all_domains list | all_methods list | all_novelty_signals list | all_query_ids list | archived bool | candidate_status string | candidate_rule_version string | candidate_eligible bool | candidate_reason string | candidate_evidence list | created_at string | description string | domains list | evidence_signals list | evidence_tier string | evidence_version string | first_observed_at string | fork bool | forks int64 | github_id int64 | homepage string | language string | license string | methods list | name string | novelty_signals list | observation_count int64 | observed_at string | paper_ids list | pushed_at string | query_ids list | readme_blob_sha string | readme_checked_at string | readme_evidence_version string | readme_etag string | readme_sections list | readme_signals list | readme_status string | readme_observed_at string | readme_repository_name_at_fetch string | selection_reason string | selection_signals list | selection_status string | selection_version string | stars int64 | topics list | updated_at string | url string | extra_json string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-01-10T16:44:16Z | Implementation and improvement of a research paper with a new theory for image deepfake detection with high generalization to include General Adversarial Networks, Diffusion Models and any other new generative techniques | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-10T16:53:26Z | false | 0 | 741,560,925 | null | Jupyter Notebook | null | [
"diffusion"
] | coderbeta1/Image_Deepfake_Detection | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-10T16:53:26Z | [] | 2024-02-02T13:10:27Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
"contribution-language",
"method-tied-novelty-claim",
"ml-method-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 5 | [] | 2025-12-14T04:12:22Z | https://github.com/coderbeta1/Image_Deepfake_Detection | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"multimodal"
] | [
"diffusion",
"generative-modeling",
"motion-generation"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.motion-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-01-11T15:27:10Z | [CVPR 2024] Official Implementation of "Seamless Human Motion Composition with Blended Positional Encodings". | [
"computer-vision",
"generative-ai",
"generative-modeling",
"multimodal"
] | [
"generative-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 14 | 742,013,413 | https://barquerogerman.github.io/FlowMDM/ | Python | NOASSERTION | [
"diffusion",
"generative-modeling",
"motion-generation"
] | BarqueroGerman/FlowMDM | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-01T17:35:19Z | [
"2402.15509"
] | 2024-05-22T08:52:05Z | [
"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 | 278 | [
"cvpr",
"cvpr2024",
"diffusion",
"generative-model",
"human-motion",
"human-motion-composition",
"human-motion-extrapolation",
"motion-generation"
] | 2026-09-30T17:53:27Z | https://github.com/BarqueroGerman/FlowMDM | null |
[
"applied-mathematics",
"applied-physics",
"computational-science",
"generative-modeling",
"health-and-biomedicine",
"medical-imaging",
"radiology"
] | [
"data-assimilation",
"deep-learning",
"diffusion",
"image-reconstruction",
"inverse-problems",
"reconstruction"
] | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"medical.medical-image-reconstruction",
"science.inverse-problems"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-01-12T01:42:05Z | Official PyTorch implementation for Decomposed Diffusion Sampling (DDS), presented in the paper "Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problems" (https://arxiv.org/abs/2303.05754). | [
"applied-mathematics",
"applied-physics",
"computational-science",
"generative-modeling",
"health-and-biomedicine",
"medical-imaging",
"radiology"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 6 | 742,214,821 | null | Python | null | [
"data-assimilation",
"deep-learning",
"diffusion",
"image-reconstruction",
"inverse-problems",
"reconstruction"
] | hyungjin-chung/DDS | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | 7 | 2026-10-01T17:35:19Z | [] | 2024-09-26T01:37:08Z | [
"medical.medical-image-reconstruction",
"science.inverse-problems"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 85 | [] | 2026-09-25T08:39:58Z | https://github.com/hyungjin-chung/DDS | null |
[
"generative-ai",
"generative-modeling",
"health-and-biomedicine",
"medical-imaging",
"radiology"
] | [
"deep-learning",
"diffusion",
"image-reconstruction",
"inverse-problems",
"transformer"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"medical.medical-image-reconstruction"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-01-12T02:15:21Z | Official PyTorch implementation of DiffGAN described in the paper ( DiffGAN: An adversarial diffusion model with local transformer for MRI reconstruction). | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model",
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 0 | 742,223,409 | https://www.sciencedirect.com/science/article/abs/pii/S0730725X24000730 | null | null | [
"diffusion",
"transformer"
] | learnerzx/DiffGAN | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-10T16:53:26Z | [] | 2024-04-09T03:15:36Z | [
"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 | [] | 2024-05-22T12:38:35Z | https://github.com/learnerzx/DiffGAN | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-01-12T13:11:47Z | [ACM MM 2023] Official implementation of "Hierarchical Masked 3D Diffusion Model for Video Outpainting" | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-30T18:03:22.392067Z | false | 7 | 742,430,195 | https://fanfanda.github.io/M3DDM/ | Python | Apache-2.0 | [
"diffusion"
] | alimama-creative/M3DDM-Video-Outpainting | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-08T18:15:14Z | [] | 2024-05-06T10:36:15Z | [
"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"
] | include | ml-contribution-v5 | 107 | [
"diffusion-models",
"stable-diffusion",
"video-generation",
"video-outpainting"
] | 2026-06-23T18:46:20Z | https://github.com/alimama-creative/M3DDM-Video-Outpainting | null |
[
"machine-learning"
] | [
"optimization",
"sharpness-aware-minimization"
] | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"specialized26.sharpness-aware-minimization"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-01-15T06:06:38Z | Official PyTorch implementation of "Fundamental Convergence Analysis of Sharpness-Aware Minimization" (NeurIPS 2024) | [
"machine-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T22:33:16Z | false | 0 | 743,390,201 | https://arxiv.org/pdf/2401.08060 | Python | MIT | [
"optimization",
"sharpness-aware-minimization"
] | lhchau/fundamental-sam | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-09-26T22:33:16Z | [] | 2025-02-21T13:47:51Z | [
"specialized26.sharpness-aware-minimization"
] | df375163b5b861de79cdae7927caa3b0dc46d9ad | 2026-09-29T18:47:14Z | gh-ml-readme-evidence-v2 | "df375163b5b861de79cdae7927caa3b0dc46d9ad" | [
"abstract",
"installation",
"other",
"usage"
] | [
"method-contribution",
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-09-29T18:47:14Z | lhchau/fundamental-sam | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 5 | [] | 2026-04-18T19:09:32Z | https://github.com/lhchau/fundamental-sam | null |
[
"distributed-ml",
"privacy-and-federated-learning"
] | [
"collaborative-learning",
"federated-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-01-15T06:50:37Z | [TIST 2023] Official Code for our TIST Paper: Watermarking in Secure Federated Learning: A Verification Framework Based on Client-Side Backdooring | [
"distributed-ml",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-11T16:48:52Z | false | 0 | 743,402,578 | null | Python | null | [
"collaborative-learning",
"federated-learning"
] | shaoshuo-ss/Watermark-Secure-FL | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-11T16:48:52Z | [] | 2024-11-05T02:22:16Z | [
"trust.federated-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 5 | [] | 2026-03-01T06:20:01Z | https://github.com/shaoshuo-ss/Watermark-Secure-FL | null |
[
"efficient-ml",
"model-compression"
] | [
"distillation",
"knowledge-distillation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-01-16T11:34:39Z | code for paper: Modality Fused Class-Proxy with Knowledge Distillation for Zero-Shot Sketch-based Image Retrieval | [
"efficient-ml",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 0 | 743,975,321 | null | Python | MIT | [
"distillation",
"knowledge-distillation"
] | li1changxing/MFCPKD | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-02T16:59:40Z | [] | 2025-06-01T17:46:52Z | [
"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 | [] | 2026-07-31T04:09:45Z | https://github.com/li1changxing/MFCPKD | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"multimodal"
] | [
"diffusion",
"generative-modeling",
"multimodal-learning",
"vision-language"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"vision.image-diffusion",
"vision.vision-language"
] | true | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-01-16T14:39:10Z | Official implementation of "Adversarial Supervision Makes Layout-to-Image Diffusion Models Thrive" (ICLR 2024) | [
"computer-vision",
"generative-ai",
"generative-modeling",
"multimodal"
] | [
"generative-model",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T16:30:43Z | false | 4 | 744,053,571 | https://yumengli007.github.io/ALDM/ | Jupyter Notebook | AGPL-3.0 | [
"diffusion",
"multimodal-learning",
"vision-language"
] | boschresearch/ALDM | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-09T17:31:36Z | [] | 2024-09-03T18:13:28Z | [
"llm.diffusion-phrase",
"vision.vision-language"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 58 | [
"aigc",
"bcai",
"controlnet",
"diffusion-models",
"generative-ai",
"generative-model",
"iclr2024",
"layout-to-image",
"paper-resource",
"semantic-image-synthesis",
"stable-diffusion"
] | 2026-02-19T08:50:43Z | https://github.com/boschresearch/ALDM | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-01-17T13:53:43Z | An official PyTorch implementation of "GNNCert: Deterministic Certification of Graph Neural Networks against Adversarial Perturbations" (ICLR 2024). | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T16:30:43Z | false | 4 | 744,534,322 | null | Python | MIT | [
"graph-neural-network",
"message-passing",
"neural-network"
] | XiaFire/GNNCERT | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-12T20:31:19Z | [] | 2024-04-04T08:35:55Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 10 | [] | 2026-05-19T13:23:23Z | https://github.com/XiaFire/GNNCERT | null |
[
"general-ml",
"generative-modeling"
] | [
"diffusion",
"novel-method"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.novel-method"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-01-18T02:31:28Z | A novel method for data augmentation using Stable Diffusion and DinoV2 | [
"general-ml",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 0 | 744,793,375 | null | null | null | [
"diffusion",
"novel-method"
] | sina973/ControlNet-Data-Augmentation | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-02T16:44:32Z | [] | 2024-01-18T02:31:29Z | [
"general.novel-method"
] | f6ef922db448d2a2102250031b553b91ec052f22 | 2026-09-27T17:16:20Z | gh-ml-readme-evidence-v2 | "f6ef922db448d2a2102250031b553b91ec052f22" | [
"other"
] | [
"method-contribution",
"ml-method-context"
] | ok | 2026-09-27T17:16:20Z | sina973/ControlNet-Data-Augmentation | specific-method-with-novelty-claim | [
"method-contribution",
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [] | 2024-01-18T02:31:29Z | https://github.com/sina973/ControlNet-Data-Augmentation | null |
[
"generative-ai",
"language",
"natural-language-processing"
] | [
"language-model",
"pretraining"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.language-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-01-18T15:22:33Z | Official implementation of the EACL Findings 2024 paper: Chem-FINESE: Validating Fine-Grained Few-shot Entity Extraction through Text Reconstruction | [
"generative-ai",
"language",
"natural-language-processing"
] | [
"large-language-model",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-06T17:10:23Z | false | 1 | 745,066,531 | https://aclanthology.org/2024.findings-eacl.1.pdf | Python | MIT | [
"language-model",
"pretraining"
] | EagleW/Chem-FINESE | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-06T17:10:23Z | [] | 2024-03-18T20:22:59Z | [
"llm.language-models"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 7 | [
"chemical-data",
"chemical-information-extraction",
"constrastive-learning",
"datasets",
"few-shot",
"few-shot-learning",
"information-extraction",
"large-language-models",
"low-resource-nlp",
"pytorch",
"reconstruction",
"transformers"
] | 2025-06-27T19:56:11Z | https://github.com/EagleW/Chem-FINESE | null |
[
"reinforcement-learning"
] | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-01-18T20:31:35Z | Code for paper on economic battery dispatch with deep reinforcement learning (under review). | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T16:30:43Z | false | 5 | 745,194,525 | null | Python | MIT | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | masa2203/battery-dispatch-with-DRL | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-23T15:59:01Z | [] | 2025-12-03T14:05:53Z | [
"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 | 14 | [] | 2026-07-30T21:39:20Z | https://github.com/masa2203/battery-dispatch-with-DRL | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-01-19T06:21:12Z | Official code for SA-Solver: Stochastic Adams Solver for Fast Sampling of Diffusion Models (NeurIPS 2023) | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-09T17:31:36Z | false | 1 | 745,348,439 | null | Python | MIT | [
"diffusion"
] | scxue/SA-Solver | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-09T17:31:36Z | [] | 2024-03-04T10:42:29Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 15 | [] | 2026-07-25T13:00:20Z | https://github.com/scxue/SA-Solver | null |
[
"forecasting",
"general-ml",
"generative-ai",
"language",
"linguistics",
"multimodal-learning",
"natural-language-processing",
"time-series",
"time-series-and-forecasting"
] | [
"deep-learning",
"forecasting",
"foundation-model",
"language-model",
"language-modeling",
"large-language-model",
"machine-learning",
"pretraining",
"sequence-modeling",
"time-series-forecasting"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"applied.time-series-forecasting",
"general.topic-deep-learning",
"general.topic-machine-learning",
"llm.language-model",
"llm.language-models",
"llm.large-language-model",
"nlp.language-modeling",
"recall.computational-linguistics",
"recall.name.foundation-model",
"recall.name.language-model",
... | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-01-20T01:26:30Z | [ICLR 2024] Official implementation of " 🦙 Time-LLM: Time Series Forecasting by Reprogramming Large Language Models" | [
"linguistics",
"multimodal-learning",
"natural-language-processing",
"time-series-and-forecasting"
] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:27:52Z | false | 464 | 745,732,093 | https://arxiv.org/abs/2310.01728 | Python | Apache-2.0 | [
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] | KimMeen/Time-LLM | [
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] | include | ml-contribution-v5 | 2,709 | [
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] | 2026-10-26T01:50:07Z | https://github.com/KimMeen/Time-LLM | null |
[
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] | Khaleesi-N/Z-Fold | [
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] | 3 | 2026-10-14T16:10:46Z | [] | 2024-01-24T16:56:08Z | [
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] | include | ml-contribution-v5 | 0 | [] | 2024-01-24T05:19:54Z | https://github.com/Khaleesi-N/Z-Fold | null |
[
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] | [
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] | [
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] | [
"recall.name.diffusion-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-01-24T07:39:15Z | [AAAI 2025] Official implementation of "OOTDiffusion: Outfitting Fusion based Latent Diffusion for Controllable Virtual Try-on" | [
"generative-ai",
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-25T16:01:25Z | false | 952 | 747,568,453 | null | Python | NOASSERTION | [
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] | levihsu/OOTDiffusion | [
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] | 6 | 2026-10-23T15:59:01Z | [
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] | 2024-05-13T08:50:07Z | [
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] | include | ml-contribution-v5 | 6,604 | [] | 2026-10-23T05:09:58Z | https://github.com/levihsu/OOTDiffusion | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-01-26T04:03:45Z | Official Code for “PriorCLIP: Visual Prior Guided Vision-Language Model for Remote Sensing Image-Text Retrieval” | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T20:57:28.489717Z | false | 4 | 748,482,313 | https://arxiv.org/abs/2405.10160 | Jupyter Notebook | MIT | [
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] | jaychempan/PriorCLIP | [
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] | 11 | 2026-10-22T16:09:07Z | [] | 2025-12-19T12:28:36Z | [
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] | [
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] | ok | 2026-09-29T18:47:14Z | jaychempan/PriorCLIP | readme-supported-paper-method-implementation | [
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] | include | ml-contribution-v5 | 31 | [
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] | 2026-10-16T13:57:01Z | https://github.com/jaychempan/PriorCLIP | null |
[
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] | [
"general.contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-01-26T07:44:42Z | The official implementation of the paper "Deep Clustering with Diffused Sampling and Hardness-aware Self-distillation" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-28T17:27:44Z | false | 0 | 748,537,718 | null | Python | MIT | [
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] | Regan-Zhang/HaDis | [
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] | 1 | 2026-10-28T17:27:44Z | [] | 2024-01-28T16:46:50Z | [
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] | include | ml-contribution-v5 | 7 | [] | 2024-11-28T02:04:38Z | https://github.com/Regan-Zhang/HaDis | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-01-26T11:31:32Z | Official implementation of "Graph Signal Diffusion Model for Collaborative Filtering" (SIGIR 2024) | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T15:29:21Z | false | 2 | 748,614,467 | null | Python | null | [
"collaborative-filtering",
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] | VinciZhu/GiffCF | [
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] | 12 | 2026-10-20T15:59:06Z | [] | 2024-05-31T05:39:13Z | [
"applied.collaborative-filtering"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 18 | [
"diffusion-model",
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"recommender-system"
] | 2026-10-10T11:40:13Z | https://github.com/VinciZhu/GiffCF | null |
[
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] | [
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] | [
"efficiency.mixture-of-experts"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-01-27T09:32:21Z | Code for paper "Merging Multi-Task Models via Weight-Ensembling Mixture of Experts" | [
"deep-learning",
"efficient-ml",
"language"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 2 | 748,998,815 | null | Jupyter Notebook | null | [
"conditional-computation",
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] | tanganke/weight-ensembling_MoE | [
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] | 2 | 2026-10-01T17:35:19Z | [] | 2024-06-07T08:05:42Z | [
"efficiency.mixture-of-experts"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 34 | [] | 2026-10-01T05:34:10Z | https://github.com/tanganke/weight-ensembling_MoE | null |
[
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] | [
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] | [
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"query-match",
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-01-28T05:32:47Z | The official code repository for our TGRS paper "DiffusionEMIS: Diffusion Model for 3D Electromagnetic Inverse Scattering". | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-09T17:31:36Z | false | 0 | 749,285,577 | null | Python | MIT | [
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] | Xueting-Bi/diffusionEMIS | [
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] | 1 | 2026-10-09T17:31:36Z | [] | 2024-03-18T03:53:28Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 18 | [] | 2026-08-11T00:44:06Z | https://github.com/Xueting-Bi/diffusionEMIS | null |
[
"explainable-ai",
"reinforcement-learning",
"trustworthy-ml"
] | [
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] | [
"description",
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"repository-metadata"
] | [
"trust.counterfactual-explanations"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-01-29T11:34:31Z | Code for paper RACCER:: Towards Reachable and Certain Counterfactual Explanations for Reinforcement Learning | [
"explainable-ai",
"reinforcement-learning",
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T14:50:46Z | false | 0 | 749,779,398 | null | Python | null | [
"counterfactual-explanations",
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] | jas97/RACCER | [
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] | 2 | 2026-10-01T17:35:19Z | [] | 2024-01-29T11:36:29Z | [
"trust.counterfactual-explanations"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 1 | [] | 2026-03-22T16:28:43Z | https://github.com/jas97/RACCER | null |
[
"3d",
"computer-vision",
"graphics"
] | [
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] | [
"description",
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"query-match",
"repository-metadata"
] | [
"multimodal.gaussian-splatting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-01-29T21:19:05Z | Official implementation of "Mesh-Based Adapting and Modification of Gaussian Splatting" | [
"3d",
"computer-vision",
"graphics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 27 | 750,030,747 | null | Python | NOASSERTION | [
"gaussian-splatting",
"neural-rendering"
] | waczjoan/gaussian-mesh-splatting | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-09-30T16:30:43Z | [] | 2025-01-23T20:43:58Z | [
"multimodal.gaussian-splatting"
] | 9e79d49caf6dfa87a6cb3bb47975d4ab9996e0ff | 2026-09-29T18:47:14Z | gh-ml-readme-evidence-v2 | "9e79d49caf6dfa87a6cb3bb47975d4ab9996e0ff" | [
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] | [
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] | ok | 2026-09-29T18:47:14Z | waczjoan/gaussian-mesh-splatting | readme-supported-paper-method-implementation | [
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] | include | ml-contribution-v5 | 512 | [] | 2026-09-24T07:00:21Z | https://github.com/waczjoan/gaussian-mesh-splatting | null |
[
"distributed-ml",
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] | [
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] | [
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"query-match",
"repository-metadata"
] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-01-30T01:32:40Z | The open-source code of our paper: ''Understanding and Improving Model Averaging in Federated Learning on Heterogeneous Data'', accepted by IEEE TMC. | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-11T16:48:52Z | false | 1 | 750,101,190 | null | Python | null | [
"collaborative-learning",
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] | TailinZhou/FedIMA | [
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] | 1 | 2026-10-11T16:48:52Z | [] | 2024-06-28T08:36:40Z | [
"trust.federated-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 9 | [] | 2026-07-22T14:17:21Z | https://github.com/TailinZhou/FedIMA | null |
[
"general-ml",
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] | [
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] | [
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] | [
"general.deep-learning-description",
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"graph.graph-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-01-30T04:38:54Z | This is the official code of the paper "MolNexTR: a generalized deep learning model for molecular image recognition" | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-01T17:35:19Z | false | 38 | 750,151,673 | null | Jupyter Notebook | Apache-2.0 | [
"generative-modeling",
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] | CYF2000127/MolNexTR | [
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"query-match",
"repository-metadata"
] | 4 | 2026-10-15T15:20:52Z | [] | 2026-01-15T07:35:29Z | [
"graph.graph-generation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-context-only",
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] | include | ml-contribution-v5 | 169 | [] | 2026-09-17T11:23:19Z | https://github.com/CYF2000127/MolNexTR | null |
[
"multi-agent-systems",
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] | [
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] | [
"description",
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] | [
"rl.multiagent"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-01-30T08:53:58Z | Official code for the paper "Coordinating Multi-Agent Reinforcement Learning via Dual Collaborative Constraints". | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 0 | 750,236,491 | null | Python | null | [
"centralized-training",
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] | chaobiubiu/DCC | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-01T17:35:19Z | [] | 2024-02-07T02:58:01Z | [
"rl.multiagent"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 4 | [] | 2025-05-18T22:15:41Z | https://github.com/chaobiubiu/DCC | null |
[
"multimodal-learning",
"reinforcement-learning"
] | [
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"policy-learning",
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] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-01-30T09:04:53Z | Official code for the paper: Multi-Modal Deep Reinforcement Learning for Visual Security of Virtual Reality Applications | [
"multimodal-learning",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-24T14:31:13Z | false | 0 | 750,240,803 | null | Jupyter Notebook | MIT | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | AmineAndam04/MultiModal-VR-Security | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-24T14:31:13Z | [] | 2024-03-20T11:55:10Z | [
"rl.deep"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 2 | [] | 2024-09-09T09:46:09Z | https://github.com/AmineAndam04/MultiModal-VR-Security | null |
[
"decision-making",
"reinforcement-learning"
] | [
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"meta-learning",
"multi-armed-bandits"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"rl.bandits"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-01-30T12:56:55Z | The source code for paper: An Effective Data Imputation Order Optimization Framework with Meta-Learning and Multi-Armed Bandits | [
"decision-making",
"reinforcement-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-30T16:30:43Z | false | 0 | 750,334,165 | null | Python | null | [
"contextual-bandits",
"meta-learning",
"multi-armed-bandits"
] | Zhang-Jiaxuan/DI-OO | [
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"query-match",
"repository-metadata"
] | 2 | 2026-10-02T16:59:40Z | [] | 2024-12-12T15:27:43Z | [
"rl.bandits"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2024-12-12T15:27:47Z | https://github.com/Zhang-Jiaxuan/DI-OO | null |
[
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"forecasting",
"sequence-modeling"
] | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"timeseries.forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-01-31T08:35:07Z | [KDD'24] Official code for our paper "Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series Forecasting". | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-30T16:30:43Z | false | 9 | 750,746,780 | https://arxiv.org/abs/2405.10800 | Python | null | [
"forecasting",
"sequence-modeling"
] | XDZhelheim/HimNet | [
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"paper-reference",
"query-match",
"repository-metadata"
] | 4 | 2026-10-17T13:17:44Z | [] | 2024-09-04T03:02:26Z | [
"timeseries.forecasting"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 84 | [] | 2026-10-16T03:14:25Z | https://github.com/XDZhelheim/HimNet | null |
[
"generative-ai",
"language"
] | [
"fine-tuning",
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-01-31T23:06:41Z | Official pytorch code for "From PEFT to DEFT: Parameter Efficient Finetuning for Reducing Activation Density in Transformers" (AAAI 2025) | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-03T15:45:52Z | false | 1 | 751,094,349 | https://arxiv.org/abs/2402.01911 | Python | Apache-2.0 | [
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] | IBM/DEFT | [
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"query-match",
"repository-metadata"
] | 1 | 2026-10-03T15:45:52Z | [] | 2025-09-18T01:01:50Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 7 | [
"machine-learning"
] | 2026-01-15T03:36:29Z | https://github.com/IBM/DEFT | null |
[
"deep-learning",
"graph-learning"
] | [
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] | [
"description",
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"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-01T17:10:16Z | The original code for ICML 2025 paper "Unifews: You Need Fewer Operations for Efficient Graph Neural Networks" | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-12T20:31:19Z | false | 0 | 751,468,687 | null | Python | null | [
"graph-neural-network",
"message-passing",
"neural-network"
] | gdmnl/Unifews | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-12T20:31:19Z | [] | 2025-07-14T15:53:01Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 4 | [] | 2026-03-19T08:03:01Z | https://github.com/gdmnl/Unifews | null |
[
"efficient-ml",
"generative-ai",
"language",
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] | [
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] | [
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] | [
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"llm.parameter-efficient-finetuning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-01T22:35:10Z | Official Implementation of "Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuning" at EMNLP 2024 Main Conference | [
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] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 4 | 751,585,783 | https://arxiv.org/abs/2402.04401 | Python | null | [
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] | TamSiuhin/OPPU | [
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] | 14 | 2026-10-25T15:11:36Z | [] | 2025-07-31T21:13:21Z | [
"llm.finetuning"
] | cb9f99f3bc93b2a5606771cfe8043c6daf9204ae | 2026-09-29T18:47:14Z | gh-ml-readme-evidence-v2 | "cb9f99f3bc93b2a5606771cfe8043c6daf9204ae" | [
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] | [
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] | ok | 2026-09-29T18:47:14Z | TamSiuhin/OPPU | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-context-only",
"ml-method-context",
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] | include | ml-contribution-v5 | 51 | [
"llm",
"peft",
"personalization"
] | 2026-08-11T11:41:41Z | https://github.com/TamSiuhin/OPPU | null |
[
"computer-vision",
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] | [
"computer-vision",
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] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.topic-computer-vision"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-05T04:30:55Z | [CVPR 2024 Highlight] MIGC and [TPAMI 2024] MIGC++ (Official Implementation) | [
"computer-vision",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-02T16:44:32Z | false | 30 | 752,906,381 | null | Python | NOASSERTION | [
"computer-vision",
"diffusion"
] | limuloo/MIGC | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-02T16:44:32Z | [] | 2025-05-15T03:29:11Z | [
"vision.topic-computer-vision"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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] | include | ml-contribution-v5 | 612 | [
"aigc",
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"cvpr",
"cvpr2024",
"stable-diffusion",
"text-to-image",
"tpami2024"
] | 2026-10-02T15:14:44Z | https://github.com/limuloo/MIGC | null |
[
"3d",
"computer-vision",
"embodied-ai",
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] | [
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] | [
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"repository-metadata"
] | [
"multimodal.point-cloud",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-05T04:56:32Z | Official Implementation of the paper RiEMann: Near Real-Time SE(3)-Equivariant Robot Manipulation without Point Cloud Segmentation | [
"3d",
"computer-vision",
"robotics",
"robotics-and-control"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 4 | 752,912,598 | null | Python | MIT | [
"3d-perception",
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] | HeegerGao/RiEMann | [
"description",
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"query-match",
"repository-metadata"
] | 4 | 2026-10-23T15:59:01Z | [] | 2026-01-13T06:46:45Z | [
"multimodal.point-cloud"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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] | include | ml-contribution-v5 | 41 | [] | 2026-09-23T02:33:26Z | https://github.com/HeegerGao/RiEMann | null |
[
"reinforcement-learning",
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] | [
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] | [
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] | [
"rl.model-based"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-05T10:39:50Z | Official implementation of Reach-Aware Value Estimation (RAVL) from the paper: "The Edge-of-Reach Problem in Offline Model-Based Reinforcement Learning." | [
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] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T16:52:35Z | false | 0 | 753,033,558 | null | Python | MIT | [
"model-based-reinforcement-learning",
"reinforcement-learning",
"world-model"
] | anyasims/edge-of-reach | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-01T17:35:19Z | [] | 2025-04-27T16:56:42Z | [
"rl.model-based"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 6 | [] | 2026-05-31T10:05:36Z | https://github.com/anyasims/edge-of-reach | null |
[
"generative-ai",
"generative-modeling"
] | [
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] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-05T11:42:53Z | Official PyTorch implementation for Label-Noise Robust Diffusion Models (TDSM) in ICLR 2024. | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-09T17:31:36Z | false | 3 | 753,058,604 | null | Python | Apache-2.0 | [
"diffusion"
] | byeonghu-na/tdsm | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-09T17:31:36Z | [] | 2024-04-29T06:19:20Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 16 | [] | 2026-07-14T06:56:06Z | https://github.com/byeonghu-na/tdsm | null |
[
"computer-vision",
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"generative-modeling",
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] | [
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] | [
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"repository-metadata"
] | [
"llm.diffusion-phrase",
"multimodal.text-to-image",
"vision.image-diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-05T13:17:03Z | Official Implementations "Get What You Want, Not What You Don't: Image Content Suppression for Text-to-Image Diffusion Models" (ICLR2024) | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-30T16:30:43Z | false | 2 | 753,097,096 | https://arxiv.org/abs/2402.05375 | Python | null | [
"diffusion"
] | sen-mao/SuppressEOT | [
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"query-match",
"repository-metadata"
] | 4 | 2026-10-09T17:31:36Z | [] | 2024-12-03T02:24:30Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 60 | [] | 2026-06-28T02:10:23Z | https://github.com/sen-mao/SuppressEOT | null |
[
"natural-language-processing"
] | [
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] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"nlp-entity-linking"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-05T15:57:02Z | This repository includes the code of our paper NLQxform: A Language Model-based Question to SPARQL Transformer. | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-10T16:53:26Z | false | 1 | 753,171,830 | null | Python | MIT | [
"entity-linking",
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] | ruijie-wang-uzh/NLQxform | [
"description",
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"query-match",
"repository-metadata"
] | 1 | 2026-10-10T16:53:26Z | [] | 2025-05-05T14:58:03Z | [
"nlp-entity-linking"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [] | 2025-09-05T11:58:27Z | https://github.com/ruijie-wang-uzh/NLQxform | null |
[
"generative-ai",
"language",
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] | [
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] | [
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"repository-metadata"
] | [
"llm.large-language-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-06T11:23:27Z | Official PyTorch implementation of DistiLLM: Towards Streamlined Distillation for Large Language Models (ICML 2024) | [
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] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-13T18:05:26Z | false | 28 | 753,564,120 | https://arxiv.org/abs/2402.03898 | Python | null | [
"distillation",
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"pretraining"
] | jongwooko/distillm | [
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"repository-metadata"
] | 2 | 2026-10-16T15:36:56Z | [] | 2025-03-13T04:00:09Z | [
"llm.large-language-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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] | include | ml-contribution-v5 | 269 | [] | 2026-09-21T17:55:14Z | https://github.com/jongwooko/distillm | null |
[
"deep-learning",
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] | [
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"message-passing"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.graph-transformer"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-06T14:01:52Z | Code for our paper "Towards Principled Graph Transformers" | [
"deep-learning",
"graph-learning"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 0 | 753,631,472 | null | Python | MIT | [
"graph-transformer",
"message-passing"
] | luis-mueller/towards-principled-gts | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-01T17:35:19Z | [] | 2024-10-30T13:17:44Z | [
"graph.graph-transformer"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 15 | [] | 2026-09-09T10:50:32Z | https://github.com/luis-mueller/towards-principled-gts | null |
[
"efficient-ml",
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] | [
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] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.lora",
"efficiency.quantization"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-06T15:40:12Z | [ICML 2024 Oral] This project is the official implementation of our Accurate LoRA-Finetuning Quantization of LLMs via Information Retention | [
"efficient-ml",
"language",
"machine-learning-systems"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T16:44:32Z | false | 4 | 753,679,203 | null | Python | null | [
"lora",
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"parameter-efficient-fine-tuning",
"quantization"
] | AI-Efficiency/IR-QLoRA | [
"description",
"query-match",
"repository-metadata"
] | 4 | 2026-10-21T15:48:06Z | [] | 2026-09-11T17:44:18Z | [
"efficiency.lora"
] | 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 | 65 | [] | 2026-09-11T17:44:23Z | https://github.com/AI-Efficiency/IR-QLoRA | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"multimodal",
"vision"
] | [
"diffusion",
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] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"multimodal.text-to-image",
"vision.image-diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-07T05:50:39Z | Official implementation of "DreamStyler: Paint by Style Inversion with Text-to-Image Diffusion Models" (AAAI24) | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-01T17:35:19Z | false | 2 | 753,962,513 | null | Python | Apache-2.0 | [
"diffusion"
] | webtoon/dreamstyler | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-11T16:48:52Z | [] | 2024-02-16T01:35:51Z | [
"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 | 55 | [] | 2026-09-24T06:46:41Z | https://github.com/webtoon/dreamstyler | null |
[
"computer-vision",
"interpretability-and-safety",
"language",
"multimodal",
"multimodal-learning",
"vision"
] | [
"contrastive-learning",
"multimodal-learning",
"vision-language",
"vision-language-model"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.vision-language",
"vision.vision-language"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-07T06:58:44Z | The official implementation of Vision-Language Alignment Learning under Affinity and Divergence Principles for Few-Shot Out-of-Distribution Generalization (IJCV2024). | [
"computer-vision",
"interpretability-and-safety",
"multimodal",
"multimodal-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 1 | 753,983,853 | null | Python | null | [
"multimodal-learning",
"vision-language"
] | LinLLLL/VLAD | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-12T20:31:19Z | [] | 2024-06-18T04:08:02Z | [
"vision.vision-language"
] | d4c21264ca64d1b4b6b27ba855c0e549e5088b24 | 2026-09-29T18:47:14Z | gh-ml-readme-evidence-v2 | "d4c21264ca64d1b4b6b27ba855c0e549e5088b24" | [
"abstract",
"citation",
"installation",
"other"
] | [
"method-contribution",
"ml-method-context",
"model-training-artifact",
"paper-reference"
] | ok | 2026-09-29T18:47:14Z | LinLLLL/VLAD | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-method-context",
"model-training-artifact",
"paper-reference"
] | include | ml-contribution-v5 | 13 | [] | 2026-08-16T15:25:26Z | https://github.com/LinLLLL/VLAD | null |
[
"forecasting",
"robust-ml",
"time-series",
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"trustworthy-ml"
] | [
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] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"timeseries.forecasting",
"trust.distribution-shift"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-08T08:15:51Z | Official code repository for KDD'24 paper "Calibration of Time-Series Forecasting: Detecting and Adapting Context-Driven Distribution Shift" | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 5 | 754,516,869 | null | Python | MIT | [
"forecasting",
"sequence-modeling"
] | HALF111/calibration_CDS | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 7 | 2026-10-17T13:17:44Z | [] | 2025-06-19T09:20:09Z | [
"timeseries.forecasting"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 38 | [] | 2026-07-25T08:33:12Z | https://github.com/HALF111/calibration_CDS | null |
[
"computer-vision",
"image-processing",
"low-level-vision"
] | [
"image-enhancement",
"image-restoration",
"low-light-enhancement",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"recall26.low-light-enhancement"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-08T16:02:31Z | [CVPR2025 && NTIRE2025] HVI: A New Color Space for Low-light Image Enhancement (Official Implementation) | [
"computer-vision",
"image-processing",
"low-level-vision"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 88 | 754,705,312 | null | Python | MIT | [
"image-enhancement",
"image-restoration",
"low-light-enhancement",
"transformer"
] | Fediory/HVI-CIDNet | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-09-30T16:47:43Z | [] | 2026-03-09T13:47:42Z | [
"recall26.low-light-enhancement"
] | 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 | 852 | [
"cvpr",
"hvi-color-space",
"image-enhancement",
"image-processing",
"low-light-image-enhancement",
"transformer"
] | 2026-09-28T15:41:28Z | https://github.com/Fediory/HVI-CIDNet | null |
[
"multi-agent-systems",
"reinforcement-learning"
] | [
"centralized-training",
"multi-agent-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"rl.core",
"rl.multiagent"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-08T17:55:12Z | Code for paper : "Memory Augmented Multi-agent Reinforcement Learning for Cooperative Environment" | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-18T13:47:34Z | false | 1 | 754,756,038 | https://link.springer.com/chapter/10.1007/978-3-031-81596-6_8 | Python | null | [
"policy-learning",
"reinforcement-learning"
] | niktaaan/Multi-Agent-LSTMTD3 | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | 2 | 2026-10-21T14:37:12Z | [] | 2026-10-14T17:27:59Z | [
"rl.core"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 5 | [
"lstm",
"multiagent-systems",
"reinforcement-learning"
] | 2026-10-14T17:28:04Z | https://github.com/niktaaan/Multi-Agent-LSTMTD3 | null |
[
"general-ml"
] | [
"novel-method"
] | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"general.novel-method"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-09T04:13:22Z | Paper of R-LIME, a novel method for interpreting the behavior of ML models. | [
"general-ml"
] | [
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 0 | 754,938,874 | https://link.springer.com/chapter/10.1007/978-3-031-78354-8_6 | TeX | null | [
"novel-method"
] | genji1024/rlime-paper | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | 2 | 2026-10-02T16:44:32Z | [] | 2025-03-08T14:53:49Z | [
"general.novel-method"
] | 7d189b6826ac46bc3c48a819a596d5cb8104fa51 | 2026-09-27T17:16:20Z | gh-ml-readme-evidence-v2 | "7d189b6826ac46bc3c48a819a596d5cb8104fa51" | [
"other"
] | [
"method-contribution",
"ml-method-context",
"paper-reference"
] | ok | 2026-09-27T17:16:20Z | genji1024/rlime-paper | specific-method-with-novelty-claim | [
"method-contribution",
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue",
"paper-reference"
] | include | ml-contribution-v5 | 0 | [] | 2026-03-25T11:40:01Z | https://github.com/genji1024/rlime-paper | null |
[
"generative-ai",
"language",
"natural-language-processing"
] | [
"language-model",
"pretraining"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.large-language-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-09T16:42:15Z | code for paper "GPT-FinRE: In-context learning for financial relation extraction using large language models" accepted at FinNLP @ IJCNLP-AACL-2023 | [
"generative-ai",
"language",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-20T15:59:06Z | false | 1 | 755,206,657 | null | Python | MIT | [
"language-model",
"pretraining"
] | Upaya07/GPT-FinRE | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-20T15:59:06Z | [] | 2024-02-09T17:42:58Z | [
"llm.large-language-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 3 | [] | 2025-04-07T11:29:08Z | https://github.com/Upaya07/GPT-FinRE | null |
[
"distributed-ml",
"privacy-and-federated-learning"
] | [
"collaborative-learning",
"federated-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-10T16:33:19Z | Official implementation of our paper "Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off" | [
"distributed-ml",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-13T18:05:26Z | false | 0 | 755,578,344 | null | null | null | [
"collaborative-learning",
"federated-learning"
] | 6lyc/FedCEO-Collaborate-with-Each-Other | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-13T18:05:26Z | [] | 2024-02-10T16:33:19Z | [
"trust.federated-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2024-02-10T16:33:19Z | https://github.com/6lyc/FedCEO-Collaborate-with-Each-Other | null |
[
"generative-ai",
"generative-modeling",
"language",
"natural-language-processing"
] | [
"diffusion",
"fine-tuning",
"lora",
"parameter-efficient-fine-tuning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.finetuning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-11T15:33:33Z | Official PyTorch Implementation for the "Recovering the Pre-Fine-Tuning Weights of Generative Models" paper (ICML 2024). | [
"generative-ai",
"generative-modeling",
"language",
"natural-language-processing"
] | [
"deep-learning",
"generative-model",
"large-language-model",
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-28T17:27:44Z | false | 4 | 755,959,286 | https://horwitz.ai/spectral_detuning/ | Python | NOASSERTION | [
"diffusion",
"fine-tuning",
"lora",
"parameter-efficient-fine-tuning"
] | eliahuhorwitz/Spectral-DeTuning | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-28T17:27:44Z | [] | 2025-04-15T08:20:32Z | [
"llm.finetuning"
] | 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 | 86 | [
"deep-learning",
"jailbreak",
"llama2",
"llm",
"lora",
"machine-learning",
"mistral",
"stable-diffusion",
"weight-space-learning"
] | 2026-04-27T05:53:50Z | https://github.com/eliahuhorwitz/Spectral-DeTuning | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"multimodal"
] | [
"generative-modeling",
"motion-generation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.motion-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-12T14:39:40Z | Official implementation of "Self-Correcting Self-Consuming Loops for Generative Model Training" (ICML 2024) | [
"computer-vision",
"generative-ai",
"generative-modeling",
"multimodal"
] | [
"generative-model",
"model-training"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 3 | 756,371,893 | https://nategillman.com/sc-sc | Python | MIT | [
"generative-modeling",
"motion-generation"
] | nate-gillman/self-correcting-self-consuming | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-01T17:35:19Z | [] | 2024-07-18T13:48:02Z | [
"multimodal.motion-generation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-context-only",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 36 | [] | 2026-08-10T04:04:29Z | https://github.com/nate-gillman/self-correcting-self-consuming | null |
[
"reinforcement-learning",
"robotics"
] | [
"model-based-reinforcement-learning",
"reinforcement-learning",
"world-model"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"rl.model-based"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-12T18:45:57Z | Source code for paper "TEAMSTER: Model-based reinforcement learning for ad hoc teamwork" (Artificial Intelligence Journal, 2023) | [
"reinforcement-learning",
"robotics"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 0 | 756,482,195 | null | Python | null | [
"model-based-reinforcement-learning",
"reinforcement-learning",
"world-model"
] | jmribeiro/teamster-model-based-ad-hoc-teamwork | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-01T17:35:19Z | [] | 2024-02-12T18:57:04Z | [
"rl.model-based"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [] | 2024-12-12T17:16:30Z | https://github.com/jmribeiro/teamster-model-based-ad-hoc-teamwork | null |
[
"generative-ai",
"interpretability-and-safety",
"language",
"safety"
] | [
"alignment",
"preference-optimization"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.preference-optimization"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-13T14:58:20Z | The official implementation of "ICDPO: Effectively Borrowing Alignment Capability of Others via In-context Direct Preference Optimization". | [
"generative-ai",
"interpretability-and-safety",
"language",
"safety"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 3 | 756,915,095 | null | Python | GPL-3.0 | [
"alignment",
"preference-optimization"
] | F2-Song/ICDPO | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-01T17:35:19Z | [] | 2024-02-15T10:23:53Z | [
"llm.preference-optimization"
] | f002fbe003d8e9e78ab895f3ec1f1b89231e658b | 2026-09-29T18:47:14Z | gh-ml-readme-evidence-v2 | "f002fbe003d8e9e78ab895f3ec1f1b89231e658b" | [
"citation",
"other",
"results",
"usage"
] | [
"method-contribution",
"ml-method-context",
"paper-reference"
] | ok | 2026-09-29T18:47:14Z | F2-Song/ICDPO | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-method-context",
"paper-reference"
] | include | ml-contribution-v5 | 16 | [] | 2025-08-19T14:13:16Z | https://github.com/F2-Song/ICDPO | null |
[
"chemistry",
"computational-chemistry",
"computer-vision",
"drug-discovery",
"language",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"vision"
] | [
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"image-text",
"multimodal-learning",
"property-prediction",
"qsar",
"ranking",
"representation-learning",
"supervised-learning",
"virtual-screening",
"vision-language",
"vision-language-model"
] | [
"description",
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"query-match",
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] | [
"chem-drug-discovery",
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"chem-property-prediction",
"chem-virtual-screening",
"multimodal.vision-language",
"multimodal.vlm",
"vision.vision-language"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-13T15:16:57Z | The official source code for "Vision Language Model is NOT All You Need: Augmentation Strategies for Molecule Language Model". | [
"chemistry",
"computer-vision",
"drug-discovery",
"multimodal-learning",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 0 | 756,924,063 | https://arxiv.org/abs/2407.09043 | Python | null | [
"ranking",
"representation-learning",
"virtual-screening"
] | Namkyeong/AMOLE | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | 8 | 2026-10-20T15:59:06Z | [] | 2024-07-23T07:40:03Z | [
"chem-drug-discovery"
] | 26e832ee63b5b55292515475d1a9acf78e2623e8 | 2026-09-29T18:47:14Z | gh-ml-readme-evidence-v2 | "26e832ee63b5b55292515475d1a9acf78e2623e8" | [
"dataset",
"method",
"other",
"overview"
] | [
"method-contribution",
"ml-method-context",
"model-training-artifact",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-09-29T18:47:14Z | Namkyeong/AMOLE | readme-supported-paper-method-implementation | [
"method-contribution",
"ml-method-context",
"model-training-artifact",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 15 | [] | 2026-08-24T00:58:10Z | https://github.com/Namkyeong/AMOLE | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-14T13:04:44Z | Official PyTorch Implementation of "Don't Play Favorites: Minority Guidance for Diffusion Models" (ICLR 2024) | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-12T20:31:19Z | false | 0 | 757,417,813 | null | Python | MIT | [
"diffusion"
] | soobin-um/minority-guidance | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-12T20:31:19Z | [] | 2025-05-05T14:09:30Z | [
"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 | 27 | [] | 2026-09-10T17:10:30Z | https://github.com/soobin-um/minority-guidance | null |
[
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"forecasting",
"sequence-modeling"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"timeseries.forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-14T19:23:36Z | Proposing a novel anomaly detection method, we merge time series forecasting with Sinkhorn Optimal Transport for enhanced accuracy in identifying abnormal events in industrial data, showcasing potential for real-world applications in proactive maintenance. | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-19T17:46:36Z | false | 0 | 757,603,041 | null | Jupyter Notebook | null | [
"forecasting",
"sequence-modeling"
] | KIssiaka/Predictive-maintenance-with-time-series-and-optimal-transport | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-19T17:46:36Z | [] | 2024-02-14T19:27:34Z | [
"timeseries.forecasting"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 1 | [] | 2024-10-21T14:39:32Z | https://github.com/KIssiaka/Predictive-maintenance-with-time-series-and-optimal-transport | null |
[
"3d",
"computer-vision",
"probabilistic-ml",
"robotics",
"trustworthy-ml"
] | [
"3d-object-detection",
"3d-perception",
"evidential-learning",
"uncertainty-estimation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"growth26.probabilistic-evidential-learning",
"multimodal.3d-object-detection"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-15T00:17:19Z | Official implementation of MEDL-U: Uncertainty-aware 3D Automatic Annotation based on Evidential Deep Learning (ICRA 2024) | [
"3d",
"computer-vision",
"robotics"
] | [
"deep-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T22:51:24Z | false | 1 | 757,774,727 | null | Jupyter Notebook | MPL-2.0 | [
"3d-object-detection",
"3d-perception"
] | paathelb/MEDL-U | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-16T15:36:56Z | [] | 2024-12-10T09:57:55Z | [
"multimodal.3d-object-detection"
] | 34b28225896620de4ca32c1608e31fd9339b3103 | 2026-09-29T18:47:14Z | gh-ml-readme-evidence-v2 | "34b28225896620de4ca32c1608e31fd9339b3103" | [
"abstract",
"citation",
"method",
"other",
"references"
] | [
"method-contribution",
"ml-method-context",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-09-29T18:47:14Z | paathelb/MEDL-U | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-context-only",
"ml-method-context",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 4 | [] | 2025-10-02T07:44:07Z | https://github.com/paathelb/MEDL-U | null |
[
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion",
"generative-modeling"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"vision.image-diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-15T08:08:49Z | Official code for the CVPR 2025 paper "SemanticDraw: Towards Real-Time Interactive Content Creation from Image Diffusion Models." | [
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 51 | 757,899,614 | https://jaerinlee.com/research/semantic-draw | Jupyter Notebook | MIT | [
"diffusion"
] | ironjr/semantic-draw | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 12 | 2026-10-11T16:48:52Z | [
"2403.09055"
] | 2025-06-01T08:22:46Z | [
"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 | 589 | [
"cvpr2025",
"diffusion-models",
"drawing",
"huggingface-spaces",
"image-generation",
"stable-diffusion",
"stablediffusion3",
"streaming",
"text-to-image"
] | 2026-10-05T11:11:57Z | https://github.com/ironjr/semantic-draw | null |
[
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"quantization"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.quantization"
] | true | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-15T10:13:57Z | Official implementation of ICML'24 paper "LQER: Low-Rank Quantization Error Reconstruction for LLMs" | [
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-12T20:31:19Z | false | 2 | 757,947,566 | null | Python | null | [
"quantization"
] | ChengZhang-98/LQER | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-12T20:31:19Z | [] | 2024-07-11T11:30:48Z | [
"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 | 19 | [] | 2026-01-08T13:00:11Z | https://github.com/ChengZhang-98/LQER | null |
[
"generative-ai",
"generative-modeling",
"missing-data",
"synthetic-data",
"time-series",
"time-series-and-forecasting",
"vision"
] | [
"diffusion",
"generative-modeling",
"imputation",
"sequence-modeling",
"time-series-generation",
"time-series-modeling"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion",
"recall.name.diffusion-model",
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"timeseries.topic"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-15T10:50:39Z | [ICLR 2024] Official Implementation of "Diffusion-TS: Interpretable Diffusion for General Time Series Generation" | [
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"time-series",
"time-series-and-forecasting"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 69 | 757,961,819 | null | Jupyter Notebook | MIT | [
"diffusion",
"sequence-modeling"
] | Y-debug-sys/Diffusion-TS | [
"description",
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"query-match",
"repository-metadata"
] | 14 | 2026-10-19T17:46:36Z | [] | 2025-02-28T14:30:06Z | [
"timeseries.topic"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 493 | [
"diffusion-model",
"pytorch",
"time-series",
"time-series-forecasting",
"time-series-imputation"
] | 2026-10-18T12:11:00Z | https://github.com/Y-debug-sys/Diffusion-TS | null |
[
"natural-language-processing"
] | [
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"repository-metadata"
] | [] | false | unknown | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-16T09:39:53Z | Official implementation of the paper "Linear Transformers with Learnable Kernel Functions are Better In-Context Models" | [
"natural-language-processing"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-19T19:27:31Z | false | 3 | 758,423,296 | null | Python | Apache-2.0 | [
"transformer"
] | corl-team/rebased | [
"description",
"github-topics",
"license-metadata",
"repository-metadata"
] | 1 | 2026-10-19T19:27:31Z | [
"2402.10644"
] | 2025-01-16T09:58:54Z | [] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 168 | [
"acl2024",
"in-context-learning",
"nlp",
"pytorch",
"transformer"
] | 2026-08-20T21:17:49Z | https://github.com/corl-team/rebased | {"discovery_source":"hf_daily_papers","paper_evidence":"unverified","queryless":true} |
[
"generative-ai",
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] | [
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] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.finetuning",
"llm.language-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-16T16:51:13Z | [ICML‘24] Official code for the paper "Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark ". | [
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"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 14 | 758,594,926 | null | Python | GPL-3.0 | [
"fine-tuning",
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] | ZO-Bench/ZO-LLM | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-28T17:27:44Z | [] | 2025-07-06T05:52:12Z | [
"llm.finetuning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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] | include | ml-contribution-v5 | 131 | [
"large-language-models",
"parameter-efficient-tuning",
"zeroth-order-optimization"
] | 2026-10-27T14:47:01Z | https://github.com/ZO-Bench/ZO-LLM | null |
[
"audio",
"generative-modeling"
] | [
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"training"
] | [
"description",
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"query-match",
"repository-metadata"
] | [
"audio-music-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-17T23:17:57Z | Official code for Symbolic Music Generation with Non-Differentiable Rule Guided Diffusion (ICML 2024, Oral). | [
"audio",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-29T17:12:21Z | false | 8 | 759,200,224 | https://scg-rule-guided-music.github.io | Python | MIT | [
"architecture",
"diffusion",
"generative-modeling",
"training"
] | yjhuangcd/rule-guided-music | [
"description",
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"query-match",
"repository-metadata"
] | 5 | 2026-10-18T14:44:29Z | [] | 2024-08-12T03:19:45Z | [
"audio-music-generation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 90 | [] | 2026-09-03T14:45:52Z | https://github.com/yjhuangcd/rule-guided-music | null |
[
"computational-science",
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] | [
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] | [
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"repository-metadata"
] | [
"science.neural-operators"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-18T12:18:54Z | A novel Fourier neural operator framework for classification of multi-sized images | [
"computational-science",
"physics"
] | [
"classifier"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 0 | 759,373,361 | https://arxiv.org/abs/2402.11568 | Python | null | [
"neural-operator",
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"surrogate-modeling"
] | Ali-Stanford/FNOMultiSizedImages | [
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"query-match",
"repository-metadata"
] | 2 | 2026-10-01T17:35:19Z | [] | 2025-02-10T06:32:57Z | [
"science.neural-operators"
] | 10671c4cac6494a33d774205e0a277397e056eb1 | 2026-09-26T15:53:33Z | gh-ml-readme-evidence-v2 | "10671c4cac6494a33d774205e0a277397e056eb1" | [
"other"
] | [
"method-contribution",
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] | ok | 2026-09-26T15:53:33Z | Ali-Stanford/FNOMultiSizedImages | specific-method-with-novelty-claim | [
"method-contribution",
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"ml-method-context",
"ml-method-cue",
"paper-reference"
] | include | ml-contribution-v5 | 8 | [] | 2026-03-22T08:56:11Z | https://github.com/Ali-Stanford/FNOMultiSizedImages | null |
[
"generative-ai",
"language",
"natural-language-processing"
] | [
"fine-tuning",
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"parameter-efficient-fine-tuning",
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] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"llm.finetuning",
"llm.large-language-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-18T12:36:35Z | [NAACL 2025] The official implementation of paper "Learning From Failure: Integrating Negative Examples when Fine-tuning Large Language Models as Agents" | [
"generative-ai",
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"natural-language-processing"
] | [
"large-language-model",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-19T17:46:36Z | false | 0 | 759,378,402 | null | Python | null | [
"fine-tuning",
"parameter-efficient-fine-tuning"
] | Reason-Wang/NAT | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 2 | 2026-10-28T17:27:44Z | [] | 2024-03-14T17:45:23Z | [
"llm.finetuning"
] | 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 | 28 | [
"agent",
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"generation",
"learning",
"llm",
"negative",
"nlp",
"pytorch",
"trajectory",
"transformers"
] | 2026-01-20T03:16:39Z | https://github.com/Reason-Wang/NAT | null |
[
"generative-ai",
"language",
"multimodal-learning",
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] | [
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] | [
"description",
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"query-match",
"repository-metadata"
] | [
"llm.large-language-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-19T15:59:51Z | Official code for our paper "Model Composition for Multimodal Large Language Models" (ACL 2024) | [
"generative-ai",
"language",
"multimodal-learning",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-19T17:46:36Z | false | 1 | 759,931,175 | null | Python | Apache-2.0 | [
"language-model",
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] | THUNLP-MT/ModelCompose | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-19T17:46:36Z | [] | 2025-01-08T09:26:28Z | [
"llm.large-language-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 31 | [] | 2026-04-30T21:13:04Z | https://github.com/THUNLP-MT/ModelCompose | null |
[
"efficient-ml",
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] | [
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] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-20T07:41:59Z | [ICLR 2024] Code for our ICLR 2024 paper 'Adversarial Feature Map Pruning for Backdoor' | [
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-02T16:44:32Z | false | 2 | 760,319,566 | null | Jupyter Notebook | NOASSERTION | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | retsuh-bqw/FMP | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-20T15:59:06Z | [] | 2025-04-30T06:24:31Z | [
"efficiency.pruning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 4 | [] | 2026-05-08T10:56:37Z | https://github.com/retsuh-bqw/FMP | null |
[
"computer-vision",
"generative-ai",
"language",
"multimodal",
"multimodal-learning",
"vision"
] | [
"contrastive-learning",
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"instruction-tuning",
"multimodal-learning",
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] | [
"description",
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"query-match",
"repository-metadata"
] | [
"llm.instruction-tuning",
"multimodal.vision-language",
"vision.vision-language"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-20T10:15:26Z | [ACL 2024 Findings] Official PyTorch Implementation code for realizing the technical part of CoLLaVO: Crayon Large Language and Vision mOdel to significantly improve zero-shot vision language performances | [
"computer-vision",
"multimodal",
"multimodal-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 14 | 760,383,982 | null | Python | MIT | [
"multimodal-learning",
"vision-language"
] | ByungKwanLee/CoLLaVO | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-11T16:48:52Z | [
"2402.11248"
] | 2024-06-28T18:03:16Z | [
"vision.vision-language"
] | faabc4b1c34bb793ca1879c841efe6ee257ff95d | 2026-09-29T18:47:14Z | gh-ml-readme-evidence-v2 | "faabc4b1c34bb793ca1879c841efe6ee257ff95d" | [
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"method",
"other"
] | [
"method-contribution",
"ml-method-context",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-09-29T18:47:14Z | ByungKwanLee/CoLLaVO | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-method-context",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 98 | [] | 2026-07-15T06:49:35Z | https://github.com/ByungKwanLee/CoLLaVO | null |
[
"generative-ai",
"generative-modeling",
"multimodal",
"vision"
] | [
"diffusion",
"image-generation",
"text-to-image"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"multimodal.text-to-image"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-20T10:50:54Z | Code for our paper "Fixed-point Inversion for Text-to-image diffusion models" | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 1 | 760,398,522 | null | Python | null | [
"diffusion"
] | dvirsamuel/FPI | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-12T20:31:19Z | [] | 2024-10-13T18:02:06Z | [
"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 | [] | 2025-03-12T07:02:53Z | https://github.com/dvirsamuel/FPI | null |
[
"information-retrieval",
"natural-language-processing"
] | [
"rag",
"retrieval-augmented-generation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"nlp.rag"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-21T01:41:24Z | The code for paper "The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG)", exploring the privacy risk on RAG. | [
"information-retrieval",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-03T15:45:52Z | false | 8 | 760,966,736 | null | Python | null | [
"rag",
"retrieval-augmented-generation"
] | phycholosogy/RAG-privacy | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-03T15:45:52Z | [] | 2025-02-01T10:14:40Z | [
"nlp.rag"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 69 | [] | 2026-08-26T06:00:23Z | https://github.com/phycholosogy/RAG-privacy | null |
[
"efficient-ml",
"generative-ai",
"language",
"machine-learning-systems",
"model-compression",
"natural-language-processing"
] | [
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] | [
"description",
"github-topics",
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"paper-reference",
"query-match",
"repository-metadata"
] | [
"efficiency.quantization",
"llm.large-language-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-21T04:13:17Z | [ICLR 2024] This is the official PyTorch implementation of "QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Models" | [
"generative-ai",
"language",
"machine-learning-systems",
"natural-language-processing"
] | [
"large-language-model",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T16:44:32Z | false | 6 | 761,009,800 | https://arxiv.org/abs/2310.08041 | Python | Apache-2.0 | [
"language-model",
"pretraining",
"quantization"
] | ModelTC/QLLM | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 4 | 2026-10-19T17:46:36Z | [] | 2024-03-11T02:56:00Z | [
"llm.large-language-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 39 | [
"llama",
"llama2",
"llm",
"post-training-quantization",
"pytorch",
"quantization",
"transformers"
] | 2026-05-25T07:39:25Z | https://github.com/ModelTC/QLLM | null |
[
"graph-learning",
"machine-learning",
"time-series"
] | [
"dynamic-graph-learning",
"hypernetwork",
"meta-learning",
"neural-network",
"temporal-graph-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.temporal-learning",
"specialized26.hypernetwork"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-21T08:51:35Z | The official implementation of the ICLR 24 submission entitled "Spatio-Temporal Few-Shot Learning via Diffusive Neural Network Generation". | [
"graph-learning",
"time-series"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T22:33:16Z | false | 5 | 761,106,048 | null | Python | null | [
"dynamic-graph-learning",
"neural-network",
"temporal-graph-learning"
] | tsinghua-fib-lab/GPD | [
"description",
"query-match",
"repository-metadata"
] | 4 | 2026-10-05T20:10:04Z | [] | 2024-05-18T15:31:04Z | [
"graph.temporal-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-context-only",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 61 | [] | 2026-08-28T07:57:21Z | https://github.com/tsinghua-fib-lab/GPD | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"multimodal",
"vision"
] | [
"diffusion",
"generative-modeling",
"image-generation",
"text-to-image"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"multimodal.text-to-image",
"vision.image-diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-22T05:46:30Z | [NeurIPS 2024] Official Implementation of Attention Interpolation of Text-to-Image Diffusion | [
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"deep-learning",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 7 | 761,592,023 | https://qy-h00.github.io/attention-interpolation-diffusion/ | Jupyter Notebook | null | [
"diffusion",
"generative-modeling"
] | QY-H00/attention-interpolation-diffusion | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 4 | 2026-10-04T15:58:58Z | [] | 2024-11-20T02:59:35Z | [
"vision.image-diffusion"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 111 | [
"deep-learning",
"diffusion",
"image-interpolation",
"pytorch",
"stable-diffusion",
"text-to-image-generation"
] | 2026-09-09T10:39:14Z | https://github.com/QY-H00/attention-interpolation-diffusion | null |
[
"generative-ai",
"language",
"multimodal-learning",
"natural-language-processing"
] | [
"fine-tuning",
"instruction-tuning",
"language-model",
"pretraining"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.instruction-tuning",
"llm.language-models",
"llm.large-language-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-22T11:14:17Z | [Arxiv 2024] Official Implementation of the paper: "Towards Robust Instruction Tuning on Multimodal Large Language Models" | [
"generative-ai",
"language",
"multimodal-learning",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 1 | 761,721,691 | null | Jupyter Notebook | MIT | [
"language-model",
"pretraining"
] | declare-lab/Auto-Scaling | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-19T17:46:36Z | [] | 2025-12-05T03:51:00Z | [
"llm.large-language-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 9 | [
"instruction-following",
"large-language-models",
"llm",
"llm-robustness",
"multimodal-instruction-tuning",
"multimodal-large-language-models"
] | 2025-12-05T03:51:04Z | https://github.com/declare-lab/Auto-Scaling | null |
[
"deep-learning",
"graph-learning",
"reinforcement-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network",
"reinforcement-learning"
] | [
"description",
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"query-match",
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] | include | ml-contribution-v5 | 0 | [] | 2025-01-05T11:45:41Z | https://github.com/scoooofield/G2SNIA | null |
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-23T07:03:45Z | Source code for paper: Single Node Injection Label Specificity Attack on Graph Neural Networks via Reinforcement Learning | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-12T20:31:19Z | false | 0 | 762,142,567 | null | Python | MIT | [
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] | include | ml-contribution-v5 | 6 | [] | 2025-10-09T08:13:32Z | https://github.com/dychen1998/G2-SNIA | null |
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-23T10:12:05Z | The official code for CoT / ZSL reasoning framework 🧠, utilized in paper: "Large Language Models in Targeted Sentiment Analysis in Russian" | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-09T17:31:36Z | false | 1 | 762,215,378 | https://arxiv.org/abs/2404.12342 | Python | MIT | [
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] | nicolay-r/Reasoning-for-Sentiment-Analysis-Framework | [
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] | 2 | 2026-10-20T15:59:06Z | [] | 2025-08-08T09:29:26Z | [
"llm.large-language-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 4 | [
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] | 2025-08-08T09:29:30Z | https://github.com/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-23T11:52:16Z | Official implementation of Nemesis: Normalizing the Soft-prompt Vectors of Vision-Language Models (ICLR 2024 Spotlight) | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 1 | 762,253,178 | null | Python | MIT | [
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] | ShyFoo/Nemesis | [
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] | 4 | 2026-10-15T15:20:52Z | [] | 2026-07-19T05:48:21Z | [
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] | bede8784177184212f70e25301ed998109c7c28c | 2026-09-30T18:16:34Z | gh-ml-readme-evidence-v2 | "bede8784177184212f70e25301ed998109c7c28c" | [
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] | [
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] | ok | 2026-09-30T18:16:34Z | ShyFoo/Nemesis | readme-supported-paper-method-implementation | [
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] | include | ml-contribution-v5 | 16 | [
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] | 2026-07-19T05:48:25Z | https://github.com/ShyFoo/Nemesis | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-23T12:34:43Z | The repository explores a novel architecture performance in the domain of federated learning | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-12T20:31:19Z | false | 0 | 762,269,605 | null | Jupyter Notebook | null | [
"collaborative-learning",
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] | amankushwaha25/FedTEC | [
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] | 1 | 2026-10-12T20:31:19Z | [] | 2024-04-09T16:49:34Z | [
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] | include | ml-contribution-v5 | 0 | [] | 2024-11-27T03:34:19Z | https://github.com/amankushwaha25/FedTEC | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-24T07:30:37Z | [ECCV 2024] Official PyTorch implementation of Hybrid Video Diffusion Models with 2D Triplane and 3D Wavelet Representation | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-12T20:31:19Z | false | 0 | 762,612,039 | null | Python | null | [
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] | hxngiee/HVDM | [
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] | 2 | 2026-10-17T13:17:44Z | [] | 2024-11-29T05:50:36Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 12 | [] | 2025-11-21T05:57:12Z | https://github.com/hxngiee/HVDM | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-24T17:21:26Z | The official implementation of the paper "Towards Efficient Mixture of Experts: A Holistic Study of Compression Techniques (TMLR)". | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 7 | 762,783,783 | https://openreview.net/forum?id=HTpMOl6xSI | Python | Apache-2.0 | [
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] | CASE-Lab-UMD/Unified-MoE-Compression | [
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] | 21 | 2026-11-01T16:11:12Z | [] | 2026-10-02T20:32:05Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 92 | [
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] | 2026-10-02T20:32:10Z | https://github.com/CASE-Lab-UMD/Unified-MoE-Compression | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-24T20:09:54Z | code for paper "Entropy-regularized Diffusion Policy with Q-Ensembles for Offline Reinforcement Learning" | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 3 | 762,831,461 | null | Python | null | [
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] | ruoqizzz/entropy-offlineRL | [
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] | 5 | 2026-10-01T17:35:19Z | [] | 2024-02-24T20:13:33Z | [
"rl.offline"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 21 | [] | 2026-03-08T02:28:53Z | https://github.com/ruoqizzz/entropy-offlineRL | null |
[
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] | [
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-25T01:53:28Z | In this work, we introduce Region-Disentangled Diffusion Model (RDDM), a novel diffusion model designed to capture the complex temporal dynamics of ECG. | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 12 | 762,898,164 | null | Python | MIT | [
"diffusion"
] | DebadityaQU/RDDM | [
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"repository-metadata"
] | 2 | 2026-10-11T16:48:52Z | [] | 2024-05-08T20:53:36Z | [
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] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
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] | include | ml-contribution-v5 | 71 | [] | 2026-08-10T01:22:16Z | https://github.com/DebadityaQU/RDDM | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-26T07:31:32Z | The repository is the official implementation of the paper "To See the Big Picture: Time Series Forecasting with Distribution Shift Prediction." | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 0 | 763,382,831 | null | Jupyter Notebook | NOASSERTION | [
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] | lib1819-hrbust/distribution-shift-predictor | [
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] | 4 | 2026-10-18T14:44:29Z | [] | 2024-02-26T07:55:27Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2024-02-26T07:53:34Z | https://github.com/lib1819-hrbust/distribution-shift-predictor | null |
[
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-27T00:00:58Z | [CVPR 2024] "Data Poisoning based Backdoor Attacks to Contrastive Learning": official code implementation. | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-29T17:49:51Z | false | 2 | 763,800,542 | null | Python | null | [
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] | jzhang538/CorruptEncoder | [
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] | 1 | 2026-10-29T17:49:51Z | [] | 2025-02-10T05:38:22Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 16 | [] | 2025-06-14T02:31:45Z | https://github.com/jzhang538/CorruptEncoder | null |
[
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-27T07:49:57Z | [CVPR 2024] "MACE: Mass Concept Erasure in Diffusion Models" (Official Implementation) | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-30T16:30:43Z | false | 32 | 763,946,969 | null | Jupyter Notebook | NOASSERTION | [
"diffusion"
] | Shilin-LU/MACE | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-11T16:48:52Z | [] | 2026-07-20T11:58:00Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 391 | [
"diffusion-models",
"generative-ai",
"stable-diffusion",
"text-to-image"
] | 2026-10-09T17:45:36Z | https://github.com/Shilin-LU/MACE | null |
[
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"multimodal",
"multimodal-learning",
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-27T10:40:22Z | [CVPR 2024] Official PyTorch Code for "PromptKD: Unsupervised Prompt Distillation for Vision-Language Models" | [
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"multimodal",
"multimodal-learning",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T20:57:28.489717Z | false | 7 | 764,021,148 | https://zhengli97.github.io/PromptKD/ | Python | Apache-2.0 | [
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] | zhengli97/PromptKD | [
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"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 8 | 2026-10-11T16:48:52Z | [] | 2025-12-14T12:52:59Z | [
"vision.vision-language"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 348 | [
"clip",
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"knowledge-distillation",
"multi-modal-learning",
"prompt-learning",
"vision-language-model"
] | 2026-09-30T04:29:48Z | https://github.com/zhengli97/PromptKD | null |
[
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] | [
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-27T11:49:51Z | Official implementation of "DS-Agent: Automated Data Science by Empowering Large Language Models with Case-Based Reasoning" in ICML'24 | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T16:30:43Z | false | 35 | 764,050,131 | null | Python | null | [
"language-model",
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] | guosyjlu/DS-Agent | [
"description",
"query-match",
"repository-metadata"
] | 5 | 2026-10-19T17:46:36Z | [] | 2024-12-03T01:21:23Z | [
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] | f84c4b864d2fbb329d4dd2a7fb230eaf979d1585 | 2026-09-30T18:16:34Z | gh-ml-readme-evidence-v2 | "f84c4b864d2fbb329d4dd2a7fb230eaf979d1585" | [
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] | [
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] | ok | 2026-09-30T18:16:34Z | guosyjlu/DS-Agent | readme-supported-paper-method-implementation | [
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"ml-method-context",
"official-implementation-claim",
"paper-code-relationship",
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] | include | ml-contribution-v5 | 245 | [] | 2026-10-18T02:28:36Z | https://github.com/guosyjlu/DS-Agent | null |
[
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] | [
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] | [
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"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.large-language-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-27T13:27:40Z | Code for ACL 2024 paper "TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful Space" | [
"generative-ai",
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"language",
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-19T17:46:36Z | false | 8 | 764,096,791 | https://ictnlp.github.io/TruthX-site/ | Python | GPL-3.0 | [
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] | ictnlp/TruthX | [
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] | 1 | 2026-10-19T17:46:36Z | [] | 2024-03-26T10:48:15Z | [
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] | 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 | 145 | [
"baichuan",
"chatglm",
"chatgpt",
"explainable-ai",
"gpt-4",
"hallucination",
"hallucinations",
"language-model",
"llama",
"llama2",
"llama3",
"llm",
"llm-inference",
"llms",
"mistral",
"representation",
"safety",
"truthfulness"
] | 2026-09-08T15:38:43Z | https://github.com/ictnlp/TruthX | null |
[
"generative-modeling",
"reinforcement-learning",
"robotics"
] | [
"behavior-cloning",
"diffusion",
"imitation-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.imitation-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-27T19:23:35Z | Code for paper "Diff-Control: A stateful Diffusion-based Policy for Imitation Learning" (Liu et al., IROS 2024) | [
"generative-modeling",
"reinforcement-learning",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 3 | 764,267,901 | https://diff-control.github.io/ | Python | MIT | [
"behavior-cloning",
"diffusion",
"imitation-learning"
] | ir-lab/Diff-Control | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-01T17:35:19Z | [] | 2025-05-28T17:30:10Z | [
"rl.imitation-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 | 77 | [] | 2026-08-19T08:09:04Z | https://github.com/ir-lab/Diff-Control | null |
[
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion",
"restoration"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"vision.image-restoration"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-29T04:26:19Z | (CVPR2024) Official implementation of paper: "Selective Hourglass Mapping for Universal Image Restoration Based on Diffusion Model" | [
"computer-vision",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-30T16:30:43Z | false | 16 | 764,989,163 | null | Python | MIT | [
"diffusion",
"restoration"
] | iSEE-Laboratory/DiffUIR | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-16T15:36:56Z | [] | 2024-12-27T16:39:37Z | [
"vision.image-restoration"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 205 | [] | 2026-10-12T05:58:50Z | https://github.com/iSEE-Laboratory/DiffUIR | null |
[
"efficient-ml",
"model-compression"
] | [
"distillation",
"knowledge-distillation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-29T12:17:30Z | This repository contains official implementation of our code for the paper "Reverse Knowledge Distillation : Training a larger model with a smaller one" accepted for WACV 2024 | [
"efficient-ml",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 1 | 765,171,619 | null | Python | null | [
"distillation",
"knowledge-distillation"
] | NiharGupte/ReverseKnowledgeDistillation | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-02T16:59:40Z | [] | 2024-03-01T11:13:39Z | [
"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 | 4 | [] | 2025-12-09T10:49:04Z | https://github.com/NiharGupte/ReverseKnowledgeDistillation | null |
[
"3d",
"computer-vision",
"robotics"
] | [
"3d-object-detection",
"3d-perception",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.3d-object-detection"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-29T15:21:19Z | Official code of DeLiVoTr: Deep and Light-weight Voxel Transformer for 3D Object Detection paper. | [
"3d",
"computer-vision",
"robotics"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-16T15:36:56Z | false | 0 | 765,257,631 | null | Python | MIT | [
"3d-object-detection",
"3d-perception",
"transformer"
] | gopi-erabati/DeLiVoTr | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-16T15:36:56Z | [] | 2024-04-02T16:49:26Z | [
"multimodal.3d-object-detection"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 2 | [] | 2025-05-11T22:36:01Z | https://github.com/gopi-erabati/DeLiVoTr | null |
[
"computer-vision",
"health-and-biomedicine",
"medical-imaging"
] | [
"segmentation",
"transformer"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"medical.medical-image-segmentation",
"vision.segmentation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-02-29T17:05:06Z | The source code of our proposed model in the paper 'CTRANS: A MULTI-RESOLUTION CONVOLUTION-TRANSFORMER NETWORK FOR MEDICAL IMAGE SEGMENTATION' | [
"computer-vision"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 0 | 765,306,070 | null | Python | null | [
"segmentation",
"transformer"
] | naisops/CTranS | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-24T14:31:13Z | [] | 2024-02-29T17:28:54Z | [
"vision.segmentation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [] | 2024-09-10T22:49:02Z | https://github.com/naisops/CTranS | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-01T07:58:33Z | Official implementation for Hierarachical Diffusion Model in CVPR24 Template free reconstruction of human object interaction | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-11T16:48:52Z | false | 2 | 765,583,066 | null | Python | NOASSERTION | [
"diffusion"
] | xiexh20/HDM | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-11T16:48:52Z | [] | 2025-03-21T02:46:24Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 96 | [] | 2026-09-23T06:46:06Z | https://github.com/xiexh20/HDM | null |
[
"classical-ml",
"representation-learning"
] | [
"metric-learning",
"representation-learning",
"transformer"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.metric-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-02T05:38:42Z | [IEEE TIM] This is official code for paper "Few-Shot Bearing Fault Diagnosis via Ensembling Transformer-based Model with Mahalanobis Distance Metric Learning from Multiscale Features". IEEE Transactions on Instrumentation and Measurement (Accepted) | [
"classical-ml",
"representation-learning"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T15:29:21Z | false | 15 | 766,018,646 | null | Python | null | [
"metric-learning",
"representation-learning",
"transformer"
] | hmvu-nv/Few-shot-via-ensembling-Transformer-with-Mahalanobis-distance | [
"description",
"query-match",
"repository-metadata"
] | 6 | 2026-10-15T15:20:52Z | [] | 2025-01-15T05:51:08Z | [
"general.metric-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 | 150 | [] | 2026-09-07T01:25:59Z | https://github.com/hmvu-nv/Few-shot-via-ensembling-Transformer-with-Mahalanobis-distance | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"multimodal",
"vision"
] | [
"diffusion",
"generative-modeling",
"image-generation",
"text-to-image",
"video-generation"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"multimodal.text-to-image",
"vision.image-diffusion",
"vision.video-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-02T15:01:19Z | [AAAI 2025] Official pytorch implementation of "VideoElevator: Elevating Video Generation Quality with Versatile Text-to-Image Diffusion Models" | [
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 5 | 766,180,478 | https://videoelevator.github.io/ | Python | null | [
"diffusion",
"generative-modeling",
"video-generation"
] | YBYBZhang/VideoElevator | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 14 | 2026-10-16T15:36:56Z | [
"2403.05438"
] | 2024-04-07T07:49:32Z | [
"vision.video-generation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 163 | [
"text-to-video-generation",
"training-free",
"video-generation"
] | 2026-06-10T12:39:34Z | https://github.com/YBYBZhang/VideoElevator | null |
[
"generative-ai",
"language",
"multimodal-learning",
"natural-language-processing"
] | [
"fine-tuning",
"instruction-tuning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.instruction-tuning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-03T04:48:56Z | The original code for paper "Towards a Holistic Framework for Multimodal LLM in 3D Brain CT Radiology Report Generation" | [
"generative-ai",
"language",
"multimodal-learning",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-10T16:53:26Z | false | 8 | 766,375,966 | https://www.nature.com/articles/s41467-025-57426-0 | Python | MIT | [
"fine-tuning",
"instruction-tuning"
] | charlierabea/FORTE | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-10T16:53:26Z | [] | 2026-08-10T10:11:37Z | [
"llm.instruction-tuning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-context-only",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 51 | [] | 2026-09-14T08:20:45Z | https://github.com/charlierabea/FORTE | null |
[
"classical-ml",
"representation-learning"
] | [
"metric-learning",
"representation-learning",
"transformer"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.metric-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-03-03T06:25:42Z | This is official code for paper "Few-Shot Bearing Fault Diagnosis via Ensembling Transformer-based Model with Mahalanobis Distance Metric Learning from Multiscale Features". IEEE Transactions on Instrumentation and Measurement (Accepted) | [
"classical-ml",
"representation-learning"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-02T16:44:32Z | false | 1 | 766,397,151 | null | null | null | [
"metric-learning",
"representation-learning",
"transformer"
] | lyzl2010/Few-shot-via-ensembling-Transformer-with-Mahalanobis-distance | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-15T15:20:52Z | [] | 2024-03-02T05:45:55Z | [
"general.metric-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 | 12 | [] | 2026-03-30T08:44:51Z | https://github.com/lyzl2010/Few-shot-via-ensembling-Transformer-with-Mahalanobis-distance | null |
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