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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
[
"3d-vision",
"computer-vision",
"generative-ai",
"generative-modeling",
"language",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"vision"
] | [
"3d-perception",
"contrastive-learning",
"depth-estimation",
"diffusion",
"generative-modeling",
"image-generation",
"image-text",
"multimodal-learning",
"text-to-image",
"vision-language",
"vision-language-model"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"multimodal.text-to-image",
"multimodal.vision-language",
"multimodal.vlm",
"vision.depth-estimation",
"vision.image-diffusion",
"vision.vision-language"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-09-23T04:06:21Z | Official Implementation of "Magnet: We Never Know How Text-to-Image Diffusion Models Work, Until We Learn How Vision-Language Models Function" [NeurIPS 2024] | [
"3d-vision",
"computer-vision",
"generative-modeling",
"multimodal-learning",
"natural-language-processing"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 1 | 861,521,430 | null | Jupyter Notebook | null | [
"3d-perception",
"depth-estimation",
"diffusion"
] | I2-Multimedia-Lab/Magnet | [
"description",
"query-match",
"repository-metadata"
] | 7 | 2026-10-28T17:27:44Z | [] | 2024-12-02T05:41:00Z | [
"vision.depth-estimation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 31 | [] | 2026-03-27T20:49:24Z | https://github.com/I2-Multimedia-Lab/Magnet | null |
[
"computer-vision",
"language",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"vision"
] | [
"contrastive-learning",
"image-text",
"vision-language-model"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.vision-language",
"multimodal.vlm"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-09-23T10:16:19Z | [Technical Report] Official PyTorch implementation code for realizing the technical part of Phantom of Latent representing equipped with enlarged hidden dimension to build super frontier vision language models. | [
"computer-vision",
"language",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 4 | 861,671,114 | null | Python | MIT | [
"contrastive-learning",
"vision-language-model"
] | ByungKwanLee/Phantom | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-02T16:59:40Z | [
"2409.14713"
] | 2024-10-09T03:01:04Z | [
"multimodal.vision-language"
] | d9de1d6a605cea6e4d6bcd9bc2e374e23a20f92b | 2026-10-06T18:32:27Z | gh-ml-readme-evidence-v2 | "d9de1d6a605cea6e4d6bcd9bc2e374e23a20f92b" | [
"other",
"overview",
"results",
"usage"
] | [
"method-contribution",
"ml-method-context",
"official-implementation-claim",
"paper-reference"
] | ok | 2026-10-06T18:32:27Z | ByungKwanLee/Phantom | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-method-context",
"official-implementation-claim",
"paper-reference"
] | include | ml-contribution-v5 | 63 | [] | 2026-07-15T06:49:30Z | https://github.com/ByungKwanLee/Phantom | 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 | [] | 2024-09-23T14:29:31Z | Official implementation of paper: LiNo: Advancing Recursive Residual Decomposition of Linear and Nonlinear Patterns for Robust Time Series Forecasting. | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-22T16:09:07Z | false | 0 | 861,798,483 | null | Shell | null | [
"forecasting",
"sequence-modeling"
] | Levi-Ackman/LiNo | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-22T16:09:07Z | [] | 2025-12-19T08:24:20Z | [
"timeseries.forecasting"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 18 | [] | 2026-02-23T17:03:59Z | https://github.com/Levi-Ackman/LiNo | null |
[
"computer-vision",
"generative-ai"
] | [
"diffusion",
"generative-modeling",
"restoration",
"super-resolution"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.image-diffusion",
"vision.image-super-resolution"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-09-23T16:28:02Z | Official implementation of S3Diff | [
"computer-vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-30T16:30:43Z | false | 20 | 861,863,183 | null | Python | Apache-2.0 | [
"restoration",
"super-resolution"
] | ArcticHare105/S3Diff | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 8 | 2026-10-25T15:11:36Z | [
"2409.17058"
] | 2024-11-09T04:27:26Z | [
"vision.image-super-resolution"
] | 97895def26841ebd5cb0e6a7c6678cde210128bf | 2026-10-06T18:32:27Z | gh-ml-readme-evidence-v2 | "97895def26841ebd5cb0e6a7c6678cde210128bf" | [
"abstract",
"citation",
"dataset",
"installation",
"other",
"overview",
"usage"
] | [
"method-contribution",
"ml-method-context",
"paper-reference"
] | ok | 2026-10-06T18:32:27Z | ArcticHare105/S3Diff | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-method-context",
"paper-reference"
] | include | ml-contribution-v5 | 225 | [] | 2026-09-10T13:18:21Z | https://github.com/ArcticHare105/S3Diff | null |
[
"climate-science",
"earth-science",
"meteorology"
] | [
"climate-modeling",
"downscaling",
"weather-forecasting"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"science.climate-modeling"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-09-23T20:59:10Z | Official implementation of Learning Dissipative Chaos In A Linear Way | [
"climate-science",
"earth-science",
"meteorology"
] | [
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T16:30:43Z | false | 3 | 861,992,527 | null | Jupyter Notebook | Apache-2.0 | [
"climate-modeling",
"downscaling",
"weather-forecasting"
] | Hy23333/PFNN | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-13T18:05:26Z | [] | 2025-02-06T17:05:35Z | [
"science.climate-modeling"
] | d615379c321ed9e38ce859742378ece5e963c8ef | 2026-10-06T18:32:27Z | gh-ml-readme-evidence-v2 | "d615379c321ed9e38ce859742378ece5e963c8ef" | [
"installation",
"other"
] | [
"method-contribution",
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-10-06T18:32:27Z | Hy23333/PFNN | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-context-only",
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 16 | [
"chaotic-dynamical-systems",
"machine-learning"
] | 2026-07-19T10:47:48Z | https://github.com/Hy23333/PFNN | null |
[
"efficient-ml",
"machine-learning-systems",
"model-compression",
"natural-language-processing"
] | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-09-24T02:16:57Z | [EMNLP 2024] Official implementation of "Rethinking Pruning Large Language Models: Benefits and Pitfalls of Reconstruction Error Minimization" | [
"efficient-ml",
"machine-learning-systems",
"model-compression",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T16:44:32Z | false | 4 | 862,080,663 | https://arxiv.org/abs/2406.15524 | Python | null | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | LOG-postech/rethinking-LLM-pruning | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | 3 | 2026-10-23T15:59:01Z | [] | 2025-02-21T08:26:40Z | [
"efficiency.pruning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 29 | [] | 2026-09-16T10:49:39Z | https://github.com/LOG-postech/rethinking-LLM-pruning | null |
[
"computer-vision",
"image-processing",
"low-level-vision"
] | [
"image-enhancement",
"image-restoration",
"low-light-enhancement",
"mamba"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"recall26.low-light-enhancement"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-09-25T18:17:10Z | Official implementation of ECMamba, which is accpeted by NeurIPS 2024. | [
"computer-vision",
"image-processing",
"low-level-vision"
] | [
"deep-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T16:30:43Z | false | 2 | 863,108,398 | https://arxiv.org/pdf/2410.21535 | Python | Apache-2.0 | [
"image-enhancement",
"image-restoration",
"low-light-enhancement",
"mamba"
] | LowLevelAI/ECMamba | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 2 | 2026-10-03T15:45:52Z | [] | 2025-06-03T19:11:26Z | [
"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 | 41 | [
"deep-learning-algorithms",
"exposure-correction",
"image-restoration",
"mamba",
"retinex"
] | 2026-09-03T04:44:05Z | https://github.com/LowLevelAI/ECMamba | null |
[
"trustworthy-ml"
] | [
"adversarial-robustness",
"robust-optimization",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"trust.adversarial-robustness"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-09-25T20:34:32Z | This repository is the official implementation of the paper "Adversarial Robust Decision Transformer: Enhancing Robustness of RvS via Minimax Returns-to-go", NeurIPS 2024. | [
"trustworthy-ml"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 0 | 863,165,202 | null | null | Apache-2.0 | [
"adversarial-robustness",
"robust-optimization",
"transformer"
] | xiaohangt/adversarial_robust_dt | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-01T17:50:46Z | [] | 2024-09-25T20:34:33Z | [
"trust.adversarial-robustness"
] | 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 | [] | 2024-09-25T20:34:36Z | https://github.com/xiaohangt/adversarial_robust_dt | null |
[
"computer-vision",
"generative-ai",
"language",
"multimodal",
"multimodal-learning",
"natural-language-processing"
] | [
"fine-tuning",
"instruction-tuning",
"multimodal-learning",
"vision-language"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.instruction-tuning",
"vision.vision-language"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-09-26T04:15:13Z | [ICML 2025] Official implementation of paper 'Look Twice Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in Multimodal Large Language Models'. | [
"computer-vision",
"multimodal",
"multimodal-learning",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-17T13:17:44Z | false | 7 | 863,301,811 | null | Python | Apache-2.0 | [
"multimodal-learning",
"vision-language"
] | 1zhou-Wang/MemVR | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-20T15:59:06Z | [] | 2025-09-25T08:04:00Z | [
"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 | 175 | [] | 2026-10-20T02:44:31Z | https://github.com/1zhou-Wang/MemVR | null |
[
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion",
"generative-modeling",
"video-generation"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.video-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-09-26T08:14:23Z | Official PyTorch implementation of the paper "MultiSpectral diffusion: joint generation of wavelet coefficients for image synthesis and upsampling" | [
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"generative-model",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 0 | 863,395,058 | null | Python | MIT | [
"diffusion",
"generative-modeling",
"video-generation"
] | ImanGoudarzvand/MultiSpectral-Diffusion | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-23T15:59:01Z | [] | 2024-10-20T17:00:48Z | [
"vision.video-generation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 5 | [
"deep-generative-models",
"diffusion-models",
"image-generation",
"image-upsampling",
"multitask-learning",
"wavelet-transform"
] | 2024-11-10T19:54:28Z | https://github.com/ImanGoudarzvand/MultiSpectral-Diffusion | null |
[
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"forecasting",
"sequence-modeling",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"timeseries.forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-09-26T16:11:03Z | [NeurIPS 2024] Official implementation of the paper "Are Self-Attentions Effective for Time Series Forecasting?" | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"deep-learning",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T16:30:43Z | false | 10 | 863,627,495 | https://arxiv.org/abs/2405.16877 | Python | MIT | [
"forecasting",
"sequence-modeling",
"transformer"
] | dongbeank/CATS | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 4 | 2026-10-22T16:09:07Z | [] | 2024-12-31T11:23:33Z | [
"timeseries.forecasting"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 64 | [
"deep-learning",
"time-series-forecasting",
"transformer-architecture"
] | 2026-06-16T23:24:24Z | https://github.com/dongbeank/CATS | null |
[
"generative-ai",
"language",
"natural-language-processing"
] | [
"language-model",
"long-context-modeling"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.long-context"
] | true | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-09-27T05:22:50Z | Official implementation for "Law of the Weakest Link: Cross capabilities of Large Language Models" | [
"generative-ai",
"language",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 2 | 863,886,841 | https://www.llm-cross-capabilities.org | Python | NOASSERTION | [
"language-model",
"long-context-modeling"
] | facebookresearch/llm-cross-capabilities | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-04T15:58:58Z | [
"2409.19951"
] | 2024-10-01T07:07:41Z | [
"llm.long-context"
] | 15847433c32d7ce4836b60a9aceb2027ad37b3ab | 2026-10-06T18:32:27Z | gh-ml-readme-evidence-v2 | "15847433c32d7ce4836b60a9aceb2027ad37b3ab" | [
"dataset",
"installation",
"method",
"other"
] | [
"method-contribution",
"ml-method-context",
"paper-reference"
] | ok | 2026-10-06T18:32:27Z | facebookresearch/llm-cross-capabilities | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-context-only",
"ml-method-context",
"paper-reference"
] | include | ml-contribution-v5 | 43 | [] | 2025-10-09T07:27:54Z | https://github.com/facebookresearch/llm-cross-capabilities | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"recall.name.diffusion-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-09-27T08:33:53Z | Official implementation of NeurIPS'24 paper Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-17T13:17:44Z | false | 5 | 863,962,594 | null | Python | MIT | [
"diffusion"
] | Darkbblue/generic-diffusion-feature | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-28T16:14:12Z | [] | 2026-10-09T09:54:21Z | [
"recall.name.diffusion-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 40 | [] | 2026-10-09T09:54:25Z | https://github.com/Darkbblue/generic-diffusion-feature | null |
[
"generative-modeling",
"time-series",
"time-series-and-forecasting"
] | [
"diffusion",
"sequence-modeling",
"transformer"
] | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"timeseries.topic"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-09-28T03:27:44Z | [ICML 2025] Official PyTorch implementation of the paper: 🎯 TimeDART: A Diffusion Autoregressive Transformer for Self-Supervised Time Series Representation | [
"generative-modeling",
"time-series",
"time-series-and-forecasting"
] | [
"self-supervised-learning",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T16:44:32Z | false | 13 | 864,366,462 | https://arxiv.org/abs/2410.05711 | Python | null | [
"diffusion",
"sequence-modeling",
"transformer"
] | ustc-time-series/TimeDART | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | 4 | 2026-10-23T15:59:01Z | [] | 2025-12-17T07:55:21Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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"ml-method-cue",
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 159 | [
"autoregressive",
"diffusion",
"pretraining",
"time-series"
] | 2026-10-11T07:59:58Z | https://github.com/ustc-time-series/TimeDART | null |
[
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] | [
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] | [
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"query-match",
"repository-metadata"
] | [
"general.arxiv",
"timeseries.forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-09-28T06:20:37Z | Official implementation of "TimeBridge: Non-Stationarity Matters for Long-term Time Series Forecasting" (ICML 2025) | [
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"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-30T16:30:43Z | false | 16 | 864,408,140 | null | Python | MIT | [
"forecasting",
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] | Hank0626/TimeBridge | [
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"repository-metadata"
] | 5 | 2026-10-22T16:09:07Z | [] | 2025-05-16T04:33:15Z | [
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] | 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 | 225 | [] | 2026-10-20T10:14:41Z | https://github.com/Hank0626/TimeBridge | null |
[
"deep-learning",
"graph-learning",
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] | [
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] | [
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"github-topics",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-09-28T08:33:16Z | Official code for our 2026 IEEE Access paper — An interpretable multi-task graph neural network framework for skin lesion classification and morph-colorimetric attribute diagnosis. | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-16T15:36:56Z | false | 0 | 864,445,340 | https://ieeexplore.ieee.org/document/11398062 | Jupyter Notebook | null | [
"graph-neural-network",
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] | Jerlshin/MultiTaskLearning_SkinLessionDiagnosis | [
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"repository-metadata"
] | 1 | 2026-10-16T15:36:56Z | [] | 2026-06-02T09:49:16Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 1 | [
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"multi-task-learning",
"pytorch-geometric",
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] | 2026-06-02T23:53:42Z | https://github.com/Jerlshin/MultiTaskLearning_SkinLessionDiagnosis | null |
[
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] | [
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] | [
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] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-09-28T11:12:11Z | Official code for the paper "Flow-Attentional Graph Neural Networks" published in TMLR (2025). | [
"deep-learning",
"graph-learning"
] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-15T15:20:52Z | false | 0 | 864,492,427 | null | Python | null | [
"graph-neural-network",
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] | pasplett/FlowGNN | [
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] | 1 | 2026-10-15T15:20:52Z | [] | 2025-09-25T06:08:56Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 3 | [] | 2026-10-08T19:26:00Z | https://github.com/pasplett/FlowGNN | null |
[
"distributed-ml",
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] | [
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"federated-learning"
] | [
"description",
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"repository-metadata"
] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-09-28T11:37:18Z | This is the official code for paper 'Shadow defense against gradient inversion attack in federated learning'. (coming soon) | [
"distributed-ml",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-14T16:10:46Z | false | 2 | 864,499,559 | null | Python | null | [
"collaborative-learning",
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] | tekap404/ShadowDef | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-14T16:10:46Z | [] | 2025-09-17T14:18:39Z | [
"trust.federated-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 3 | [] | 2026-05-11T14:00:47Z | https://github.com/tekap404/ShadowDef | null |
[
"information-retrieval",
"natural-language-processing"
] | [
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] | [
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"repository-metadata"
] | [
"nlp.rag",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-09-28T19:45:00Z | AT-RAG (Adaptive Retrieval-Augmented Generation) is a novel RAG model developed to address the challenges of complex multi-hop queries | [
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] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-01T17:35:19Z | false | 1 | 864,653,119 | https://arxiv.org/abs/2410.12886 | Python | NOASSERTION | [
"rag",
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] | MrRezaeiUofT/AT-RAG | [
"description",
"github-topics",
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"query-match",
"repository-metadata"
] | 2 | 2026-10-14T16:10:46Z | [] | 2025-07-06T00:48:07Z | [
"nlp.rag"
] | 0e18a98197fe40f0ea3af81e7699d151e3819d3e | 2026-10-01T19:11:32Z | gh-ml-readme-evidence-v2 | "0e18a98197fe40f0ea3af81e7699d151e3819d3e" | [
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] | [
"method-contribution",
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] | ok | 2026-10-01T19:11:32Z | MrRezaeiUofT/AT-RAG | specific-method-with-novelty-claim | [
"method-contribution",
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue",
"paper-reference"
] | include | ml-contribution-v5 | 10 | [
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"multihop",
"multihop-question-answering",
"qa",
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] | 2026-03-03T22:23:17Z | https://github.com/MrRezaeiUofT/AT-RAG | null |
[
"general-ml",
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"language",
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] | [
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] | [
"general.topic-deep-learning",
"general.topic-machine-learning",
"llm.language-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-09-29T00:21:20Z | Official Implementation for EMNLP 2024 (Main Track, Oral) "AgentReview: Exploring Academic Peer Review with LLM Agent." | [
"generative-ai",
"language",
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"social-sciences"
] | [
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"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T18:03:22.392067Z | false | 75 | 864,708,082 | https://agentreview.github.io/ | Jupyter Notebook | Apache-2.0 | [
"language-model",
"pretraining"
] | Ahren09/AgentReview | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-13T18:05:26Z | [] | 2026-05-10T21:39:43Z | [
"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"
] | include | ml-contribution-v5 | 575 | [
"agent",
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"emnlp",
"emnlp2024",
"gpt-4",
"gpt-4o",
"large-language-models",
"llm",
"machine-learning"
] | 2026-10-13T18:06:12Z | https://github.com/Ahren09/AgentReview | null |
[
"generative-ai",
"multimodal",
"reinforcement-learning",
"vision"
] | [
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"reinforcement-learning",
"text-to-image"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.text-to-image"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-09-29T02:04:09Z | [NeurIPS 2023] Official code release for the paper: "Can Pre-Trained Text-to-Image Models Generate Visual Goals for Reinforcement Learning?" | [
"generative-ai",
"multimodal",
"reinforcement-learning",
"vision"
] | [
"reinforcement-learning",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-07T18:07:52Z | false | 0 | 864,726,113 | null | Python | null | [
"image-generation",
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] | gaojl19/LfVoid | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-07T18:07:52Z | [] | 2024-09-29T02:38:42Z | [
"multimodal.text-to-image"
] | 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 | [] | 2025-06-26T07:24:04Z | https://github.com/gaojl19/LfVoid | 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 | [] | 2024-09-29T10:13:19Z | [NeurIPS 2024] The official implementation of "Image Copy Detection for Diffusion Models" | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model",
"embedding"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-18T14:44:29Z | false | 1 | 864,844,319 | null | Python | NOASSERTION | [
"diffusion"
] | WangWenhao0716/PDF-Embedding | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-18T14:44:29Z | [] | 2024-10-01T05:35:34Z | [
"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 | 18 | [] | 2026-02-03T06:16:15Z | https://github.com/WangWenhao0716/PDF-Embedding | null |
[
"generative-ai",
"generative-modeling",
"multimodal",
"video"
] | [
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] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"multimodal.image-to-video"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-09-29T10:33:49Z | The official code of paper "LVCD: Reference-based Lineart Video Colorization with Diffusion Models" | [
"generative-ai",
"generative-modeling"
] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-01T17:35:19Z | false | 20 | 864,849,999 | null | Python | null | [
"diffusion"
] | luckyhzt/LVCD | [
"description",
"query-match",
"repository-metadata"
] | 5 | 2026-10-17T13:17:44Z | [] | 2025-01-06T04:39:52Z | [
"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 | 200 | [] | 2026-09-01T04:00:34Z | https://github.com/luckyhzt/LVCD | null |
[
"interpretability-and-safety",
"multimodal",
"natural-language-processing",
"reinforcement-learning",
"robotics"
] | [
"environment-modeling",
"world-model"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.world-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-09-29T13:39:25Z | Official code for the paper: WALL-E: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents | [
"interpretability-and-safety",
"multimodal",
"natural-language-processing",
"reinforcement-learning",
"robotics"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 0 | 864,906,091 | null | Python | null | [
"environment-modeling",
"world-model"
] | elated-sawyer/WALL-E | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-02T16:44:32Z | [] | 2025-12-03T00:36:36Z | [
"multimodal.world-models"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 70 | [] | 2026-10-01T11:31:33Z | https://github.com/elated-sawyer/WALL-E | null |
[
"planning",
"robotics",
"robotics-and-control"
] | [
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"neural-network",
"robotics"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"robotics.robot-motion-planning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-09-30T12:37:30Z | Official implementation accompanying the paper Safe and Stable Neural Network Dynamical Systems for Robot Motion Planning (S2-NNDS) accepted for publication at IEEE Robotics and Automation Letters(RA-L). | [
"planning",
"robotics",
"robotics-and-control"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 0 | 865,376,566 | null | Python | null | [
"motion-planning",
"neural-network",
"robotics"
] | allemmbinn/S2NNDS | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-01T17:35:19Z | [] | 2026-04-15T04:11:33Z | [
"robotics.robot-motion-planning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 6 | [] | 2026-06-11T05:34:46Z | https://github.com/allemmbinn/S2NNDS | null |
[
"computer-vision",
"generative-modeling",
"multimodal-learning"
] | [
"diffusion"
] | [
"description",
"github-topics",
"license-metadata",
"repository-metadata"
] | [] | false | unknown | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-09-30T14:17:35Z | [AAAI 2025] Official Implementation for "Click2Mask: Local Editing with Dynamic Mask Generation" Paper. | [
"computer-vision",
"generative-modeling",
"multimodal-learning"
] | [
"deep-learning",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-31T18:35:38Z | false | 4 | 865,431,221 | https://omeregev.github.io/click2mask/ | Python | NOASSERTION | [
"diffusion"
] | omeregev/click2mask | [
"description",
"github-topics",
"license-metadata",
"repository-metadata"
] | 1 | 2026-10-31T18:35:38Z | [
"2409.08272"
] | 2026-01-22T08:41:55Z | [] | 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 | 21 | [
"computer-vision",
"deep-learning",
"diffusion-models",
"generative-ai",
"image-generation",
"multimodal-deep-learning",
"text-guided-image-manipulation",
"text-to-image"
] | 2026-03-30T17:36:58Z | https://github.com/omeregev/click2mask | {"discovery_source":"hf_daily_papers","paper_evidence":"unverified","queryless":true} |
[
"computer-vision",
"generative-modeling"
] | [
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"flow-matching",
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] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.image-restoration",
"vision.topic-computer-vision"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-09-30T23:39:45Z | [ICLR 2025] Official implementation of Posterior-Mean Rectified Flow: Towards Minimum MSE Photo-Realistic Image Restoration | [
"computer-vision",
"generative-modeling"
] | [
"generative-model",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 42 | 865,674,064 | https://pmrf-ml.github.io | Python | MIT | [
"diffusion",
"flow-matching",
"restoration"
] | ohayonguy/PMRF | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 15 | 2026-10-21T15:48:06Z | [
"2410.00418"
] | 2025-02-05T08:32:35Z | [
"vision.image-restoration"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 753 | [
"blind-face-restoration",
"colorization",
"computer-vision",
"denoising",
"diffusion-models",
"flow-matching",
"generative-models",
"image-manipulation",
"image-processing",
"image-restoration",
"inpainting",
"inverse-problems",
"rectified-flow"
] | 2026-10-20T13:38:00Z | https://github.com/ohayonguy/PMRF | null |
[
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion",
"generative-modeling",
"image-to-image-translation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.image-to-image-translation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-01T12:00:12Z | the official code of "Diffusion-Based Image-to-Image Translation by Noise Correction via Prompt Interpolation" (ECCV2024) | [
"computer-vision",
"generative-ai",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-01T17:35:19Z | false | 0 | 865,954,301 | null | Python | MIT | [
"diffusion",
"generative-modeling",
"image-to-image-translation"
] | jslee525/PIC | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-23T15:59:01Z | [] | 2025-01-14T06:04:03Z | [
"vision.image-to-image-translation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 13 | [] | 2025-11-11T06:11:07Z | https://github.com/jslee525/PIC | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"multimodal",
"vision"
] | [
"diffusion",
"generative-modeling",
"image-generation",
"text-to-image"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"multimodal.text-to-image",
"vision.image-diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-02T04:27:26Z | [ICLR 2025] Official PyTorch Implementation for CPE: Concept Pinpoint Eraser for Text-to-image Diffusion Models via Residual Attention Gate | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-07T18:07:52Z | false | 0 | 866,339,000 | null | Python | null | [
"diffusion"
] | Hyun1A/CPE | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-10-18T14:44:29Z | [] | 2025-04-07T06:04:02Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 13 | [] | 2026-04-20T11:42:13Z | https://github.com/Hyun1A/CPE | null |
[
"generative-ai",
"language",
"natural-language-processing",
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] | [
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] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.preference-optimization"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-02T06:28:53Z | [EMNLP Findings 2024] Official Code for "LLMs Cannot (Yet) Match the Specificity and Simplicity of Online Finance Communities in Long Form Question Answering" | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-03T15:45:52Z | false | 0 | 866,375,329 | null | Python | MIT | [
"alignment",
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] | kris-fillip/SocialFinanceQA | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-03T15:45:52Z | [] | 2024-11-25T06:12:42Z | [
"llm.preference-optimization"
] | 9dc243d25ea79f5ff634fe24570eaed3c0e293a5 | 2026-10-06T18:32:27Z | gh-ml-readme-evidence-v2 | "9dc243d25ea79f5ff634fe24570eaed3c0e293a5" | [
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] | [
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"ml-method-context",
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] | ok | 2026-10-06T18:32:27Z | kris-fillip/SocialFinanceQA | readme-supported-paper-method-implementation | [
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] | include | ml-contribution-v5 | 1 | [] | 2026-04-01T19:41:25Z | https://github.com/kris-fillip/SocialFinanceQA | 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 | [] | 2024-10-02T15:08:05Z | [NeurIPS 2024] Official PyTorch implementation for the paper "AdjointDEIS: Efficient Gradients for Diffusion Models" | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-19T17:46:36Z | false | 0 | 866,610,586 | https://zblasingame.github.io/AdjointDEIS/ | Python | MIT | [
"diffusion"
] | zblasingame/AdjointDEIS | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-19T17:46:36Z | [] | 2026-05-24T13:13:04Z | [
"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 | [
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"gradient-descent",
"guided-diffusion",
"numerical-methods",
"ode-solver",
"sde",
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] | 2026-05-24T13:13:07Z | https://github.com/zblasingame/AdjointDEIS | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"multimodal",
"time-series-and-forecasting",
"video"
] | [
"diffusion",
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] | [
"description",
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"repository-metadata"
] | [
"multimodal.image-to-video",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-03T09:31:30Z | Code for Paper 'Redefining Temporal Modeling in Video Diffusion: The Vectorized Timestep Approach' | [
"computer-vision",
"generative-ai",
"generative-modeling",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 1 | 867,006,926 | null | Python | Apache-2.0 | [
"diffusion",
"generative-modeling",
"video-generation"
] | Yaofang-Liu/FVDM | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-22T16:09:07Z | [
"2410.03160"
] | 2026-01-02T10:21:51Z | [
"vision.video-generation"
] | 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 | 36 | [] | 2026-04-21T08:59:05Z | https://github.com/Yaofang-Liu/FVDM | null |
[
"generative-ai",
"generative-modeling",
"graph-learning"
] | [
"flow-matching",
"generative-modeling",
"graph-generation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.graph-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-03T10:38:41Z | Official implementation of the paper: "DeFoG: Discrete Flow Matching for Graph Generation" | [
"generative-ai",
"generative-modeling",
"graph-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 22 | 867,036,453 | null | Python | MIT | [
"flow-matching",
"generative-modeling",
"graph-generation"
] | manuelmlmadeira/DeFoG | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-20T15:59:06Z | [] | 2026-02-03T09:14:39Z | [
"graph.graph-generation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 73 | [] | 2026-10-19T07:35:02Z | https://github.com/manuelmlmadeira/DeFoG | null |
[
"deep-learning",
"graph-learning",
"natural-language-processing"
] | [
"graph-neural-network",
"message-passing",
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] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-04T01:59:42Z | LGLoc as a New Language Model-Driven Graph Neural Network for mRNA Localization | [
"deep-learning",
"graph-learning",
"natural-language-processing"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-15T15:20:52Z | false | 0 | 867,400,973 | null | Python | MIT | [
"graph-neural-network",
"message-passing",
"neural-network"
] | Aref-Shahbakhsh/LGLoc | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-15T15:20:52Z | [] | 2025-05-28T19:56:19Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 2 | [] | 2025-08-31T05:23:30Z | https://github.com/Aref-Shahbakhsh/LGLoc | null |
[
"generative-ai",
"generative-modeling"
] | [
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"low-rank-adaptation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-04T03:32:51Z | The official implementation of the "AutoLoRA: AutoGuidance Meets Low-Rank Adaptation for Diffusion Models" paper | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-18T14:44:29Z | false | 0 | 867,424,746 | null | null | null | [
"diffusion",
"low-rank-adaptation"
] | gmum/AutoLoRA | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-18T14:44:29Z | [] | 2024-10-04T03:48:23Z | [
"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 | 7 | [] | 2024-12-12T20:25:26Z | https://github.com/gmum/AutoLoRA | null |
[
"computer-vision",
"language",
"multimodal",
"multimodal-learning",
"science-and-engineering",
"vision"
] | [
"change-detection",
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"vision-language",
"vision-language-model"
] | [
"description",
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"query-match",
"repository-metadata"
] | [
"multimodal.vision-language",
"vision.change-detection",
"vision.vision-language"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-04T18:55:04Z | Official code for TEOChat, the first vision-language assistant for temporal earth observation data (ICLR 2025). | [
"computer-vision",
"multimodal",
"multimodal-learning",
"science-and-engineering"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-30T16:30:43Z | false | 6 | 867,805,986 | https://arxiv.org/abs/2410.06234 | Python | Apache-2.0 | [
"multimodal-learning",
"vision-language"
] | ermongroup/TEOChat | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 3 | 2026-10-20T15:59:06Z | [] | 2025-12-01T21:50:17Z | [
"vision.vision-language"
] | 350c9ce335675b3cd07430ad43cc85a8aafb52a4 | 2026-10-07T19:34:31Z | gh-ml-readme-evidence-v2 | "350c9ce335675b3cd07430ad43cc85a8aafb52a4" | [
"citation",
"dataset",
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"method",
"other",
"usage"
] | [
"method-contribution",
"ml-method-context",
"paper-reference"
] | ok | 2026-10-07T19:34:31Z | ermongroup/TEOChat | readme-supported-paper-method-implementation | [
"method-contribution",
"ml-method-context",
"paper-reference"
] | include | ml-contribution-v5 | 157 | [] | 2026-10-14T06:36:11Z | https://github.com/ermongroup/TEOChat | null |
[
"generative-ai",
"generative-modeling",
"multimodal-learning"
] | [
"diffusion"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"recall.name.diffusion-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-05T08:39:17Z | This is the official code of the NeurIPS 2024 paper "Text-DiFuse: An Interactive Multi-Modal Image Fusion Framework based on Text-modulated Diffusion Model" | [
"generative-ai",
"generative-modeling",
"multimodal-learning"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-18T13:47:34Z | false | 1 | 868,012,167 | null | Jupyter Notebook | MIT | [
"diffusion"
] | Leiii-Cao/Text-DiFuse | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-28T16:14:12Z | [] | 2026-10-10T07:26:33Z | [
"recall.name.diffusion-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 40 | [] | 2026-10-27T07:45:21Z | https://github.com/Leiii-Cao/Text-DiFuse | null |
[
"computer-vision",
"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 | [] | 2024-10-05T11:55:18Z | [NeurIPS2025 spotlight★] Official implementation for "RepLDM: Reprogramming Pretrained Latent Diffusion Models for High-Quality, High-Efficiency, High-Resolution Image Generation". | [
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-15T15:20:52Z | false | 41 | 868,069,349 | https://kmittle.github.io/project_pages/RepLDM/ | Python | Apache-2.0 | [
"diffusion"
] | kmittle/RepLDM | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-17T13:17:44Z | [] | 2026-09-07T11:01:28Z | [
"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 | 298 | [] | 2026-05-23T08:10:25Z | https://github.com/kmittle/RepLDM | 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 | [] | 2024-10-05T16:34:33Z | Official pytorch implementation of the paper: "Classification Diffusion Models: Revitalizing Density Ratio Estimation" | [
"generative-ai",
"generative-modeling"
] | [
"classifier",
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-18T14:44:29Z | false | 2 | 868,164,268 | null | Python | MIT | [
"diffusion"
] | shaharYadin/CDM | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-18T14:44:29Z | [] | 2024-10-17T07:20:46Z | [
"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 | 6 | [] | 2025-09-22T16:44:02Z | https://github.com/shaharYadin/CDM | null |
[
"computer-vision"
] | [
"action-recognition",
"mamba",
"video-understanding"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.action-recognition"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-05T18:52:42Z | This is the official implementation of our paper Simba: Mamba augmented U-ShiftGCN for Skeletal Action Recognition in Videos | [
"computer-vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-30T16:30:43Z | false | 1 | 868,208,433 | null | Python | Apache-2.0 | [
"action-recognition",
"mamba",
"video-understanding"
] | Soumyabrata2003/Simba | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-05T20:10:04Z | [] | 2025-04-02T09:39:34Z | [
"vision.action-recognition"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 15 | [] | 2026-03-13T14:29:52Z | https://github.com/Soumyabrata2003/Simba | null |
[
"generative-modeling"
] | [
"flow-matching"
] | [
"description",
"github-topics",
"license-metadata",
"repository-metadata"
] | [] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-05T23:50:10Z | Official implementation of the NeurIPS 24 paper of statistical flow matching (SFM) for discrete generation. | [
"generative-modeling"
] | [
"generative-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T20:57:28.489717Z | false | 3 | 868,274,208 | null | Jupyter Notebook | MIT | [
"flow-matching"
] | ccr-cheng/statistical-flow-matching | [
"description",
"github-topics",
"license-metadata",
"repository-metadata"
] | 1 | 2026-09-26T20:57:28.489717Z | [] | 2024-11-07T23:12:12Z | [] | 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 | 49 | [
"discrete-generative-models",
"flow-matching",
"information-geometry"
] | 2026-08-23T11:13:16Z | https://github.com/ccr-cheng/statistical-flow-matching | {"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["flow-matching"]} |
[
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-06T01:34:32Z | Official Implementation of KnobGen: Controlling the Sophistication of Artwork in Sketch-Based Diffusion Models (CVPR 2025) | [
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-19T17:46:36Z | false | 0 | 868,290,663 | https://arxiv.org/abs/2410.01595 | Python | MIT | [
"diffusion"
] | OSUPCVLab/KnobGen | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-10-19T17:46:36Z | [] | 2025-04-15T22:47:20Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 1 | [
"conditional-diffusion-model",
"diffusion-models",
"image-generation",
"stable-diffusion",
"text-guidance"
] | 2025-05-25T19:11:38Z | https://github.com/OSUPCVLab/KnobGen | null |
[
"generative-ai",
"generative-modeling",
"graph-learning"
] | [
"diffusion",
"generative-modeling",
"graph-generation"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.graph-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-06T02:00:50Z | The official implementation of "Advancing Graph Generation Through Beta Diffusion (ICLR 2025)" | [
"generative-ai",
"generative-modeling",
"graph-learning"
] | [
"generative-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-01T17:35:19Z | false | 1 | 868,295,059 | null | Python | MIT | [
"diffusion",
"generative-modeling",
"graph-generation"
] | xinyangATK/GraphBetaDiffusion | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-20T15:59:06Z | [] | 2025-07-08T15:39:38Z | [
"graph.graph-generation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 23 | [
"generative-model",
"graph-modeling"
] | 2026-09-24T06:51:13Z | https://github.com/xinyangATK/GraphBetaDiffusion | null |
[
"biology",
"computational-biology",
"generative-ai",
"generative-modeling",
"health-and-biomedicine",
"protein-engineering"
] | [
"diffusion",
"generative-modeling",
"inverse-folding",
"protein-design"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"bio.inverse-folding",
"bio.protein-design",
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-06T23:58:20Z | Code for paper: "Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein Design" | [
"generative-ai",
"generative-modeling",
"health-and-biomedicine"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 12 | 868,659,626 | null | Python | null | [
"diffusion"
] | ChenyuWang-Monica/DRAKES | [
"description",
"query-match",
"repository-metadata"
] | 7 | 2026-10-17T13:17:44Z | [] | 2025-05-10T08:53:39Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 78 | [] | 2026-09-06T11:18:06Z | https://github.com/ChenyuWang-Monica/DRAKES | null |
[
"efficient-ml",
"generative-ai",
"language",
"machine-learning-systems",
"model-compression",
"natural-language-processing"
] | [
"efficient-inference",
"quantization"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.quantization",
"llm.quantization"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-07T13:48:14Z | [ICML 2025] Official PyTorch implementation of "FlatQuant: Flatness Matters for LLM Quantization" | [
"efficient-ml",
"machine-learning-systems",
"model-compression",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 34 | 868,967,667 | null | Python | MIT | [
"quantization"
] | ruikangliu/FlatQuant | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 10 | 2026-10-15T15:20:52Z | [] | 2025-11-25T12:18:56Z | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-24T14:31:13Z | false | 8 | 869,892,130 | null | Python | Apache-2.0 | [
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] | yueyang130/DeeR-VLA | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-09T05:17:09Z | [ICLR 2025 Spotlight] The official implementation of the paper “LOKI：A Comprehensive Synthetic Data Detection Benchmark using Large Multimodal Models” | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-01T17:35:19Z | false | 5 | 869,902,870 | null | Python | null | [
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] | opendatalab/LOKI | [
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] | 3 | 2026-10-08T18:15:14Z | [] | 2026-02-07T07:16:03Z | [
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] | include | ml-contribution-v5 | 180 | [] | 2026-07-06T09:36:58Z | https://github.com/opendatalab/LOKI | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-09T07:28:01Z | Official implementation for "Cluster-wise Graph Transformer with Dual-granularity Kernelized Attention" (NeurIPS2024 Spotlight) | [
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] | [
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] | 2 | 2026-10-01T17:35:19Z | [] | 2024-10-10T16:34:49Z | [
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] | include | ml-contribution-v5 | 18 | [] | 2026-06-21T03:32:35Z | https://github.com/LUMIA-Group/Cluster-wise-Graph-Transformer | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-09T10:47:49Z | The official code of the "Efficient and Interpretable Grammatical Error Correction with Mixture of Experts" paper | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 0 | 870,044,394 | https://aclanthology.org/2024.findings-emnlp.997/ | Python | GPL-3.0 | [
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] | nusnlp/moece | [
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] | 3 | 2026-10-14T16:10:46Z | [] | 2025-04-26T06:14:02Z | [
"llm.language-models"
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] | include | ml-contribution-v5 | 8 | [
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] | 2026-03-23T12:37:59Z | https://github.com/nusnlp/moece | null |
[
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 2 | 870,131,620 | null | Python | MIT | [
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] | include | ml-contribution-v5 | 114 | [
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] | 2026-09-27T14:34:29Z | https://github.com/shikiw/Modality-Integration-Rate | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-10T03:40:28Z | [ICCV 2025] Official implementation of "Holistic Unlearning Benchmark: A Multi-Faceted Evaluation for Text-to-Image Diffusion Model Unlearning" | [
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] | include | ml-contribution-v5 | 23 | [] | 2026-04-14T07:02:18Z | https://github.com/ml-postech/HUB | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-10T04:07:41Z | Official code for [NeurIPS 2024] QuadMamba: Learning Quadtree-based Selective Scan for Visual State Space Model | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 5 | 870,448,358 | null | Python | MIT | [
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] | include | ml-contribution-v5 | 47 | [] | 2026-07-31T13:57:03Z | https://github.com/VISION-SJTU/QuadMamba | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-10T05:09:57Z | [NeurIPS' 24] The PyTorch implementation of our paper: "Kaleidoscope: Learnable Masks for Heterogeneous Multi-agent Reinforcement Learning". | [
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] | LXXXXR/Kaleidoscope | [
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] | 3 | 2026-10-02T16:59:40Z | [] | 2024-10-10T05:14:22Z | [
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] | include | ml-contribution-v5 | 22 | [] | 2026-08-05T22:56:39Z | https://github.com/LXXXXR/Kaleidoscope | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-11T16:51:44Z | The official code of the ACCV 2024 paper "Image Deraining with Frequency-Enhanced State Space Model (DFSSM)" | [
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] | ShugoYamashita/DFSSM | [
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] | 1 | 2026-10-01T17:50:46Z | [] | 2025-12-06T06:18:28Z | [
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] | include | ml-contribution-v5 | 17 | [
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] | 2026-07-07T12:39:16Z | https://github.com/ShugoYamashita/DFSSM | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-12T02:28:52Z | Official implementation of "Analysis of Influential Factors for the Prediction of YouTube Views Using Tree-based Machine Learning" (JDCS 2025) — a machine learning framework that predicts YouTube video views across categories using metadata and derived variables, and interprets key influencing factors through SHAP anal... | [
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] | 1 | 2026-10-04T15:58:58Z | [] | 2025-11-08T06:49:40Z | [
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] | include | ml-contribution-v5 | 1 | [] | 2025-11-08T17:47:36Z | https://github.com/jirungx/YouTube-View-Prediction | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-12T07:45:16Z | [NeurIPS 2024] Official implementation of "BELM: Bidirectional Explicit Linear Multi-step Sampler for Exact Inversion in Diffusion Models". | [
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] | include | ml-contribution-v5 | 143 | [
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] | 2026-08-24T07:46:46Z | https://github.com/zituitui/BELM | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-12T19:34:36Z | Official Pytorch implementation of MRM 2024 paper "Physics-Guided Self-Supervised Learning: Demonstration for Generalized RF Pulse Design" | [
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] | lphxx6222712/GPS_RF | [
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] | 1 | 2026-10-04T15:58:58Z | [] | 2024-10-14T21:39:48Z | [
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] | include | ml-contribution-v5 | 1 | [] | 2025-11-11T21:08:33Z | https://github.com/lphxx6222712/GPS_RF | null |
[
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"query-match",
"repository-metadata"
] | [
"general.self-supervised-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-12T21:38:02Z | Official Pytorch implementation of MRM 2024 paper "Physics-Guided Self-Supervised Learning: Demonstration for Generalized RF Pulse Design" | [
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[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-13T13:43:07Z | [WWW'25]The official implementation of Distributionally Robust Graph Out-of-Distribution Recommendation via Diffusion Model | [
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] | include | ml-contribution-v5 | 10 | [
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] | 2026-07-24T07:30:40Z | https://github.com/user683/DRGO | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-13T17:35:00Z | [ICLR 2025] Official implementation of DICL (Disentangled In-Context Learning), featured in the paper "Zero-shot Model-based Reinforcement Learning using Large Language Models". | [
"generative-ai",
"language",
"natural-language-processing",
"reinforcement-learning"
] | [
"large-language-model",
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 5 | 872,066,094 | https://iclr.cc/virtual/2025/poster/27971 | Jupyter Notebook | MIT | [
"language-model",
"pretraining",
"reinforcement-learning"
] | abenechehab/dicl | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-14T16:10:46Z | [] | 2025-02-14T14:03:12Z | [
"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 | 25 | [
"in-context-learning",
"large-language-models",
"model-based-reinforcement-learning",
"reinforcement-learning"
] | 2026-03-11T11:54:55Z | https://github.com/abenechehab/dicl | null |
[
"generative-ai",
"multimodal",
"vision"
] | [
"image-generation",
"text-to-image"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.text-to-image"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-13T17:35:53Z | [ICLR 2025] Official Implementation of Meissonic: Revitalizing Masked Generative Transformers for Efficient High-Resolution Text-to-Image Synthesis | [
"generative-ai",
"multimodal",
"vision"
] | [
"diffusion-model",
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-01T17:35:19Z | false | 11 | 872,066,430 | https://viiika.github.io/Meissonic/ | Python | Apache-2.0 | [
"image-generation",
"text-to-image"
] | viiika/Meissonic | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-07T18:07:52Z | [] | 2026-07-15T07:19:12Z | [
"multimodal.text-to-image"
] | 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 | 347 | [
"image-to-image",
"non-autoregressive-transformers",
"text-to-image"
] | 2026-09-25T18:43:50Z | https://github.com/viiika/Meissonic | null |
[
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion",
"generative-modeling"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"vision.image-diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-13T18:03:07Z | Code for our paper: "Where's Waldo: Diffusion Features For Personalized Segmentation and Retrieval". | [
"computer-vision",
"generative-ai",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-07T18:07:52Z | false | 5 | 872,075,257 | null | Python | null | [
"diffusion",
"generative-modeling"
] | dvirsamuel/PDM | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-08T18:15:14Z | [] | 2025-02-26T11:30:23Z | [
"vision.image-diffusion"
] | 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 | [] | 2026-03-24T05:55:10Z | https://github.com/dvirsamuel/PDM | null |
[
"computer-vision",
"generative-ai",
"multimodal",
"video"
] | [
"generative-modeling",
"text-to-video",
"video-generation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.text-to-video",
"vision.video-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-14T02:29:15Z | [CVPR 2025] Official implementation of ByTheWay: Boost Your Text-to-Video Generation Model to Higher Quality in a Training-free Way | [
"computer-vision",
"generative-ai"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 0 | 872,199,101 | https://bujiazi.github.io/bytheway.github.io/ | null | null | [
"generative-modeling",
"video-generation"
] | Bujiazi/ByTheWay | [
"description",
"query-match",
"repository-metadata"
] | 4 | 2026-10-22T16:09:07Z | [] | 2025-10-10T11:33:06Z | [
"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 | 48 | [] | 2026-06-03T03:31:45Z | https://github.com/Bujiazi/ByTheWay | null |
[
"deep-learning",
"geometric-learning",
"graph-learning"
] | [
"geometric-deep-learning",
"graph-neural-network",
"manifold-learning",
"message-passing",
"neural-network"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.geometric-learning",
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-14T03:19:16Z | Official implementation of HEGNN, a novel high-degree equivariant graph neural network proposed in the NeurIPS 2024 paper 'Are High-Degree Representations Really Unnecessary in Equivariant Graph Neural Networks?' | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 3 | 872,211,998 | null | Python | MIT | [
"graph-neural-network",
"message-passing",
"neural-network"
] | GLAD-RUC/HEGNN | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-10-15T15:20:52Z | [] | 2024-11-08T08:41:48Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 34 | [] | 2026-04-02T23:55:38Z | https://github.com/GLAD-RUC/HEGNN | 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 | [] | 2024-10-14T04:03:24Z | Official Implementation of paper "Tex4D: Zero-shot 4D Scene Texturing with Video Diffusion Models" | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-17T13:17:44Z | false | 3 | 872,223,938 | https://tex4d.github.io | Python | Apache-2.0 | [
"diffusion"
] | ZqlwMatt/Tex4D | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-17T13:17:44Z | [] | 2025-01-28T14:55:38Z | [
"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 | 55 | [] | 2026-01-08T03:51:33Z | https://github.com/ZqlwMatt/Tex4D | null |
[
"computer-vision",
"generative-ai",
"machine-learning"
] | [
"diffusion",
"distillation",
"generative-modeling",
"machine-unlearning",
"privacy"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"specialized.machine-unlearning",
"vision.image-diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-14T06:39:47Z | Official PyTorch Implementation of Score Forgetting Distillation (ICLR 2025) | [
"computer-vision",
"generative-ai"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 0 | 872,272,610 | null | Python | Apache-2.0 | [
"diffusion",
"distillation",
"generative-modeling"
] | tqch/score-forgetting-distillation | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-08T18:15:14Z | [] | 2025-04-07T06:02:55Z | [
"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 | 8 | [] | 2026-04-01T09:35:02Z | https://github.com/tqch/score-forgetting-distillation | null |
[
"computer-vision",
"generative-ai"
] | [
"generative-modeling",
"video-generation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.video-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-14T07:58:21Z | [CVPR 2025] Official code for "Synergizing Motion and Appearance: Multi-Scale Compensatory Codebooks for Talking Head Video Generation" | [
"computer-vision",
"generative-ai"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-22T16:09:07Z | false | 4 | 872,305,475 | https://shaelynz.github.io/synergize-motion-appearance/ | Python | NOASSERTION | [
"generative-modeling",
"video-generation"
] | ShaelynZ/synergize-motion-appearance | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-22T16:09:07Z | [] | 2026-07-03T09:45:46Z | [
"vision.video-generation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 65 | [] | 2026-10-15T02:36:41Z | https://github.com/ShaelynZ/synergize-motion-appearance | null |
[
"efficient-ml",
"generative-ai",
"generative-modeling",
"machine-learning-systems",
"model-compression",
"multimodal",
"vision"
] | [
"diffusion",
"image-generation",
"post-training-quantization",
"quantization",
"text-to-image"
] | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"efficiency.post-training-quantization",
"efficiency.quantization",
"llm.diffusion-phrase",
"multimodal.text-to-image"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-14T10:37:46Z | [ECCV 2024] Official implementation of "Post-training quantization with progressive calibration and activation relaxing for text-to-image diffusion models" | [
"generative-ai",
"generative-modeling",
"machine-learning-systems"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 0 | 872,379,521 | https://arxiv.org/abs/2311.06322 | Python | null | [
"diffusion",
"quantization"
] | tsa18/PCR | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | 8 | 2026-10-18T14:44:29Z | [] | 2025-04-07T02:51:00Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 6 | [] | 2026-08-09T21:07:59Z | https://github.com/tsa18/PCR | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"multimodal",
"vision"
] | [
"diffusion",
"generative-modeling",
"image-generation",
"text-to-image"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"multimodal.text-to-image",
"vision.image-diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-14T11:29:58Z | [NIPS24] Official Implementation of Unsupervised Modality Adaptation with Text-to-Image Diffusion Models for Semantic Segmentation | [
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-07T18:07:52Z | false | 1 | 872,402,917 | null | Python | null | [
"diffusion"
] | XiaRho/MADM | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-10-18T14:44:29Z | [] | 2024-10-31T12:39:16Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 20 | [] | 2025-10-24T04:49:49Z | https://github.com/XiaRho/MADM | null |
[
"generative-modeling",
"synthetic-data",
"time-series",
"time-series-and-forecasting"
] | [
"diffusion",
"generative-modeling",
"time-series-generation"
] | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"timeseries.synthetic-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-14T18:16:33Z | Official implementation: Population Aware Diffusion for Time Series Generation (AAAI-25) | [
"generative-modeling",
"synthetic-data",
"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T15:29:21Z | false | 2 | 872,602,479 | https://arxiv.org/abs/2501.00910 | Python | null | [
"diffusion",
"generative-modeling",
"time-series-generation"
] | wmd3i/PaD-TS | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | 6 | 2026-10-01T17:35:19Z | [] | 2025-09-01T14:57:30Z | [
"timeseries.synthetic-generation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 18 | [] | 2026-09-23T04:54:13Z | https://github.com/wmd3i/PaD-TS | null |
[
"efficient-ml",
"machine-learning-systems",
"model-compression",
"natural-language-processing"
] | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-15T10:50:29Z | Implementation for our paper “DRPruning: Efficient Large Language Model Pruning through Distributionally Robust Optimization,” accepted to the Main Conference of ACL 2025. 🎉 | [
"efficient-ml",
"machine-learning-systems",
"model-compression",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 1 | 872,945,510 | https://aclanthology.org/2025.acl-long.1414/ | Python | MIT | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | hexuandeng/DRPruning | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-23T15:59:01Z | [] | 2026-02-04T14:48:24Z | [
"efficiency.pruning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 9 | [
"data-centric",
"dro",
"efficiency",
"llama",
"llm",
"nlp",
"pre-training",
"pruning",
"qwen"
] | 2026-08-31T14:20:21Z | https://github.com/hexuandeng/DRPruning | null |
[
"computer-vision",
"generative-modeling",
"health-and-biomedicine",
"multimodal",
"multimodal-learning",
"natural-language-processing"
] | [
"diffusion",
"multimodal-learning",
"vision-language"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.vision-language"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-15T11:21:13Z | [NeurIPS 2024 AIM-FM Workshop] Official implementation of vision-language-conditioned medical image generation. Combines clinical text and segmentation masks with deep generative models to synthesize chest CT images and reveal imaging patterns associated with clinical attributes. | [
"computer-vision",
"generative-modeling",
"health-and-biomedicine",
"multimodal",
"multimodal-learning",
"natural-language-processing"
] | [
"generative-model",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-20T15:59:06Z | false | 2 | 872,959,236 | https://openreview.net/pdf/10184d813cdcc01d695afdcc9bb24fdf39711cf8.pdf | Python | MIT | [
"diffusion",
"multimodal-learning",
"vision-language"
] | junzhin/DGM-VLC | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-20T15:59:06Z | [] | 2024-10-31T18:30:24Z | [
"vision.vision-language"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 8 | [
"chest-ct",
"clinical-data",
"conditional-generation",
"diffusion-models",
"generative-adversarial-networks",
"generative-models",
"medical-image-generation",
"medical-image-synthesis",
"medical-imaging",
"multimodal-learning",
"pytorch",
"vision-language-models"
] | 2026-10-19T01:23:30Z | https://github.com/junzhin/DGM-VLC | null |
[
"sequence-modeling",
"time-series"
] | [
"sequence-modeling",
"state-space-model"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"timeseries.state-space-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-15T13:25:26Z | This is the official implementation of paper "Structural Inference of Dynamical Systems with Conjoined State Space Models" accepted by NeurIPS2024. | [
"sequence-modeling",
"time-series"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 0 | 873,020,107 | null | null | MIT | [
"sequence-modeling",
"state-space-model"
] | wang422003/SICSM-JAX | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-02T16:59:40Z | [] | 2024-10-15T13:26:39Z | [
"timeseries.state-space-models"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 3 | [] | 2025-01-03T06:21:04Z | https://github.com/wang422003/SICSM-JAX | 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 | [] | 2024-10-15T14:07:42Z | Official reference implementation of NeurIPS 2024 paper "Energy-based Epistemic Uncertainty for Graph Neural Networks" | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-15T15:20:52Z | false | 1 | 873,045,692 | null | Python | NOASSERTION | [
"graph-neural-network",
"message-passing",
"neural-network"
] | dfuchsgruber/graph-ebm | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-15T15:20:52Z | [] | 2025-01-30T16:05:35Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 6 | [] | 2026-07-19T14:43:52Z | https://github.com/dfuchsgruber/graph-ebm | null |
[
"deep-learning",
"graph-learning",
"mathematics",
"operations-research"
] | [
"combinatorial-optimization",
"graph-transformer",
"message-passing",
"neural-network",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"gap2026.math-neural-combinatorial-optimization",
"graph.graph-transformer"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-16T07:28:13Z | Official code for the paper "GraphXForm: Graph transformer for computer-aided molecular design" | [
"mathematics",
"operations-research"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 4 | 873,448,518 | null | Python | MIT | [
"combinatorial-optimization",
"neural-network",
"transformer"
] | grimmlab/graphxform | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-10-02T16:44:32Z | [] | 2026-02-26T15:24:25Z | [
"gap2026.math-neural-combinatorial-optimization"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 11 | [
"drug-design",
"gumbeldore",
"molecule-design",
"self-improvement",
"solvent-design"
] | 2026-02-26T15:28:29Z | https://github.com/grimmlab/graphxform | null |
[
"multimodal",
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] | [
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] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.world-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-17T05:22:57Z | Official code repository for "Web Agents with World Models [ICLR 2025]". | [
"multimodal",
"reinforcement-learning",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 2 | 874,009,512 | null | Python | null | [
"environment-modeling",
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] | kyle8581/WMA-Agents | [
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"repository-metadata"
] | 2 | 2026-10-02T16:44:32Z | [] | 2025-03-02T08:51:25Z | [
"multimodal.world-models"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 32 | [] | 2026-08-07T02:54:04Z | https://github.com/kyle8581/WMA-Agents | null |
[
"distributed-ml",
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] | [
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] | [
"description",
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"repository-metadata"
] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-17T07:27:25Z | The code for paper "Uncovering Hidden Correlations: Post-Training Data Reconstruction Attacks against Vertical Federated Learning" | [
"distributed-ml",
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-15T15:20:52Z | false | 0 | 874,060,321 | null | Python | null | [
"collaborative-learning",
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] | SPHelixLab/DARE | [
"description",
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] | 1 | 2026-10-15T15:20:52Z | [] | 2024-10-17T07:33:44Z | [
"trust.federated-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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] | include | ml-contribution-v5 | 1 | [] | 2025-01-06T07:06:02Z | https://github.com/SPHelixLab/DARE | null |
[
"deep-learning",
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] | [
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] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-17T07:43:59Z | The official source code of paper ”Fusion-Based Graph Neural Networks for Synergistic Underwater Image Enhancement“ | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-15T15:20:52Z | false | 0 | 874,067,675 | null | Python | null | [
"graph-neural-network",
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] | whZ62/FUGN | [
"description",
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"repository-metadata"
] | 1 | 2026-10-15T15:20:52Z | [] | 2024-12-17T08:19:21Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 7 | [] | 2026-06-26T05:10:27Z | https://github.com/whZ62/FUGN | null |
[
"computer-vision",
"generative-ai",
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] | [
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] | [
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] | [
"llm.diffusion-phrase",
"multimodal.text-to-image",
"vision.image-diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-17T10:36:42Z | Official code for the Paper "Context-Aware Full Body Anonymization using Text-to-Image Diffusion Models" presented at ACVR 2024. | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 0 | 874,149,071 | null | Python | GPL-3.0 | [
"diffusion"
] | fzi-forschungszentrum-informatik/fadm | [
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] | 3 | 2026-10-19T17:46:36Z | [] | 2025-05-23T10:59:53Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 2 | [] | 2025-05-23T10:59:57Z | https://github.com/fzi-forschungszentrum-informatik/fadm | null |
[
"machine-learning"
] | [
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] | [
"description",
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"repository-metadata"
] | [
"specialized.machine-unlearning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-17T14:42:12Z | [NAACL 2025 Main] Official Implementation of MLLMU-Bench | [
"machine-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 3 | 874,275,570 | null | Python | null | [
"machine-unlearning",
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] | franciscoliu/MLLMU-Bench | [
"description",
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] | 6 | 2026-10-01T17:50:46Z | [] | 2025-03-13T03:16:09Z | [
"specialized.machine-unlearning"
] | 80795bdc980fb18403b180c07fe1e0618fbc0d0c | 2026-10-07T19:34:31Z | gh-ml-readme-evidence-v2 | "80795bdc980fb18403b180c07fe1e0618fbc0d0c" | [
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] | [
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] | ok | 2026-10-07T19:34:31Z | franciscoliu/MLLMU-Bench | readme-supported-paper-method-implementation | [
"contribution-language",
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] | include | ml-contribution-v5 | 57 | [] | 2026-09-15T10:23:23Z | https://github.com/franciscoliu/MLLMU-Bench | null |
[
"deep-learning",
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] | [
"dynamic-graph-learning",
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] | [
"description",
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"repository-metadata"
] | [
"graph.gnn-description",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-17T15:34:22Z | The source code for paper " Coden: Efficient Temporal Graph Neural Networks for Continuous Prediction" | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-10T16:53:26Z | false | 0 | 874,304,008 | null | C++ | null | [
"graph-neural-network",
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] | ZulunZhu/Coden | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-16T15:36:56Z | [] | 2025-09-24T10:20:50Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 0 | [] | 2025-09-24T10:20:54Z | https://github.com/ZulunZhu/Coden | null |
[
"embodied-ai",
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] | [
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] | [
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"repository-metadata"
] | [
"robotics.diffusion-policy"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-17T19:51:41Z | [RA-L2024/ICRA2025] Official implementation for paper "Diverse Controllable Diffusion Policy with Signal Temporal Logic." | [
"embodied-ai",
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 0 | 874,421,750 | https://mengyuest.github.io/pSTL-diffusion-policy/ | Python | null | [
"diffusion",
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] | MIT-REALM/pstl-diffusion | [
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"query-match",
"repository-metadata"
] | 2 | 2026-09-30T16:30:43Z | [] | 2024-10-17T19:52:57Z | [
"robotics.diffusion-policy"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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] | include | ml-contribution-v5 | 5 | [
"autonomous-driving",
"diffusion-models",
"robotics",
"signal-temporal-logic"
] | 2026-06-22T03:39:28Z | https://github.com/MIT-REALM/pstl-diffusion | null |
[
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"language",
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] | [
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"machine-learning",
"model-adaptation",
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] | [
"description",
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] | [
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"recall.name.language-model",
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"recall.name.transformer-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-18T04:11:52Z | The open-source Mixture of Depths code and the official implementation of the paper "Router-Tuning: A Simple and Effective Approach for Enabling Dynamic Depth in Transformers. (EMNLP 2025)" | [
"general-ml",
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] | [
"deep-learning",
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"neural-network",
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-01T16:56:07Z | false | 4 | 874,565,778 | https://aclanthology.org/2025.emnlp-main.99 | Python | null | [
"neural-network",
"transformer"
] | CASE-Lab-UMD/Router-Tuning-Mixture-of-Depths | [
"description",
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"query-match",
"repository-metadata"
] | 13 | 2026-10-31T15:48:22Z | [] | 2026-10-02T20:32:01Z | [
"recall.name.transformer-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 34 | [
"deep-learning",
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"machine-learning",
"mixture-of-depths",
"neural-network",
"parameter-efficient-fine-tuning"
] | 2026-10-02T20:32:05Z | https://github.com/CASE-Lab-UMD/Router-Tuning-Mixture-of-Depths | null |
[
"generative-modeling",
"machine-learning",
"reliable-ml",
"robust-ml",
"trustworthy-ml"
] | [
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] | [
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"repository-metadata"
] | [
"probable.evaluation-ood-readme",
"trust.out-of-distribution-detection"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-18T07:21:26Z | The official PyTorch implementation for the paper Diffusion Denoising Process for Perceptron Bias in Out-of-distribution Detection | [
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"machine-learning",
"reliable-ml"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T14:50:46Z | false | 0 | 874,632,586 | https://arxiv.org/abs/2211.11255 | Python | Apache-2.0 | [
"diffusion",
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"uncertainty-estimation"
] | luping-liu/DiffOOD | [
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"repository-metadata"
] | 3 | 2026-10-14T16:10:46Z | [] | 2024-10-18T07:21:49Z | [
"probable.evaluation-ood-readme"
] | 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 | 1 | [] | 2024-10-18T07:22:36Z | https://github.com/luping-liu/DiffOOD | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
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"neural-network"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-18T09:47:22Z | This is the code for paper: 'Network Measure-Enriched Graph Neural Networks: A New Benchmark for Power Grid Stability Assessment' | [
"deep-learning",
"graph-learning"
] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-15T15:20:52Z | false | 0 | 874,696,485 | null | Python | null | [
"graph-neural-network",
"message-passing",
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] | JYZHU03/NSGNN | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-15T15:20:52Z | [] | 2025-06-23T16:21:49Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 9 | [] | 2026-03-17T06:28:47Z | https://github.com/JYZHU03/NSGNN | null |
[
"deep-learning",
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] | [
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] | [
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"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-18T23:20:41Z | Official implementation of Spectro-Riemannian Graph Neural Networks (ICLR 2025) | [
"deep-learning",
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] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T16:30:43Z | false | 2 | 875,022,343 | https://www.arxiv.org/abs/2502.00401 | Python | null | [
"graph-neural-network",
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] | amazon-science/cusp | [
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] | 3 | 2026-10-15T15:20:52Z | [] | 2026-07-17T21:41:17Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 17 | [] | 2026-06-16T06:49:56Z | https://github.com/amazon-science/cusp | null |
[
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] | [
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] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.quantization"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-19T04:35:21Z | An official implementation of "Catastrophic Failure of LLM Unlearning via Quantization" (ICLR 2025) | [
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"natural-language-processing"
] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T16:44:32Z | false | 3 | 875,089,846 | null | Python | null | [
"quantization"
] | zzwjames/FailureLLMUnlearning | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-15T15:20:52Z | [] | 2025-02-22T05:02:23Z | [
"efficiency.quantization"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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] | include | ml-contribution-v5 | 40 | [] | 2026-10-08T20:39:35Z | https://github.com/zzwjames/FailureLLMUnlearning | null |
[
"interpretability-and-safety",
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] | [
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"interpretability",
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] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"trust.explainable-ai"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-19T15:49:07Z | Our code files for the paper: Enhancing Skin Disease Classification Leveraging Transformer-based Deep Learning Architectures and Explainable AI | [
"interpretability-and-safety",
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] | [
"deep-learning",
"classifier",
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-01T17:35:19Z | false | 0 | 875,294,720 | null | Jupyter Notebook | MIT | [
"explainable-ai",
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] | JAYANTH-MOHAN/Enhancing-Skin-Disease-Classification-Using-Transformers | [
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"repository-metadata"
] | 3 | 2026-10-03T15:45:52Z | [] | 2024-10-23T22:12:50Z | [
"trust.explainable-ai"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 6 | [] | 2026-07-14T07:48:23Z | https://github.com/JAYANTH-MOHAN/Enhancing-Skin-Disease-Classification-Using-Transformers | null |
[
"forecasting",
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] | [
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] | [
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"query-match",
"repository-metadata"
] | [
"timeseries.forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-20T12:29:13Z | Official implementation of "DDN: Dual-domain Dynamic Normalization for Non-stationary Time Series Forecasting" (NeurIPS 2024) | [
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"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-20T15:59:06Z | false | 2 | 875,602,910 | null | Python | MIT | [
"forecasting",
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] | Hank0626/DDN | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-22T16:09:07Z | [] | 2024-10-28T03:29:10Z | [
"timeseries.forecasting"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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] | include | ml-contribution-v5 | 26 | [] | 2026-05-31T17:43:17Z | https://github.com/Hank0626/DDN | null |
[
"computer-vision",
"generative-ai",
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] | [
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] | [
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"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.image-to-video",
"vision.video-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-21T01:32:52Z | [ICLR 2025] "Robust Watermarking Using Generative Priors Against Image Editing: From Benchmarking to Advances" (Official Implementation) | [
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T15:29:21Z | false | 35 | 875,857,865 | null | Python | NOASSERTION | [
"diffusion",
"generative-modeling",
"video-generation"
] | Shilin-LU/VINE | [
"description",
"github-topics",
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"query-match",
"repository-metadata"
] | 8 | 2026-10-22T16:09:07Z | [] | 2026-03-27T03:21:41Z | [
"vision.video-generation"
] | 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 | 391 | [
"diffusion-models",
"image-generation",
"sdxl-turbo",
"watermark",
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] | 2026-10-21T16:14:16Z | https://github.com/Shilin-LU/VINE | null |
[
"forecasting",
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] | [
"forecasting",
"sequence-modeling"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"timeseries.forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-21T08:06:05Z | This is the official code for the paper Conditional Denoising Meets Polynomial Modeling: A Flexible Decoupled Framework for Time Series Forecasting. | [
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"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-22T16:09:07Z | false | 0 | 875,989,098 | null | Python | null | [
"forecasting",
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] | zjt-gpu/CDPM | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-22T16:09:07Z | [] | 2025-05-20T08:33:04Z | [
"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 | 11 | [] | 2026-05-25T03:39:46Z | https://github.com/zjt-gpu/CDPM | null |
[
"generative-modeling",
"machine-learning",
"representation-learning"
] | [
"diffusion",
"domain-generalization",
"transfer-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"probable.adaptation-domain-generalization-readme"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-21T15:56:06Z | [NeurIPS 2024] official implementation of Weight Diffusion for Future: Learn to Generalize in Non-Stationary Environments | [
"generative-modeling",
"machine-learning",
"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-18T14:44:29Z | false | 0 | 876,226,407 | null | Python | null | [
"diffusion",
"domain-generalization",
"transfer-learning"
] | BIT-DA/W-Diff | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-18T14:44:29Z | [] | 2024-12-11T11:04:08Z | [
"probable.adaptation-domain-generalization-readme"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 13 | [] | 2026-05-18T02:09:59Z | https://github.com/BIT-DA/W-Diff | 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 | [] | 2024-10-22T04:01:57Z | Official PyTorch implementation for ICLR2025 paper "Scaling up Masked Diffusion Models on Text" | [
"generative-ai",
"generative-modeling"
] | [
"machine-learning",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T16:30:43Z | false | 29 | 876,489,746 | null | Python | Apache-2.0 | [
"diffusion"
] | ML-GSAI/SMDM | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-17T13:17:44Z | [] | 2024-12-22T12:38:18Z | [
"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 | 389 | [] | 2026-09-26T01:56:42Z | https://github.com/ML-GSAI/SMDM | null |
[
"computer-vision",
"multimodal",
"multimodal-learning"
] | [
"multimodal-learning",
"vision-language"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.vision-language"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-22T05:28:28Z | Official PyTorch implementation of the IEEE TETCI 2024 paper LoCATe-GAT | [
"computer-vision",
"multimodal",
"multimodal-learning"
] | [
"classifier",
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-21T15:48:06Z | false | 0 | 876,517,212 | https://ieeexplore.ieee.org/document/10769605 | Python | MIT | [
"multimodal-learning",
"vision-language"
] | sandipan211/LoCATe-GAT | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-21T15:48:06Z | [] | 2025-04-27T10:32:43Z | [
"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 | 7 | [
"clip",
"dilated-convolution",
"graph-attention-networks",
"polysemy",
"transformers",
"video-classification",
"vision-language",
"zero-shot-action-recognition",
"zero-shot-learning"
] | 2025-05-27T00:48:00Z | https://github.com/sandipan211/LoCATe-GAT | null |
[
"forecasting",
"generative-ai",
"language",
"time-series",
"time-series-and-forecasting"
] | [
"forecasting",
"language-model",
"sequence-modeling",
"time-series-forecasting",
"transformer"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"applied.time-series-forecasting",
"llm.transformers",
"timeseries.forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-22T10:36:21Z | Official implementation for "TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables" (NeurIPS 2024) | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 76 | 876,652,686 | null | Python | null | [
"forecasting",
"sequence-modeling"
] | thuml/TimeXer | [
"description",
"query-match",
"repository-metadata"
] | 10 | 2026-10-22T16:09:07Z | [] | 2024-11-27T07:25:50Z | [
"timeseries.forecasting"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 523 | [] | 2026-10-22T10:48:02Z | https://github.com/thuml/TimeXer | null |
[
"tabular-and-structured-data",
"tabular-ml"
] | [
"tabular-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"applied.tabular-ml"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-23T02:26:52Z | Official implementation of Optimized Feature Generation for Tabular Data via LLMs with Decision Tree Reasoning (NeurIPS 2024). | [
"tabular-and-structured-data",
"tabular-ml"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T16:44:32Z | false | 5 | 877,055,792 | null | Python | null | [
"tabular-learning"
] | jaehyun513/OCTree | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-02T16:44:32Z | [] | 2025-03-04T08:26:02Z | [
"applied.tabular-ml"
] | 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 | 33 | [] | 2026-08-20T21:20:13Z | https://github.com/jaehyun513/OCTree | null |
[
"computer-vision",
"generative-ai",
"multimodal",
"reinforcement-learning",
"robotics",
"robotics-and-control"
] | [
"embodied-ai",
"environment-modeling",
"generative-modeling",
"video-generation",
"world-model"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"multimodal.embodied-world-models",
"multimodal.world-models",
"vision.video-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-23T07:18:19Z | [ICML 2025] Official code for EVA: An Embodied World Model for Future Video Anticipation | [
"computer-vision",
"generative-ai",
"robotics-and-control"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 0 | 877,153,420 | https://sites.google.com/view/eva-public | null | null | [
"generative-modeling",
"video-generation",
"world-model"
] | litwellchi/EmbodiedVideoAnticipator | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 9 | 2026-10-23T15:59:01Z | [] | 2025-09-03T16:05:23Z | [
"vision.video-generation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 6 | [
"embodied-ai",
"video-generation",
"world-models"
] | 2025-09-03T16:05:27Z | https://github.com/litwellchi/EmbodiedVideoAnticipator | null |
[
"efficient-ml",
"generative-ai",
"inference-systems",
"language"
] | [
"efficient-inference",
"inference-optimization",
"speculative-decoding"
] | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"efficiency.speculative-decoding",
"llm.speculative-decoding"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-10-23T13:14:18Z | Official Implementation of SAM-Decoding: Speculative Decoding via Suffix Automaton | [
"efficient-ml",
"inference-systems",
"language"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 3 | 877,324,856 | https://arxiv.org/abs/2411.10666 | Python | null | [
"inference-optimization",
"speculative-decoding"
] | hyx1999/SAM-Decoding | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | 11 | 2026-10-02T16:44:32Z | [] | 2026-05-12T05:25:14Z | [
"efficiency.speculative-decoding"
] | 903123426e1a9c743fe47ed0c4d241b3bb4f5f4d | 2026-10-07T19:34:31Z | gh-ml-readme-evidence-v2 | "903123426e1a9c743fe47ed0c4d241b3bb4f5f4d" | [
"dataset",
"method",
"other",
"overview",
"results",
"usage"
] | [
"method-contribution",
"ml-method-context",
"paper-reference"
] | ok | 2026-10-07T19:34:31Z | hyx1999/SAM-Decoding | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-method-context",
"paper-reference"
] | include | ml-contribution-v5 | 54 | [
"speculative-decoding"
] | 2026-09-10T12:39:13Z | https://github.com/hyx1999/SAM-Decoding | 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 | [] | 2024-10-25T14:37:52Z | Source code for our paper "Revisiting Ensembling in One-Shot Federated Learning", NeurIPS 2024 | [
"distributed-ml",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-14T16:10:46Z | false | 1 | 878,517,830 | null | Python | null | [
"collaborative-learning",
"federated-learning"
] | sacs-epfl/fens | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-14T16:10:46Z | [] | 2024-10-25T15:00:15Z | [
"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 | 3 | [] | 2026-05-28T06:23:57Z | https://github.com/sacs-epfl/fens | null |
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