all_domains list | all_methods list | all_novelty_signals list | all_query_ids list | archived bool | candidate_status string | candidate_rule_version string | candidate_eligible bool | candidate_reason string | candidate_evidence list | created_at string | description string | domains list | evidence_signals list | evidence_tier string | evidence_version string | first_observed_at string | fork bool | forks int64 | github_id int64 | homepage string | language string | license string | methods list | name string | novelty_signals list | observation_count int64 | observed_at string | paper_ids list | pushed_at string | query_ids list | readme_blob_sha string | readme_checked_at string | readme_evidence_version string | readme_etag string | readme_sections list | readme_signals list | readme_status string | readme_observed_at string | readme_repository_name_at_fetch string | selection_reason string | selection_signals list | selection_status string | selection_version string | stars int64 | topics list | updated_at string | url string | extra_json string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
[
"generative-ai",
"language",
"linguistics",
"multimodal-learning",
"natural-language-processing"
] | [
"language-model",
"language-modeling",
"large-language-model",
"pretraining"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.language-model",
"llm.large-language-model",
"nlp.language-modeling",
"recall.computational-linguistics",
"recall.name.language-model",
"recall.name.large-language-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-25T17:08:28Z | GAMBIT: A Gamified Jailbreak Framework for Multimodal Large Language Models (ACL 2026 Oral) — official red-teaming code. | [
"multimodal-learning",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-27T14:44:36Z | false | 0 | 1,387,927,565 | null | Python | MIT | [
"language-model"
] | East-Hu/GAMBIT-A-Gamified-Jailbreak-Framework-for-Multimodal-Large-Language-Models-ACL-2026-Oral | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-08T17:03:06Z | [] | 2026-09-25T17:24:36Z | [
"recall.name.language-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 1 | [] | 2026-09-26T07:12:56Z | https://github.com/East-Hu/GAMBIT-A-Gamified-Jailbreak-Framework-for-Multimodal-Large-Language-Models-ACL-2026-Oral | null |
[
"information-retrieval",
"language"
] | [
"rag",
"retrieval-augmented-generation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm-retrieval-augmented-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-25T19:29:08Z | this project is implmentation of okf( open knowledge format ) new method of retrive the information instead of rag where it's uses markdown file and agent's that traverse the those files | [
"information-retrieval",
"language"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-06T16:04:50Z | false | 0 | 1,388,103,275 | null | Python | null | [
"rag",
"retrieval-augmented-generation"
] | Yuraj023/DataGraph | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-06T16:04:50Z | [] | 2026-09-26T11:28:05Z | [
"llm-retrieval-augmented-generation"
] | 5d8b1797546dd1693904063a43151028e2eda65c | 2026-10-06T18:32:27Z | gh-ml-readme-evidence-v2 | "5d8b1797546dd1693904063a43151028e2eda65c" | [
"installation",
"method",
"other",
"usage"
] | [
"ml-method-context"
] | ok | 2026-10-06T18:32:27Z | Yuraj023/DataGraph | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-09-26T11:28:08Z | https://github.com/Yuraj023/DataGraph | null |
[
"deep-learning",
"general-ml",
"graph-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.neural-network",
"graph.gnn-description",
"recall.name.neural-network"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-26T00:38:04Z | official code release for Neurips Paper A Unified Uncertainty Representation for Graph Neural Networks via Doubly-Spectral Stochastic Expansion | [
"general-ml",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-29T15:58:23Z | false | 0 | 1,388,326,043 | null | Python | null | [
"graph-neural-network",
"neural-network"
] | heraclixus/DSSGNN | [
"description",
"query-match",
"repository-metadata"
] | 4 | 2026-10-08T17:03:06Z | [] | 2026-09-28T06:12:59Z | [
"recall.name.neural-network"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-10-01T00:59:24Z | https://github.com/heraclixus/DSSGNN | null |
[
"reinforcement-learning"
] | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-26T15:36:34Z | Official code for the paper "Deep Reinforcement Learning on Item-Compatibility Graphs for One-Dimensional Bin Packing" | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-27T14:44:36Z | false | 0 | 1,389,438,921 | null | Python | MIT | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | aslimaydin/1dBpp_GraphDRL | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-01T16:56:07Z | [] | 2026-09-26T15:38:53Z | [
"rl.deep"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-09-26T15:38:57Z | https://github.com/aslimaydin/1dBpp_GraphDRL | null |
[
"natural-language-processing",
"reinforcement-learning"
] | [
"language-model",
"reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"recall.name.language-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-26T16:24:16Z | Code for paper: Towards Smart Exploration: Reinforcement Learning for LLM Agents Guided by Meta-Actions. | [
"natural-language-processing",
"reinforcement-learning"
] | [
"large-language-model",
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-13T17:04:48Z | false | 0 | 1,389,505,186 | null | Python | null | [
"language-model",
"reinforcement-learning"
] | HavenTong/MetaAct | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-13T17:04:48Z | [] | 2026-09-29T10:04:36Z | [
"recall.name.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 | 0 | [] | 2026-10-01T01:01:03Z | https://github.com/HavenTong/MetaAct | null |
[
"computer-vision",
"general-ml",
"interpretability-and-safety",
"machine-learning"
] | [
"computer-vision",
"deep-learning",
"fine-tuning",
"model-adaptation",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"general.deep-learning-description",
"general.deep-learning-readme",
"general.topic-deep-learning",
"probable.adaptation-finetuning-readme",
"recall.name.deep-learning",
"recall.name.transformer-model",
"vision.topic-computer-vision"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-27T04:56:27Z | Official implementation of IEEE ICCIT 2025 paper: A Multi-Task ResNet152V2 Deep Learning Model for Joint Fruit Type & Freshness Detection with Grad-CAM Explainability (98.45% Accuracy). | [
"computer-vision",
"general-ml",
"interpretability-and-safety"
] | [
"deep-learning",
"classifier"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-03T14:44:25Z | false | 0 | 1,390,144,369 | https://doi.org/10.1109/ICCIT68739.2025.11491294 | Jupyter Notebook | MIT | [
"transformer"
] | RifatHossaiN47/conference-fruit-freshness-xai | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 4 | 2026-10-11T15:52:01Z | [] | 2026-09-27T07:02:03Z | [
"recall.name.transformer-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-context-only",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [
"computer-vision",
"conference-paper",
"deep-learning",
"explainable-ai",
"food-quality-assessment",
"grad-cam",
"ieee-iccit-2025",
"image-classification",
"multi-task-learning",
"pytorch-equivalent",
"resnet152v2",
"smart-agriculture",
"streamlit",
"tensorflow",
"transfer-learning"
] | 2026-09-27T07:02:07Z | https://github.com/RifatHossaiN47/conference-fruit-freshness-xai | null |
[
"computer-vision",
"efficient-ml",
"general-ml",
"graph-learning",
"language",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"vision"
] | [
"computer-vision",
"contrastive-learning",
"distillation",
"graph-neural-network",
"knowledge-distillation",
"language-model",
"lora",
"low-rank-adaptation",
"multimodal-learning",
"neural-network",
"parameter-efficient-fine-tuning",
"transformer",
"vision-language",
"vision-language-model... | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.lora",
"multimodal.vision-language",
"recall.name.language-model",
"recall.name.transformer-model",
"vision.topic-computer-vision",
"vision.vision-language"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-27T07:42:35Z | [NeurIPS 2026] Official PyTorch implementation of SD-LoRA: Training-Time Structural Distillation into LoRA for Few-Shot Vision-Language Adaptation. | [
"computer-vision",
"general-ml",
"graph-learning",
"multimodal-learning",
"natural-language-processing"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-29T15:58:23Z | false | 0 | 1,390,399,775 | null | Python | AGPL-3.0 | [
"distillation",
"graph-neural-network",
"knowledge-distillation",
"language-model",
"lora",
"neural-network",
"transformer"
] | MR-Sherif/SD-LoRA | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 8 | 2026-10-11T15:52:01Z | [] | 2026-09-27T09:11:22Z | [
"recall.name.language-model",
"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"
] | include | ml-contribution-v5 | 3 | [
"clip",
"computer-vision",
"few-shot-learning",
"graph-neural-networks",
"knowledge-distillation",
"lora",
"neurips-2026",
"pytorch",
"vision-language-models"
] | 2026-10-03T02:02:28Z | https://github.com/MR-Sherif/SD-LoRA | null |
[
"generative-ai",
"language",
"natural-language-processing",
"reinforcement-learning"
] | [
"language-model",
"pretraining",
"reinforcement-learning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.language-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-27T15:57:51Z | Official implementation of ERPO: Entropy-Regularized Rank-Masked Policy Optimization for Test-Time RL in Code Generation (EMNLP 2026 Oral). | [
"generative-ai",
"language",
"natural-language-processing",
"reinforcement-learning"
] | [
"large-language-model",
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-21T14:37:12Z | false | 0 | 1,391,083,813 | https://xiaobanni.github.io/projects/erpo/ | Python | Apache-2.0 | [
"language-model",
"pretraining",
"reinforcement-learning"
] | xiaobanni/erpo | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-21T14:37:12Z | [] | 2026-10-16T10:37:44Z | [
"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 | 1 | [
"code-generation",
"emnlp2026",
"entropy-regularization",
"large-language-models",
"policy-optimization",
"reinforcement-learning",
"test-time-reinforcement-learning"
] | 2026-10-16T13:43:56Z | https://github.com/xiaobanni/erpo | null |
[
"generative-ai",
"imitation-learning",
"reinforcement-learning"
] | [
"diffusion",
"imitation-learning",
"inverse-reinforcement-learning",
"reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"recall.name.diffusion-model",
"rl.inverse"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-27T22:53:39Z | The official implementation for the paper Loop-Free Inverse Reinforcement Learning via Sequential Value Recovery with Q-Score Matching | [
"generative-ai",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-29T15:58:23Z | false | 0 | 1,391,474,658 | null | Python | null | [
"diffusion",
"reinforcement-learning"
] | NothingThrough/LFIRL | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-10-06T16:04:50Z | [] | 2026-09-27T23:09:59Z | [
"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 | 0 | [] | 2026-09-27T23:10:02Z | https://github.com/NothingThrough/LFIRL | null |
[
"efficient-ml",
"generative-ai",
"language",
"machine-learning-systems",
"model-compression"
] | [
"language-model",
"neural-network-pruning",
"pruning",
"structured-pruning",
"transformer"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning",
"llm.transformers"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-28T02:02:15Z | Official implementation of "Spectral-aligned pruning for universal error-correcting code transformers” (NeurIPS 2026) | [
"generative-ai",
"language",
"machine-learning-systems"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-19T16:36:56Z | false | 0 | 1,391,594,391 | null | Python | null | [
"language-model",
"pruning",
"transformer"
] | iil-postech/sap | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 2 | 2026-10-24T13:28:22Z | [] | 2026-10-12T13:37:56Z | [
"llm.transformers"
] | 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 | [
"ecc",
"transformer"
] | 2026-10-14T00:42:14Z | https://github.com/iil-postech/sap | null |
[
"computer-vision",
"generative-ai",
"language"
] | [
"language-model",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.transformers"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-28T05:53:19Z | Official implementation of the paper "ReSS: Residual-Restoring Sparse Attention for 3D Vision Transformers" | [
"computer-vision",
"generative-ai",
"language"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-04T14:53:29Z | false | 0 | 1,391,942,774 | null | Python | NOASSERTION | [
"language-model",
"transformer"
] | libary753/ReSS | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-23T14:45:14Z | [] | 2026-10-12T07:24:20Z | [
"llm.transformers"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 2 | [] | 2026-10-23T05:40:01Z | https://github.com/libary753/ReSS | null |
[
"computer-vision",
"machine-learning",
"multimodal",
"representation-learning"
] | [
"adaptation",
"domain-generalization",
"multimodal-learning",
"test-time-adaptation",
"transfer-learning",
"vision-language"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"probable.adaptation-domain-generalization-readme",
"specialized.test-time-adaptation",
"vision.vision-language"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-28T08:59:43Z | [NeurIPS2026] Official source code for Efficient Test-time Adaptation through Candidate Verification and Divergence Shifts. | [
"computer-vision",
"multimodal"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-30T15:58:03Z | false | 0 | 1,392,272,042 | https://mmai-laboratory.github.io/TTC/ | Python | Apache-2.0 | [
"multimodal-learning",
"test-time-adaptation",
"vision-language"
] | MMAI-Laboratory/TTC | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-23T14:45:14Z | [] | 2026-10-12T07:28:08Z | [
"vision.vision-language"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 3 | [
"efficient-ai",
"test-time-adaptation",
"vlm"
] | 2026-10-12T07:29:19Z | https://github.com/MMAI-Laboratory/TTC | null |
[
"generative-modeling",
"natural-language-processing"
] | [
"diffusion",
"text-generation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"nlp.text-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-28T11:31:45Z | [NeurIPS 2026] This repository is the official implementation of "BiMoGen: Bidirectional Motion-Text Generation via Unified Masked Discrete Diffusion" | [
"generative-modeling",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-29T15:58:23Z | false | 0 | 1,392,535,051 | null | null | null | [
"diffusion",
"text-generation"
] | wengwanjiang/BiMoGen | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-05T18:59:49Z | [] | 2026-09-28T11:47:56Z | [
"nlp.text-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 | 2 | [] | 2026-10-04T08:10:17Z | https://github.com/wengwanjiang/BiMoGen | null |
[
"multimodal",
"multimodal-learning",
"natural-language-processing",
"reinforcement-learning",
"robotics"
] | [
"environment-modeling",
"multimodal-learning",
"world-model"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.world-models",
"recall.name.multimodal-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-28T12:11:21Z | Official implementation of PD-WM: LLM-Guided Multimodal World Model for Parkinson’s Severity and Personalized Management Profiling, accepted as a regular paper at IEEE BIBM 2026. | [
"multimodal",
"multimodal-learning",
"natural-language-processing",
"reinforcement-learning",
"robotics"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-29T15:58:23Z | false | 0 | 1,392,618,126 | null | Python | null | [
"environment-modeling",
"world-model"
] | ayoubncbae/PD-WM-BIBM2026 | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-02T15:56:46Z | [] | 2026-09-28T12:20:43Z | [
"multimodal.world-models"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 2 | [] | 2026-10-01T00:47:10Z | https://github.com/ayoubncbae/PD-WM-BIBM2026 | null |
[
"deep-learning",
"efficient-ml",
"language"
] | [
"conditional-computation",
"mixture-of-experts",
"sparse-activation"
] | [
"description",
"license-metadata",
"query-match",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-29T05:39:15Z | Code for paper: Anomaly Detection for High-Altitude UAV Flight Data Using a Mixture of Experts | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-29T15:58:23Z | false | 0 | 1,394,430,523 | null | Python | MIT | [
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] | zuocancan/UAV-MoE-Anomaly-Detection | [
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] | 3 | 2026-10-01T16:56:07Z | [] | 2026-09-29T06:20:49Z | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-10-01T01:03:11Z | https://github.com/zuocancan/UAV-MoE-Anomaly-Detection | null |
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-29T09:58:56Z | Official implementation of the paper on "Test-time adaptation for speech enhancement with an autoregressive speech prior" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-29T15:58:23Z | false | 0 | 1,394,875,851 | null | Python | MIT | [
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] | SofieneKammoun/TAAP-SE-CODE | [
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] | 6 | 2026-10-11T15:52:01Z | [] | 2026-09-29T13:47:16Z | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-10-01T01:13:35Z | https://github.com/SofieneKammoun/TAAP-SE-CODE | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-29T10:14:44Z | GFT: a new transformer architecture (Residual Content Gating) benchmarked against a vanilla transformer on CPU under an enforced 150 MB memory budget. numpy-only, hand-written backprop, gradient-checked. | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-01T16:31:09Z | false | 0 | 1,394,903,775 | null | Python | null | [
"transformer"
] | Brainshock246/gated-fusion-transformer | [
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] | 3 | 2026-10-13T17:04:48Z | [] | 2026-09-29T10:14:56Z | [
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] | [
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] | ok | 2026-10-01T19:11:32Z | Brainshock246/gated-fusion-transformer | specific-method-with-novelty-claim | [
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] | include | ml-contribution-v5 | 1 | [] | 2026-10-04T08:09:04Z | https://github.com/Brainshock246/gated-fusion-transformer | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-29T13:40:55Z | Official implementation of CATok: Rethinking Causal Action Tokenization with Conditional Annealing in Flow Matching (arXiv:2609.35469) | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-29T15:58:23Z | false | 0 | 1,395,440,577 | https://chenyuzhangx.github.io/CATok/ | Python | Apache-2.0 | [
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] | ChenyuZhangX/CausalActionTokenizer | [
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] | 6 | 2026-10-04T14:53:29Z | [] | 2026-09-29T13:42:54Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 3 | [
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"flow-matching",
"robot-learning",
"robotics",
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"vla"
] | 2026-10-01T01:13:37Z | https://github.com/ChenyuZhangX/CausalActionTokenizer | null |
[
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-30T05:54:16Z | Open-source code for NeurIPS 2026 paper: High-Dimensional Robotic Reinforcement Learning with Developing Synergies | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T18:03:22.392067Z | false | 0 | 1,397,108,266 | null | Python | NOASSERTION | [
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] | JonyeeShen/Devyn | [
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] | 6 | 2026-10-20T14:47:52Z | [] | 2026-10-09T14:45:57Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [
"reinforcement-learning"
] | 2026-10-09T14:46:01Z | https://github.com/JonyeeShen/Devyn | null |
[
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] | [
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-30T07:13:16Z | Official implementation for Wasserstein-based Knowledge Distillation (WASD) in NeurIPS 2026. | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-01T16:31:09Z | false | 1 | 1,397,256,754 | null | Python | Apache-2.0 | [
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] | aailab-kaist/WASD | [
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] | 3 | 2026-10-18T13:47:34Z | [] | 2026-10-08T23:52:47Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 5 | [] | 2026-10-17T00:18:17Z | https://github.com/aailab-kaist/WASD | null |
[
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-01T01:28:57Z | [NeurIPS 2026 main] Official Implementation of "Towards Real-Time Full-Waveform LiDAR Transformers via Intensity-Guided Token Reduction and Physics-Aware Augmentation" | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-22T14:58:59Z | false | 0 | 1,398,903,806 | https://keio-csg.github.io/FWL-ToPM/ | JavaScript | null | [
"neural-operators",
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] | Keio-CSG/FWL-ToPM | [
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] | 2 | 2026-10-23T14:45:14Z | [] | 2026-10-07T07:04:35Z | [
"science.physics"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 4 | [
"full-waveform-lidar",
"full-waveform-lidar-data",
"lidar",
"token-merging",
"token-pruning"
] | 2026-10-12T04:08:40Z | https://github.com/Keio-CSG/FWL-ToPM | null |
[
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] | [
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] | [
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] | [
"llm.transformers",
"recall.name.transformer-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-01T02:52:13Z | Official implementation of DPPE: Rethinking Camera-Based Positional Encoding for Scaling Multi-View Transformers (NeurIPS2026) | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-01T16:31:09Z | false | 0 | 1,398,967,141 | null | Python | Apache-2.0 | [
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] | sbintuitions/dppe | [
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] | 6 | 2026-10-21T14:37:12Z | [] | 2026-10-01T02:58:42Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 2 | [] | 2026-10-11T06:46:58Z | https://github.com/sbintuitions/dppe | null |
[
"natural-language-processing"
] | [] | [
"description",
"repository-metadata"
] | [] | false | unknown | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-01T05:28:26Z | Official code repository of "StepCAD: Mesh-to-CAD Code Generation via LLM Policy and Geometry-Guided Search" @ NeurIPS 2026 | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-08T19:45:10Z | false | 1 | 1,399,195,709 | https://ghadinehme.com/stepcad.github.io/ | null | null | [] | ghadinehme/StepCAD | [
"description",
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] | 3 | 2026-10-10T18:17:44Z | [
"2610.03799"
] | 2026-10-01T05:39:30Z | [] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 5 | [] | 2026-10-10T16:50:48Z | https://github.com/ghadinehme/StepCAD | {"discovery_source":"hf_daily_papers","paper_evidence":"unverified","queryless":true} |
[
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-01T09:05:37Z | Official code for "Revisiting Neural Processes via Fourier Transform and Volterra Series" (ICML 2026) | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-01T16:31:09Z | false | 0 | 1,399,608,851 | null | Python | MIT | [
"machine-learning",
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] | peiman-m/fourier-volterra-nps | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 15 | 2026-10-25T14:09:27Z | [] | 2026-10-01T09:05:43Z | [
"recall.name.machine-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [
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"volterra-s... | 2026-10-11T07:08:21Z | https://github.com/peiman-m/fourier-volterra-nps | null |
[
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] | [
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-02T01:02:06Z | Code, benchmark and results for our ACML 2026 paper on test-time adaptation for frozen object detectors on video streams. | [
"computer-vision",
"machine-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T14:49:08Z | false | 0 | 1,400,879,352 | null | Python | MIT | [
"adaptation",
"test-time-adaptation"
] | ShahriarShakirSumit/FAN-TTA | [
"description",
"github-topics",
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"query-match",
"repository-metadata"
] | 2 | 2026-10-18T13:47:34Z | [] | 2026-10-17T09:51:04Z | [
"specialized.test-time-adaptation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [
"acml2026",
"benchmark",
"domain-shift",
"object-detection",
"streaming",
"test-time-adaptation",
"yolov8"
] | 2026-10-17T09:51:08Z | https://github.com/ShahriarShakirSumit/FAN-TTA | null |
[
"imitation-learning",
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] | [
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] | [
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] | [
"rl.inverse"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-02T13:14:11Z | Official implementation of the NeurIPS 2026 paper: "A Geometric Perspective on Reward Function Updates in Inverse Reinforcement Learning" | [
"imitation-learning",
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] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-03T14:20:41Z | false | 0 | 1,401,759,352 | https://arxiv.org/pdf/2610.14041 | Python | CC-BY-SA-4.0 | [
"imitation-learning",
"inverse-reinforcement-learning",
"reinforcement-learning"
] | anishhdiwan/geometric-perspective | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 2 | 2026-10-18T13:47:34Z | [] | 2026-10-14T07:41:44Z | [
"rl.inverse"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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"ml-method-cue",
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] | include | ml-contribution-v5 | 0 | [
"inverse-reinforcement-learning",
"natural-gradients",
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] | 2026-10-15T14:09:43Z | https://github.com/anishhdiwan/geometric-perspective | null |
[
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] | [
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] | [
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"query-match",
"repository-metadata"
] | [
"multimodal.world-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-02T14:07:43Z | Official implementation of the paper "Next Embedding Prediction Makes World Models Stronger" | [
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"reinforcement-learning",
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] | [
"embedding"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-03T14:20:41Z | false | 0 | 1,401,849,274 | https://corl-team.github.io/nedreamer/ | null | MIT | [
"environment-modeling",
"world-model"
] | mokebot007/nedreamer | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-03T14:20:41Z | [] | 2026-10-02T14:10:05Z | [
"multimodal.world-models"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-10-02T15:02:40Z | https://github.com/mokebot007/nedreamer | null |
[
"foundation-models",
"generative-ai",
"generative-modeling"
] | [
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"pretraining"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.foundation-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-02T19:01:14Z | Code for NeurIPS 2026 paper "Diffusion Meta-Prompting and Steering for Generalizable Foundation Model Adaptation" | [
"foundation-models",
"generative-ai",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-03T14:20:41Z | false | 0 | 1,402,240,682 | null | null | null | [
"diffusion",
"multimodal",
"pretraining"
] | DeepakSridhar/dmp | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-20T14:47:52Z | [] | 2026-10-02T19:01:15Z | [
"llm.foundation-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 | 0 | [] | 2026-10-02T19:01:15Z | https://github.com/DeepakSridhar/dmp | null |
[
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"efficient-ml",
"machine-learning-systems",
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] | [
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] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.quantization",
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-04T17:57:28Z | Re-created code for our PLoS One 2025 paper: nuclei segmentation (SegNet) and classification (DenseNet121) on PanNuke with Hyperband tuning, TFLite INT8 quantization and federated learning. | [
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] | [
"classifier"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-05T18:33:27Z | false | 0 | 1,404,697,119 | null | Python | NOASSERTION | [
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"quantization"
] | anzir29/pannuke-nuclei-fl | [
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"query-match",
"repository-metadata"
] | 3 | 2026-10-20T14:47:52Z | [] | 2026-10-04T18:21:32Z | [
"efficiency.quantization",
"trust.federated-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-10-04T18:16:42Z | https://github.com/anzir29/pannuke-nuclei-fl | null |
[
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"generative-ai",
"generative-modeling",
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] | [
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"query-match",
"repository-metadata"
] | [
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"vision.video-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-05T10:54:05Z | Official implementation of "GRACE: Generation-Aware Latent Compression for Efficient Video Generation" (arXiv 2610.10524) | [
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] | include | ml-contribution-v5 | 28 | [
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] | 2026-10-21T13:20:40Z | https://github.com/cvlab-kaist/GRACE | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-05T16:03:39Z | CPU-only code for the analytical and synthetic Gaussian-mixture experiments in our TMLR paper on schedule design for Brownian Bridge Diffusion Models. | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-05T18:33:27Z | false | 0 | 1,406,036,436 | null | Python | MIT | [
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] | 5 | 2026-10-24T13:28:22Z | [] | 2026-10-05T17:56:22Z | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-10-11T08:30:38Z | https://github.com/ron787/bbdm-schedule-design | null |
[
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"generative-modeling",
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] | [
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] | [
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] | [
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"recall.name.diffusion-model",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-05T17:30:19Z | Official code for SAGE (ECCV 2026): Structure-Aware Geometric Regularization for safety alignment of text-to-image diffusion models | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-05T18:33:27Z | false | 0 | 1,406,148,974 | https://adeelyousaf.github.io/SAGE_ECCV26_Project_Page/ | Jupyter Notebook | NOASSERTION | [
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] | Adeelyousaf/SAGE | [
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] | include | ml-contribution-v5 | 2 | [
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] | 2026-10-13T13:58:28Z | https://github.com/Adeelyousaf/SAGE | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-06T12:41:57Z | Few-shot meta-learning bake-off across vision/tabular/chess/coding + novel PAHO architecture | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-07T16:37:11Z | false | 0 | 1,407,283,578 | null | Python | null | [
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] | mahithpaulin/fewshot-arch-lab | [
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] | 3 | 2026-10-19T16:36:56Z | [] | 2026-10-06T12:55:55Z | [
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] | [
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] | ok | 2026-10-07T19:34:31Z | mahithpaulin/fewshot-arch-lab | specific-method-with-novelty-claim | [
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"model-training-artifact"
] | include | ml-contribution-v5 | 0 | [] | 2026-10-11T08:58:28Z | https://github.com/mahithpaulin/fewshot-arch-lab | null |
[
"generative-ai",
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] | [
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] | [
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"query-match",
"repository-metadata"
] | [
"recall.name.diffusion-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-06T13:11:07Z | [NeurIPS 2026] Official PyTorch implementation of Feature Information Dynamics in Diffusion. | [
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"generative-modeling"
] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-26T16:16:45Z | false | 1 | 1,407,329,438 | https://panjiashu.github.io/feature-information-dynamics/ | Python | MIT | [
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] | AI4Science-WestlakeU/feature-information-dynamics | [
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] | 1 | 2026-10-26T16:16:45Z | [] | 2026-10-08T08:39:23Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 10 | [
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] | 2026-10-14T14:43:25Z | https://github.com/AI4Science-WestlakeU/feature-information-dynamics | null |
[
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-06T13:15:19Z | Official implementation of [Oakland’27] paper "Revisiting Locally Differentially Private Graph Neural Networks" | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-07T16:37:11Z | false | 0 | 1,407,335,578 | null | Python | NOASSERTION | [
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] | ZJU-TrustAID/Revisit-LDPGNN | [
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] | 3 | 2026-10-19T16:36:56Z | [] | 2026-10-07T02:01:35Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-10-11T08:58:00Z | https://github.com/ZJU-TrustAID/Revisit-LDPGNN | null |
[
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] | [
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] | [
"applied.tabular-ml",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-06T18:48:30Z | official implementation of AISTATS'26 paper "TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer" | [
"generative-ai",
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-07T16:37:11Z | false | 0 | 1,407,786,778 | null | Python | NOASSERTION | [
"language-model",
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] | BetterdataLabs/tabtreeformer | [
"description",
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"query-match",
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] | 3 | 2026-10-22T14:58:59Z | [] | 2026-10-06T18:51:44Z | [
"applied.tabular-ml",
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-10-06T18:55:22Z | https://github.com/BetterdataLabs/tabtreeformer | null |
[
"computer-vision",
"language",
"multimodal",
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] | [
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-07T07:38:30Z | [NeurIPS 2026] Official implementation of Visual-Advantage On-Policy Distillation for Vision-Language Models. | [
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"multimodal-learning",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-07T16:37:11Z | false | 0 | 1,408,427,282 | null | Python | Apache-2.0 | [
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] | handsome-rich/VA-OPD | [
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] | 5 | 2026-10-22T14:58:59Z | [] | 2026-10-07T08:46:41Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 2 | [] | 2026-10-20T12:44:59Z | https://github.com/handsome-rich/VA-OPD | null |
[
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] | [
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] | [
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"repository-metadata"
] | [
"timeseries.imputation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-07T08:41:34Z | [NeurIPS 2026] Official implementation of the paper "Discretizing Continuous Time Series for Imputation with Masked Diffusion Training" | [
"generative-modeling",
"missing-data",
"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-09T16:18:36Z | false | 0 | 1,408,498,270 | https://dongbeank.github.io/MDTIM | Python | MIT | [
"diffusion",
"imputation",
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] | dongbeank/MDTIM | [
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"query-match",
"repository-metadata"
] | 3 | 2026-10-18T13:47:34Z | [] | 2026-10-14T20:34:20Z | [
"timeseries.imputation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 2 | [] | 2026-10-14T20:34:24Z | https://github.com/dongbeank/MDTIM | null |
[
"deep-learning",
"efficient-ml",
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] | [
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] | [
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] | [
"efficiency.mixture-of-experts"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-07T17:47:23Z | Official implementation of "Ask, Answer, and Detect: Role-Playing LLMs for Personality Detection with Question-Conditioned Mixture-of-Experts" (NeurIPS 2026). | [
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"efficient-ml",
"language"
] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-08T16:35:27Z | false | 0 | 1,409,168,315 | null | null | null | [
"conditional-computation",
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] | JasmineLyf/ROME-NeurIPS2026 | [
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] | 3 | 2026-10-19T16:36:56Z | [] | 2026-10-07T17:47:25Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-10-07T17:47:25Z | https://github.com/JasmineLyf/ROME-NeurIPS2026 | null |
[
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] | [
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] | [
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"repository-metadata"
] | [
"efficiency.speculative-decoding"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-09T04:12:50Z | Official implementation of the paper "Fork and Regenerate: MTP-Guided Early Forking and Suffix Regeneration for Block Diffusion Speculative Decoding" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-19T16:36:56Z | false | 0 | 1,411,267,903 | null | Python | Apache-2.0 | [
"diffusion",
"inference-optimization",
"speculative-decoding"
] | uncc-efficient-ai/FoRE | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-19T16:36:56Z | [] | 2026-10-13T21:20:08Z | [
"efficiency.speculative-decoding"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-10-14T04:18:33Z | https://github.com/uncc-efficient-ai/FoRE | null |
[
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"multimodal",
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-09T08:18:04Z | Official implementation of MVP: Multi-View-Guided Token Pruning for Vision-Language Models (EMNLP 2026 Main Conference). | [
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"language",
"machine-learning-systems",
"multimodal-learning",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-10T15:26:41Z | false | 0 | 1,411,510,010 | null | Python | Apache-2.0 | [
"language-model",
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] | Chenxi-CCW/MVP | [
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] | 5 | 2026-10-22T14:58:59Z | [] | 2026-10-09T08:27:30Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-10-11T10:00:48Z | https://github.com/Chenxi-CCW/MVP | null |
[
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] | [
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"query-match",
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-09T12:29:26Z | Official implementation of "Mixture-of-Control: State-Aware Fine-Tuning for Transformer-based Models" (NeurIPS 2026). | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-10T15:26:41Z | false | 0 | 1,411,814,617 | null | null | MIT | [
"fine-tuning",
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] | duc-anh-2002/Mixture-of-Control | [
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"query-match",
"repository-metadata"
] | 2 | 2026-10-22T14:58:59Z | [] | 2026-10-09T12:29:28Z | [
"llm.finetuning",
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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 | 0 | [] | 2026-10-09T12:29:32Z | https://github.com/duc-anh-2002/Mixture-of-Control | null |
[
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] | [
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] | [
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"repository-metadata"
] | [
"rl.multiagent"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-09T15:46:49Z | Code for paper "Multi-Agent Reinforcement Learning for Cross-Region Placement of Branched Service Function Chains" | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-10T15:26:41Z | false | 0 | 1,412,091,542 | null | Python | null | [
"centralized-training",
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"reinforcement-learning"
] | Shuxin-Wang/MAPPO-SFC | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-18T13:47:34Z | [] | 2026-10-09T15:49:52Z | [
"rl.multiagent"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-10-09T15:49:57Z | https://github.com/Shuxin-Wang/MAPPO-SFC | null |
[
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] | [
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"paper-reference",
"query-match",
"repository-metadata"
] | [
"recall.name.diffusion-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-09T17:43:51Z | [coming soon] The official implementation of the paper titled "Controllable Exaggeration for Generative Motion Models via Training-Time Adaptation and Inference-Time Guidance"" | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-28T16:14:12Z | false | 0 | 1,412,236,437 | https://arxiv.org/abs/2610.12316 | null | null | [
"diffusion",
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] | AHHHZ975/Controllable-Motion-Exaggeration | [
"description",
"github-topics",
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"query-match",
"repository-metadata"
] | 1 | 2026-10-28T16:14:12Z | [] | 2026-10-09T18:11:37Z | [
"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 | 2 | [
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"diffusion-models",
"flow-matching",
"generative-ai",
"motion-generation",
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] | 2026-10-25T20:36:38Z | https://github.com/AHHHZ975/Controllable-Motion-Exaggeration | null |
[
"generative-ai",
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] | [
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] | [
"description",
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"repository-metadata"
] | [
"llm.transformers"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-10T07:44:39Z | Official implementations of experiments in paper "Transformers Provably Implement In-Context Reinforcement Learning with Policy Improvement". | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-24T13:28:22Z | false | 0 | 1,412,915,713 | null | Jupyter Notebook | null | [
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] | harris-liang/Transformer-in-context-RL | [
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] | 1 | 2026-10-24T13:28:22Z | [] | 2026-10-10T08:00:01Z | [
"llm.transformers"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
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] | include | ml-contribution-v5 | 1 | [] | 2026-10-10T08:00:05Z | https://github.com/harris-liang/Transformer-in-context-RL | null |
[
"automl",
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"general-ml",
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] | [
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"synthetic-data"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.meta-learning",
"llm.synthetic-data"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-10T14:08:34Z | Code for paper: MotherTree: Meta-learning on synthetic data improves decision tree training | [
"automl",
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"general-ml",
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-10T15:26:41Z | false | 0 | 1,413,265,398 | null | Python | null | [
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] | Stanwang218/Mother-Tree | [
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"repository-metadata"
] | 3 | 2026-10-18T13:47:34Z | [] | 2026-10-14T08:15:05Z | [
"general.meta-learning",
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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] | include | ml-contribution-v5 | 4 | [] | 2026-10-14T10:26:20Z | https://github.com/Stanwang218/Mother-Tree | null |
[
"distributed-ml",
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] | [
"collaborative-learning",
"federated-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-10T14:16:17Z | Official implementation for the ICCA 2026 paper: Federated Learning for Privacy-Preserving Adult Content Detection under Non-IID Data Distributions. | [
"distributed-ml",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-11T15:47:04Z | false | 0 | 1,413,273,543 | null | Jupyter Notebook | null | [
"collaborative-learning",
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] | Sagor000000/icca-2026-privacy-preserving-fl | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-13T16:59:50Z | [] | 2026-10-10T14:17:07Z | [
"trust.federated-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-10-10T14:17:10Z | https://github.com/Sagor000000/icca-2026-privacy-preserving-fl | null |
[
"computer-vision",
"generative-ai",
"language"
] | [
"language-model",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.transformers"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-11T03:41:16Z | Official implementation of Patch Rebirth: Fast and Transferable Model Inversion of Vision Transformers (NeurIPS 2026). | [
"computer-vision",
"generative-ai",
"language"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-24T13:28:22Z | false | 0 | 1,413,894,549 | null | Python | MIT | [
"language-model",
"transformer"
] | bigdata-inha/PRI | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-24T13:28:22Z | [] | 2026-10-11T03:44:06Z | [
"llm.transformers"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-10-21T16:32:22Z | https://github.com/bigdata-inha/PRI | null |
[
"computer-vision",
"efficient-ml",
"image-processing",
"low-level-vision",
"machine-learning-systems",
"model-compression"
] | [
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"mamba",
"quantization",
"restoration",
"self-supervised-learning",
"super-resolution"
] | [
"description",
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"repository-metadata"
] | [
"efficiency.quantization",
"recall26.image-denoising",
"vision.image-restoration",
"vision.image-super-resolution"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-11T06:46:29Z | [NeurIPS 2026] Official PyTorch implementation of "Hardware-Friendly Token-Group Activation Quantization for Low-Bit Mamba Super-Resolution" | [
"computer-vision",
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-12T19:07:30Z | false | 0 | 1,414,024,388 | null | Python | Apache-2.0 | [
"mamba",
"quantization",
"restoration"
] | imaboybut/TGQ-MambaSR | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-21T14:37:12Z | [] | 2026-10-14T03:11:38Z | [
"efficiency.quantization",
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 2 | [] | 2026-10-14T04:18:28Z | https://github.com/imaboybut/TGQ-MambaSR | null |
[
"efficient-ml",
"machine-learning-systems",
"model-compression",
"natural-language-processing"
] | [
"quantization"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.quantization"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-12T22:20:49Z | Official implementation of paper "MoBiQuant: Mixture-of-Bits Quantization for Token-Adaptive Any-Precision LLM" | [
"efficient-ml",
"machine-learning-systems",
"model-compression",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-14T14:33:31Z | false | 0 | 1,416,187,325 | null | Python | MIT | [
"quantization"
] | SimWangArizona/MoBiQuant | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-21T14:37:12Z | [] | 2026-10-17T17:57:39Z | [
"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 | 2 | [] | 2026-10-20T19:50:46Z | https://github.com/SimWangArizona/MoBiQuant | null |
[
"embodied-ai",
"general-ml",
"multimodal",
"robotics"
] | [
"foundation-model",
"robot-foundation-model",
"robot-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"recall.name.foundation-model",
"robotics.robot-foundation-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-13T13:41:33Z | Official implementation of the paper "Value as Memory: Online Value-Guided Post-training for General Robot Policies" | [
"embodied-ai",
"multimodal",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-14T14:33:31Z | false | 0 | 1,417,143,487 | null | Python | Apache-2.0 | [
"robot-foundation-model",
"robot-learning"
] | Virlus/VaM | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-23T14:45:14Z | [] | 2026-10-16T02:52:52Z | [
"robotics.robot-foundation-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 | 24 | [] | 2026-10-21T17:21:02Z | https://github.com/Virlus/VaM | null |
[
"data-centric-ai",
"deep-learning",
"efficient-ml",
"language",
"machine-learning"
] | [
"conditional-computation",
"feature-selection",
"machine-learning",
"mixture-of-experts",
"sparse-activation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.mixture-of-experts",
"probable.data-feature-selection"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-13T15:22:54Z | Official code for the paper "RouterInterp: Understanding Superposed Specialisation in Mixture of Experts Routing" | [
"data-centric-ai",
"machine-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-14T14:33:31Z | false | 0 | 1,417,340,009 | null | Python | MIT | [
"feature-selection",
"machine-learning",
"mixture-of-experts"
] | ilyalasy/routerinterp | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-25T14:09:27Z | [] | 2026-10-13T15:23:40Z | [
"probable.data-feature-selection"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-10-21T17:14:14Z | https://github.com/ilyalasy/routerinterp | null |
[
"biology",
"computational-biology",
"generative-modeling",
"protein-engineering"
] | [
"diffusion",
"inverse-folding",
"protein-design"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"bio.inverse-folding"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-13T20:06:00Z | Official implementation of GOLD: Geometric Optimized Latent Diffusion for Structure-Aware RNA Inverse Folding (NeurIPS 2026 Spotlight). | [
"biology",
"computational-biology",
"generative-modeling",
"protein-engineering"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-14T14:33:31Z | false | 0 | 1,417,897,308 | null | Python | Apache-2.0 | [
"diffusion",
"inverse-folding",
"protein-design"
] | darkflash03/GOLD | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-18T13:47:34Z | [] | 2026-10-15T09:13:35Z | [
"bio.inverse-folding"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 2 | [] | 2026-10-15T12:37:15Z | https://github.com/darkflash03/GOLD | null |
[
"generative-ai",
"language",
"multimodal",
"multimodal-learning",
"video"
] | [
"fine-tuning",
"parameter-efficient-fine-tuning",
"video-question-answering",
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] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.finetuning",
"multimodal.video-question-answering"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-14T08:46:46Z | [NeurIPS 2026] Official implementation of "BoxTuning: Object-Aware Visual Prompting for Multimodal Model Fine-Tuning" | [
"generative-ai",
"language",
"multimodal-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-14T14:33:31Z | false | 0 | 1,418,619,130 | null | null | null | [
"fine-tuning",
"parameter-efficient-fine-tuning"
] | zekunqian/BoxTuning | [
"description",
"query-match",
"repository-metadata"
] | 4 | 2026-10-23T14:45:14Z | [] | 2026-10-14T08:54:44Z | [
"llm.finetuning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-10-14T08:54:48Z | https://github.com/zekunqian/BoxTuning | null |
[
"multimodal",
"multimodal-learning"
] | [
"multimodal-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"recall.name.multimodal-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-15T17:05:44Z | [NeurIPS 2026] Official implementation of Latent Reasoning in Continuous Space for Unified Multimodal Models (LARC) | [
"multimodal",
"multimodal-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-16T14:00:50Z | false | 0 | 1,421,071,945 | null | null | null | [
"multimodal-learning"
] | rootyJeon/LARC | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-16T14:00:50Z | [] | 2026-10-15T17:09:48Z | [
"recall.name.multimodal-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 1 | [] | 2026-10-16T01:59:54Z | https://github.com/rootyJeon/LARC | null |
[
"deep-learning",
"representation-learning"
] | [
"representation-learning",
"self-supervised-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.self-supervised-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-16T04:33:57Z | The source code of our TNNLS-2026 paper "Efficient and Scalable Multi-View Masked Autoencoder for Heterogeneous Graph Self-Supervised Learning". | [
"deep-learning",
"representation-learning"
] | [
"self-supervised-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-16T14:00:50Z | false | 0 | 1,421,646,699 | null | Python | null | [
"representation-learning",
"self-supervised-learning"
] | hcmdgh/MVMAE | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-10-18T13:47:34Z | [] | 2026-10-16T04:42:27Z | [
"general.self-supervised-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [] | 2026-10-16T14:58:05Z | https://github.com/hcmdgh/MVMAE | null |
[
"computer-vision",
"generative-modeling",
"interpretability-and-safety"
] | [
"action-recognition",
"flow-matching",
"video-understanding"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.action-recognition"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-16T07:30:25Z | 【ECCV 2026】This repo is the official implementation for "Beyond Alignment: A Generative Matching Paradigm via Flow Matching for Zero-Shot Skeleton-Based Action Recognition", presented as a poster at ECCV 2026. | [
"computer-vision",
"generative-modeling",
"interpretability-and-safety"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-17T13:01:14Z | false | 0 | 1,421,807,717 | null | Python | MIT | [
"action-recognition",
"flow-matching",
"video-understanding"
] | reques/ProtoFM | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-19T16:31:26Z | [] | 2026-10-16T08:26:07Z | [
"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"
] | include | ml-contribution-v5 | 1 | [] | 2026-10-18T07:41:15Z | https://github.com/reques/ProtoFM | null |
[
"reinforcement-learning"
] | [
"batch-reinforcement-learning",
"offline-reinforcement-learning",
"reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"rl.offline"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-16T08:09:44Z | Official implementation for NeurIPS 2022 paper ‘Variational Consequence-Driven Offline Reinforcement Learning’ | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-16T14:00:50Z | false | 0 | 1,421,847,714 | null | null | null | [
"batch-reinforcement-learning",
"offline-reinforcement-learning",
"reinforcement-learning"
] | DarrenJum/StBR | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-10-18T13:47:34Z | [] | 2026-10-16T08:09:46Z | [
"rl.offline"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-10-16T08:09:50Z | https://github.com/DarrenJum/StBR | null |
[
"computer-vision",
"deep-learning",
"efficient-ml",
"health-and-biomedicine",
"language",
"medical-imaging"
] | [
"conditional-computation",
"image-analysis",
"mixture-of-experts",
"sparse-activation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.mixture-of-experts",
"medical.medical-imaging"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-16T15:41:17Z | Official code release for BMVC 2026 paper "DCT-MoE: Frequency-Domain Medical Image Generation with Mixture-of-Experts" | [
"computer-vision",
"health-and-biomedicine",
"medical-imaging"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-18T13:21:37Z | false | 0 | 1,422,351,750 | null | Python | MIT | [
"image-analysis",
"mixture-of-experts"
] | Jean-Yifan-Sun/DCT-MoE-Release | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-25T14:09:27Z | [] | 2026-10-16T15:41:51Z | [
"medical.medical-imaging"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-10-21T18:32:12Z | https://github.com/Jean-Yifan-Sun/DCT-MoE-Release | null |
[
"language",
"natural-language-processing"
] | [
"distillation",
"language-model",
"language-modeling"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.language-model",
"nlp.language-modeling"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-16T19:30:20Z | [NeurIPS 2026] Official code for "Progress-Aware Distillation for Mitigating Stagnation in Small Language Model Agents" | [
"language",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-20T17:39:40Z | false | 0 | 1,422,592,981 | null | Python | Apache-2.0 | [
"distillation",
"language-model"
] | CaoYuanpu/ProD | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-25T14:09:27Z | [
"2610.19439"
] | 2026-10-16T19:39:58Z | [
"llm.language-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-10-16T19:54:01Z | https://github.com/CaoYuanpu/ProD | null |
[
"computer-vision",
"generative-ai",
"image-processing",
"language",
"low-level-vision"
] | [
"image-enhancement",
"image-restoration",
"language-model",
"low-light-enhancement",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.transformers",
"recall26.low-light-enhancement"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-17T01:56:49Z | Official PyTorch implementation of our paper : HFCNet: A hybrid CNN-transformer network with frequency-aware gating and cross-stream interaction for low-light image enhancement | [
"generative-ai",
"language"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-18T13:21:37Z | false | 0 | 1,422,851,801 | null | Python | MIT | [
"language-model",
"transformer"
] | clearlove7780/HFCNet | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-25T14:09:27Z | [] | 2026-10-17T03:22:30Z | [
"llm.transformers"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-10-21T18:31:06Z | https://github.com/clearlove7780/HFCNet | null |
[
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"forecasting",
"multivariate-forecasting",
"sequence-modeling"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"timeseries.forecasting",
"timeseries.multivariate"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-17T13:09:27Z | Official implementation of the paper "CTLite: Lightweight Irregular Multivariate Time Series Forecasting with Reusable Continuous-Time Summaries" | [
"forecasting",
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-18T13:47:34Z | false | 0 | 1,423,341,269 | null | Shell | null | [
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] | SSMa913/NICLab-CTLite | [
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] | 3 | 2026-10-19T16:36:56Z | [] | 2026-10-17T13:30:51Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-10-17T13:30:54Z | https://github.com/SSMa913/NICLab-CTLite | null |
[
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"description",
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-18T03:19:12Z | Source code for paper "Breaking the Vicious Spiral: Achieving Stable Transformer-based World Models for Reinforcement Learning". | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-18T13:21:37Z | false | 0 | 1,423,948,698 | null | Python | null | [
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] | lsb829/stw | [
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] | 4 | 2026-10-25T14:09:27Z | [] | 2026-10-18T03:20:21Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-10-19T02:38:42Z | https://github.com/lsb829/stw | null |
[
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"description",
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"repository-metadata"
] | [
"nlp.cross-encoder-reranking"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-18T14:42:59Z | A new architecture of RAG | [
"information-retrieval",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-26T16:16:45Z | false | 0 | 1,424,476,053 | null | Python | null | [
"cross-encoder",
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] | Sanidhya555/Documind | [
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] | 1 | 2026-10-26T16:16:45Z | [] | 2026-10-18T14:57:21Z | [
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] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-10-18T14:57:25Z | https://github.com/Sanidhya555/Documind | null |
[
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] | [
"general.contrastive-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-20T00:48:44Z | [SIGIR 2023 Oral] This is our Pytorch implementation for the paper: "Meta-optimized Contrastive Learning for Sequential Recommendation". | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-20T14:21:44Z | false | 0 | 1,426,338,283 | https://arxiv.org/abs/2304.07763v1 | null | MIT | [
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] | AliasJeff/MCLRec | [
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] | 2 | 2026-10-23T14:17:19Z | [] | 2026-10-20T05:31:23Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-10-20T01:04:47Z | https://github.com/AliasJeff/MCLRec | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-20T01:37:19Z | Official implementation of CCG Diffusion: Combinatory Categorial Grammar-Driven Compositional and Syntactically Disambiguated Text-to-Image Generation (arXiv:2610.29415). | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-21T14:32:05Z | false | 0 | 1,426,371,704 | https://arxiv.org/abs/2610.29415 | Python | MIT | [
"diffusion",
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] | ryosuke-yamaki/ccg-diffusion | [
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] | 3 | 2026-10-24T13:23:18Z | [] | 2026-10-20T10:02:45Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [
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] | 2026-10-22T00:56:52Z | https://github.com/ryosuke-yamaki/ccg-diffusion | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-20T06:57:21Z | This is the official code repository of the paper "A Byzantine-Robust Federated Learning with Nesterov Momentum". | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-21T14:32:05Z | false | 0 | 1,426,625,722 | null | Python | null | [
"collaborative-learning",
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] | IHXU/Byrd-NAFL-IOTJ | [
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] | 2 | 2026-10-24T13:23:18Z | [] | 2026-10-20T07:11:23Z | [
"trust.federated-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-10-20T07:19:25Z | https://github.com/IHXU/Byrd-NAFL-IOTJ | null |
[
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] | [
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] | [
"multimodal.vision-language",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-21T04:50:43Z | Official code for "Let It Be Simple: One-Step Action Generation for Vision-Language-Action Models" — self-contained openpi-based implementation, paper run configs, diagnostics, and LIBERO evaluation. | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-23T14:17:19Z | false | 0 | 1,427,890,827 | https://arxiv.org/abs/2606.05737 | Python | Apache-2.0 | [
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] | bondtesty/let-it-be-simple | [
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] | 1 | 2026-10-23T14:17:19Z | [] | 2026-10-22T11:04:35Z | [
"multimodal.vision-language",
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
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] | include | ml-contribution-v5 | 2 | [
"diffusion-policy",
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"imitation-learning",
"libero",
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"vision-language-action"
] | 2026-10-23T13:55:04Z | https://github.com/bondtesty/let-it-be-simple | null |
[
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] | [
"description",
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"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-22T02:53:56Z | The official implementation of KD paper in IJCV: "Relaxed Knowledge Distillation" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-23T14:17:19Z | false | 0 | 1,429,168,600 | null | Python | MIT | [
"distillation",
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] | we1sher/ReKD | [
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] | 1 | 2026-10-23T14:17:19Z | [] | 2026-10-22T03:28:21Z | [
"efficiency.knowledge-distillation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-10-22T05:57:02Z | https://github.com/we1sher/ReKD | null |
[
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] | [
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] | [
"rl.offline",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-22T10:24:17Z | Official implementation of "Trajectory-Consistent Diffusion Policies for Offline Reinforcement Learning" (NeurIPS 2026). | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-23T14:17:19Z | false | 0 | 1,429,584,024 | null | null | null | [
"batch-reinforcement-learning",
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] | yichaofu/TCR-Diffusion | [
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] | 1 | 2026-10-23T14:17:19Z | [] | 2026-10-22T10:24:19Z | [
"rl.offline",
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-10-22T10:24:23Z | https://github.com/yichaofu/TCR-Diffusion | null |
[
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] | [
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] | [
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"repository-metadata"
] | [
"llm.large-language-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-23T04:39:58Z | An official implementation of "Global Context Guidance for Diffusion Large Language Models" (NeurIPS 2026) in PyTorch. | [
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"generative-modeling",
"language",
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-23T14:17:19Z | false | 0 | 1,430,581,146 | null | Python | GPL-3.0 | [
"diffusion",
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] | cvlab-yonsei/GCG | [
"description",
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"query-match",
"repository-metadata"
] | 2 | 2026-10-25T13:45:15Z | [] | 2026-10-23T04:44:56Z | [
"llm.large-language-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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] | include | ml-contribution-v5 | 1 | [] | 2026-10-23T10:38:10Z | https://github.com/cvlab-yonsei/GCG | null |
[
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] | [
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] | [
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] | [
"science.neural-operators"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-23T05:03:34Z | The official code repository of paper "Time-Lapse Seismic Inversion Using a 3D Neural Operator " . | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-23T14:17:19Z | false | 0 | 1,430,597,582 | null | Jupyter Notebook | MIT | [
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] | maxiao6668/Time_Lapse_inversion | [
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] | 2 | 2026-10-25T13:45:15Z | [] | 2026-10-23T05:10:20Z | [
"science.neural-operators"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-10-23T05:10:23Z | https://github.com/maxiao6668/Time_Lapse_inversion | null |
[
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] | [
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"query-match",
"repository-metadata"
] | [
"vision.image-retrieval"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-23T17:52:40Z | Official PyTorch implementation of The Locality Trade-off in Masked Patch Self-Distillation (NeurIPS 2026) | [
"computer-vision",
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-24T13:23:18Z | false | 0 | 1,431,374,793 | null | Python | Apache-2.0 | [
"distillation",
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] | winci-ai/jea | [
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"query-match",
"repository-metadata"
] | 2 | 2026-10-26T16:11:12Z | [] | 2026-10-25T12:06:11Z | [
"vision.image-retrieval"
] | 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 | 1 | [] | 2026-10-25T12:06:15Z | https://github.com/winci-ai/jea | null |
[
"computer-vision",
"efficient-ml",
"general-ml",
"generative-ai",
"language",
"machine-learning-systems",
"model-compression"
] | [
"language-model",
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] | [
"description",
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"repository-metadata"
] | [
"efficiency.post-training-quantization",
"efficiency.quantization",
"llm.transformers",
"recall.name.transformer-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-24T11:30:20Z | Official PyTorch implementation of "GPFA-Q: Post-Training Quantization with Gradient-Projected Fisher Approximation for Vision Transformers", [NeurIPS 2026.] | [
"computer-vision",
"efficient-ml",
"general-ml",
"generative-ai",
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] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-25T13:45:15Z | false | 0 | 1,432,113,861 | null | Python | Apache-2.0 | [
"language-model",
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"quantization",
"transformer"
] | Nobody-96/GPFA-Q | [
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"repository-metadata"
] | 1 | 2026-10-25T13:45:15Z | [] | 2026-10-25T11:10:16Z | [
"efficiency.post-training-quantization",
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"llm.transformers",
"recall.name.transformer-model"
] | 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 | 7 | [] | 2026-10-25T12:37:17Z | https://github.com/Nobody-96/GPFA-Q | null |
[
"generative-ai",
"generative-modeling",
"language"
] | [
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] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.finetuning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-24T15:30:53Z | Official PyTorch implementation of "Revisiting Diffusion Fine-Tuning for Unsupervised Domain Adaptation" (NeurIPS 2026). | [
"generative-ai",
"generative-modeling",
"language"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-25T13:45:15Z | false | 0 | 1,432,322,267 | https://xuanqi99.github.io/MUSE/ | Python | MIT | [
"diffusion",
"fine-tuning",
"parameter-efficient-fine-tuning"
] | xuanqi99/MUSE-Code | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-25T13:45:15Z | [] | 2026-10-24T16:04:22Z | [
"llm.finetuning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 0 | [
"diffusion-models",
"generative-modeling",
"parameter-efficient-fine-tuning",
"unsupervised-domain-adaptation"
] | 2026-10-24T19:25:15Z | https://github.com/xuanqi99/MUSE-Code | null |
[
"computational-science",
"physics"
] | [
"neural-operators",
"surrogate-modeling"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"science.neural-operators"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-25T05:05:59Z | Official implementation of "CoupledFlow: One-Step Neural Operators for Coupled Multi-Physics PDEs", which is accpeted at NeurIPS 2026 | [
"computational-science",
"physics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-25T13:45:15Z | false | 0 | 1,432,848,066 | null | null | null | [
"neural-operators",
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] | khiembk/CoupledFlow | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-27T15:25:06Z | [] | 2026-10-25T05:06:46Z | [
"science.neural-operators"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-10-25T05:06:51Z | https://github.com/khiembk/CoupledFlow | null |
[
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"time-series"
] | [
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] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"timeseries.state-space-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-25T09:19:18Z | Official PyTorch implementation of the ECCV 2026 paper "S2Gest: Split-Scan State Space Models for Dynamic Hand Gesture Recognition". | [
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"time-series"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-26T16:11:12Z | false | 0 | 1,433,020,480 | null | Python | MIT | [
"sequence-modeling",
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] | Chen-Ke-Fan/S2Gest | [
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"query-match",
"repository-metadata"
] | 2 | 2026-10-28T16:08:23Z | [] | 2026-10-25T10:27:39Z | [
"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 | 0 | [] | 2026-10-25T15:23:33Z | https://github.com/Chen-Ke-Fan/S2Gest | null |
[
"computer-vision",
"generative-ai"
] | [
"generative-modeling",
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] | [
"description",
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-25T10:20:44Z | [NeurIPS 2026] Official implementation of FreeSpec: Training-Free Long Video Generation via Singular-Spectrum Reconstruction | [
"computer-vision",
"generative-ai"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-26T16:11:12Z | false | 0 | 1,433,069,030 | https://fdchen24.github.io/FreeSpec-Website/ | null | Apache-2.0 | [
"generative-modeling",
"video-generation"
] | fdchen24/FreeSpec | [
"description",
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"query-match",
"repository-metadata"
] | 2 | 2026-10-28T16:08:23Z | [] | 2026-10-26T04:39:22Z | [
"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 | 0 | [] | 2026-10-26T04:39:26Z | https://github.com/fdchen24/FreeSpec | null |
[
"computer-vision",
"language",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"vision"
] | [
"contrastive-learning",
"image-text",
"multimodal-learning",
"vision-language",
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"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.vision-language",
"multimodal.vlm",
"vision.vision-language"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-26T03:15:55Z | [NeurIPS 2026] Official implementation of paper "SwiftVLM: Efficient Vision-Language Model Inference via Cross-Layer Token Bypass" | [
"computer-vision",
"multimodal",
"multimodal-learning",
"natural-language-processing"
] | [
"model-inference"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-27T15:25:06Z | false | 0 | 1,433,849,114 | null | null | MIT | [
"multimodal-learning",
"vision-language"
] | qianchen214/SwiftVLM | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-28T16:08:23Z | [] | 2026-10-26T03:15:57Z | [
"vision.vision-language"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-context-only",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 2 | [] | 2026-10-28T07:19:18Z | https://github.com/qianchen214/SwiftVLM | null |
[
"classical-ml",
"deep-learning",
"forecasting",
"representation-learning",
"time-series",
"time-series-and-forecasting"
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"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.contrastive-learning",
"general.metric-learning",
"timeseries.forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-26T14:06:26Z | In this code repo we present a new representation learning method for time series forecasting, specifically applied to electricity load forecasting. This new representation learning method is called similarity preserving, based on similarity metric learning like contrastive learning but much more efficient and with oth... | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"representation-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-27T15:25:06Z | false | 0 | 1,434,468,308 | null | Python | MIT | [
"contrastive-learning",
"forecasting",
"sequence-modeling"
] | lear-ner97/Similarity-preserving-representation-learning-for-weekly-electricity-load-forecasting | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-28T16:08:23Z | [] | 2026-10-26T14:11:48Z | [
"timeseries.forecasting"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-10-26T17:46:32Z | https://github.com/lear-ner97/Similarity-preserving-representation-learning-for-weekly-electricity-load-forecasting | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-27T08:27:48Z | An official implementation for NeurIPS 2026 paper "AdderDiff: All-Addition Spiking Diffusion Models with Attention Enhancement" | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-27T15:25:06Z | false | 0 | 1,435,451,886 | null | Python | null | [
"diffusion"
] | hanbaobao950123/AdderDiff | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-27T15:25:06Z | [] | 2026-10-27T09:28:26Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-10-27T12:00:12Z | https://github.com/hanbaobao950123/AdderDiff | null |
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