all_domains list | all_methods list | all_novelty_signals list | all_query_ids list | archived bool | candidate_status string | candidate_rule_version string | candidate_eligible bool | candidate_reason string | candidate_evidence list | created_at string | description string | domains list | evidence_signals list | evidence_tier string | evidence_version string | first_observed_at string | fork bool | forks int64 | github_id int64 | homepage string | language string | license string | methods list | name string | novelty_signals list | observation_count int64 | observed_at string | paper_ids list | pushed_at string | query_ids list | readme_blob_sha string | readme_checked_at string | readme_evidence_version string | readme_etag string | readme_sections list | readme_signals list | readme_status string | readme_observed_at string | readme_repository_name_at_fetch string | selection_reason string | selection_signals list | selection_status string | selection_version string | stars int64 | topics list | updated_at string | url string | extra_json string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-08T02:56:56Z | This is the official code for paper 'Frequency Feature Fusion Graph Neural Network for Depression Diagnosis Via fNIRS'. | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-29T17:49:51Z | false | 0 | 1,327,364,513 | null | Python | Apache-2.0 | [
"graph-neural-network",
"message-passing",
"neural-network"
] | ChineseFootball10/Frequency-Feature-Fusion-Graph-Neural-Network-for-Depression-Diagnosis-Via-fNIRS | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-29T17:49:51Z | [] | 2026-08-08T04:04:45Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-08-08T04:04:49Z | https://github.com/ChineseFootball10/Frequency-Feature-Fusion-Graph-Neural-Network-for-Depression-Diagnosis-Via-fNIRS | null |
[
"computer-vision",
"deep-learning",
"efficient-ml",
"language"
] | [
"conditional-computation",
"mixture-of-experts",
"sparse-activation",
"video-understanding"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.mixture-of-experts",
"vision.video-understanding"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-09T18:23:38Z | Official code release for "MoE-ViE Mixture of Experts Vision Encoder for Efficient Image and Video Understanding" (ECCV 2026) | [
"computer-vision",
"deep-learning",
"efficient-ml",
"language"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 3 | 1,329,082,227 | null | Python | NOASSERTION | [
"conditional-computation",
"mixture-of-experts",
"sparse-activation"
] | facebookresearch/moe_vie | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 7 | 2026-10-04T15:58:58Z | [] | 2026-08-19T00:51:18Z | [
"efficiency.mixture-of-experts"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 31 | [] | 2026-09-29T17:57:20Z | https://github.com/facebookresearch/moe_vie | null |
[
"generative-ai",
"language",
"natural-language-processing",
"software-engineering"
] | [
"code-generation",
"language-model"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm-code-generation",
"recall.name.language-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-10T10:52:48Z | Supplementary material for our IEEE TENCON 2026 paper: prompts and experimental settings for a plan-and-test LLM code generation pipeline | [
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T15:56:46Z | false | 0 | 1,329,719,039 | null | null | null | [
"language-model"
] | bp22068/tencon2026-plan-and-test | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-08T17:03:06Z | [] | 2026-09-25T15:51:56Z | [
"recall.name.language-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-context-only",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-09-25T15:52:00Z | https://github.com/bp22068/tencon2026-plan-and-test | null |
[
"demand-forecasting",
"forecasting",
"retail",
"supply-chain",
"tabular-and-structured-data",
"time-series-and-forecasting"
] | [
"gradient-boosting"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"applied.demand-forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-10T12:06:34Z | This repository presents a demand forecasting and marketing optimization framework that uses Stacked Autoencoder (SAE) feature extraction and New Caledonian Crow Learning Algorithm (NCCLA)-tuned Extreme Gradient Boosting (NCCL-XGBoost) to forecast consumer demand. | [
"demand-forecasting",
"forecasting",
"retail",
"supply-chain",
"tabular-and-structured-data",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-11-01T17:09:41Z | false | 0 | 1,329,781,833 | null | HTML | null | [
"gradient-boosting"
] | colabsss417/demand-forecasting-marketing | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-11-01T17:09:41Z | [] | 2026-08-10T12:07:40Z | [
"applied.demand-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 | [] | 2026-08-10T12:08:06Z | https://github.com/colabsss417/demand-forecasting-marketing | null |
[
"energy",
"forecasting",
"tabular-and-structured-data",
"time-series",
"time-series-and-forecasting"
] | [
"energy-forecasting",
"load-forecasting",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"timeseries.energy-forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-11T02:00:22Z | Benchmarking ARIMA, XGBoost, LSTM & Transformer for global renewable energy forecasting (World Bank, 1960–2020). Code for our CEIS 2026 paper. | [
"energy",
"forecasting",
"tabular-and-structured-data",
"time-series",
"time-series-and-forecasting"
] | [
"deep-learning",
"machine-learning",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 0 | 1,330,403,484 | https://doi.org/10.7176/CEIS/17-1-05 | Python | NOASSERTION | [
"energy-forecasting",
"load-forecasting",
"transformer"
] | Asadullah-Irshad/Global-Renewable-Energy-Consumption-Forecasting | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-09-27T16:09:59Z | [] | 2026-08-31T16:43:46Z | [
"timeseries.energy-forecasting"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [
"arima",
"data-science",
"deep-learning",
"energy-transition",
"forecasting",
"lstm",
"machine-learning",
"python",
"pytorch",
"renewable-energy",
"scikit-learn",
"time-series-analysis",
"transformers",
"xgboost"
] | 2026-08-31T16:47:48Z | https://github.com/Asadullah-Irshad/Global-Renewable-Energy-Consumption-Forecasting | null |
[
"multimodal",
"multimodal-learning",
"reinforcement-learning",
"robotics"
] | [
"environment-modeling",
"world-model"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.world-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-13T07:02:27Z | Code for paper "HounsWorld: A Multimodal World Model for Hidden Patient-State Readout, Reconstruction, and Simulation" | [
"multimodal",
"multimodal-learning",
"reinforcement-learning",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-13T18:05:26Z | false | 0 | 1,332,808,477 | null | null | null | [
"environment-modeling",
"world-model"
] | byhwhite/HounsWorld | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-13T18:05:26Z | [] | 2026-08-13T07:04:21Z | [
"multimodal.world-models"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 3 | [] | 2026-08-17T02:12:47Z | https://github.com/byhwhite/HounsWorld | null |
[
"data-centric-ai",
"machine-learning",
"reinforcement-learning"
] | [
"batch-reinforcement-learning",
"data-selection",
"machine-learning",
"offline-reinforcement-learning",
"reinforcement-learning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"probable.data-training-selection",
"rl.offline"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-13T13:09:45Z | This repository is the official implementation of the paper “Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions". TMLR, 2026. | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T10:12:50Z | false | 0 | 1,333,130,446 | https://openreview.net/forum?id=4vVhm4817Q | Jupyter Notebook | NOASSERTION | [
"batch-reinforcement-learning",
"offline-reinforcement-learning",
"reinforcement-learning"
] | jesserem/CDQAC_scheduling | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 8 | 2026-10-18T13:47:34Z | [] | 2026-10-13T12:48:27Z | [
"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 | 1 | [
"combinatorial-optimization",
"flexible-job-shop",
"flexible-job-shop-scheduling",
"job-shop",
"job-shop-problem",
"job-shop-scheduling",
"job-shop-scheduling-problem",
"job-shop-schedulling",
"offline-reinforcement-learning",
"offline-rl"
] | 2026-10-13T12:48:33Z | https://github.com/jesserem/CDQAC_scheduling | null |
[
"multimodal",
"reinforcement-learning",
"robotics"
] | [
"environment-modeling",
"world-model"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.world-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-14T03:26:18Z | Pang-W1 is a self-reinforcing world model in which, within a simulated universe, one discovers physics and mathematics on its own and, through that discovery, enriches the universe once again. I don't give AI work; I give it the universe. | [
"multimodal",
"reinforcement-learning",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-15T15:20:52Z | false | 0 | 1,333,749,912 | null | null | null | [
"environment-modeling",
"world-model"
] | 9xp2m4dwh2-bot/NEW-WORLD-MODEL | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-15T15:20:52Z | [] | 2026-08-14T03:28:47Z | [
"multimodal.world-models"
] | 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 | [] | 2026-08-14T03:29:03Z | https://github.com/9xp2m4dwh2-bot/NEW-WORLD-MODEL | null |
[
"computer-vision",
"efficient-ml",
"language",
"machine-learning-systems",
"model-compression",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"vision"
] | [
"contrastive-learning",
"image-text",
"multimodal-learning",
"neural-network-pruning",
"pruning",
"structured-pruning",
"vision-language",
"vision-language-model"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning",
"multimodal.vision-language",
"multimodal.vlm",
"vision.vision-language"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-14T11:36:35Z | Code for Paper "AdaptInfer: Adaptive Token Pruning for Vision Language Model Inference via Dynamical Text Guidance" | [
"computer-vision",
"language",
"machine-learning-systems",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"vision"
] | [
"model-inference"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-19T16:36:56Z | false | 0 | 1,334,116,740 | null | Python | Apache-2.0 | [
"contrastive-learning",
"pruning",
"vision-language-model"
] | weiczh02/AdaptInfer-base | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-28T17:27:44Z | [] | 2026-10-28T14:42:48Z | [
"multimodal.vision-language"
] | 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 | [] | 2026-10-28T14:42:52Z | https://github.com/weiczh02/AdaptInfer-base | null |
[
"efficient-ml",
"model-compression"
] | [
"distillation",
"knowledge-distillation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-15T15:06:59Z | Official implementation of SA-optimized knowledge distillation hyperparameters for lightweight breast cancer histopathology classification (AFRICAI @ MICCAI 2026). | [
"efficient-ml",
"model-compression"
] | [
"classifier"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-10T16:53:26Z | false | 0 | 1,335,182,513 | null | Python | null | [
"distillation",
"knowledge-distillation"
] | falmatamkn/SA-KD-BreastCancer | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-10T16:53:26Z | [] | 2026-08-15T21:44:35Z | [
"efficiency.knowledge-distillation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-08-15T21:44:52Z | https://github.com/falmatamkn/SA-KD-BreastCancer | null |
[
"cybersecurity",
"efficient-ml",
"model-compression"
] | [
"distillation",
"knowledge-distillation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-16T15:22:56Z | Official implementation of HEDM-KD: A Parallel Heterogeneous Knowledge Distillation Framework for Class-Imbalanced Intrusion Detection (Code will be released upon paper acceptance) | [
"cybersecurity",
"efficient-ml",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-11T16:48:52Z | false | 0 | 1,336,070,629 | null | null | null | [
"distillation",
"knowledge-distillation"
] | liys159357/HEDM-KD | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-11T16:48:52Z | [] | 2026-08-16T15:23:51Z | [
"efficiency.knowledge-distillation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-08-16T15:24:23Z | https://github.com/liys159357/HEDM-KD | null |
[
"generative-ai",
"generative-modeling",
"graph-learning",
"topological-ml"
] | [
"diffusion",
"graph-diffusion",
"graph-generation",
"persistent-homology",
"topological-data-analysis"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.graph-diffusion",
"graph.persistent-homology"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-18T07:32:55Z | Official implementation of "Topology-aware Graph Diffusion Model with Persistent Homology" (NeurIPS 2025). | [
"generative-modeling",
"topological-ml"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 0 | 1,338,111,093 | null | Python | NOASSERTION | [
"diffusion",
"persistent-homology",
"topological-data-analysis"
] | pjh1023/TAGG | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 7 | 2026-09-26T14:50:46Z | [] | 2026-08-18T15:22:40Z | [
"graph.persistent-homology"
] | 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 | [] | 2026-08-18T15:29:22Z | https://github.com/pjh1023/TAGG | null |
[
"multimodal-learning",
"natural-language-processing"
] | [
"entity-linking",
"entity-resolution",
"retrieval-augmented-generation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"nlp-entity-linking"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-18T09:43:00Z | Official implementation of the ACM MM 2026 paper "DualG-MRAG: Decoupling Macro-Reasoning and Micro-Matching for Multimodal Retrieval-Augmented Generation" (MM '26, November 10–14, 2026, Rio de Janeiro, Brazil). | [
"multimodal-learning",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-11-01T17:09:41Z | false | 1 | 1,338,234,482 | null | Python | Apache-2.0 | [
"entity-linking",
"entity-resolution",
"retrieval-augmented-generation"
] | Sea-Valley/DualG-MRAG | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-11-01T17:09:41Z | [] | 2026-08-18T10:15:32Z | [
"nlp-entity-linking"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 5 | [] | 2026-09-09T16:55:54Z | https://github.com/Sea-Valley/DualG-MRAG | null |
[
"earth-observation",
"geospatial",
"machine-learning",
"machine-learning-systems",
"remote-sensing",
"science-and-engineering"
] | [
"change-detection",
"coreset-selection",
"data-selection",
"image-analysis",
"pruning",
"representation-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"geo.change-detection",
"specialized.coreset-selection"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-20T19:18:40Z | Official Implementation for the paper Fidelity-Diversity-Consistency (FDC): Data Pruning for Remote Sensing Change Detection | [
"machine-learning",
"machine-learning-systems",
"science-and-engineering"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 0 | 1,341,069,376 | null | Python | null | [
"coreset-selection",
"data-selection",
"pruning"
] | ddydyd32/fidelity-diversity-consistency | [
"description",
"query-match",
"repository-metadata"
] | 4 | 2026-09-27T16:09:59Z | [] | 2026-08-20T23:52:49Z | [
"specialized.coreset-selection"
] | 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 | [] | 2026-08-20T23:53:02Z | https://github.com/ddydyd32/fidelity-diversity-consistency | null |
[
"sequence-modeling",
"time-series"
] | [
"sequence-modeling",
"state-space-model"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"timeseries.state-space-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-21T05:44:23Z | This is the official code for the paper "Direction-Aware Hybrid Architecture with Cross-Scan State Space Model for Retinal Vessel Segmentation". | [
"sequence-modeling",
"time-series"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 0 | 1,341,444,894 | null | Python | null | [
"sequence-modeling",
"state-space-model"
] | ecustyy/VesselSeg | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2026-08-21T09:44:17Z | [
"timeseries.state-space-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 | [] | 2026-09-16T10:21:20Z | https://github.com/ecustyy/VesselSeg | null |
[
"earth-science",
"geospatial",
"geospatial-science",
"remote-sensing",
"science-and-engineering",
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] | [
"distillation",
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"geospatial-learning",
"land-use-classification",
"remote-sensing",
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] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"geo.land-use",
"science.remote-sensing"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-21T08:07:25Z | Official code for the paper “RS-OPSD: RELIABLE PRIVILEGED ON-POLICY SELF-DISTILLATION FOR ULTRA-HIGH-RESOLUTION REMOTE SENSING VQA”. | [
"geospatial",
"remote-sensing",
"science-and-engineering",
"urban-computing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-02T15:56:46Z | false | 0 | 1,341,558,184 | null | Python | NOASSERTION | [
"distillation",
"land-use-classification",
"semantic-segmentation"
] | Ronniejiang/RS-OPSD | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-21T14:37:12Z | [] | 2026-10-09T06:08:44Z | [
"geo.land-use"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 2 | [] | 2026-10-09T06:08:48Z | https://github.com/Ronniejiang/RS-OPSD | null |
[
"information-retrieval",
"natural-language-processing"
] | [
"cross-encoder",
"distillation",
"neural-reranking",
"question-answering",
"reading-comprehension"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"nlp.cross-encoder-reranking",
"nlp.question-answering"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-21T12:57:20Z | [EMNLP 2026] Official implementation of "PROBE: Prospective Retrieval with Oracle-Based End-Goal Distillation for Multi-Hop Question Answering" | [
"information-retrieval",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-22T14:58:59Z | false | 0 | 1,341,813,410 | null | Python | Apache-2.0 | [
"cross-encoder",
"distillation",
"neural-reranking"
] | luxu-zju/PROBE | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-26T16:16:45Z | [] | 2026-10-18T11:01:01Z | [
"nlp.cross-encoder-reranking"
] | 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 | [] | 2026-10-18T11:01:04Z | https://github.com/luxu-zju/PROBE | null |
[
"sequence-modeling",
"time-series"
] | [
"sequence-modeling",
"state-space-model"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"timeseries.state-space-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-24T10:04:13Z | Code for paper: Bounded Reachability & Jailbreak Detection via Contraction-Constrained State Space Models | [
"sequence-modeling",
"time-series"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 1,344,806,712 | null | Python | MIT | [
"sequence-modeling",
"state-space-model"
] | omanshuthapliyal/contraction_constrained-safety-head | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-18T13:47:34Z | [] | 2026-10-08T23:16:32Z | [
"timeseries.state-space-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 | [] | 2026-10-08T23:16:37Z | https://github.com/omanshuthapliyal/contraction_constrained-safety-head | null |
[
"applied-mathematics",
"applied-physics",
"computational-science",
"generative-modeling"
] | [
"data-assimilation",
"diffusion",
"inverse-problems",
"reconstruction"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"science.inverse-problems"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-25T09:07:25Z | This is the official implementation of "Outlier-Robust Diffusion Solvers for Inverse Problems" (CVPR 2026). | [
"applied-mathematics",
"applied-physics",
"computational-science",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 0 | 1,345,910,440 | null | Python | null | [
"data-assimilation",
"diffusion",
"inverse-problems",
"reconstruction"
] | StarNextDay/Robust-DAPS | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-01T17:50:46Z | [] | 2026-08-25T09:47:02Z | [
"science.inverse-problems"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-08-25T09:50:29Z | https://github.com/StarNextDay/Robust-DAPS | null |
[
"generative-ai",
"language"
] | [
"language-model",
"long-context-modeling",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.long-context"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-25T11:56:42Z | Official implementation for “Write Policy Determines Source Retention in External FIFO Memory: A Delayed-Copy Diagnostic” (EMNLP 2026 Findings). | [
"generative-ai",
"language"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-30T17:16:29Z | false | 0 | 1,346,074,337 | null | Python | MIT | [
"language-model",
"long-context-modeling",
"transformer"
] | LiuSunshine1223/WritePolicyFIFO | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-30T17:16:29Z | [] | 2026-08-30T05:07:50Z | [
"llm.long-context"
] | 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 | [
"emnlp2026",
"externa-memory",
"fifo-memory",
"long-context",
"transformer",
"write-policy"
] | 2026-08-30T05:08:46Z | https://github.com/LiuSunshine1223/WritePolicyFIFO | null |
[
"computer-vision",
"embodied-ai",
"general-ml",
"machine-learning",
"multimodal",
"natural-language-processing",
"robotics",
"robotics-and-control"
] | [
"fine-tuning",
"foundation-model",
"language-model",
"manipulation",
"model-adaptation",
"multimodal-learning",
"robot-foundation-model",
"robot-learning",
"vision-language"
] | [
"description",
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"query-match",
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] | [
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"recall.name.language-model",
"robotics.manipulation",
"robotics.robot-foundation-model",
"vision.vision-language"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-26T03:49:26Z | Official implementation of 3DWay: Generalizing Robot Manipulation via 3D Consistent Waypoints, accepted at ECCV 2026. | [
"machine-learning",
"robotics-and-control"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 0 | 1,346,873,459 | null | Python | Apache-2.0 | [
"fine-tuning",
"model-adaptation"
] | ziqin-h/3DWay | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 7 | 2026-10-09T16:23:55Z | [] | 2026-09-25T13:35:50Z | [
"probable.adaptation-finetuning-readme"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 10 | [] | 2026-10-09T02:39:21Z | https://github.com/ziqin-h/3DWay | null |
[
"generative-ai",
"generative-modeling",
"language",
"natural-language-processing"
] | [
"diffusion",
"language-model",
"long-context-modeling"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.long-context"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-26T06:43:24Z | Official implementation of “Beyond Prefix Answering: Parallel Infilling Reading for Long Contexts with Diffusion Language Models” (EMNLP 2026 Main). | [
"generative-ai",
"generative-modeling",
"language",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-29T17:49:51Z | false | 0 | 1,347,001,112 | null | null | MIT | [
"diffusion",
"language-model",
"long-context-modeling"
] | RUCAIBox/Parallel-Infilling-Reading | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-29T17:49:51Z | [] | 2026-08-26T06:43:26Z | [
"llm.long-context"
] | 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 | [] | 2026-09-24T09:17:05Z | https://github.com/RUCAIBox/Parallel-Infilling-Reading | null |
[
"language",
"multimodal",
"natural-language-processing",
"video"
] | [
"video-language-model",
"video-understanding"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.video-language-model",
"multimodal.video-understanding"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-26T08:55:19Z | [EMNLP 2026] Official implementation of CounterVid, a counterfactual video generation framework for mitigating action and temporal hallucinations in video-language models. | [
"language",
"multimodal",
"natural-language-processing",
"video"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 0 | 1,347,116,087 | https://aimagelab.github.io/CounterVid/ | Python | Apache-2.0 | [
"video-language-model",
"video-understanding"
] | aimagelab/CounterVid | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-09-26T14:34:36Z | [] | 2026-09-14T08:08:00Z | [
"multimodal.video-language-model",
"multimodal.video-understanding"
] | 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 | 4 | [] | 2026-09-25T09:56:05Z | https://github.com/aimagelab/CounterVid | null |
[
"computer-vision",
"embodied-ai",
"generative-ai",
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"machine-learning",
"multimodal",
"multimodal-learning",
"robotics",
"robotics-and-control",
"time-series-and-forecasting",
"vision"
] | [
"contrastive-learning",
"diffusion",
"fine-tuning",
"model-adaptation",
"multimodal-learning",
"robot-learning",
"vision-language-action",
"vision-language-model"
] | [
"description",
"github-topics",
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"query-match",
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] | [
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"recall.name.diffusion-model",
"recall.name.multimodal-model",
"robotics.visual-language-action"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-26T16:13:52Z | [NeurIPS 2026] Official code of "StreamPI: Streaming Multimodal Temporal Modeling for Vision-Language-Action Models" | [
"computer-vision",
"language",
"multimodal",
"multimodal-learning",
"robotics-and-control",
"time-series-and-forecasting",
"vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 9 | 1,347,541,757 | https://happinesslz.github.io/projects/StreamPI/ | Python | Apache-2.0 | [
"contrastive-learning",
"vision-language-model"
] | hku-sail/StreamPI | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 16 | 2026-10-26T17:25:59Z | [] | 2026-09-27T01:03:16Z | [
"multimodal.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 | 184 | [
"vla"
] | 2026-10-26T18:16:50Z | https://github.com/hku-sail/StreamPI | null |
[
"efficient-ml",
"model-compression"
] | [
"distillation",
"knowledge-distillation"
] | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-28T17:20:01Z | Code for our paper: "Knowledge Distillation During Mid-Training Favors Reasoning over Factual Recall" | [
"efficient-ml",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 0 | 1,349,862,287 | https://arxiv.org/abs/2609.01532 | Python | MIT | [
"distillation",
"knowledge-distillation"
] | facebookresearch/midtraining-distillation | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 5 | 2026-10-18T13:47:34Z | [] | 2026-10-10T04:03:18Z | [
"efficiency.knowledge-distillation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 13 | [] | 2026-10-10T19:31:33Z | https://github.com/facebookresearch/midtraining-distillation | null |
[
"computer-vision",
"language",
"machine-learning-systems",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"vision"
] | [
"contrastive-learning",
"image-text",
"pruning",
"vision-language-model"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.vision-language",
"multimodal.vlm"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-30T02:58:16Z | [EMNLP 2026 Findings] Official implementation of HACP: Mitigating Object Hallucinations in Vision-Language Models via Head-Level Attribution and Counteractive Pruning | [
"computer-vision",
"language",
"machine-learning-systems",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-20T15:59:06Z | false | 0 | 1,351,060,553 | null | null | Apache-2.0 | [
"contrastive-learning",
"pruning",
"vision-language-model"
] | S1yang/HACP | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-29T17:49:51Z | [] | 2026-10-21T00:02:50Z | [
"multimodal.vision-language"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-10-21T00:02:54Z | https://github.com/S1yang/HACP | null |
[
"efficient-ml",
"generative-modeling",
"inference-systems",
"language",
"natural-language-processing"
] | [
"diffusion",
"kv-cache-optimization",
"memory-optimization"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.kv-cache"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-30T03:39:24Z | [EMNLP 26 Main] This is the official implementation for the paper "SampKV: Saliency-Aware Mixed-Precision KV Cache for Efficient Diffusion Language Model Inference" in EMNLP 2026 main conference. | [
"efficient-ml",
"generative-modeling",
"inference-systems",
"language",
"natural-language-processing"
] | [
"model-inference"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-14T16:10:46Z | false | 0 | 1,351,078,954 | null | Python | null | [
"diffusion",
"kv-cache-optimization",
"memory-optimization"
] | jerryw95/SampKV | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-14T16:10:46Z | [] | 2026-09-01T19:48:39Z | [
"efficiency.kv-cache"
] | 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 | [] | 2026-09-01T19:49:11Z | https://github.com/jerryw95/SampKV | null |
[
"deep-learning",
"efficient-ml",
"language",
"machine-learning-systems"
] | [
"conditional-computation",
"mixture-of-experts",
"quantization",
"sparse-activation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.mixture-of-experts"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-30T07:00:44Z | Official PyTorch implementation of "Q-Strata: Hierarchical Bit Allocation for Mixed-Precision Quantization of Mixture-of-Experts LLMs" (EMNLP 2026) | [
"deep-learning",
"efficient-ml",
"language",
"machine-learning-systems"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 1,351,177,360 | null | null | null | [
"conditional-computation",
"mixture-of-experts",
"quantization",
"sparse-activation"
] | snu-mllab/Q-Strata | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-04T15:58:58Z | [] | 2026-08-30T07:01:50Z | [
"efficiency.mixture-of-experts"
] | 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 | 4 | [] | 2026-10-02T07:18:48Z | https://github.com/snu-mllab/Q-Strata | null |
[
"3d",
"computer-vision",
"generative-ai",
"generative-modeling",
"graphics"
] | [
"3d-generation",
"diffusion",
"generative-modeling"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"multimodal.3d-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-30T14:35:22Z | Official implementation of TOG26 paper "DecomVoxel: Harnessing 3D-Native Priors with Guided In-situ Denoising Optimization for Decompositional Scene Reconstruction" | [
"3d",
"computer-vision",
"generative-ai",
"generative-modeling",
"graphics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-01T16:56:07Z | false | 0 | 1,351,469,880 | https://decomvoxel.github.io/DecomVoxel-Webpage/ | Python | null | [
"3d-generation",
"diffusion",
"generative-modeling"
] | DecomVoxel/DecomVoxel | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 4 | 2026-10-20T15:59:06Z | [] | 2026-10-02T03:12:48Z | [
"multimodal.3d-generation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 18 | [
"3d-reconstruction",
"diffusion-prior",
"scene-editing"
] | 2026-10-16T08:39:01Z | https://github.com/DecomVoxel/DecomVoxel | null |
[
"efficient-ml",
"generative-modeling",
"inference-systems",
"language",
"natural-language-processing"
] | [
"diffusion",
"kv-cache-optimization",
"memory-optimization"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.kv-cache"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-31T00:46:56Z | Official implementation of “Enabling KV Caching of Shared Prefix for Diffusion Language Models” (EMNLP 2026 Main Conference) | [
"efficient-ml",
"generative-modeling",
"inference-systems",
"language",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 0 | 1,351,851,493 | null | Python | AGPL-3.0 | [
"diffusion",
"kv-cache-optimization",
"memory-optimization"
] | OSSS-KU/BiCache | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-11T16:48:52Z | [] | 2026-08-31T04:41:45Z | [
"efficiency.kv-cache"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 5 | [] | 2026-10-02T20:57:11Z | https://github.com/OSSS-KU/BiCache | null |
[
"generative-ai",
"language",
"linguistics",
"natural-language-processing",
"reinforcement-learning"
] | [
"language-model",
"language-modeling",
"pretraining",
"reinforcement-learning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-08-31T12:34:25Z | Official implementation of TCS: Two-Stage Reinforcement Learning for Sound and Adversarial Test Generation in Code LLMs (Findings of EMNLP 2026). | [
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"language",
"natural-language-processing",
"reinforcement-learning"
] | [
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"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T13:50:07Z | false | 0 | 1,352,392,252 | https://xiaobanni.github.io/projects/tcs/ | Python | Apache-2.0 | [
"language-model",
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] | xiaobanni/tcs | [
"description",
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"query-match",
"repository-metadata"
] | 3 | 2026-10-26T17:25:59Z | [] | 2026-09-24T15:57:21Z | [
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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 | 1 | [
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"emnlp2026",
"large-language-models",
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"test-generation"
] | 2026-10-16T13:46:42Z | https://github.com/xiaobanni/tcs | null |
[
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] | [
"diffusion"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"recall.name.diffusion-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-01T04:20:56Z | Official implementation for "InterCMDM: Block-Causal Diffusion for Autoregressive Human Interaction Generation" (ECCV 2026) | [
"generative-ai",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-06T16:04:50Z | false | 1 | 1,353,125,611 | https://yu1ut.com/InterCMDM-HP/ | Python | CC0-1.0 | [
"diffusion"
] | lycorp-jp/InterCMDM | [
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"query-match",
"repository-metadata"
] | 1 | 2026-10-06T16:04:50Z | [] | 2026-09-28T07:19:39Z | [
"recall.name.diffusion-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 3 | [] | 2026-10-01T00:47:41Z | https://github.com/lycorp-jp/InterCMDM | null |
[
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] | [
"description",
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"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning",
"recall.computational-linguistics"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-01T06:34:18Z | [EMNLP2026] Official source code for Train Overcomplete, Deploy Compact: Scaling Recovery Capacity for Structured LLM Pruning. | [
"linguistics",
"machine-learning-systems",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-18T13:47:34Z | false | 0 | 1,353,220,058 | https://mmai-laboratory.github.io/OverRep/ | Python | Apache-2.0 | [
"language-modeling",
"pruning"
] | MMAI-Laboratory/OverRep | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-21T14:37:12Z | [] | 2026-10-12T07:30:33Z | [
"recall.computational-linguistics"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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] | include | ml-contribution-v5 | 2 | [
"llm",
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"structured-pruning"
] | 2026-10-12T07:30:50Z | https://github.com/MMAI-Laboratory/OverRep | null |
[
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] | [
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"query-match",
"repository-metadata"
] | [
"nlp-query-understanding"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-01T07:33:04Z | Official code for "Session-Guided Pre-training for Query Understanding: Constraining Mask Prediction and Improving Query Fine-tunability" (EMNLP 2026 Industry Track). | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-30T17:16:29Z | false | 0 | 1,353,271,737 | null | Python | CC0-1.0 | [
"information-retrieval",
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] | lycorp-jp/segqu | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-11-01T17:09:41Z | [] | 2026-09-02T07:18:03Z | [
"nlp-query-understanding"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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] | include | ml-contribution-v5 | 0 | [
"emnlp2026",
"pretraining-bert",
"query-understanding",
"search-sessions"
] | 2026-09-02T08:14:19Z | https://github.com/lycorp-jp/segqu | null |
[
"computer-vision",
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] | [
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] | [
"description",
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"query-match",
"repository-metadata"
] | [
"vision.image-diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-05T00:26:04Z | Official implementation of Efficient and Training-Free Single-Image Diffusion Models (CVPR 2026). | [
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-16T15:36:56Z | false | 1 | 1,357,699,828 | null | Python | NOASSERTION | [
"diffusion",
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] | haojunqiu/efficient-SID-code | [
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"query-match",
"repository-metadata"
] | 1 | 2026-10-16T15:36:56Z | [] | 2026-09-05T00:44:51Z | [
"vision.image-diffusion"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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] | include | ml-contribution-v5 | 4 | [] | 2026-10-15T17:50:02Z | https://github.com/haojunqiu/efficient-SID-code | null |
[
"multimodal",
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] | [
"environment-modeling",
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] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.world-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-07T14:17:48Z | new AI world model centered on human and humen activity | [
"multimodal",
"reinforcement-learning",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-14T16:10:46Z | false | 0 | 1,360,315,958 | null | null | null | [
"environment-modeling",
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] | laozux-cloud/event-world-modek-foe-AI | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-14T16:10:46Z | [] | 2026-09-07T14:17:50Z | [
"multimodal.world-models"
] | 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 | [] | 2026-09-07T14:19:06Z | https://github.com/laozux-cloud/event-world-modek-foe-AI | null |
[
"3d",
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] | [
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"mamba"
] | [
"description",
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"query-match",
"repository-metadata"
] | [
"multimodal.3d-object-detection"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-09T21:22:55Z | Official implementation of PointLAM: Local Attentive Mamba for Efficient Point-based 3D Object Detection (ECCV 2026) | [
"3d",
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"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 0 | 1,363,229,222 | https://pointlam.github.io/ | Python | Apache-2.0 | [
"3d-object-detection",
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"mamba"
] | PointLAM/PointLAM | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-25T15:11:36Z | [] | 2026-09-21T02:47:56Z | [
"multimodal.3d-object-detection"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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] | include | ml-contribution-v5 | 5 | [] | 2026-10-09T14:17:10Z | https://github.com/PointLAM/PointLAM | null |
[
"machine-learning"
] | [
"adaptation",
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] | [
"description",
"query-match",
"repository-metadata"
] | [
"specialized.test-time-adaptation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-11T01:54:43Z | Official implementation of the paper “EEMF: ENTROPY--ENERGY-DRIVEN MULTI-CACHE FRAMEWORK WITH DISTRIBUTION CONSTRAINTS FOR TRAINING-FREE POINT CLOUD TEST-TIME ADAPTATION” | [
"machine-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-10T16:53:26Z | false | 0 | 1,365,185,637 | null | Python | null | [
"adaptation",
"test-time-adaptation"
] | TanLinJi/eemf | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-10T16:53:26Z | [] | 2026-09-11T02:20:00Z | [
"specialized.test-time-adaptation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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] | include | ml-contribution-v5 | 0 | [] | 2026-09-11T02:20:03Z | https://github.com/TanLinJi/eemf | null |
[
"multimodal",
"reinforcement-learning",
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] | [
"environment-modeling",
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] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.world-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-11T03:34:57Z | Code for paper: Pelican-Sim 1.0: A General World Model Simulator for Embodied Intelligence | [
"multimodal",
"reinforcement-learning",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-13T18:05:26Z | false | 0 | 1,365,262,937 | https://zoushilong1024.github.io/Pelican-Sim1.0/ | null | Apache-2.0 | [
"environment-modeling",
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] | ZouShilong1024/Pelican-Sim1.0 | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-13T18:05:26Z | [] | 2026-09-11T03:34:58Z | [
"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",
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] | include | ml-contribution-v5 | 4 | [
"world-model-evaluation",
"world-model-rl",
"world-models"
] | 2026-10-07T13:39:18Z | https://github.com/ZouShilong1024/Pelican-Sim1.0 | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"multimodal",
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] | [
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] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.text-to-image",
"vision.image-diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-12T06:21:38Z | The official implementation repository for our EMNLP 2025 paper, R-Bind: Unified Enhancement of Attribute and Relation Binding in Text-to-Image Diffusion Models. | [
"generative-ai",
"generative-modeling",
"multimodal",
"multimodal-learning",
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] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 0 | 1,367,017,408 | null | Python | MIT | [
"diffusion",
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"text-to-image"
] | PKU-ONELab/R-Bind | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-22T16:09:07Z | [] | 2026-09-12T06:23:02Z | [
"multimodal.text-to-image"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [
"attention-mechanism",
"emnlp2025",
"text-to-image",
"text-to-image-diffusion",
"training-free-method"
] | 2026-09-12T07:03:25Z | https://github.com/PKU-ONELab/R-Bind | null |
[
"reinforcement-learning"
] | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-13T07:40:43Z | Source code for paper: Influence Maximization in Social Networks via Offline Deep Reinforcement Learning | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-18T13:47:34Z | false | 0 | 1,368,161,005 | null | Python | null | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | harryyan0322/ODRL-IM | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-18T13:47:34Z | [] | 2026-10-17T02:57:38Z | [
"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 | [] | 2026-10-17T02:57:42Z | https://github.com/harryyan0322/ODRL-IM | null |
[
"computer-vision",
"efficient-ml",
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] | [
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] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-15T06:08:06Z | This repository provides the official and fully reproducible implementation of the paper: Knowledge Distillation via Temporal Frequency for Generative Semantic Image Segmentation | [
"computer-vision",
"efficient-ml",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-11T16:48:52Z | false | 0 | 1,370,983,636 | null | null | null | [
"distillation",
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] | hoangdv05/TFKD | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-11T16:48:52Z | [] | 2026-09-15T06:08:08Z | [
"efficiency.knowledge-distillation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
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] | include | ml-contribution-v5 | 0 | [] | 2026-10-08T16:47:37Z | https://github.com/hoangdv05/TFKD | null |
[
"deep-learning",
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] | [
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] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-15T08:52:26Z | Source code for paper: [Degraded but Not Entirely Ineffective: PE-Based Deformable Graph Neural Networks]. | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-29T17:49:51Z | false | 0 | 1,371,164,197 | null | Python | MIT | [
"graph-neural-network",
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] | 1113wu/PE-Based-Defense | [
"description",
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"query-match",
"repository-metadata"
] | 1 | 2026-10-29T17:49:51Z | [] | 2026-09-15T09:16:58Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-09-15T08:52:45Z | https://github.com/1113wu/PE-Based-Defense | null |
[
"general-ml",
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] | [
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] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"nlp.named-entity-recognition",
"recall.name.transformer-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-15T14:09:45Z | Research code for our ICIMCIS 2026 paper: distant supervision with heuristic-augmented denoising for crypto NER on Indonesian Twitter using IndoBERTweet-CRF | [
"general-ml",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-20T14:47:52Z | false | 0 | 1,371,587,400 | null | Jupyter Notebook | MIT | [
"transformer"
] | bryandalee/crypto-ner-indobertweet-crf | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-11-01T16:11:12Z | [] | 2026-10-03T14:06:16Z | [
"recall.name.transformer-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 | 1 | [
"bert",
"indonesian-nlp",
"named-entity-recognition",
"nlp",
"pytorch"
] | 2026-10-03T14:06:20Z | https://github.com/bryandalee/crypto-ner-indobertweet-crf | null |
[
"machine-learning"
] | [
"adaptation",
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] | [
"description",
"query-match",
"repository-metadata"
] | [
"specialized.test-time-adaptation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-16T00:35:34Z | Official implementation of the paper “EEMC: ENTROPY--ENERGY-DRIVEN MULTI-CACHE FRAMEWORK WITH DISTRIBUTION CONSTRAINTS FOR TRAINING-FREE POINT CLOUD TEST-TIME ADAPTATION” | [
"machine-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-10T16:53:26Z | false | 0 | 1,372,313,683 | null | Python | null | [
"adaptation",
"test-time-adaptation"
] | pla0801/eemc | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-10T16:53:26Z | [] | 2026-09-20T10:13:30Z | [
"specialized.test-time-adaptation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-09-20T10:13:33Z | https://github.com/pla0801/eemc | null |
[
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
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"generative-modeling",
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] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"recall.name.diffusion-model",
"vision.video-generation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-17T11:52:19Z | Official implementation of the paper UnStep Training-Free Acceleration of Causal Video Diffusion with Fewer Steps Than Distillation | [
"generative-ai",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-05T18:59:49Z | false | 1 | 1,374,356,103 | https://y-mansour.github.io/UnStep/ | Python | NOASSERTION | [
"diffusion",
"distillation"
] | facebookresearch/UnStep | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-29T16:41:07Z | [] | 2026-10-24T10:07:27Z | [
"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 | 19 | [] | 2026-10-20T09:00:33Z | https://github.com/facebookresearch/UnStep | null |
[
"multimodal",
"reinforcement-learning",
"robotics"
] | [
"environment-modeling",
"world-model"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.world-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-18T14:12:39Z | new research on world models | [
"multimodal",
"reinforcement-learning",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-13T18:05:26Z | false | 0 | 1,375,968,423 | null | Python | null | [
"environment-modeling",
"world-model"
] | Vixel2006/new-research | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-13T18:05:26Z | [] | 2026-09-27T14:49:23Z | [
"multimodal.world-models"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
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"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-09-27T14:50:12Z | https://github.com/Vixel2006/new-research | null |
[
"data-centric-ai",
"embodied-ai",
"machine-learning",
"robotics",
"robotics-and-control"
] | [
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] | [
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] | [
"probable.data-training-selection",
"robotics.manipulation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-19T05:45:22Z | Official code implementation of the ICML 2026 paper Move-Then-Operate: Behavioral Phasing for Human-Like Robotic Manipulation | [
"data-centric-ai",
"machine-learning",
"robotics-and-control"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 1,376,779,641 | null | Python | Apache-2.0 | [
"data-selection",
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] | healenrens/Move-then-Operate | [
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"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-20T15:59:06Z | [] | 2026-10-07T02:03:23Z | [
"probable.data-training-selection"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 3 | [] | 2026-10-18T10:49:01Z | https://github.com/healenrens/Move-then-Operate | null |
[
"foundation-models",
"generative-ai",
"multimodal-learning",
"natural-language-processing"
] | [
"large-language-model",
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] | [
"description",
"github-topics",
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"query-match",
"repository-metadata"
] | [
"llm.foundation-models",
"recall.name.large-language-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-20T03:50:35Z | Official implementation of “Pretrain Once, Route Anywhere: Towards a Foundation Model for LLM Routing” (arXiv:2609.37362). | [
"multimodal-learning",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T15:56:46Z | false | 0 | 1,377,878,450 | https://arxiv.org/abs/2609.37362 | Python | Apache-2.0 | [
"large-language-model"
] | LAMDA-Model-Reuse/RouteFM | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 8 | 2026-11-01T16:11:12Z | [
"2609.37362"
] | 2026-10-14T04:08:57Z | [
"recall.name.large-language-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 | 11 | [
"foundation-models",
"in-context-learning",
"llm-routing",
"model-routing",
"multimodal-routing",
"routefm"
] | 2026-10-18T08:42:28Z | https://github.com/LAMDA-Model-Reuse/RouteFM | null |
[
"computer-vision",
"efficient-ml",
"generative-ai",
"language",
"machine-learning-systems",
"model-compression",
"multimodal",
"multimodal-learning",
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] | [
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"pruning",
"referring-expression-comprehension",
"structured-pruning",
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] | [
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] | [
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"recall.name.language-model",
"recall.name.large-language-model",
"recall.name.multimodal-model",
"recall26.visual-grounding"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-20T07:26:58Z | Code for Paper "Layer-Aware Position Embeddings for Visual Token Pruning in Multimodal Large Language Model" | [
"computer-vision",
"machine-learning-systems",
"multimodal-learning",
"natural-language-processing"
] | [
"large-language-model",
"embedding"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-25T15:00:52Z | false | 0 | 1,378,060,420 | null | Python | Apache-2.0 | [
"pruning",
"referring-expression-comprehension",
"vision-language-learning",
"visual-grounding"
] | YahongWang1/LayerPos | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-18T14:44:29Z | [] | 2026-09-23T02:00:06Z | [
"recall26.visual-grounding"
] | 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 | [] | 2026-09-23T02:00:10Z | https://github.com/YahongWang1/LayerPos | null |
[
"3d-vision",
"computer-vision",
"efficient-ml",
"model-compression"
] | [
"3d-perception",
"depth-estimation",
"distillation",
"knowledge-distillation"
] | [
"description",
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"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation",
"vision.depth-estimation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-22T09:48:12Z | This repository contains the official implementation for the paper "KD-CAUNet: Geometry-Aware Attentive UNet with Channel-Level Knowledge Distillation for Joint Acoustic Source Range and Depth Estimation in Shallow Water" | [
"efficient-ml",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-25T15:00:52Z | false | 0 | 1,381,225,394 | null | null | MIT | [
"distillation",
"knowledge-distillation"
] | binzhangbin/KD-CAUNet | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-11T16:48:52Z | [] | 2026-09-24T20:51:22Z | [
"efficiency.knowledge-distillation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [] | 2026-10-02T12:55:28Z | https://github.com/binzhangbin/KD-CAUNet | null |
[
"general-ml",
"generative-ai",
"generative-modeling"
] | [
"diffusion",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"recall.name.diffusion-model",
"recall.name.transformer-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-22T16:11:41Z | [ECCV 2026] Official implementation of LayerVerse: Finding the Sweet Spot for KV-Injection in Training-Free Image Editing. | [
"general-ml",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-27T15:08:59Z | false | 0 | 1,381,903,299 | null | Python | Apache-2.0 | [
"diffusion",
"transformer"
] | v-gen-ai/LayerVerse | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-14T15:02:21Z | [] | 2026-09-30T20:42:49Z | [
"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 | [
"flux",
"image-editing",
"rectified-flow",
"stable-diffusion-3-5",
"training-free"
] | 2026-10-11T06:47:04Z | https://github.com/v-gen-ai/LayerVerse | null |
[
"general-ml",
"generative-modeling"
] | [
"flow-matching",
"transformer"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"recall.name.transformer-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-22T23:10:22Z | official code repo for paper "SKELETONS IN FLOW: GRAPH STRUCTURED FLOW MATCHING FOR HUMAN MOTION PREDICTION" | [
"general-ml",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-29T16:25:50Z | false | 0 | 1,382,453,181 | null | Python | null | [
"flow-matching",
"transformer"
] | noMushroomw/GSFM | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-06T16:04:50Z | [] | 2026-09-24T17:39:28Z | [
"recall.name.transformer-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 | [] | 2026-09-24T17:40:34Z | https://github.com/noMushroomw/GSFM | null |
[
"computational-physics",
"general-ml",
"physics",
"science-and-engineering",
"scientific-computing"
] | [
"neural-network",
"neural-operators",
"physics-informed-learning",
"surrogate-modeling"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.neural-network",
"recall.name.neural-network",
"science.physics",
"science.physics-informed-neural-networks"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-23T08:29:49Z | This code is for our paper:Efficient multiphysics system modeling enabled by data-free physics informed neural networks | [
"computational-physics",
"physics",
"science-and-engineering"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T13:20:52Z | false | 0 | 1,383,058,852 | null | Python | null | [
"neural-network",
"physics-informed-learning"
] | cloud139969/PINN-for-Multiphysics-Modeling | [
"description",
"query-match",
"repository-metadata"
] | 5 | 2026-10-11T16:48:52Z | [] | 2026-09-23T12:05:39Z | [
"science.physics-informed-neural-networks"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-context-only",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-09-30T22:48:45Z | https://github.com/cloud139969/PINN-for-Multiphysics-Modeling | null |
[
"classical-ml",
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"deep-learning",
"general-ml",
"generative-ai",
"graph-learning",
"language",
"metric-learning",
"physics",
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] | [
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"machine-learning",
"message-passing",
"nearest-neighbors",
"neural-operators",
"physics-informed-learning",
"surrogate-modeling",
"transformer"
] | [
"description",
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] | [
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"llm.transformers",
"recall.name.diffusion-model",
"recall.name.machine-learning",
"recall.name.transformer-model",
"science.physics"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-23T09:36:13Z | Official code for the IEEE MLSP 2026 paper: Physics-Augmented Graph Transformers for Patch-Antenna Forward and Inverse Design | [
"deep-learning",
"graph-learning"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T13:07:37Z | false | 0 | 1,383,153,451 | null | Jupyter Notebook | MIT | [
"graph-transformer",
"message-passing"
] | AviEpstein/GNN-for-Antenna-design | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 7 | 2026-10-18T13:47:34Z | [] | 2026-10-07T16:03:33Z | [
"graph.graph-transformer"
] | 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 | [] | 2026-10-07T16:03:37Z | https://github.com/AviEpstein/GNN-for-Antenna-design | null |
[
"general-ml",
"machine-learning"
] | [
"distillation",
"fine-tuning",
"model-adaptation",
"neural-network"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"probable.adaptation-finetuning-readme",
"recall.name.neural-network"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-24T05:51:43Z | Official code for the NeurIPS 2026 paper 'The Shape of Events: Edge-Based Inductive Biases via Cross-Domain Distillation'. | [
"machine-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-08T17:03:06Z | false | 0 | 1,384,785,758 | null | Python | NOASSERTION | [
"distillation",
"fine-tuning",
"model-adaptation"
] | snskysk/event2rgb-distillation | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-10T15:52:45Z | [] | 2026-09-28T10:43:16Z | [
"probable.adaptation-finetuning-readme"
] | 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 | [] | 2026-09-30T23:16:50Z | https://github.com/snskysk/event2rgb-distillation | null |
[
"general-ml",
"generative-ai",
"generative-modeling",
"language"
] | [
"diffusion",
"language-model",
"transformer"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"llm.transformers",
"recall.name.diffusion-model",
"recall.name.transformer-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-24T21:23:59Z | Official AutoTarget implementation: solver-aware cache target selection for few-step diffusion transformers (submitted to ICLR 2027). | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model",
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-05T18:59:49Z | false | 0 | 1,386,382,273 | null | Python | null | [
"diffusion"
] | wali1024-offical/AutoTarget | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 6 | 2026-10-22T14:58:59Z | [] | 2026-10-01T03:17:30Z | [
"recall.name.diffusion-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 2 | [
"diffusion-models",
"diffusion-transformers",
"inference-acceleration"
] | 2026-10-15T04:54:02Z | https://github.com/wali1024-offical/AutoTarget | null |
[
"deep-learning",
"general-ml",
"generative-ai",
"graph-learning",
"language"
] | [
"graph-transformer",
"language-model",
"message-passing",
"neural-network",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.graph-transformer",
"llm.transformers",
"recall.name.neural-network",
"recall.name.transformer-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-09-25T15:33:23Z | The official GitHub repository of our ACCV 2026 paper "Learned Suppression for 3D Keypoint Detection with a Graph-Transformer Backbone" | [
"general-ml"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-18T13:47:34Z | false | 2 | 1,387,784,568 | null | Python | MIT | [
"transformer"
] | cansdev/learned-suppression-3d | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 8 | 2026-10-31T15:48:22Z | [] | 2026-10-02T06:54:55Z | [
"recall.name.transformer-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 | 4 | [] | 2026-10-11T07:17:34Z | https://github.com/cansdev/learned-suppression-3d | null |
[
"generative-ai",
"language",
"linguistics",
"multimodal-learning",
"natural-language-processing"
] | [
"language-model",
"language-modeling",
"large-language-model",
"pretraining"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
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"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",
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] | [
"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",
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] | include | ml-contribution-v5 | 0 | [] | 2026-10-01T01:01:03Z | https://github.com/HavenTong/MetaAct | null |
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] | 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). | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-29T15:58:23Z | false | 9 | 1,390,181,962 | https://chenkangjie1123.github.io/VGGT-Diff/ | Python | NOASSERTION | [
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] | 2026-10-30T07:56:20Z | https://github.com/chenkangjie1123/VGGT-Diff | null |
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] | 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. | [
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] | 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). | [
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] | include | ml-contribution-v5 | 1 | [
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] | 2026-10-16T13:43:56Z | https://github.com/xiaobanni/erpo | null |
[
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] | [
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] | 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 | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-09-27T23:10:02Z | https://github.com/NothingThrough/LFIRL | null |
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] | [
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] | 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) | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-19T16:36:56Z | false | 0 | 1,391,594,391 | null | Python | null | [
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] | iil-postech/sap | [
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] | 2 | 2026-10-24T13:28:22Z | [] | 2026-10-12T13:37:56Z | [
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] | include | ml-contribution-v5 | 1 | [
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] | 2026-10-14T00:42:14Z | https://github.com/iil-postech/sap | null |
[
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] | [
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] | 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" | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-04T14:53:29Z | false | 0 | 1,391,942,774 | null | Python | NOASSERTION | [
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] | libary753/ReSS | [
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] | 2 | 2026-10-23T14:45:14Z | [] | 2026-10-12T07:24:20Z | [
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] | include | ml-contribution-v5 | 2 | [] | 2026-10-23T05:40:01Z | https://github.com/libary753/ReSS | null |
[
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] | 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. | [
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] | [] | 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 | [
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] | 4 | 2026-10-23T14:45:14Z | [] | 2026-10-12T07:28:08Z | [
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] | 2026-10-12T07:29:19Z | https://github.com/MMAI-Laboratory/TTC | null |
[
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] | [
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] | 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" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-29T15:58:23Z | false | 0 | 1,392,535,051 | null | null | null | [
"diffusion",
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] | wengwanjiang/BiMoGen | [
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] | 2 | 2026-10-05T18:59:49Z | [] | 2026-09-28T11:47:56Z | [
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] | include | ml-contribution-v5 | 2 | [] | 2026-10-04T08:10:17Z | https://github.com/wengwanjiang/BiMoGen | null |
[
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] | [
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] | 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. | [
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] | ayoubncbae/PD-WM-BIBM2026 | [
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] | include | ml-contribution-v5 | 2 | [] | 2026-10-01T00:47:10Z | https://github.com/ayoubncbae/PD-WM-BIBM2026 | null |
[
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] | 1 | 2026-11-01T16:11:12Z | [] | 2026-10-18T18:17:16Z | [
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] | 2026-10-27T22:23:51Z | https://github.com/m1nhengChen/exvivo-cortical-folding | null |
[
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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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[
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[
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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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] | include | ml-contribution-v5 | 5 | [] | 2026-10-17T00:18:17Z | https://github.com/aailab-kaist/WASD | null |
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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 | [
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] | 2 | 2026-10-23T14:45:14Z | [] | 2026-10-07T07:04:35Z | [
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] | include | ml-contribution-v5 | 4 | [
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] | 2026-10-12T04:08:40Z | https://github.com/Keio-CSG/FWL-ToPM | null |
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] | [
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] | 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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] | [] | [
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] | [] | 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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] | 3 | 2026-10-10T18:17:44Z | [
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] | 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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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-01T16:31:09Z | false | 0 | 1,399,608,851 | null | Python | MIT | [
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] | peiman-m/fourier-volterra-nps | [
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] | 15 | 2026-10-25T14:09:27Z | [] | 2026-10-01T09:05:43Z | [
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] | include | ml-contribution-v5 | 0 | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T14:49:08Z | false | 0 | 1,400,879,352 | null | Python | MIT | [
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] | ShahriarShakirSumit/FAN-TTA | [
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] | 2 | 2026-10-18T13:47:34Z | [] | 2026-10-17T09:51:04Z | [
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] | include | ml-contribution-v5 | 0 | [
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] | 2026-10-17T09:51:08Z | https://github.com/ShahriarShakirSumit/FAN-TTA | null |
[
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] | 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" | [
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] | [
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] | 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 | [
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] | anishhdiwan/geometric-perspective | [
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] | 2 | 2026-10-18T13:47:34Z | [] | 2026-10-14T07:41:44Z | [
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] | include | ml-contribution-v5 | 0 | [
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] | 2026-10-15T14:09:43Z | https://github.com/anishhdiwan/geometric-perspective | null |
[
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] | [
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] | 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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] | 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 | [
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] | 1 | 2026-10-03T14:20:41Z | [] | 2026-10-02T14:10:05Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-10-02T15:02:40Z | https://github.com/mokebot007/nedreamer | null |
[
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-02T19:01:14Z | Code for NeurIPS 2026 paper "Diffusion Meta-Prompting and Steering for Generalizable Foundation Model Adaptation" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-03T14:20:41Z | false | 0 | 1,402,240,682 | null | null | null | [
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] | DeepakSridhar/dmp | [
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] | 2 | 2026-10-20T14:47:52Z | [] | 2026-10-02T19:01:15Z | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-10-02T19:01:15Z | https://github.com/DeepakSridhar/dmp | null |
[
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] | [
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] | 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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] | [
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] | 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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] | anzir29/pannuke-nuclei-fl | [
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] | 3 | 2026-10-20T14:47:52Z | [] | 2026-10-04T18:21:32Z | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-10-04T18:16:42Z | https://github.com/anzir29/pannuke-nuclei-fl | null |
[
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] | [
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] | 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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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-08T16:35:27Z | false | 2 | 1,405,594,963 | https://cvlab-kaist.github.io/GRACE/ | Python | NOASSERTION | [
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] | 6 | 2026-10-23T14:45:14Z | [
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] | 2026-10-12T08:12:04Z | [
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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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] | 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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] | include | ml-contribution-v5 | 0 | [] | 2026-10-11T08:30:38Z | https://github.com/ron787/bbdm-schedule-design | null |
[
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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 | [
"diffusion"
] | Adeelyousaf/SAGE | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 9 | 2026-10-24T13:28:22Z | [] | 2026-10-05T20:33:23Z | [
"recall.name.diffusion-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 | 2 | [
"ai-safety",
"diffusion-models",
"eccv2026",
"stable-diffusion",
"text-to-image"
] | 2026-10-13T13:58:28Z | https://github.com/Adeelyousaf/SAGE | null |
[
"automl",
"computer-vision",
"general-ml"
] | [
"few-shot-learning",
"meta-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.meta-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-06T12:41:57Z | Few-shot meta-learning bake-off across vision/tabular/chess/coding + novel PAHO architecture | [
"automl",
"computer-vision",
"general-ml"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-07T16:37:11Z | false | 0 | 1,407,283,578 | null | Python | null | [
"few-shot-learning",
"meta-learning"
] | mahithpaulin/fewshot-arch-lab | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-10-19T16:36:56Z | [] | 2026-10-06T12:55:55Z | [
"general.meta-learning"
] | c4ff2cfa32d74ab26c7910ee1ce511217b95c0d0 | 2026-10-07T19:34:31Z | gh-ml-readme-evidence-v2 | "c4ff2cfa32d74ab26c7910ee1ce511217b95c0d0" | [
"other"
] | [
"ml-method-context",
"model-training-artifact"
] | ok | 2026-10-07T19:34:31Z | mahithpaulin/fewshot-arch-lab | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue",
"model-training-artifact"
] | include | ml-contribution-v5 | 0 | [] | 2026-10-11T08:58:28Z | https://github.com/mahithpaulin/fewshot-arch-lab | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion",
"flow-matching"
] | [
"description",
"github-topics",
"license-metadata",
"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. | [
"generative-ai",
"generative-modeling"
] | [
"representation-learning",
"diffusion-model"
] | 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 | [
"diffusion",
"flow-matching"
] | AI4Science-WestlakeU/feature-information-dynamics | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-26T16:16:45Z | [] | 2026-10-08T08:39:23Z | [
"recall.name.diffusion-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 10 | [
"diffusion-models",
"flow-matching",
"information-theory",
"representation-learning"
] | 2026-10-14T14:43:25Z | https://github.com/AI4Science-WestlakeU/feature-information-dynamics | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-10-06T13:15:19Z | Official implementation of [Oakland’27] paper "Revisiting Locally Differentially Private Graph Neural Networks" | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-07T16:37:11Z | false | 0 | 1,407,335,578 | null | Python | NOASSERTION | [
"graph-neural-network",
"message-passing",
"neural-network"
] | ZJU-TrustAID/Revisit-LDPGNN | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-19T16:36:56Z | [] | 2026-10-07T02:01:35Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-10-11T08:58:00Z | https://github.com/ZJU-TrustAID/Revisit-LDPGNN | null |
[
"generative-ai",
"language",
"tabular-and-structured-data",
"tabular-ml"
] | [
"language-model",
"tabular-learning",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"applied.tabular-ml",
"llm.transformers"
] | 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",
"language",
"tabular-and-structured-data",
"tabular-ml"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-07T16:37:11Z | false | 0 | 1,407,786,778 | null | Python | NOASSERTION | [
"language-model",
"tabular-learning",
"transformer"
] | BetterdataLabs/tabtreeformer | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-22T14:58:59Z | [] | 2026-10-06T18:51:44Z | [
"applied.tabular-ml",
"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 | [] | 2026-10-06T18:55:22Z | https://github.com/BetterdataLabs/tabtreeformer | null |
[
"computer-vision",
"language",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"vision"
] | [
"contrastive-learning",
"distillation",
"image-text",
"language-model",
"language-modeling",
"vision-language-model"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.language-model",
"multimodal.vision-language",
"multimodal.vlm",
"nlp.language-modeling"
] | 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. | [
"computer-vision",
"language",
"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 | [
"distillation",
"language-model",
"language-modeling"
] | handsome-rich/VA-OPD | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-22T14:58:59Z | [] | 2026-10-07T08:46:41Z | [
"llm.language-model",
"nlp.language-modeling"
] | 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 | [] | 2026-10-20T12:44:59Z | https://github.com/handsome-rich/VA-OPD | null |
[
"generative-ai",
"generative-modeling",
"missing-data",
"time-series",
"time-series-and-forecasting"
] | [
"diffusion",
"imputation",
"time-series-modeling"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"recall.name.diffusion-model",
"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-ai",
"generative-modeling",
"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"
] | dongbeank/MDTIM | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-30T16:05:33Z | [] | 2026-10-14T20:34:20Z | [
"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 | [] | 2026-10-14T20:34:24Z | https://github.com/dongbeank/MDTIM | null |
[
"deep-learning",
"efficient-ml",
"language"
] | [
"conditional-computation",
"mixture-of-experts",
"sparse-activation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"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). | [
"deep-learning",
"efficient-ml",
"language"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-08T16:35:27Z | false | 0 | 1,409,168,315 | null | null | null | [
"conditional-computation",
"mixture-of-experts",
"sparse-activation"
] | JasmineLyf/ROME-NeurIPS2026 | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-10-19T16:36:56Z | [] | 2026-10-07T17:47:25Z | [
"efficiency.mixture-of-experts"
] | 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 | [] | 2026-10-07T17:47:25Z | https://github.com/JasmineLyf/ROME-NeurIPS2026 | null |
[
"efficient-ml",
"generative-modeling",
"inference-systems",
"language"
] | [
"diffusion",
"inference-optimization",
"speculative-decoding"
] | [
"description",
"license-metadata",
"query-match",
"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" | [
"efficient-ml",
"generative-modeling",
"inference-systems",
"language"
] | [] | 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 | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-10-14T04:18:33Z | https://github.com/uncc-efficient-ai/FoRE | null |
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