
"computer-vision",
"detection",
"diffusion"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"vision.topic-computer-vision",
"vision.visual-anomaly-detection"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-14T14:29:44Z | [AAAI 2026] The Official Implementation for "Anomagic: Crossmodal Prompt-driven Zero-shot Anomaly Generation" | [
"anomaly-detection",
"computer-vision",
"generative-modeling",
"multimodal-learning",
"natural-language-processing"
] | [
"large-language-model",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T15:29:21Z | false | 9 | 1,096,509,761 | https://yuxin-jiang.github.io/Anomagic/ | Python | null | [
"anomaly-detection",
"computer-vision",
"detection",
"diffusion"
] | yuxin-jiang/Anomagic | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 4 | 2026-09-29T17:12:21Z | [] | 2026-09-04T12:02:33Z | [
"vision.topic-computer-vision",
"vision.visual-anomaly-detection"
] | 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 | 159 | [
"anomaly-detection",
"computer-vision",
"diffusion-models",
"multimodal-llm",
"text-to-image",
"zero-shot-learning"
] | 2026-09-09T11:31:24Z | https://github.com/yuxin-jiang/Anomagic | null |
[
"chemistry",
"drug-discovery",
"generative-modeling",
"structural-biology"
] | [
"diffusion",
"docking",
"structure-prediction"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"chem-molecular-docking"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-14T16:46:41Z | Official implementation of the NeurIPS 25 Paper: "Diffusion Generative Modeling on Lie Group Representations" | [
"chemistry",
"drug-discovery",
"generative-modeling",
"structural-biology"
] | [
"generative-model",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T16:16:53Z | false | 2 | 1,096,592,418 | null | Jupyter Notebook | NOASSERTION | [
"diffusion",
"docking",
"structure-prediction"
] | pfizer-opensource/symmetry-induced-score-matching | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-06T17:10:23Z | [] | 2025-12-12T23:58:57Z | [
"chem-molecular-docking"
] | 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 | [
"diffusion-models",
"flow-models",
"generative-model",
"lie-groups",
"neural-dynamics",
"riemannian-geometry",
"score-matching"
] | 2026-03-29T12:14:53Z | https://github.com/pfizer-opensource/symmetry-induced-score-matching | null |
[
"deep-learning",
"efficient-ml",
"general-ml",
"generative-ai",
"language",
"ml-systems",
"multimodal",
"multimodal-learning",
"natural-language-processing"
] | [
"benchmarking",
"conditional-computation",
"evaluation",
"expert-parallelism",
"mixture-of-experts",
"multimodal-learning",
"sparse-activation",
"transformer"
] | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"efficiency.mixture-of-experts",
"growth26.systems-expert-parallelism",
"llm.evaluation",
"recall.name.multimodal-model",
"recall.name.transformer-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-14T21:57:16Z | The official implementation of the paper "Capacity-Aware Inference: Mitigating the Straggler Effect in Mixture of Experts" (ICLR 2026). | [
"general-ml",
"multimodal-learning",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 3 | 1,096,739,687 | https://arxiv.org/abs/2503.05066 | Python | null | [
"mixture-of-experts",
"transformer"
] | CASE-Lab-UMD/Capacity-Aware-MoE | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | 17 | 2026-10-31T15:48:22Z | [] | 2026-10-02T20:31:57Z | [
"recall.name.transformer-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 23 | [
"inference-acceleration",
"language-models",
"load-balancing",
"mixture-of-experts",
"multimodal-models",
"test-time-optimization"
] | 2026-10-02T20:32:02Z | https://github.com/CASE-Lab-UMD/Capacity-Aware-MoE | 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 | [] | 2025-11-15T12:22:31Z | This repository contains the official implementation of the paper: 2D-CrossScan Mamba: Enhancing State Space Models with Spatially Consistent Multi-Path 2D Information Propagation. | [
"sequence-modeling",
"time-series"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 1 | 1,097,036,951 | null | Cuda | MIT | [
"mamba",
"sequence-modeling",
"state-space-model"
] | longlong-yu/official-cross-scan | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-04T15:58:58Z | [] | 2026-03-23T01:53:09Z | [
"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",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 2 | [] | 2026-07-06T01:31:43Z | https://github.com/longlong-yu/official-cross-scan | null |
[
"deep-learning",
"representation-learning"
] | [
"representation-learning",
"self-supervised-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.self-supervised-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-15T12:45:28Z | Official implementation of our AAAI 2026 paper "SigFusion: Unified Signal-Level Self-Supervised Learning Paradigm for Image Fusion“ | [
"deep-learning",
"representation-learning"
] | [
"self-supervised-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 0 | 1,097,046,560 | null | Python | null | [
"representation-learning",
"self-supervised-learning"
] | fengjiawei123/SigFusion | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-10-05T20:10:04Z | [] | 2026-08-14T22:58:53Z | [
"general.self-supervised-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 4 | [] | 2026-09-21T11:04:19Z | https://github.com/fengjiawei123/SigFusion | null |
[
"distributed-ml",
"privacy-and-federated-learning"
] | [
"collaborative-learning",
"federated-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-15T13:27:02Z | This is the official implementation of the paper: Robust Client-Server Watermarking for Split Federated Learning | [
"distributed-ml",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-21T15:48:06Z | false | 0 | 1,097,064,538 | null | Python | null | [
"collaborative-learning",
"federated-learning"
] | dickton/RISE | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-21T15:48:06Z | [] | 2025-11-15T13:27:25Z | [
"trust.federated-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 4 | [] | 2025-12-31T01:41:06Z | https://github.com/dickton/RISE | null |
[
"computer-vision",
"generative-modeling"
] | [
"diffusion",
"image-classification"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.image-classification"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-15T17:18:14Z | An official implementation of the paper: "Advancing Image Classification with Discrete Diffusion Classification Modeling". | [
"computer-vision",
"generative-modeling"
] | [
"classifier"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 3 | 1,097,177,685 | null | Python | MIT | [
"diffusion",
"image-classification"
] | omerb01/didicm | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-09-27T15:53:49Z | [] | 2026-04-10T19:24:25Z | [
"vision.image-classification"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 25 | [] | 2026-09-21T13:45:31Z | https://github.com/omerb01/didicm | 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 | [] | 2025-11-16T05:40:14Z | Official implementation of paper: Repurposing 2D Diffusion Models for 3D Shape Completion | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-26T17:25:59Z | false | 0 | 1,097,408,787 | null | Python | MIT | [
"diffusion"
] | shockwaveHe/ShapeAtlas | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-26T17:25:59Z | [] | 2026-04-21T07:00:59Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 4 | [] | 2026-07-08T07:01:04Z | https://github.com/shockwaveHe/ShapeAtlas | null |
[
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"neural-network",
"neural-network-pruning",
"pruning",
"structured-pruning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-16T18:05:43Z | Final research project for MIT's 6.7960 Deep Learning. We investigated the effectiveness and tradeoffs of novel biologically-inspired pruning methods on custom neural networks. | [
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"deep-learning",
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-11-01T17:09:41Z | false | 0 | 1,097,726,398 | https://cinxwei.github.io/6.7960-Final-Project--Ellis-Cindy/ | null | null | [
"neural-network",
"neural-network-pruning",
"pruning",
"structured-pruning"
] | cinxwei/6.7960-Final-Project--Ellis-Cindy | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-11-01T17:09:41Z | [] | 2026-02-20T07:04:43Z | [
"efficiency.pruning"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-02-20T19:48:22Z | https://github.com/cinxwei/6.7960-Final-Project--Ellis-Cindy | null |
[
"reinforcement-learning"
] | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-17T03:41:12Z | Official implementation for the paper “Path Following for Autonomous Mobile Robots with Deep Reinforcement Learning”. A fully Python-based DRL framework for autonomous mobile robot path following on predefined (random) paths. | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-30T17:16:29Z | false | 0 | 1,097,939,268 | https://doi.org/10.3390/s24020561 | Python | null | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | cao-yu/DRL-Autonomous-Path-Following | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-10-30T17:16:29Z | [] | 2026-02-13T05:49:28Z | [
"rl.deep"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 7 | [
"autonomous-mobile-robot",
"deep-reinforcement-learning",
"path-following",
"pure-pursuit",
"soft-actor-critic"
] | 2026-05-13T03:45:01Z | https://github.com/cao-yu/DRL-Autonomous-Path-Following | null |
[
"reinforcement-learning"
] | [
"policy-gradient",
"policy-optimization"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"rl.ppo"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-17T05:28:57Z | Official implementation and Extended Version of "Learning Branching Policies for MILPs with Proximal Policy Optimization" (AAAI 2026). | [
"reinforcement-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 0 | 1,097,985,419 | null | Python | null | [
"policy-gradient",
"policy-optimization"
] | benmhamed-a/tgppo-aaai-26 | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-09-30T16:47:43Z | [] | 2026-04-08T11:15:58Z | [
"rl.ppo"
] | 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 | [] | 2026-09-21T12:49:18Z | https://github.com/benmhamed-a/tgppo-aaai-26 | null |
[
"computer-vision",
"efficient-ml",
"foundation-models",
"generative-ai",
"language",
"model-compression",
"multimodal",
"multimodal-learning",
"vision"
] | [
"contrastive-learning",
"distillation",
"knowledge-distillation",
"multimodal",
"pretraining",
"vision-language-model"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation",
"llm.foundation-models",
"multimodal.vision-language"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-17T07:23:24Z | The official implementation of paper: FusionKD: Fusion Knowledge Distillation of Vision-Language Foundation Model for Strip Steel Surface Defect Detection | [
"computer-vision",
"language",
"multimodal",
"multimodal-learning",
"vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 0 | 1,098,043,686 | null | Python | null | [
"contrastive-learning",
"distillation",
"knowledge-distillation",
"vision-language-model"
] | Miss-Jo/FusionKD | [
"description",
"query-match",
"repository-metadata"
] | 6 | 2026-10-14T16:10:46Z | [] | 2025-12-16T07:57:58Z | [
"multimodal.vision-language"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 12 | [] | 2026-09-29T08:30:48Z | https://github.com/Miss-Jo/FusionKD | null |
[
"efficient-ml",
"machine-learning-systems",
"model-compression",
"natural-language-processing"
] | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-18T03:28:07Z | code for paper: "PP-LLMs: A Progressive Pruning Approach with Medium-Granularity for Large Language Models" | [
"efficient-ml",
"machine-learning-systems",
"model-compression",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-11-01T17:09:41Z | false | 0 | 1,098,708,562 | null | Python | null | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | ykai66/ppllms | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-11-01T17:09:41Z | [] | 2025-11-18T06:21:23Z | [
"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 | 0 | [] | 2025-11-18T06:21:27Z | https://github.com/ykai66/ppllms | 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 | [] | 2025-11-18T14:27:04Z | The official PyTorch implementation of the paper "Human Motion Diffusion Model" | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-26T17:25:59Z | false | 0 | 1,099,073,410 | null | null | MIT | [
"diffusion"
] | Sloppyan/motion-diffusion-model-phc | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-26T17:25:59Z | [] | 2026-05-06T14:41:39Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-03-11T15:10:35Z | https://github.com/Sloppyan/motion-diffusion-model-phc | null |
[
"computer-vision",
"generative-ai"
] | [
"diffusion",
"generative-modeling",
"video-generation",
"world-model"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"recall.name.diffusion-model",
"vision.video-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-19T00:26:16Z | [ECCV 2026] Official Pytorch Code of the Paper "WorldWander: Bridging Egocentric and Exocentric Worlds in Video Generation" | [
"generative-ai"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-19T16:36:56Z | false | 4 | 1,099,404,083 | https://lulupig12138.github.io/WorldWander | Python | null | [
"diffusion",
"world-model"
] | showlab/WorldWander | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 3 | 2026-10-26T16:16:45Z | [] | 2026-10-07T06:09:31Z | [
"recall.name.diffusion-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 | 45 | [
"cross-view-generation",
"egocentric-exocentric",
"perspective-transformation",
"video-genration",
"video-translation",
"virtual-reality",
"world-model"
] | 2026-10-22T09:43:50Z | https://github.com/showlab/WorldWander | null |
[
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"probabilistic-forecasting",
"uncertainty-estimation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"timeseries.probabilistic-forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-20T11:27:59Z | Official implementation of the paper "Probabilistic Forecasting of Household Energy and Evaluation of Energy Self-Sufficiency Rate Using Pre-trained Time Series Models" (submitted to Energies). | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 0 | 1,100,509,962 | null | Python | MIT | [
"probabilistic-forecasting",
"uncertainty-estimation"
] | MTBT-27/probabilistic-essr-forecasting | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-08T18:15:14Z | [] | 2025-12-06T05:49:21Z | [
"timeseries.probabilistic-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 | 0 | [] | 2025-12-06T05:49:25Z | https://github.com/MTBT-27/probabilistic-essr-forecasting | null |
[
"computer-vision",
"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 | [] | 2025-11-20T12:16:48Z | [ICML 2026] Official implementation of "SC²-WM: A Self-Correcting World Model with Closed-Loop Feedback for Vision-and-Language Navigation in Continuous Environments" . | [
"computer-vision",
"multimodal",
"reinforcement-learning",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 0 | 1,100,537,789 | null | Python | MIT | [
"environment-modeling",
"world-model"
] | sunrise-yuze/SC2_WM | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-02T16:59:40Z | [] | 2026-09-16T07:18:34Z | [
"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 | 19 | [] | 2026-09-28T08:03:41Z | https://github.com/sunrise-yuze/SC2_WM | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-transformer",
"message-passing",
"transformer"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.graph-transformer"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-20T13:03:42Z | This repository contains the official PyTorch implementation of the paper "V-BUILD: A graph Transformer-based decision framework for selective updating of vector building data". | [
"deep-learning",
"graph-learning"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 1 | 1,100,565,913 | null | Python | null | [
"graph-transformer",
"message-passing",
"transformer"
] | ParkourX/V-BUILD | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-09-27T16:09:59Z | [] | 2026-02-23T09:28:33Z | [
"graph.graph-transformer"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [] | 2026-08-26T10:07:42Z | https://github.com/ParkourX/V-BUILD | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"multimodal"
] | [
"generative-modeling",
"motion-generation",
"transformer"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"multimodal.motion-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-20T16:36:53Z | [ECCV 2026] Official implementation of "Language-Guided Transformer Tokenizer for Human Motion Generation." | [
"computer-vision",
"generative-ai",
"generative-modeling",
"multimodal"
] | [
"generative-model",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 0 | 1,100,707,496 | https://eanson023.github.io/LG-Tok/ | Python | null | [
"generative-modeling",
"motion-generation",
"transformer"
] | eanson023/LG-Tok | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 3 | 2026-09-28T18:33:08Z | [] | 2026-07-09T01:47:24Z | [
"multimodal.motion-generation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 24 | [
"generative-model",
"motion-analysis",
"pytorch-implementation",
"text-to-motion"
] | 2026-09-27T09:22:33Z | https://github.com/eanson023/LG-Tok | null |
[
"computer-vision",
"generative-modeling"
] | [
"diffusion",
"restoration",
"super-resolution",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.image-super-resolution"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-21T04:20:35Z | [CVPR2026] ODTSR: This repo is the official implementation of "One-Step Diffusion Transformer for Controllable Real-World Image Super-Resolution" | [
"computer-vision",
"generative-modeling"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 4 | 1,101,012,339 | null | Python | Apache-2.0 | [
"diffusion",
"restoration",
"super-resolution",
"transformer"
] | RedMediaTech/ODTSR | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-11-02T21:04:38Z | [] | 2026-02-21T09:06:54Z | [
"vision.image-super-resolution"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 199 | [] | 2026-10-23T20:33:34Z | https://github.com/RedMediaTech/ODTSR | null |
[
"computational-neuroscience",
"machine-learning",
"privacy-and-federated-learning"
] | [
"federated-learning",
"neural-network",
"neuromorphic-computing",
"spiking-neural-network"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"specialized.spiking-neural-network"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-21T16:44:21Z | Official code for "Exploiting Label Skewness for Spiking Neural Networks in Federated Learning" (IJCAI'2025) | [
"computational-neuroscience",
"machine-learning",
"privacy-and-federated-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-22T16:09:07Z | false | 1 | 1,101,422,106 | null | null | Apache-2.0 | [
"federated-learning",
"neural-network",
"neuromorphic-computing",
"spiking-neural-network"
] | alliethebanana/FedLEC | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-22T16:09:07Z | [] | 2025-08-08T09:14:40Z | [
"specialized.spiking-neural-network"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 0 | [] | 2025-11-21T16:44:21Z | https://github.com/alliethebanana/FedLEC | null |
[
"deep-learning",
"graph-learning",
"materials-science"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-23T11:22:18Z | Multi-View Conditional Information Bottleneck (MVCIB) is a novel architecture for pre-training Graph Neural Networks on 2D and 3D molecular structures and developed by NS Lab, CUK based on pure PyTorch backend. | [
"deep-learning",
"graph-learning",
"materials-science"
] | [
"neural-network",
"representation-learning",
"self-supervised-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-20T15:59:06Z | false | 2 | 1,102,387,302 | https://nslab-cuk.github.io/2025/11/22/MVCIB/ | Python | MIT | [
"graph-neural-network",
"message-passing",
"neural-network"
] | NSLab-CUK/MVCIB | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-20T15:59:06Z | [] | 2025-11-26T08:35:00Z | [
"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 | 5 | [
"graph",
"graph-foundation-model",
"graph-information-bottleneck",
"graph-neural-networks",
"graph-representation-learning",
"molecular-graph-learning",
"molecular-graph-neural-networks",
"molecular-property-prediction",
"pretrained-graph-model",
"self-supervised-graph-learning"
] | 2026-03-07T16:08:35Z | https://github.com/NSLab-CUK/MVCIB | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"recall.name.diffusion-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-24T03:56:02Z | Official implementation of "Rethinking Garment Conditioning in Diffusion-based Virtual Try-On: Decouple, Don't Denoise (DeCo-VTON)", ECCV 2026 | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-02T15:56:46Z | false | 1 | 1,102,797,928 | https://arxiv.org/abs/2511.18775 | Python | NOASSERTION | [
"diffusion"
] | Levinna/DeCo-VTON | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-10-02T15:56:46Z | [] | 2026-09-24T15:48:05Z | [
"recall.name.diffusion-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 6 | [
"conditional-diffusion-model",
"diffusion-models",
"pytorch",
"stable-diffusion",
"virtual-try-on"
] | 2026-09-30T23:24:27Z | https://github.com/Levinna/DeCo-VTON | null |
[
"generative-ai",
"interpretability-and-safety",
"language",
"natural-language-processing",
"robust-ml",
"safety",
"trustworthy-ml"
] | [
"alignment",
"distribution-shift-detection",
"domain-generalization",
"preference-optimization"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.preference-optimization",
"trust.distribution-shift"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-24T08:51:36Z | Code for NeurIPS 2025 paper: Leveraging Robust Optimization for LLM Alignment under Distribution Shifts | [
"generative-ai",
"interpretability-and-safety",
"language",
"natural-language-processing",
"safety"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 0 | 1,102,954,785 | null | Python | null | [
"alignment",
"preference-optimization"
] | stevie1023/DoRA | [
"description",
"query-match",
"repository-metadata"
] | 4 | 2026-10-14T16:10:46Z | [] | 2025-11-24T10:32:38Z | [
"llm.preference-optimization"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2025-11-24T10:32:42Z | https://github.com/stevie1023/DoRA | null |
[
"generative-ai",
"language"
] | [
"language-model",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.transformers"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-24T14:30:01Z | [CVPR 2026] Official code and models for Video Encoder-only Mask Transformer (VidEoMT). | [
"generative-ai",
"language"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 25 | 1,103,178,377 | http://www.tue-mps.org/videomt/ | Python | MIT | [
"language-model",
"transformer"
] | tue-mps/videomt | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-09-29T17:12:21Z | [] | 2026-09-04T13:30:22Z | [
"llm.transformers"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 265 | [] | 2026-09-22T07:43:22Z | https://github.com/tue-mps/videomt | null |
[
"computer-vision",
"document-understanding",
"efficient-ml",
"generative-modeling",
"machine-learning-systems",
"model-compression",
"natural-language-processing"
] | [
"diffusion",
"ocr",
"quantization"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"efficiency.quantization",
"vision.ocr"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-24T17:26:43Z | [WACV 2026] Official Implementation of paper: "CONSTANT: Towards High-Quality One-Shot Handwriting Generation with Patch Contrastive Enhancement and Style-Aware Quantization" | [
"computer-vision",
"document-understanding",
"generative-modeling",
"machine-learning-systems",
"natural-language-processing"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-25T15:11:36Z | false | 1 | 1,103,298,451 | https://duylebkhcm.github.io/constant_website/ | Python | null | [
"diffusion",
"ocr",
"quantization"
] | duylebkHCM/CONSTANT | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 2 | 2026-10-28T17:27:44Z | [] | 2026-05-26T19:16:51Z | [
"vision.ocr"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 3 | [
"diffusion-models",
"generative-ai",
"handwritten-text-generation",
"ocr",
"style-content-disentanglement",
"visual-concept",
"wacv2026"
] | 2026-09-22T19:07:55Z | https://github.com/duylebkHCM/CONSTANT | null |
[
"generative-ai",
"generative-modeling",
"graph-learning"
] | [
"diffusion"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-25T10:11:35Z | Official implementation of paper "ACDm: Autoregressive-Conditioned Diffusion Model for Future State Generation in Temporal Knowledge Graph Reasoning" | [
"generative-ai",
"generative-modeling",
"graph-learning"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-28T17:27:44Z | false | 0 | 1,103,772,109 | null | Python | MIT | [
"diffusion"
] | cuiying0719/ACDm2025 | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-28T17:27:44Z | [] | 2025-11-25T10:12:59Z | [
"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 | [] | 2026-03-20T13:41:38Z | https://github.com/cuiying0719/ACDm2025 | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-26T15:01:57Z | This is the code for our paper "LayDi2Co: A Discrete-to-Continuous Diffusion Model for Structured Layout Generation" | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-28T17:27:44Z | false | 0 | 1,104,695,458 | null | Python | null | [
"diffusion"
] | xuyuankun631/LayDi2Co | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-28T17:27:44Z | [] | 2025-11-26T15:09:17Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [] | 2026-07-10T16:43:56Z | https://github.com/xuyuankun631/LayDi2Co | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-27T05:46:09Z | Official code for the NeurIPS'25 workshop paper: "Tracking Memorization Geometry throughout the Diffusion Model Generative Process". | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-28T17:27:44Z | false | 0 | 1,105,086,490 | null | Python | null | [
"diffusion"
] | JonathanBrok/Tracking-Memorization-Geometry-throughout-the-Diffusion-Model-Generative-Process | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-28T17:27:44Z | [] | 2025-12-09T08:48:29Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2025-12-09T08:48:33Z | https://github.com/JonathanBrok/Tracking-Memorization-Geometry-throughout-the-Diffusion-Model-Generative-Process | null |
[
"reinforcement-learning"
] | [
"policy-gradient",
"policy-optimization"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.policy-gradient"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-27T09:45:32Z | This repository contains code from our paper "Sample-Efficient Model-Free Policy Gradient Methods for Stochastic LQR: via Robust Linear Regression" | [
"reinforcement-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 1,105,214,680 | null | MATLAB | MIT | [
"policy-gradient",
"policy-optimization"
] | col-tasas/2025-MFPGM-RobustLR | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-04T15:58:58Z | [] | 2025-12-01T15:39:30Z | [
"rl.policy-gradient"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2025-12-01T15:39:33Z | https://github.com/col-tasas/2025-MFPGM-RobustLR | null |
[
"computational-science",
"physics"
] | [
"neural-operator",
"neural-operators",
"surrogate-modeling"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"science.neural-operators"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-27T13:14:57Z | Official code release for the paper: MscaleFNO: Multi-scale Fourier Neural Operator Learning for Oscillatory Functions and Wave Scattering | [
"computational-science",
"physics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 1 | 1,105,337,074 | null | Python | MIT | [
"neural-operator",
"neural-operators",
"surrogate-modeling"
] | yyc-rgb/MscaleFNO | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-09-28T18:33:08Z | [] | 2025-11-27T14:57:29Z | [
"science.neural-operators"
] | 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 | [] | 2026-08-18T04:09:39Z | https://github.com/yyc-rgb/MscaleFNO | null |
[
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"quantization"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.quantization"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-28T14:48:06Z | Official implementation of the paper "Quantization as a Vulnerability: Safety Degradation in Edge-Deployed Medical LLMs." Benchmarks the robustness of low-bit medical transformers against automated adversarial jailbreaking (GCG/TAP) and simulated hardware bit-flip faults. | [
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"large-language-model",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-26T17:25:59Z | false | 0 | 1,106,049,401 | null | null | null | [
"quantization"
] | starsofchance/Quantization-as-a-Vulnerability | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-26T17:25:59Z | [] | 2025-11-28T14:48:06Z | [
"efficiency.quantization"
] | 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 | [] | 2025-11-28T14:48:10Z | https://github.com/starsofchance/Quantization-as-a-Vulnerability | null |
[
"generative-modeling",
"synthetic-data",
"time-series",
"time-series-and-forecasting"
] | [
"diffusion",
"generative-modeling",
"time-series-generation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"timeseries.synthetic-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-11-30T06:16:07Z | Official Pytorch Implementation of "TimeBridge: Better Diffusion Prior Design with Bridge Models for Time Series Generation" (KDD 2026) | [
"generative-modeling",
"synthetic-data",
"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 1 | 1,106,884,387 | null | Python | MIT | [
"diffusion",
"generative-modeling",
"time-series-generation"
] | JinseongP/TimeBridge | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-09-29T17:30:08Z | [] | 2026-02-06T06:39:05Z | [
"timeseries.synthetic-generation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 7 | [] | 2026-04-15T07:53:34Z | https://github.com/JinseongP/TimeBridge | null |
[
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"forecasting",
"multivariate-forecasting"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"timeseries.multivariate"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-12-01T05:34:24Z | This repository contains the code and models for our paper "Adaptive wavelet decomposition and event-aware high-frequency modeling network for multivariate time series forecasting". | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 0 | 1,107,418,172 | null | null | MIT | [
"forecasting",
"multivariate-forecasting"
] | WangChengci/AweHF | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2025-12-15T09:25:55Z | [
"timeseries.multivariate"
] | 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 | [] | 2026-03-02T15:25:59Z | https://github.com/WangChengci/AweHF | null |
[
"deep-learning",
"efficient-ml",
"language",
"sequence-modeling",
"time-series"
] | [
"conditional-computation",
"mamba",
"mixture-of-experts",
"self-attention",
"sequence-modeling",
"sparse-activation",
"state-space-model"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.mixture-of-experts",
"timeseries.state-space-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-12-01T07:13:59Z | MambaDecoderBlock is a novel decoder architecture that replaces traditional self-attention mechanisms with Mamba state space models, augmented by Mixture of Experts (MoE) layers. | [
"sequence-modeling",
"time-series"
] | [
"large-language-model",
"machine-learning",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 1 | 1,107,466,639 | https://discord.gg/EamjgSaEQf | Python | Apache-2.0 | [
"mamba",
"mixture-of-experts",
"self-attention",
"sequence-modeling",
"state-space-model"
] | kyegomez/MambaDecoderBlock | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-03T15:45:52Z | [] | 2025-12-02T12:01:36Z | [
"timeseries.state-space-models"
] | 57f815b5bf50534b1783d108ed49beec5506cfd7 | 2026-10-02T18:21:54Z | gh-ml-readme-evidence-v2 | "57f815b5bf50534b1783d108ed49beec5506cfd7" | [
"citation",
"installation",
"method",
"other",
"overview",
"usage"
] | [
"ml-method-context"
] | ok | 2026-10-02T18:21:54Z | kyegomez/MambaDecoderBlock | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue"
] | include | ml-contribution-v5 | 5 | [
"attention",
"attention-mechanism",
"llms",
"mamba",
"ml",
"pytorch",
"ssm",
"swarms",
"transformers"
] | 2026-01-24T07:26:46Z | https://github.com/kyegomez/MambaDecoderBlock | null |
[
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"forecasting",
"multivariate-forecasting"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"timeseries.multivariate"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-12-01T14:12:57Z | This repository contains the code and models for our paper "Adaptive wavelet decomposition and event-aware high-frequency modeling network for multivariate time series forecasting". | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 0 | 1,107,714,337 | null | null | null | [
"forecasting",
"multivariate-forecasting"
] | Gongmd/AweHF | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2025-12-01T14:12:57Z | [
"timeseries.multivariate"
] | 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 | [] | 2025-12-01T14:13:01Z | https://github.com/Gongmd/AweHF | 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 | [] | 2025-12-01T22:08:19Z | Official implementation of the paper ChefNMR: "Atomic Diffusion Models for Small Molecule Structure Elucidation from NMR Spectra" (NeurIPS 2025). | [
"generative-ai",
"generative-modeling"
] | [
"machine-learning",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-26T17:25:59Z | false | 2 | 1,107,994,065 | null | Python | MIT | [
"diffusion"
] | ml-struct-bio/chefnmr | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-26T17:25:59Z | [] | 2025-12-11T17:55:42Z | [
"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 | 25 | [] | 2026-09-11T07:59:23Z | https://github.com/ml-struct-bio/chefnmr | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-12-02T03:06:51Z | Code for paper " Fourier spatial attention guided diffusion model for illumination optimization in endoscopic images " | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-28T17:27:44Z | false | 0 | 1,108,106,116 | null | null | null | [
"diffusion"
] | yangh0119/FSADiff- | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-28T17:27:44Z | [] | 2025-12-02T03:07:14Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2025-12-22T11:19:46Z | https://github.com/yangh0119/FSADiff- | null |
[
"generative-ai",
"language",
"multimodal-learning",
"natural-language-processing",
"speech-and-audio"
] | [
"fine-tuning",
"instruction-tuning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.instruction-tuning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-12-03T06:01:39Z | [NeurIPS'25 Spotlight] Official implementation of "JavisGPT: A Unified Multi-modal LLM for Sounding-Video Comprehension and Generation" | [
"generative-ai",
"language",
"multimodal-learning",
"natural-language-processing",
"speech-and-audio"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-31T16:49:26Z | false | 6 | 1,108,943,947 | https://javisverse.github.io/JavisGPT-page/ | Python | Apache-2.0 | [
"fine-tuning",
"instruction-tuning"
] | JavisVerse/JavisGPT | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-31T16:49:26Z | [] | 2026-02-26T12:38:06Z | [
"llm.instruction-tuning"
] | 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 | 77 | [
"audio-video",
"audiovisual-synchronization",
"audiovisual-uderstanding",
"joint-audio-video-generation",
"mllm",
"multimodal",
"multimodal-large-language-models",
"sounding-video-generation",
"unified-mllm"
] | 2026-10-28T11:38:44Z | https://github.com/JavisVerse/JavisGPT | null |
[
"forecasting",
"generative-modeling",
"time-series",
"time-series-and-forecasting"
] | [
"diffusion",
"probabilistic-forecasting",
"uncertainty-estimation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"timeseries.probabilistic-forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-12-03T08:26:06Z | This repository contains the official implementation of the paper "DiffWPF: A Diffusion-Based Model for Multi-Step Probabilistic Wind Power Forecasting". | [
"forecasting",
"generative-modeling",
"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-06T17:10:23Z | false | 0 | 1,109,021,300 | null | Jupyter Notebook | null | [
"diffusion",
"probabilistic-forecasting",
"uncertainty-estimation"
] | tiany0605/DiffWPF | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-08T18:15:14Z | [] | 2025-12-03T08:34:27Z | [
"timeseries.probabilistic-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 | 3 | [] | 2026-09-12T08:48:28Z | https://github.com/tiany0605/DiffWPF | null |
[
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"neural-network",
"neural-network-pruning",
"pruning",
"structured-pruning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-12-03T08:52:53Z | LiePrune is a Lie group structured & quantum geometry empowered neural network structured pruning paradigm, which establishes a novel network compression paradigm grounded in operator functional equivalence and manifold representation theory. | [
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-11-01T17:09:41Z | false | 0 | 1,109,036,710 | null | Python | null | [
"neural-network",
"neural-network-pruning",
"pruning",
"structured-pruning"
] | Harmenlv/lieprune | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-11-01T17:09:41Z | [] | 2026-09-16T08:27:54Z | [
"efficiency.pruning"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 1 | [] | 2026-09-16T08:27:58Z | https://github.com/Harmenlv/lieprune | 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 | [] | 2025-12-04T10:30:12Z | Code for paper "Diffusion Models Enable High-Fidelity Prediction of Fuel Cell Impedance Spectrum from Short Time-Domain Profiles" | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-26T17:25:59Z | false | 1 | 1,109,822,552 | null | Python | MIT | [
"diffusion"
] | yuanhao911/ISDiffM | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-26T17:25:59Z | [] | 2026-01-06T14:28:48Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 4 | [] | 2026-02-13T08:54:01Z | https://github.com/yuanhao911/ISDiffM | null |
[
"autonomous-driving",
"reinforcement-learning",
"robotics",
"robotics-and-control",
"safety"
] | [
"constrained-reinforcement-learning",
"perception",
"planning",
"reinforcement-learning",
"safe-reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"rl.safe",
"robotics.autonomous-driving"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-12-04T13:36:40Z | Code for Paper "Lyapunov-Guided Safe Reinforcement Learning for High-Speed Autonomous Driving with Stability Guarantees" | [
"autonomous-driving",
"reinforcement-learning",
"robotics",
"robotics-and-control"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 0 | 1,109,931,441 | null | Python | null | [
"perception",
"planning",
"reinforcement-learning"
] | upballoon/Lyapunov-Guided-SRL-for-High-Speed-Autonomous-Driving-with-Stability-Guarantees | [
"description",
"query-match",
"repository-metadata"
] | 4 | 2026-10-17T13:17:44Z | [] | 2025-12-04T14:14:07Z | [
"robotics.autonomous-driving"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2025-12-04T14:14:11Z | https://github.com/upballoon/Lyapunov-Guided-SRL-for-High-Speed-Autonomous-Driving-with-Stability-Guarantees | null |
[
"generative-ai",
"generative-modeling",
"graph-learning",
"time-series"
] | [
"diffusion",
"dynamic-graph-learning",
"graph-diffusion",
"graph-generation",
"temporal-graph-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.graph-diffusion",
"graph.temporal-learning",
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-12-06T07:45:26Z | This repository contains the implementation and experimental framework for our research paper: Sentiment-Aware Competitive Graph Diffusion Model for Negative Information Control in Online Social Networks | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 0 | 1,111,067,114 | null | Python | null | [
"diffusion"
] | Aman18sh/SocialNetworkAnalysis | [
"description",
"query-match",
"repository-metadata"
] | 7 | 2026-10-28T17:27:44Z | [] | 2025-12-06T07:50:19Z | [
"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 | [] | 2026-06-16T11:34:45Z | https://github.com/Aman18sh/SocialNetworkAnalysis | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-12-06T11:34:41Z | Official implementation of paper 'DiffLOB: Diffusion Models for Counterfactual Generation in Limit Order Books' | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-26T17:25:59Z | false | 3 | 1,111,155,239 | null | Python | null | [
"diffusion"
] | ZhuoHan1998/DiffLOB | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-26T17:25:59Z | [] | 2026-07-13T18:55:57Z | [
"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 | 14 | [] | 2026-09-17T09:31:17Z | https://github.com/ZhuoHan1998/DiffLOB | null |
[
"generative-ai",
"language",
"multimodal-learning",
"speech-and-audio",
"time-series-and-forecasting"
] | [
"fine-tuning",
"parameter-efficient-fine-tuning",
"transformer"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.parameter-efficient-finetuning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-12-07T15:43:49Z | Research on a novel multimodal transformer architecture (Text, Audio, Time-series) for industrial reliability. | [
"generative-ai",
"language",
"multimodal-learning",
"speech-and-audio",
"time-series-and-forecasting"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 0 | 1,111,766,032 | null | TypeScript | null | [
"fine-tuning",
"parameter-efficient-fine-tuning",
"transformer"
] | danimelatru/Multimodal-Foundation-Model-Architecture-Research | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-11-02T21:04:38Z | [] | 2025-12-14T17:28:26Z | [
"llm.parameter-efficient-finetuning"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 6 | [] | 2026-01-27T17:58:50Z | https://github.com/danimelatru/Multimodal-Foundation-Model-Architecture-Research | null |
[
"computer-vision",
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-12-08T09:06:09Z | Official implementation for TCSVT paper: "DM3D: Lightweight and Distortion Minimized Weight Pruning for 3D Object Detection" | [
"computer-vision",
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-11-01T17:09:41Z | false | 0 | 1,112,179,482 | null | null | null | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | xuk114/DM3D | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-11-01T17:09:41Z | [] | 2025-12-08T09:06:29Z | [
"efficiency.pruning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2025-12-08T09:06:33Z | https://github.com/xuk114/DM3D | 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 | [] | 2025-12-08T13:38:10Z | Official code for the NLDL 2026 paper titled "Wildfire Spread Scenarios: Increasing Sample Diversity of Segmentation Diffusion Models with Training-Free Methods" | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-28T17:27:44Z | false | 0 | 1,112,337,424 | null | Python | NOASSERTION | [
"diffusion"
] | SebastianGer/wildfire-spread-scenarios | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-28T17:27:44Z | [] | 2026-03-18T14:33:42Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-03-18T14:33:48Z | https://github.com/SebastianGer/wildfire-spread-scenarios | null |
[
"autonomous-driving",
"computer-vision",
"embodied-ai",
"language",
"multimodal",
"multimodal-learning",
"reinforcement-learning",
"robotics",
"robotics-and-control",
"vision"
] | [
"contrastive-learning",
"perception",
"planning",
"reinforcement-learning",
"robot-learning",
"vision-language-action",
"vision-language-model"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.vision-language",
"robotics.autonomous-driving",
"robotics.visual-language-action"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-12-10T03:32:28Z | [ECCV 2026] Official code of “MindDrive: A Vision-Language-Action Model for Autonomous Driving via Online Reinforcement Learning” | [
"autonomous-driving",
"computer-vision",
"multimodal-learning",
"reinforcement-learning",
"robotics",
"robotics-and-control"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 26 | 1,113,484,458 | null | Python | Apache-2.0 | [
"perception",
"planning",
"reinforcement-learning"
] | xiaomi-mlab/MindDrive | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 11 | 2026-10-15T15:20:52Z | [] | 2026-06-23T02:21:07Z | [
"robotics.autonomous-driving"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 276 | [] | 2026-10-13T13:08:51Z | https://github.com/xiaomi-mlab/MindDrive | null |
[
"mathematics",
"operations-research"
] | [
"combinatorial-optimization",
"neural-network"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"gap2026.math-neural-combinatorial-optimization"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-12-10T18:20:13Z | Official implementation of the paper [Neural Combinatorial Optimization for Time Dependent Traveling Salesman Problem] | [
"mathematics",
"operations-research"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-25T16:01:25Z | false | 0 | 1,113,979,475 | https://neurips.cc/virtual/2025/loc/san-diego/poster/117758 | Python | null | [
"combinatorial-optimization",
"neural-network"
] | Brelliothe/NCO4TDTSP | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 2 | 2026-09-29T17:12:21Z | [] | 2025-12-12T14:57:28Z | [
"gap2026.math-neural-combinatorial-optimization"
] | 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 | [
"neural-combinatorial-optimization",
"neurips-2025",
"time-dependent",
"traveling-"
] | 2026-07-13T16:36:51Z | https://github.com/Brelliothe/NCO4TDTSP | null |
[
"reinforcement-learning"
] | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-12-12T09:04:16Z | Code for our ICLR 2026 paper "Understanding and Improving Hyperbolic Deep Reinforcement Learning" | [
"reinforcement-learning"
] | [
"machine-learning",
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 0 | 1,115,044,928 | null | Python | null | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | Probabilistic-and-Interactive-ML/hyper-rl | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-10-30T17:16:29Z | [] | 2026-04-07T08:56:10Z | [
"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 | 12 | [] | 2026-10-22T06:35:41Z | https://github.com/Probabilistic-and-Interactive-ML/hyper-rl | 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 | [] | 2025-12-12T10:43:23Z | Official code for the paper "DifuzCam: replacing camera lens with a mask and a diffusion model for generative AI based flat camera design", by Erez Yosef and Raja Giryes | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-28T17:27:44Z | false | 0 | 1,115,101,449 | null | Python | Apache-2.0 | [
"diffusion"
] | ErezYosef/Difuzcam-AI | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-28T17:27:44Z | [] | 2025-12-16T13:53:21Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 2 | [] | 2026-08-19T06:17:37Z | https://github.com/ErezYosef/Difuzcam-AI | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"multimodal",
"vision"
] | [
"diffusion",
"generative-modeling",
"image-generation",
"text-to-image"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"multimodal.text-to-image",
"vision.image-diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-12-12T12:26:47Z | [Arxiv 2025] Official PyTorch Implementation of "SVG-T2I: Scaling up Text-to-Image Latent Diffusion Model Without Variational Autoencoder". | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T17:03:46Z | false | 7 | 1,115,156,359 | null | Python | Apache-2.0 | [
"diffusion"
] | KlingAIResearch/SVG-T2I | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 10 | 2026-10-26T17:25:59Z | [] | 2025-12-18T07:17:15Z | [
"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 | 155 | [] | 2026-10-22T03:40:54Z | https://github.com/KlingAIResearch/SVG-T2I | 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",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning",
"multimodal.vision-language",
"multimodal.vlm"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-12-13T13:04:07Z | Official implementation of the paper: Attention Debiasing for Token Pruning in Vision–Language Models. | [
"computer-vision",
"efficient-ml",
"machine-learning-systems",
"model-compression",
"multimodal-learning",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 1 | 1,115,739,133 | null | Python | Apache-2.0 | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | intcomp/attention-bias | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-10-31T16:49:26Z | [] | 2026-01-19T08:41:21Z | [
"efficiency.pruning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 10 | [] | 2026-07-02T02:15:11Z | https://github.com/intcomp/attention-bias | 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 | [] | 2025-12-14T12:38:11Z | Official code for the paper "SITS-Diff: Self-Informed Textual Guidance and Structure-Aware Diffusion Model for Arbitrary-Dose PET Denoising". | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-28T17:27:44Z | false | 0 | 1,116,215,358 | null | null | Apache-2.0 | [
"diffusion"
] | Long0121/SITS-Diff | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-28T17:27:44Z | [] | 2025-12-14T14:05:21Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2025-12-14T14:09:10Z | https://github.com/Long0121/SITS-Diff | null |
[
"embodied-ai",
"generative-modeling",
"robotics"
] | [
"diffusion",
"diffusion-policy",
"imitation-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"robotics.diffusion-policy"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-12-15T07:32:04Z | [ICLR 2026] The official implementation of "Dichotomous Diffusion Policy Optimization" | [
"embodied-ai",
"generative-modeling",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 5 | 1,116,654,502 | https://lrmbbj.github.io/DIPOLE/ | null | null | [
"diffusion",
"diffusion-policy",
"imitation-learning"
] | LRMbbj/DIPOLE | [
"description",
"query-match",
"repository-metadata"
] | 5 | 2026-09-27T15:53:49Z | [] | 2026-05-02T14:52:28Z | [
"robotics.diffusion-policy"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 46 | [] | 2026-08-09T10:16:24Z | https://github.com/LRMbbj/DIPOLE | null |
[
"forecasting",
"general-ml",
"generative-modeling",
"time-series",
"time-series-and-forecasting"
] | [
"diffusion",
"probabilistic-forecasting",
"research-implementation",
"uncertainty-estimation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.research-code",
"timeseries.probabilistic-forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-12-15T23:09:00Z | Code for paper on AI Ensemble Forecasting | [
"forecasting",
"generative-modeling",
"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 0 | 1,117,157,770 | null | Jupyter Notebook | MIT | [
"diffusion",
"probabilistic-forecasting",
"uncertainty-estimation"
] | kruparell/Ensemble_Forecasting-Rollout-and-Diffusion | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-10T16:53:26Z | [] | 2026-01-13T16:04:01Z | [
"timeseries.probabilistic-forecasting"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-04-10T20:16:41Z | https://github.com/kruparell/Ensemble_Forecasting-Rollout-and-Diffusion | null |
[
"computer-vision",
"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 | [] | 2025-12-16T07:43:22Z | Official implementation of "RS-SSM: Refining Forgotten Specifics in State Space Model for Video Semantic Segmentation" [CVPR2026] | [
"computer-vision",
"sequence-modeling",
"time-series"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-03T15:45:52Z | false | 1 | 1,117,355,757 | null | Python | null | [
"sequence-modeling",
"state-space-model"
] | zhoujiahuan1991/CVPR2026-RS-SSM | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-03T15:45:52Z | [] | 2026-03-30T04:26:12Z | [
"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 | 6 | [] | 2026-09-22T02:36:12Z | https://github.com/zhoujiahuan1991/CVPR2026-RS-SSM | null |
[
"computer-vision",
"embodied-ai",
"language",
"multimodal",
"multimodal-learning",
"robotics",
"vision"
] | [
"contrastive-learning",
"robot-learning",
"vision-language-action",
"vision-language-model"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.vision-language",
"robotics.visual-language-action"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-12-16T07:44:16Z | About Official implementation of the CVPR 2026 paper "Test-Time Perturbation Tuning with Delayed Feedback for Vision-Language-Action Models" | [
"computer-vision",
"language",
"multimodal",
"multimodal-learning",
"vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T14:50:46Z | false | 1 | 1,117,356,201 | null | Python | MIT | [
"contrastive-learning",
"vision-language-model"
] | zhoujiahuan1991/CVPR2026-PDF | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-15T15:20:52Z | [] | 2026-05-25T16:01:23Z | [
"multimodal.vision-language"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 8 | [] | 2026-09-28T13:43:45Z | https://github.com/zhoujiahuan1991/CVPR2026-PDF | null |
[
"sequence-modeling",
"time-series"
] | [
"sequence-modeling",
"state-space-model"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"timeseries.state-space-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-12-16T08:49:44Z | This is an implementation of our paper: Multi-Expert Learning Framework with the State Space Model for Optical and SAR Image Registration | [
"sequence-modeling",
"time-series"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-03T15:45:52Z | false | 1 | 1,117,391,184 | null | Python | null | [
"sequence-modeling",
"state-space-model"
] | Miraitowa515/ME-SSM | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-03T15:45:52Z | [] | 2026-06-11T10:12:47Z | [
"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",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 6 | [] | 2026-08-30T01:32:03Z | https://github.com/Miraitowa515/ME-SSM | null |
[
"computer-vision",
"interpretability-and-safety"
] | [
"mamba",
"mixture-of-experts"
] | [
"description",
"github-topics",
"repository-metadata"
] | [] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-12-18T09:54:45Z | [AAAI 2026] This is the official implementation of ”Seeing in Double: Dual-Granularity BEV Segmentation via Mamba-Driven Alignment and Polar-Decoupled Experts” | [
"computer-vision",
"interpretability-and-safety"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-02T18:08:49.004055Z | false | 0 | 1,118,804,334 | null | null | null | [
"mamba",
"mixture-of-experts"
] | imcjx/SiD | [
"description",
"github-topics",
"repository-metadata"
] | 1 | 2026-10-02T18:08:49.004055Z | [] | 2025-12-18T10:43:23Z | [] | 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 | [
"bev",
"birds-eye-view",
"computer-vision",
"dual-resolution",
"mamba",
"mixture-of-experts",
"semantic-segmentation"
] | 2025-12-23T06:55:26Z | https://github.com/imcjx/SiD | {"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["mixture-of-experts"]} |
[
"graph-learning",
"language",
"reinforcement-learning"
] | [
"agent",
"graph-neural-network",
"neural-network",
"tool-use",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.agents"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-12-18T15:35:30Z | We present GAHT (Geo-Aware Hybrid Transformer), a novel deep learning architecture that combines E(n)-equivariant graph neural networks with Transformer attention mechanisms for hepatotoxicity prediction. | [
"graph-learning",
"language",
"reinforcement-learning"
] | [
"deep-learning",
"neural-network",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-30T17:16:29Z | false | 0 | 1,118,999,544 | https://hapatox.onrender.com/ | HTML | MIT | [
"agent",
"graph-neural-network",
"neural-network",
"tool-use",
"transformer"
] | suraj187104/hapatox | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-30T17:16:29Z | [] | 2025-12-20T15:42:40Z | [
"llm.agents"
] | 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 | [
"ai-agents",
"heathcare",
"reseach-development"
] | 2026-04-14T08:02:04Z | https://github.com/suraj187104/hapatox | 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 | [] | 2025-12-18T22:13:16Z | [TMLR 2026] The official implementation of the paper "Diffusion Models are Secretly Zero-Shot 3DGS Harmonizers" | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-26T17:25:59Z | false | 1 | 1,119,187,531 | null | Jupyter Notebook | Apache-2.0 | [
"diffusion"
] | sevashasla/D3DR | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-26T17:25:59Z | [] | 2026-06-20T15:28:29Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 10 | [] | 2026-09-15T05:44:27Z | https://github.com/sevashasla/D3DR | null |
[
"information-retrieval",
"natural-language-processing"
] | [
"dense-retrieval",
"distillation",
"knowledge-distillation",
"learning-to-rank",
"neural-reranking"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"nlp.dense-retrieval",
"nlp.neural-reranking"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-12-19T19:43:58Z | Provision-level retrieval and neural reranking for UK legislation, with an open distilled ModernBERT reranker. Official code for the Artificial Intelligence and Law (2026) paper. | [
"information-retrieval",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 0 | 1,119,755,268 | https://doi.org/10.1007/s10506-025-09501-6 | Python | MIT | [
"dense-retrieval",
"distillation",
"knowledge-distillation"
] | alshehriamal1994/uk-statute-retrieval | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 6 | 2026-10-14T16:10:46Z | [] | 2026-06-07T16:52:47Z | [
"nlp.dense-retrieval"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [
"knowledge-distillation",
"legal-information-retrieval",
"legal-nlp",
"modernbert",
"reranking",
"statutory-retrieval",
"uk-legislation"
] | 2026-06-07T16:53:08Z | https://github.com/alshehriamal1994/uk-statute-retrieval | null |
[
"biology",
"computational-biology",
"deep-learning",
"graph-learning",
"representation-learning",
"transcriptomics"
] | [
"contrastive-learning",
"gene-expression-modeling",
"graph-contrastive-learning",
"representation-learning",
"self-supervised-learning",
"spatial-modeling"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"bio.spatial-transcriptomics",
"bio.transcriptomics",
"general.contrastive-learning",
"graph.graph-contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-12-20T02:58:19Z | The official implementation of the paper "SpaDGC: A Dynamic Graph Contrastive Learning Framework for Spatial Transcriptomics Analysis" (Accepted by BIBM 2026) | [
"biology",
"computational-biology",
"transcriptomics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 0 | 1,119,879,072 | null | Python | null | [
"contrastive-learning",
"gene-expression-modeling",
"representation-learning",
"spatial-modeling"
] | NozomiMizore/SpaDGC | [
"description",
"query-match",
"repository-metadata"
] | 6 | 2026-10-21T15:48:06Z | [] | 2026-09-26T09:47:11Z | [
"bio.spatial-transcriptomics"
] | 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 | [] | 2026-09-30T23:56:08Z | https://github.com/NozomiMizore/SpaDGC | null |
[
"artificial-intelligence",
"general-ml",
"generative-ai",
"health-and-biomedicine",
"interpretability-and-safety",
"language",
"machine-learning",
"natural-language-processing"
] | [
"fine-tuning",
"interpretability",
"language-model",
"language-modeling",
"mechanistic-interpretability",
"model-adaptation",
"parameter-efficient-fine-tuning",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.finetuning",
"llm.language-model",
"llm.parameter-efficient-finetuning",
"nlp.language-modeling",
"probable.adaptation-finetuning-readme",
"recall.name.language-model",
"recall.name.transformer-model",
"specialized.mechanistic-interpretability"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-12-21T01:03:26Z | Official implementation of the paper "Mechanistic Interpretability of Fine-Tuned Protein Language Models for Nanobody Thermostability Prediction". | [
"artificial-intelligence",
"health-and-biomedicine",
"interpretability-and-safety",
"machine-learning",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T09:41:18Z | false | 0 | 1,120,328,737 | null | Jupyter Notebook | GPL-3.0 | [
"interpretability",
"mechanistic-interpretability"
] | matsunagalab/paper_nanobody-thermostability-sae | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 8 | 2026-10-10T16:53:26Z | [] | 2026-09-24T01:57:17Z | [
"specialized.mechanistic-interpretability"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-09-24T01:57:19Z | https://github.com/matsunagalab/paper_nanobody-thermostability-sae | null |
[
"automl",
"general-ml"
] | [
"few-shot-learning",
"meta-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.meta-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-12-22T02:47:28Z | The official code repository of the paper Explainable Spatial-Temporal-Spectral Meta-Learning for Subject-Independent EEG-based Emotion Recognition | [
"automl",
"general-ml"
] | [
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T17:43:44Z | false | 2 | 1,120,833,863 | null | Python | null | [
"few-shot-learning",
"meta-learning"
] | ZhiqiZeying/STS-ML | [
"description",
"query-match",
"repository-metadata"
] | 4 | 2026-11-02T21:04:38Z | [] | 2026-03-17T09:11:30Z | [
"general.meta-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 4 | [] | 2026-09-18T03:24:31Z | https://github.com/ZhiqiZeying/STS-ML | null |
[
"deep-learning",
"representation-learning"
] | [
"representation-learning",
"self-supervised-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.self-supervised-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2025-12-22T14:22:37Z | The official implementations of the Paper "SSL4RL: Revisiting Self-supervised Learning as Intrinsic Reward for Visual-Language Reasoning" | [
"deep-learning",
"representation-learning"
] | [
"machine-learning",
"self-supervised-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 1 | 1,121,154,650 | null | Python | Apache-2.0 | [
"representation-learning",
"self-supervised-learning"
] | PKU-ML/SSL4RL | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2025-12-23T06:29:42Z | [
"general.self-supervised-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 2 | [] | 2026-03-09T09:41:32Z | https://github.com/PKU-ML/SSL4RL | null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.