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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
[
"deep-learning",
"efficient-ml",
"language",
"machine-learning"
] | [
"adaptation",
"conditional-computation",
"mixture-of-experts",
"sparse-activation",
"test-time-adaptation"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"efficiency.mixture-of-experts",
"specialized.test-time-adaptation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-15T08:19:50Z | 🔥 [AAAI 2026 Oral] Official code for Decomposing the Neurons: Activation Sparsity via Mixture of Experts for Continual Test Time Adaptation | [
"deep-learning",
"efficient-ml",
"language",
"machine-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 5 | 800,906,385 | null | Python | null | [
"adaptation",
"conditional-computation",
"mixture-of-experts",
"sparse-activation",
"test-time-adaptation"
] | RoyZry98/MoASE-Pytorch | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 7 | 2026-10-01T17:35:19Z | [] | 2026-05-13T04:35:02Z | [
"efficiency.mixture-of-experts",
"specialized.test-time-adaptation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 80 | [
"activation-sparsification",
"continual-test-time-adaptation",
"mixture-of-experts"
] | 2026-08-24T06:44:50Z | https://github.com/RoyZry98/MoASE-Pytorch | null |
[
"computer-vision",
"language",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"vision"
] | [
"contrastive-learning",
"image-text",
"multimodal-learning",
"vision-language",
"vision-language-model"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.vision-language",
"multimodal.vlm",
"vision.vision-language"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-15T15:08:56Z | [ICML 2024] Official implementation for "Image Fusion via Vision-Language Model". | [
"computer-vision",
"multimodal",
"multimodal-learning",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 6 | 801,087,965 | null | Python | null | [
"multimodal-learning",
"vision-language"
] | Zhaozixiang1228/IF-FILM | [
"description",
"query-match",
"repository-metadata"
] | 4 | 2026-10-11T16:48:52Z | [] | 2025-01-15T18:30:29Z | [
"vision.vision-language"
] | c9d5530e5d868b433101643c894820427ad2b7de | 2026-10-02T18:21:54Z | gh-ml-readme-evidence-v2 | "c9d5530e5d868b433101643c894820427ad2b7de" | [
"abstract",
"citation",
"dataset",
"method",
"other",
"references",
"usage"
] | [
"method-contribution",
"ml-method-context",
"paper-reference"
] | ok | 2026-10-02T18:21:54Z | Zhaozixiang1228/IF-FILM | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-method-context",
"paper-reference"
] | include | ml-contribution-v5 | 99 | [] | 2026-08-25T14:06:33Z | https://github.com/Zhaozixiang1228/IF-FILM | null |
[
"graph-learning",
"interpretability-and-safety",
"knowledge-graphs"
] | [
"knowledge-graph-embedding"
] | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"graph.knowledge-graph-embedding"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-16T00:13:27Z | Official implementation of the paper Bring back Semantics to Knowledge Graph Embeddings : An Interpretability Approach | [
"graph-learning",
"interpretability-and-safety",
"knowledge-graphs"
] | [
"embedding"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 0 | 801,288,141 | https://link.springer.com/chapter/10.1007/978-3-031-71170-1_17 | Jupyter Notebook | null | [
"knowledge-graph-embedding"
] | toniodo/InterpretE | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | 2 | 2026-10-01T17:35:19Z | [] | 2025-03-04T16:00:30Z | [
"graph.knowledge-graph-embedding"
] | 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 | [
"embeddings",
"interpretable-ai",
"kg",
"kge",
"svm"
] | 2025-04-09T14:36:27Z | https://github.com/toniodo/InterpretE | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-17T04:46:11Z | Official Code for Guided Trajectory Generation with Diffusion Models for Offline Model-based Optimization (NIPS 2024) | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-11T16:48:52Z | false | 3 | 801,872,320 | https://openreview.net/forum?id=ioKQzb8SMr | Python | null | [
"diffusion"
] | dbsxodud-11/GTG | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-11T16:48:52Z | [] | 2024-08-15T05:37:54Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 23 | [] | 2026-04-10T08:13:05Z | https://github.com/dbsxodud-11/GTG | 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 | [] | 2024-05-18T00:54:55Z | The official implementation of Flexible Motion In-betweening with Diffusion Models, SIGGRAPH 2024 | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-30T16:30:43Z | false | 22 | 802,313,982 | https://setarehc.github.io/CondMDI/ | Python | NOASSERTION | [
"diffusion"
] | setarehc/diffusion-motion-inbetweening | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-11T16:48:52Z | [] | 2024-09-17T21:45:09Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 281 | [] | 2026-10-10T08:59:06Z | https://github.com/setarehc/diffusion-motion-inbetweening | 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 | [] | 2024-05-18T02:08:44Z | The official implementation of paper "Diff-DEM: A Diffusion Probabilistic Approach to Digital Elevation Model Void Filling" | [
"computer-vision",
"generative-ai",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 2 | 802,327,429 | null | Python | null | [
"diffusion",
"generative-modeling"
] | kylelo/Diff-DEM | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-04T15:58:58Z | [] | 2024-06-04T02:48:32Z | [
"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",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 20 | [] | 2026-04-14T12:56:32Z | https://github.com/kylelo/Diff-DEM | null |
[
"deep-learning",
"health-and-biomedicine",
"medical-imaging",
"multimodal-learning",
"representation-learning"
] | [
"contrastive-learning",
"image-analysis",
"image-registration",
"reconstruction",
"self-supervised-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning",
"medical.medical-image-registration"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-18T11:29:04Z | Official implementation of paper "Unsupervised Multi-Modal Medical Image Registration via query-selected attention and decoupled Contrastive Learning" | [
"deep-learning",
"multimodal-learning",
"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T14:50:46Z | false | 0 | 802,467,243 | null | Python | null | [
"contrastive-learning",
"self-supervised-learning"
] | hzrhit/QSDCL-DFMIR | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-10-31T16:49:26Z | [] | 2024-05-18T11:40:38Z | [
"general.contrastive-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [] | 2026-01-15T04:45:04Z | https://github.com/hzrhit/QSDCL-DFMIR | null |
[
"data-centric-ai",
"generative-ai",
"generative-modeling",
"language"
] | [
"diffusion",
"language-model",
"synthetic-data"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"llm.synthetic-data"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-18T12:10:01Z | [ICCV-2025] Official implementation of Bootstrap3D: Improving Multi-view Diffusion Model with Synthetic Data | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 2 | 802,478,788 | https://Sunzey.github.io/Bootstrap3D/ | Python | null | [
"diffusion"
] | SunzeY/Bootstrap3D | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 4 | 2026-10-11T16:48:52Z | [] | 2025-07-26T15:22:39Z | [
"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"
] | include | ml-contribution-v5 | 94 | [
"3d-aigc",
"3d-generation",
"3d-graphics",
"3dgen",
"3dgeneration",
"text-to-3d"
] | 2026-04-29T21:58:00Z | https://github.com/SunzeY/Bootstrap3D | null |
[
"deep-learning",
"graph-learning",
"time-series"
] | [
"dynamic-graph-learning",
"graph-neural-network",
"message-passing",
"neural-network",
"temporal-graph-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description",
"graph.temporal-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-19T08:44:51Z | Code for paper "NAD-GNN: A novel temporal graph neural network approach for network attack detection" | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-06T17:10:23Z | false | 0 | 802,779,709 | null | Python | null | [
"graph-neural-network",
"message-passing",
"neural-network"
] | mangoding71/NAD-GNN | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-13T18:05:26Z | [] | 2025-09-25T00:55:29Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"method-tied-novelty-claim",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2025-09-25T00:55:32Z | https://github.com/mangoding71/NAD-GNN | null |
[
"chemistry",
"organic-chemistry"
] | [
"generative-modeling",
"planning",
"reaction-prediction"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"chem-retrosynthesis-planning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-20T02:28:21Z | The official source code for [2024 NeurIPS] "Retrieval-Retro: Retrieval-based Inorganic Retrosynthesis with Expert Knowledge" | [
"chemistry",
"organic-chemistry"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 5 | 803,071,700 | null | Python | null | [
"generative-modeling",
"planning",
"reaction-prediction"
] | HeewoongNoh/Retrieval-Retro | [
"description",
"query-match",
"repository-metadata"
] | 8 | 2026-09-30T16:30:43Z | [] | 2025-06-23T03:27:57Z | [
"chem-retrosynthesis-planning"
] | 638d7084d03e294f944579db1ed7fee3b9dc378a | 2026-10-02T18:21:54Z | gh-ml-readme-evidence-v2 | "638d7084d03e294f944579db1ed7fee3b9dc378a" | [
"dataset",
"method",
"other",
"overview"
] | [
"method-contribution",
"ml-method-context",
"paper-reference"
] | ok | 2026-10-02T18:21:54Z | HeewoongNoh/Retrieval-Retro | readme-supported-paper-method-implementation | [
"method-contribution",
"ml-method-context",
"paper-reference"
] | include | ml-contribution-v5 | 38 | [] | 2026-09-18T07:04:30Z | https://github.com/HeewoongNoh/Retrieval-Retro | null |
[
"3d",
"computer-vision",
"generative-modeling",
"robotics"
] | [
"3d-object-detection",
"3d-perception",
"diffusion"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.3d-object-detection"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-20T12:18:13Z | Official code of DDet3D: Embracing 3D Object Detector with Diffusion paper. | [
"3d",
"computer-vision",
"generative-modeling",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-16T15:36:56Z | false | 0 | 803,276,185 | null | Python | MIT | [
"3d-object-detection",
"3d-perception",
"diffusion"
] | gopi-erabati/DDet3D | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-16T15:36:56Z | [] | 2024-05-20T13:37:02Z | [
"multimodal.3d-object-detection"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 5 | [] | 2025-04-10T07:03:45Z | https://github.com/gopi-erabati/DDet3D | null |
[
"graph-learning",
"time-series",
"time-series-and-forecasting"
] | [
"dynamic-graph-learning",
"temporal-graph-learning"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"graph.temporal-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-20T13:01:56Z | Official implementation for "UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Prediction" (KDD 2024) | [
"graph-learning",
"time-series",
"time-series-and-forecasting"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 23 | 803,296,179 | null | Python | null | [
"dynamic-graph-learning",
"temporal-graph-learning"
] | tsinghua-fib-lab/UniST | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 3 | 2026-10-05T20:10:04Z | [] | 2025-01-09T02:36:56Z | [
"graph.temporal-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 230 | [
"forecasting",
"one-for-all",
"pre-training",
"prediction",
"prompt-learning",
"prompt-tuning",
"spatio-temporal",
"transformers",
"universal-forecasting",
"universal-model"
] | 2026-09-02T06:22:01Z | https://github.com/tsinghua-fib-lab/UniST | null |
[
"audio",
"interpretability-and-safety",
"multimodal",
"multimodal-learning"
] | [
"audio-captioning",
"audio-language"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"audio-audio-captioning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-20T15:42:18Z | [NeurIPS2024] Official code for (IMA) Implicit Multimodal Alignment: On the Generalization of Frozen LLMs to Multimodal Inputs | [
"audio",
"interpretability-and-safety",
"multimodal",
"multimodal-learning"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 4 | 803,372,571 | null | Python | null | [
"audio-captioning",
"audio-language"
] | mshukor/ima-lmms | [
"description",
"query-match",
"repository-metadata"
] | 7 | 2026-09-29T17:12:21Z | [] | 2024-10-15T06:27:37Z | [
"audio-audio-captioning"
] | d0e0e32101279498ef18a89bd1fa2e892f34367f | 2026-10-02T18:21:54Z | gh-ml-readme-evidence-v2 | "d0e0e32101279498ef18a89bd1fa2e892f34367f" | [
"citation",
"installation",
"method",
"other",
"overview"
] | [
"method-contribution",
"ml-method-context",
"model-training-artifact",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-10-02T18:21:54Z | mshukor/ima-lmms | readme-supported-paper-method-implementation | [
"method-contribution",
"ml-context-only",
"ml-method-context",
"model-training-artifact",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 23 | [] | 2026-02-09T02:47:49Z | https://github.com/mshukor/ima-lmms | null |
[
"deep-learning",
"representation-learning",
"time-series-and-forecasting"
] | [
"contrastive-learning",
"self-supervised-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-21T06:43:00Z | The official code of the paper "MF-CLR: Multi-Frequency Contrastive Learning Representation for Time Series" [ICML 2024]. | [
"deep-learning",
"representation-learning",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-30T17:16:29Z | false | 2 | 803,661,738 | null | Python | MIT | [
"contrastive-learning",
"self-supervised-learning"
] | duanjufang/MF-CLR | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-30T17:16:29Z | [] | 2025-03-24T08:51:44Z | [
"general.contrastive-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 | 15 | [] | 2026-07-08T08:15:12Z | https://github.com/duanjufang/MF-CLR | null |
[
"forecasting",
"robust-ml",
"time-series",
"time-series-and-forecasting",
"trustworthy-ml"
] | [
"distribution-shift-detection",
"domain-generalization",
"forecasting",
"sequence-modeling"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"timeseries.forecasting",
"trust.distribution-shift"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-21T14:41:11Z | Code for paper <Evolving Multi-Scale Normalization for Time Series Forecasting Under Distribution Shifts> | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 1 | 803,881,202 | null | Python | Apache-2.0 | [
"forecasting",
"sequence-modeling"
] | hkuedl/Evolving_Multiscale_Normalization | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-17T13:17:44Z | [] | 2025-01-03T09:10:55Z | [
"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 | 16 | [] | 2026-09-30T08:46:01Z | https://github.com/hkuedl/Evolving_Multiscale_Normalization | null |
[
"distributed-ml",
"machine-learning",
"privacy-and-federated-learning"
] | [
"collaborative-learning",
"data-efficiency",
"dataset-distillation",
"federated-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"specialized.dataset-distillation",
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-21T20:38:21Z | The official code for ICML 2024 "FedREDefense: Defending against Model Poisoning Attacks for Federated Learning using Model Update Reconstruction Error" | [
"distributed-ml",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 9 | 804,034,992 | null | Python | Apache-2.0 | [
"collaborative-learning",
"federated-learning"
] | xyq7/FedREDefense | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-10-10T16:53:26Z | [] | 2026-09-16T06:22:49Z | [
"trust.federated-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 32 | [] | 2026-10-08T13:55:47Z | https://github.com/xyq7/FedREDefense | null |
[
"computer-vision",
"deep-learning",
"generative-modeling",
"representation-learning"
] | [
"contrastive-learning",
"diffusion",
"self-supervised-learning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-21T22:53:47Z | [NeurIPS 2024] Official code for DiffCut: Catalyzing Zero-Shot Semantic Segmentation with Diffusion Features and Recursive Normalized Cut | [
"computer-vision",
"deep-learning",
"generative-modeling",
"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-30T16:30:43Z | false | 7 | 804,073,848 | https://diffcut-segmentation.github.io | Jupyter Notebook | MIT | [
"contrastive-learning",
"diffusion",
"self-supervised-learning"
] | PaulCouairon/DiffCut | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-30T17:16:29Z | [] | 2025-01-19T18:29:10Z | [
"general.contrastive-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 53 | [
"computer-vision",
"image-segmentation",
"normalized-cuts",
"stable-diffusion",
"unsupervised-segmentation",
"zero-shot-segmentation"
] | 2026-09-04T05:43:00Z | https://github.com/PaulCouairon/DiffCut | 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 | [] | 2024-05-22T06:04:58Z | This repository contains the implementation of the Structure-aware Dual Graph Neural Network (SDGNN), as described in our paper: SDGNN: Structure-aware Dual Graph Neural Network for Code Summarization. | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-12T20:31:19Z | false | 0 | 804,191,072 | null | Python | Apache-2.0 | [
"graph-neural-network",
"message-passing",
"neural-network"
] | zssyqpgdll/SDGNN | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-12T20:31:19Z | [] | 2025-02-28T17:48:27Z | [
"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 | 2 | [] | 2025-03-02T16:47:20Z | https://github.com/zssyqpgdll/SDGNN | null |
[
"automl",
"computer-vision",
"efficient-ml"
] | [
"neural-architecture-search",
"transformer"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.neural-architecture-search"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-22T07:39:04Z | [NeurIPS 2024] Official Code for the Paper OoD-ViT-NAS: Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights | [
"automl",
"computer-vision",
"efficient-ml"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-26T14:50:46Z | false | 1 | 804,227,336 | null | Python | null | [
"neural-architecture-search",
"transformer"
] | vovantuan1999a/OoD-ViT-NAS | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-01T17:35:19Z | [] | 2024-12-03T08:44:50Z | [
"efficiency.neural-architecture-search"
] | 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 | 15 | [] | 2026-07-19T19:16:13Z | https://github.com/vovantuan1999a/OoD-ViT-NAS | null |
[
"foundation-models",
"generative-ai",
"language"
] | [
"fine-tuning",
"multimodal",
"parameter-efficient-fine-tuning",
"pretraining"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.foundation-models",
"llm.parameter-efficient-finetuning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-22T07:49:56Z | [NeurIPS'24]Official implementation for Personalized Adapter for Large Meteorology Model on Devices: Towards Weather Foundation Model | [
"generative-ai",
"language"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 1 | 804,231,518 | null | Python | MIT | [
"fine-tuning",
"parameter-efficient-fine-tuning"
] | shengchaochen82/LM-Weather | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-03T15:45:52Z | [] | 2026-02-02T02:03:39Z | [
"llm.parameter-efficient-finetuning"
] | b5fd39ed93ef7a9338e374c5a945c6356947d575 | 2026-10-02T18:21:54Z | gh-ml-readme-evidence-v2 | "b5fd39ed93ef7a9338e374c5a945c6356947d575" | [
"abstract",
"citation",
"installation",
"method",
"other",
"overview",
"results"
] | [
"method-contribution",
"ml-method-context",
"model-training-artifact",
"paper-reference"
] | ok | 2026-10-02T18:21:54Z | shengchaochen82/LM-Weather | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-method-context",
"model-training-artifact",
"paper-reference"
] | include | ml-contribution-v5 | 5 | [] | 2026-02-02T02:03:43Z | https://github.com/shengchaochen82/LM-Weather | null |
[
"generative-ai",
"interpretability-and-safety",
"language",
"safety"
] | [
"alignment",
"preference-optimization"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"growth26.llm-direct-preference-optimization",
"llm.preference-optimization"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-22T08:17:20Z | [NeurIPS 2024] Official code of $\beta$-DPO: Direct Preference Optimization with Dynamic $\beta$ | [
"generative-ai",
"interpretability-and-safety",
"language",
"safety"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T22:51:24Z | false | 6 | 804,242,627 | null | Python | null | [
"alignment",
"preference-optimization"
] | junkangwu/beta-DPO | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 3 | 2026-10-01T17:35:19Z | [] | 2024-10-23T07:38:15Z | [
"llm.preference-optimization"
] | 62776335721b25c2e212d3917c1cf316516ada73 | 2026-10-02T18:21:54Z | gh-ml-readme-evidence-v2 | "62776335721b25c2e212d3917c1cf316516ada73" | [
"other",
"usage"
] | [
"ml-method-context",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-10-02T18:21:54Z | junkangwu/beta-DPO | readme-supported-paper-method-implementation | [
"ml-method-context",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 51 | [
"alignment",
"dpo",
"preference-alignment",
"rlhf"
] | 2026-04-01T09:28:39Z | https://github.com/junkangwu/beta-DPO | null |
[
"generative-ai",
"interpretability-and-safety",
"language",
"natural-language-processing",
"safety"
] | [
"alignment",
"preference-optimization"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"llm.preference-optimization"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-22T08:18:00Z | [ICLR 2025] Official code of "Towards Robust Alignment of Language Models: Distributionally Robustifying Direct Preference Optimization" | [
"generative-ai",
"interpretability-and-safety",
"language",
"natural-language-processing",
"safety"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 3 | 804,242,950 | null | Python | null | [
"alignment",
"preference-optimization"
] | junkangwu/Dr_DPO | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 1 | 2026-09-28T18:50:00Z | [] | 2024-06-01T08:13:56Z | [
"llm.preference-optimization"
] | b0e4917ada216dc5bc2844f74d802525d8824079 | 2026-10-02T18:21:54Z | gh-ml-readme-evidence-v2 | "b0e4917ada216dc5bc2844f74d802525d8824079" | [
"other",
"usage"
] | [
"ml-method-context",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-10-02T18:21:54Z | junkangwu/Dr_DPO | readme-supported-paper-method-implementation | [
"ml-method-context",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 19 | [
"alignment",
"distributionally-robust-optimization",
"dpo",
"preference-alignment",
"rlhf"
] | 2026-05-13T16:56:42Z | https://github.com/junkangwu/Dr_DPO | null |
[
"probabilistic-ml",
"trustworthy-ml"
] | [
"evidential-learning",
"uncertainty-estimation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"growth26.probabilistic-evidential-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-22T10:49:03Z | [ICML 2024] Official code for Uncertainty Estimation by Density Aware Evidential Deep Learning | [
"probabilistic-ml",
"trustworthy-ml"
] | [
"deep-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T22:51:24Z | false | 2 | 804,305,114 | null | Python | MIT | [
"evidential-learning",
"uncertainty-estimation"
] | TaeseongYoon/DAEDL | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-09-26T22:51:24Z | [] | 2026-03-20T12:03:49Z | [
"growth26.probabilistic-evidential-learning"
] | 8a36d2e2c4cc9a1e4f27bce32e0d207088c90558 | 2026-10-02T18:21:54Z | gh-ml-readme-evidence-v2 | "8a36d2e2c4cc9a1e4f27bce32e0d207088c90558" | [
"abstract",
"citation",
"method",
"other"
] | [
"method-contribution",
"ml-method-context",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-10-02T18:21:54Z | TaeseongYoon/DAEDL | readme-supported-paper-method-implementation | [
"method-contribution",
"ml-context-only",
"ml-method-context",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 17 | [] | 2026-07-24T14:57:29Z | https://github.com/TaeseongYoon/DAEDL | null |
[
"graph-learning",
"privacy-ml"
] | [
"federated-learning",
"graph-neural-network"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"growth26.privacy-federated-graphs"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-22T10:50:28Z | Official Code for FedVN | [
"graph-learning",
"privacy-ml"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T22:51:24Z | false | 0 | 804,305,701 | null | Python | null | [
"federated-learning",
"graph-neural-network"
] | xbfu/FedVN | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-09-26T22:51:24Z | [] | 2025-03-16T04:18:14Z | [
"growth26.privacy-federated-graphs"
] | 1327951ed80e1cd1fcb94e965fcbf354a11f9dab | 2026-10-02T18:21:54Z | gh-ml-readme-evidence-v2 | "1327951ed80e1cd1fcb94e965fcbf354a11f9dab" | [
"dataset",
"other",
"overview",
"usage"
] | [
"method-contribution",
"ml-method-context",
"paper-reference"
] | ok | 2026-10-02T18:21:54Z | xbfu/FedVN | readme-supported-paper-method-implementation | [
"method-contribution",
"ml-method-context",
"paper-reference"
] | include | ml-contribution-v5 | 2 | [] | 2025-06-28T14:25:41Z | https://github.com/xbfu/FedVN | null |
[
"computer-vision",
"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 | [] | 2024-05-22T14:46:23Z | Official implementation of NeurIPS'24 paper "Defensive Unlearning with Adversarial Training for Robust Concept Erasure in Diffusion Models". This work adversarially unlearns the text encoder to enhance the robustness of unlearned DMs against adversarial prompt attacks and achieves a better balance between unlearning pe... | [
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"machine-learning",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-12T20:31:19Z | false | 3 | 804,417,259 | null | Jupyter Notebook | CC-BY-4.0 | [
"diffusion"
] | OPTML-Group/AdvUnlearn | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-12T20:31:19Z | [] | 2024-11-04T14:32:51Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 56 | [
"adversarial-machine-learning",
"robust-optimization",
"stable-diffusion",
"unlearned-diffusion-model",
"unlearning"
] | 2026-09-20T07:12:04Z | https://github.com/OPTML-Group/AdvUnlearn | null |
[
"audio",
"efficient-ml",
"model-compression",
"multimodal-learning",
"speech"
] | [
"distillation",
"emotion-recognition",
"inference",
"knowledge-distillation",
"representation",
"speech-understanding"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"audio-speech-emotion-recognition",
"audio-speech-enhancement",
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-22T18:27:48Z | Official code repository for the paper "Enhancing Speech Emotion Recognition through Knowledge Distillation" (ICTC 2024). | [
"audio",
"multimodal-learning",
"speech"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-29T17:12:21Z | false | 0 | 804,516,109 | null | Python | MIT | [
"distillation",
"emotion-recognition",
"knowledge-distillation",
"speech-understanding"
] | nmihtrug/DistilSER | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-10-09T17:31:36Z | [] | 2025-12-09T06:05:44Z | [
"audio-speech-emotion-recognition"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 7 | [
"knowledge-distillation",
"multi-modal",
"speech-emotion-recognition"
] | 2025-12-09T06:05:47Z | https://github.com/nmihtrug/DistilSER | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"recall.name.diffusion-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-22T19:10:43Z | Official implementation for the paper "Model-based Diffusion for Trajectory Optimization". Model-based diffusion (MBD) is a novel diffusion-based trajectory optimization framework that employs a dynamics model to run the reverse denoising process to generate high-quality trajectories. | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-01T17:35:19Z | false | 33 | 804,533,296 | https://lecar-lab.github.io/mbd/ | Python | Apache-2.0 | [
"diffusion"
] | LeCAR-Lab/model-based-diffusion | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-01T17:35:19Z | [] | 2025-03-27T12:48:05Z | [
"recall.name.diffusion-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 341 | [
"control",
"diffusion",
"diffusion-models",
"optimization",
"planning",
"rl",
"sampling-methods",
"trajectory",
"trajectory-optimization",
"zeroth-order-optimization"
] | 2026-09-27T15:32:52Z | https://github.com/LeCAR-Lab/model-based-diffusion | 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 | [] | 2024-05-23T08:25:41Z | [CVPR 2024] Official PyTorch implementation of Towards More Accurate Diffusion Model Acceleration with A Timestep Tuner | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-16T15:36:56Z | false | 0 | 804,768,086 | null | Python | MIT | [
"diffusion"
] | THU-LYJ-Lab/time-tuner | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-16T15:36:56Z | [] | 2024-07-22T11:19:59Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 10 | [] | 2026-03-17T23:54:06Z | https://github.com/THU-LYJ-Lab/time-tuner | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"multimodal",
"vision"
] | [
"diffusion",
"generative-modeling",
"image-generation",
"text-to-image"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"multimodal.text-to-image",
"vision.image-diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-23T08:34:44Z | Official implementation of "Slicedit: Zero-Shot Video Editing With Text-to-Image Diffusion Models Using Spatio-Temporal Slices" (ICML 2024). | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-30T16:30:43Z | false | 4 | 804,771,702 | null | Python | MIT | [
"diffusion"
] | fallenshock/Slicedit | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-10-15T15:20:52Z | [] | 2024-11-24T14:51:26Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 57 | [] | 2026-08-18T08:45:57Z | https://github.com/fallenshock/Slicedit | null |
[
"chemistry",
"computational-chemistry",
"deep-learning",
"graph-learning",
"materials-science"
] | [
"graph-neural-network",
"message-passing",
"molecular-dynamics",
"neural-network",
"scientific-machine-learning",
"simulation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
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] | include | ml-contribution-v5 | 32 | [] | 2026-06-23T04:18:47Z | https://github.com/GLAD-RUC/FastEGNN | null |
[
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] | 2026-09-15T07:38:36Z | https://github.com/liuqidong07/LLM-ESR | null |
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-01T17:35:19Z | false | 0 | 804,928,960 | null | Python | MIT | [
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] | 3 | 2026-10-02T16:59:40Z | [] | 2024-07-10T12:06:39Z | [
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] | include | ml-contribution-v5 | 9 | [] | 2026-05-12T06:40:59Z | https://github.com/tudkcui/M3FC-MARL | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-23T22:12:55Z | This is the official implementation of "DMPlug: A Plug-in Method for Solving Inverse Problems with Diffusion Models" (NeurIPS 2024). | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 8 | 805,100,563 | null | Python | null | [
"diffusion"
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] | 7 | 2026-10-15T15:20:52Z | [] | 2025-09-20T19:28:24Z | [
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] | include | ml-contribution-v5 | 77 | [] | 2026-09-14T03:36:23Z | https://github.com/sun-umn/DMPlug | null |
[
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-26T22:33:16Z | false | 7 | 805,272,458 | null | Python | null | [
"generative-modeling",
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] | A4Bio/GraphsGPT | [
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] | 7 | 2026-10-19T17:46:36Z | [] | 2025-03-19T02:58:40Z | [
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] | include | ml-contribution-v5 | 49 | [] | 2026-09-28T02:22:26Z | https://github.com/A4Bio/GraphsGPT | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-24T08:37:08Z | [ICML 2024] Official implementation of: "Revitalizing Multivariate Time Series Forecasting: Learnable Decomposition with Inter-Series Dependencies and Intra-Series Variations Modeling". | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 7 | 805,276,329 | https://openreview.net/forum?id=87CYNyCGOo | Python | MIT | [
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] | 12 | 2026-10-20T15:59:06Z | [] | 2026-09-29T12:02:45Z | [
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] | include | ml-contribution-v5 | 90 | [
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] | 2026-09-29T12:02:49Z | https://github.com/Levi-Ackman/Leddam | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-24T12:28:25Z | [ICLR 2025] Official implementation of MotionClone: Training-Free Motion Cloning for Controllable Video Generation | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T15:29:21Z | false | 40 | 805,367,406 | null | Python | null | [
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] | 11 | 2026-10-20T15:59:06Z | [
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] | include | ml-contribution-v5 | 526 | [] | 2026-10-14T16:11:18Z | https://github.com/LPengYang/MotionClone | null |
[
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] | [
"multimodal.vision-language"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-24T13:09:11Z | [NeurIPS 2024] Official PyTorch implementation code for realizing the technical part of Mamba-based traversal of rationale (Meteor) to improve performance of numerous vision language performances for diverse capabilities. | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 6 | 805,384,763 | null | Python | MIT | [
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] | ByungKwanLee/Meteor | [
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] | 2 | 2026-10-02T16:59:40Z | [
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] | include | ml-contribution-v5 | 117 | [] | 2026-08-01T19:22:42Z | https://github.com/ByungKwanLee/Meteor | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-24T17:56:01Z | Official code for our paper Pre-training with Synthetic Data Helps Offline Reinforcement Learning at ICLR 2024 | [
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] | include | ml-contribution-v5 | 2 | [] | 2024-08-08T18:36:43Z | https://github.com/Victor-wang-902/synthetic-pretrain-rl | null |
[
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] | [
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] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-26T10:38:08Z | Official implementation of the paper "Memory-efficient High-resolution OCT Volume Synthesis with Cascaded Amortized Latent Diffusion Models". | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-16T15:36:56Z | false | 1 | 806,085,898 | null | Python | null | [
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] | include | ml-contribution-v5 | 9 | [] | 2025-03-12T03:32:27Z | https://github.com/nicetomeetu21/CA-LDM | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-26T12:10:09Z | The official implementation of "A Light-weight Universal Medical Segmentation Network for Laptops Based on Knowledge Distillation" (CVPR 2024 Workshop) | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-01T17:35:19Z | false | 0 | 806,111,486 | null | Python | Apache-2.0 | [
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] | include | ml-contribution-v5 | 11 | [] | 2026-07-29T01:50:44Z | https://github.com/YSongxiao/MedSAMonLaptop_RepViT | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-26T14:13:06Z | 🧙🏻 Code and benchmark for our Findings of ACL 2024 paper - "TimeChara: Evaluating Point-in-Time Character Hallucination of Role-Playing Large Language Models" | [
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] | include | ml-contribution-v5 | 23 | [
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[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-27T03:38:25Z | the implementation of the experiments with 2D synthetic data conducted in our paper, "Understanding Diffusion Models by Feynman's Path Integral," https://arxiv.org/abs/2403.11262 | [
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] | include | ml-contribution-v5 | 3 | [] | 2025-12-15T15:32:30Z | https://github.com/AkinoriTanaka-phys/diffusion_path_integral | null |
[
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] | [
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] | include | ml-contribution-v5 | 0 | [] | 2024-05-27T04:40:44Z | https://github.com/fengxiaoming520/RL-I2IT | null |
[
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] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-27T06:16:27Z | Official PyTorch implementation of "In-distribution Public Data Synthesis with Diffusion Models for Differentially Private Image Classification", CVPR 2024. | [
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] | include | ml-contribution-v5 | 5 | [] | 2025-04-26T12:12:22Z | https://github.com/JinseongP/DPTrainer | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-27T06:57:28Z | [AAAI 2025] Official pytorch implementation of "Diffusion Model Patching via Mixture-of-Prompts" | [
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] | include | ml-contribution-v5 | 14 | [] | 2026-10-11T02:14:56Z | https://github.com/sangminwoo/DMP | null |
[
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[
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] | include | ml-contribution-v5 | 65 | [] | 2026-10-12T14:12:58Z | https://github.com/arvijj/rl-cpp | null |
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-31T11:28:17Z | Official Pytorch implementation of NeuralWalker (ICLR 2025) | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T16:44:32Z | false | 4 | 808,586,006 | https://openreview.net/forum?id=kJ5H7oGT2M | Python | BSD-3-Clause | [
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] | 2 | 2026-10-07T18:07:52Z | [] | 2025-06-25T13:04:22Z | [
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] | 2026-07-07T14:11:21Z | https://github.com/BorgwardtLab/NeuralWalker | null |
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 808,624,526 | https://arxiv.org/abs/2410.01434 | Python | CC-BY-SA-4.0 | [
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] | 1 | 2026-10-04T15:58:58Z | [] | 2025-02-11T15:40:22Z | [
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] | include | ml-contribution-v5 | 1 | [
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] | 2025-05-23T16:31:10Z | https://github.com/SondreWold/comp_rep_study | null |
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-31T16:42:12Z | [NeurIPS2024 Spotlight] The official implementation of MambaTree: Tree Topology is All You Need in State Space Model | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T14:50:46Z | false | 6 | 808,710,027 | null | Python | null | [
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] | EasonXiao-888/MambaTree | [
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] | 5 | 2026-10-01T17:50:46Z | [] | 2024-06-13T11:09:04Z | [
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] | include | ml-contribution-v5 | 114 | [] | 2026-09-27T00:08:21Z | https://github.com/EasonXiao-888/MambaTree | null |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-05-31T17:06:16Z | Official PyTorch implementation of "LayerMerge: Neural Network Depth Compression through Layer Pruning and Merging" (ICML 2024) | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T16:44:32Z | false | 1 | 808,719,620 | https://arxiv.org/abs/2406.12837 | Python | MIT | [
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] | include | ml-contribution-v5 | 32 | [
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] | 2026-08-26T05:06:02Z | https://github.com/snu-mllab/LayerMerge | null |
[
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 2 | 808,782,473 | null | Python | null | [
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] | include | ml-contribution-v5 | 25 | [] | 2026-01-27T19:17:18Z | https://github.com/alenai97/PEFT-MLLM | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-01T04:53:06Z | Code for ICML 2024 Paper "PID: Prompt-Independent Data Protection Against Latent Diffusion Models" | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-16T15:36:56Z | false | 1 | 808,891,936 | null | Python | null | [
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] | include | ml-contribution-v5 | 12 | [] | 2025-11-05T01:21:36Z | https://github.com/PKU-ML/Diffusion-PID-Protection | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-01T17:32:41Z | This is the code repo of our paper DiffEgoPose: An End-to-end Diffusion Model for Ego Pose Estimation. | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-16T15:36:56Z | false | 0 | 809,102,129 | null | null | MIT | [
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] | include | ml-contribution-v5 | 2 | [] | 2024-06-03T08:53:59Z | https://github.com/EninumXJ/Diff-EgoPose | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-02T01:51:08Z | Official implementation of the paper "End-to-End Neuro-Symbolic Reinforcement Learning with Textual Explanations" (ICML 2024) | [
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] | [
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] | 2 | 2026-10-02T16:44:32Z | [] | 2024-07-10T04:48:48Z | [
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] | include | ml-contribution-v5 | 8 | [] | 2025-09-21T07:35:22Z | https://github.com/ins-rl/insight | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-03T05:09:22Z | Official implementation of "One-dimensional Adapter to Rule Them All: Concepts, Diffusion Models and Erasing Applications (CVPR2024 Highlight)" | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 1 | 809,604,373 | null | Python | Apache-2.0 | [
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] | THU-MIG/SPM | [
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] | 3 | 2026-10-19T17:46:36Z | [] | 2024-06-04T14:34:50Z | [
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] | include | ml-contribution-v5 | 9 | [] | 2025-09-23T09:00:44Z | https://github.com/THU-MIG/SPM | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-03T09:07:03Z | Code of ComboStoc, the diffusion models and training/sampling code for our paper exploring the Combinatorial Stochasticity for Diffusion Generative Models. | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 5 | 809,691,425 | null | Python | AGPL-3.0 | [
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] | 2 | 2026-10-15T15:20:52Z | [] | 2026-07-07T07:31:48Z | [
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] | include | ml-contribution-v5 | 39 | [] | 2026-09-25T00:36:43Z | https://github.com/Xrvitd/ComboStoc | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-03T14:02:09Z | [NeurIPS 2024] Source code for our paper "Finding NeMo: Localizing Neurons Responsible For Memorization in Diffusion Models". | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-16T15:36:56Z | false | 1 | 809,817,902 | https://arxiv.org/abs/2406.02366 | Python | MIT | [
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] | 1 | 2026-10-16T15:36:56Z | [] | 2025-07-18T17:39:29Z | [
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] | include | ml-contribution-v5 | 13 | [
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] | 2025-08-19T01:08:46Z | https://github.com/ml-research/localizing_memorization_in_diffusion_models | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-04T11:50:54Z | Official PyTorch implementation of the CVPRW 2023 paper | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-26T14:50:46Z | false | 0 | 810,281,989 | null | Python | MIT | [
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] | 3 | 2026-09-30T16:30:43Z | [] | 2025-05-22T10:49:56Z | [
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] | include | ml-contribution-v5 | 0 | [
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] | 2025-05-22T10:49:59Z | https://github.com/ShineiArakawa/patch-based-ddpm | null |
[
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 0 | 810,354,699 | null | Python | null | [
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] | 2 | 2026-10-16T15:36:56Z | [] | 2024-11-10T23:50:10Z | [
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] | include | ml-contribution-v5 | 0 | [] | 2024-11-11T06:21:54Z | https://github.com/himyl/OTGD | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-04T14:54:38Z | Official Pytorch Implementation of Paper - Stable-Pose: Leveraging Transformers for Pose-Guided Text-to-Image Generation - NeurIPS 2024 | [
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] | ai-med/StablePose | [
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] | 3 | 2026-10-07T18:07:52Z | [] | 2024-12-23T17:49:49Z | [
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] | include | ml-contribution-v5 | 112 | [] | 2026-08-17T05:51:41Z | https://github.com/ai-med/StablePose | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-05T03:18:32Z | Code for paper "Adversarial Moment-Matching Knowledge Distillation for Large Language Models" in NeurIPS'2024. | [
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] | jiachenwestlake/MMKD | [
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] | 2 | 2026-10-29T17:49:51Z | [] | 2024-11-07T07:48:58Z | [
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] | include | ml-contribution-v5 | 1 | [] | 2024-11-07T09:40:00Z | https://github.com/jiachenwestlake/MMKD | null |
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] | slp-rl/HebTTS | [
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] | 2026-09-18T16:05:51Z | https://github.com/slp-rl/HebTTS | null |
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] | 1 | 2026-10-15T15:20:52Z | [] | 2025-11-27T03:14:47Z | [
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] | include | ml-contribution-v5 | 11 | [] | 2025-05-14T07:27:38Z | https://github.com/aryol/inductive-scratchpad | null |
[
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[
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] | 2026-10-01T07:24:23Z | https://github.com/NVlabs/MambaVision | null |
[
"deep-learning",
"efficient-ml",
"language",
"machine-learning-systems",
"model-compression"
] | [
"conditional-computation",
"mixture-of-experts",
"post-training-quantization",
"quantization",
"sparse-activation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.mixture-of-experts",
"efficiency.post-training-quantization",
"efficiency.quantization"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-11T05:02:28Z | Official code for the paper "Examining Post-Training Quantization for Mixture-of-Experts: A Benchmark" | [
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 5 | 813,439,517 | null | Python | MIT | [
"mixture-of-experts",
"quantization"
] | UNITES-Lab/MoE-Quantization | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 10 | 2026-10-15T15:20:52Z | [] | 2025-06-30T05:58:22Z | [
"efficiency.quantization"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 31 | [] | 2026-07-20T08:40:28Z | https://github.com/UNITES-Lab/MoE-Quantization | null |
[
"computer-vision",
"foundation-models",
"generative-ai"
] | [
"multimodal",
"pretraining"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.foundation-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-11T09:36:41Z | [NeurIPS 2024] Official implementation of "Is One GPU Enough? Pushing Image Generation at Higher-Resolutions with Foundation Models." | [
"computer-vision",
"foundation-models",
"generative-ai"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-03T15:45:52Z | false | 5 | 813,548,664 | null | Jupyter Notebook | GPL-3.0 | [
"multimodal",
"pretraining"
] | Thanos-DB/Pixelsmith | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-03T15:45:52Z | [] | 2025-08-14T00:15:43Z | [
"llm.foundation-models"
] | 53953a0a1761c9a94dfd473d4d308c8523118200 | 2026-10-03T17:01:43Z | gh-ml-readme-evidence-v2 | "53953a0a1761c9a94dfd473d4d308c8523118200" | [
"abstract",
"citation",
"other"
] | [
"method-contribution",
"ml-method-context",
"paper-reference"
] | ok | 2026-10-03T17:01:43Z | Thanos-DB/Pixelsmith | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-method-context",
"paper-reference"
] | include | ml-contribution-v5 | 54 | [] | 2026-06-11T03:37:07Z | https://github.com/Thanos-DB/Pixelsmith | null |
[
"generative-ai",
"language",
"safety"
] | [
"alignment",
"language-model",
"preference-optimization"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"growth26.llm-direct-preference-optimization",
"growth26.llm-preference-optimization",
"llm.preference-optimization"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-11T10:57:11Z | [NeurIPS 2024] The official implementation of paper: Chain of Preference Optimization: Improving Chain-of-Thought Reasoning in LLMs. | [
"generative-ai",
"language",
"safety"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 10 | 813,583,730 | null | Python | null | [
"alignment",
"preference-optimization"
] | sail-sg/CPO | [
"description",
"query-match",
"repository-metadata"
] | 9 | 2026-10-01T17:50:46Z | [] | 2025-03-21T09:19:02Z | [
"llm.preference-optimization"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 137 | [] | 2026-06-12T20:00:44Z | https://github.com/sail-sg/CPO | null |
[
"computer-vision",
"sequence-modeling",
"time-series"
] | [
"sequence-modeling",
"state-space-model"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"timeseries.state-space-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-11T18:43:59Z | Official implementation of our paper "Scalable Event-by-event Processing of Neuromorphic Sensory Signals With Deep State-Space Models" | [
"computer-vision",
"sequence-modeling",
"time-series"
] | [
"deep-learning",
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 3 | 813,790,036 | null | Python | MIT | [
"sequence-modeling",
"state-space-model"
] | Efficient-Scalable-Machine-Learning/event-ssm | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-01T17:50:46Z | [] | 2025-10-02T07:53:15Z | [
"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 | 30 | [
"deep-learning",
"event-based-camera",
"event-based-vision",
"state-space-model"
] | 2026-09-15T01:52:10Z | https://github.com/Efficient-Scalable-Machine-Learning/event-ssm | null |
[
"computational-physics",
"optimization",
"physics",
"science-and-engineering"
] | [
"neural-network",
"physics-informed-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"science.physics-informed-neural-networks"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-12T02:35:48Z | Official implementation for paper PINN-BO: A Black-box Optimization Algorithm using Physics-Informed Neural Networks (accepted at ECML-PKDD 2024) | [
"computational-physics",
"optimization",
"physics",
"science-and-engineering"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 0 | 813,928,403 | null | Python | MIT | [
"neural-network",
"physics-informed-learning"
] | phantrdat/pinn-bo | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-02T16:59:40Z | [] | 2024-09-02T14:40:33Z | [
"science.physics-informed-neural-networks"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 5 | [] | 2026-05-28T14:56:01Z | https://github.com/phantrdat/pinn-bo | null |
[
"generative-ai",
"language",
"multimodal-learning",
"natural-language-processing"
] | [
"language-model",
"pretraining"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.large-language-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-12T17:18:05Z | [AAAI 2025] Code for paper:Enhancing Multimodal Large Language Models Complex Reasoning via Similarity Computation | [
"generative-ai",
"language",
"multimodal-learning",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-28T17:27:44Z | false | 0 | 814,274,307 | null | Python | null | [
"language-model",
"pretraining"
] | 0x4e8/Simignore | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-28T17:27:44Z | [] | 2025-01-14T06:03:18Z | [
"llm.large-language-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 19 | [] | 2026-10-01T13:48:30Z | https://github.com/0x4e8/Simignore | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-13T03:47:33Z | The official implementation of NeurIPS2024 paper "SubgDiff: A Subgraph Diffusion Model to Improve Molecular Representation Learning." | [
"generative-ai",
"generative-modeling"
] | [
"representation-learning",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-16T15:36:56Z | false | 0 | 814,454,609 | null | Python | MIT | [
"diffusion"
] | IDEA-XL/SubgDiff | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-16T15:36:56Z | [] | 2025-05-28T02:02:14Z | [
"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 | 11 | [] | 2026-03-04T06:33:37Z | https://github.com/IDEA-XL/SubgDiff | null |
[
"general-ml",
"generative-ai",
"language",
"natural-language-processing",
"safety"
] | [
"alignment",
"deep-learning",
"language-model",
"preference-optimization",
"pretraining"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.topic-deep-learning",
"growth26.llm-preference-optimization",
"llm.language-models",
"llm.preference-optimization"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2024-06-13T06:13:29Z | The official implementation of Self-Play Preference Optimization (SPPO) | [
"generative-ai",
"language",
"natural-language-processing"
] | [
"deep-learning",
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 48 | 814,496,416 | https://uclaml.github.io/SPPO/ | Python | Apache-2.0 | [
"language-model",
"pretraining"
] | uclaml/SPPO | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 9 | 2026-10-11T16:48:52Z | [
"2405.00675"
] | 2025-01-23T01:25:48Z | [
"llm.language-models"
] | 7e9f69174d5c38c6e2912371620cef29336e7ec7 | 2026-10-03T17:01:43Z | gh-ml-readme-evidence-v2 | "7e9f69174d5c38c6e2912371620cef29336e7ec7" | [
"citation",
"installation",
"method",
"other",
"overview",
"results"
] | [
"method-contribution",
"ml-method-context",
"model-training-artifact",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-10-03T17:01:43Z | uclaml/SPPO | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-context-only",
"ml-method-context",
"model-training-artifact",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 588 | [
"deep-learning",
"fine-tuning",
"large-language-models",
"rlhf",
"self-play"
] | 2026-10-05T11:09:54Z | https://github.com/uclaml/SPPO | null |
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