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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
[
"computational-science",
"generative-modeling",
"physics"
] | [
"diffusion",
"neural-operator",
"neural-operators",
"surrogate-modeling"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"science.neural-operators"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-07T11:52:44Z | Official PyTorch implementation for the IFAC paper 'Stability of Slowly-Varying Reaction-Diffusion PDEs under Neural Operator-based Gain-Scheduling Control'. | [
"computational-science",
"generative-modeling",
"physics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 0 | 1,129,656,523 | null | Python | MIT | [
"diffusion",
"neural-operator",
"neural-operators",
"surrogate-modeling"
] | gaozheng2001/DeepONet-Gain-Scheduling-PDE | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-09-29T17:30:08Z | [] | 2026-05-06T11:04:32Z | [
"science.neural-operators"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-05-06T11:04:36Z | https://github.com/gaozheng2001/DeepONet-Gain-Scheduling-PDE | null |
[
"efficient-ml",
"machine-learning-systems",
"model-compression",
"sequence-modeling",
"time-series"
] | [
"neural-network-pruning",
"pruning",
"sequence-modeling",
"state-space-model",
"structured-pruning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning",
"timeseries.state-space-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-08T09:10:13Z | The official implementation of the IEEE TIP paper "Selecting and Pruning: A Differentiable Causal Sequentialized State-Space Model for Two-View Correspondence Learning" | [
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 1,130,268,048 | null | null | null | [
"neural-network-pruning",
"pruning",
"state-space-model",
"structured-pruning"
] | ShineFox/CorrMamba | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-31T16:49:26Z | [] | 2026-01-08T09:10:14Z | [
"efficiency.pruning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 2 | [] | 2026-02-13T10:25:28Z | https://github.com/ShineFox/CorrMamba | null |
[
"interpretability-and-safety",
"trustworthy-ml"
] | [
"adversarial-robustness",
"robust-optimization",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"trust.adversarial-robustness"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-08T09:27:15Z | Code for paper "Adversarial Robustness Evaluation of Hybrid CNN-LSTM-Transformer NIDS on Evolving Threats". | [
"interpretability-and-safety",
"trustworthy-ml"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 0 | 1,130,278,214 | null | Python | MIT | [
"adversarial-robustness",
"robust-optimization",
"transformer"
] | darthjuanan/hybrid-nids-2026 | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-09-30T16:47:43Z | [] | 2026-02-24T22:46:16Z | [
"trust.adversarial-robustness"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-09-20T11:17:43Z | https://github.com/darthjuanan/hybrid-nids-2026 | null |
[
"computer-vision",
"generative-ai",
"language",
"safety"
] | [
"alignment",
"preference-optimization",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.preference-optimization"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-08T18:37:06Z | Neural Geometric State Space Transformer (NGSST), is a novel vision architecture that fundamentally rethinks how artificial systems perceive and understand visual information. NGSST models vision as a continuous geometric process governed by physic dynamics | [
"computer-vision",
"generative-ai",
"language",
"safety"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-16T15:36:56Z | false | 0 | 1,130,605,299 | null | Python | NOASSERTION | [
"alignment",
"preference-optimization",
"transformer"
] | calisweetleaf/NGSST | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-16T15:36:56Z | [] | 2026-01-11T05:36:50Z | [
"llm.preference-optimization"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 1 | [] | 2026-01-11T05:36:54Z | https://github.com/calisweetleaf/NGSST | null |
[
"computational-neuroscience",
"machine-learning"
] | [
"distillation",
"neuromorphic-computing",
"spiking-neural-network"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"specialized.spiking-neural-network"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-09T09:58:52Z | [AAAI 2026] Official implementation of "Bi-Spectrum Distillation: Addressing Spectral Mismatch in ANN-SNN Knowledge Transfer" | [
"computational-neuroscience",
"machine-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-22T16:09:07Z | false | 0 | 1,130,987,268 | null | Python | null | [
"distillation",
"neuromorphic-computing",
"spiking-neural-network"
] | deng-ai-lab/BSDSNN | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-22T16:09:07Z | [] | 2026-01-09T10:00:33Z | [
"specialized.spiking-neural-network"
] | 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 | [] | 2026-09-13T15:23:19Z | https://github.com/deng-ai-lab/BSDSNN | null |
[
"physics",
"scientific-computing"
] | [
"inverse-problems",
"scientific-machine-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"gap2026.physics-inverse-problem"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-10T18:42:14Z | A research repository proposing a new family of AI models based on residual-Jacobian optimization proposed by Dadoyi Dadesso, as an alternative to traditional loss-gradient-based learning. | [
"physics",
"scientific-computing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 1,131,814,769 | null | null | null | [
"inverse-problems",
"scientific-machine-learning"
] | taid08/-Residual-Jacobian-AI | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-04T15:58:58Z | [] | 2026-01-10T18:57:36Z | [
"gap2026.physics-inverse-problem"
] | a08e895995a34c0013307a47c48b47a02019a5fc | 2026-10-04T17:18:56Z | gh-ml-readme-evidence-v2 | "a08e895995a34c0013307a47c48b47a02019a5fc" | [
"other"
] | [
"ml-method-context"
] | ok | 2026-10-04T17:18:56Z | taid08/-Residual-Jacobian-AI | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-01-10T18:57:40Z | https://github.com/taid08/-Residual-Jacobian-AI | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-11T15:10:13Z | Source code for our paper "Revealing the Attention Floating Mechanism in Masked Diffusion Models". | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-26T17:25:59Z | false | 0 | 1,132,224,606 | null | Python | null | [
"diffusion"
] | NEUIR/Attention-Floating | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-28T17:27:44Z | [] | 2026-01-14T11:18:56Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 13 | [] | 2026-10-17T10:59:19Z | https://github.com/NEUIR/Attention-Floating | null |
[
"generative-ai",
"generative-modeling",
"multimodal",
"vision"
] | [
"diffusion",
"image-generation",
"text-to-image"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.text-to-image"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-12T08:30:30Z | Official PyTorch implementation for paper: "Towards Hallucination-Robust Diffusion Augmented Interactive Text-to-Image Retrieval" | [
"generative-ai",
"generative-modeling",
"multimodal",
"vision"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-16T15:36:56Z | false | 0 | 1,132,631,288 | null | Python | null | [
"diffusion",
"image-generation",
"text-to-image"
] | longkukuhi/NRGR | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-16T15:36:56Z | [] | 2026-05-07T11:48:29Z | [
"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 | 3 | [] | 2026-05-07T11:48:33Z | https://github.com/longkukuhi/NRGR | null |
[
"machine-learning"
] | [
"adaptation",
"test-time-adaptation"
] | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"specialized.test-time-adaptation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-12T11:30:37Z | Official implementation of TEMG-TTA from paper "Temporal Motif-aware Graph Test-time Adaptation for OOD Blockchain Anomaly Detection" | [
"machine-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T10:12:50Z | false | 0 | 1,132,734,990 | https://arxiv.org/abs/2605.29526 | Jupyter Notebook | null | [
"adaptation",
"test-time-adaptation"
] | LuoXishuang0712/TEMG-TTA | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | 2 | 2026-10-05T20:10:04Z | [] | 2026-07-16T02:00:08Z | [
"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 | 3 | [
"graph-anomaly-detection",
"graph-fraud-detection",
"ijcai2026"
] | 2026-09-22T12:30:39Z | https://github.com/LuoXishuang0712/TEMG-TTA | null |
[
"distributed-ml",
"privacy-and-federated-learning"
] | [
"collaborative-learning",
"federated-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-14T02:01:06Z | The official implementation of 《Fedsa: A unified representation learning via semantic anchors for prototype-based federated learning》 AAAI'25 Oral | [
"distributed-ml",
"privacy-and-federated-learning"
] | [
"representation-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 0 | 1,133,919,799 | null | Python | GPL-3.0 | [
"collaborative-learning",
"federated-learning"
] | lokinko/FedSA | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-21T15:48:06Z | [] | 2026-01-14T02:22:31Z | [
"trust.federated-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 5 | [] | 2026-10-13T01:18:02Z | https://github.com/lokinko/FedSA | null |
[
"computer-vision",
"language",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"robotics-and-control",
"vision"
] | [
"contrastive-learning",
"distillation",
"image-text",
"vision-language-model"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"multimodal.vision-language",
"multimodal.vlm"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-16T08:25:09Z | MetricGround: Bridging the Dimensionality Gap for Metric-Aware Embodied Vision-Language Models - Official implementation of the paper | [
"computer-vision",
"language",
"multimodal",
"multimodal-learning",
"natural-language-processing",
"robotics-and-control",
"vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-11T16:48:52Z | false | 0 | 1,135,532,013 | null | Python | null | [
"contrastive-learning",
"distillation",
"vision-language-model"
] | muthusamir/MetricGround | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 2 | 2026-10-17T13:17:44Z | [] | 2026-01-16T10:08:58Z | [
"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",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [
"3d-euclidean-reasoning",
"cross-modal-distillation",
"dimensionality-gap",
"embodied-ai",
"metric-grounding",
"occlusion-ambiguity",
"spatial-question-answering",
"vision-language-models"
] | 2026-01-16T10:12:38Z | https://github.com/muthusamir/MetricGround | null |
[
"deep-learning",
"efficient-ml",
"language",
"multimodal-learning"
] | [
"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 | [] | 2026-01-16T20:29:01Z | This is the official PyTorch implementation of the paper "SMG-Fusion: A Superpixel-guided Mixture of Experts Graph Network for Multi-Modality Image Fusion". | [
"deep-learning",
"efficient-ml",
"language",
"multimodal-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 0 | 1,135,948,516 | null | Python | null | [
"conditional-computation",
"mixture-of-experts",
"sparse-activation"
] | Sheny127/SMGFusion | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-02T16:59:40Z | [] | 2026-01-16T20:52:37Z | [
"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",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 2 | [] | 2026-01-26T08:02:15Z | https://github.com/Sheny127/SMGFusion | null |
[
"forecasting",
"multimodal-learning",
"synthetic-data",
"time-series",
"time-series-and-forecasting"
] | [
"forecasting",
"generative-modeling",
"sequence-modeling",
"time-series-generation"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"timeseries.forecasting",
"timeseries.synthetic-generation",
"timeseries.topic"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-17T15:51:13Z | [IJCAI 2026] Official implementation of "EventTSF: Event-Aware Non-Stationary Time Series Forecasting" | [
"multimodal-learning",
"time-series",
"time-series-and-forecasting"
] | [
"deep-learning",
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 2 | 1,136,387,996 | https://arxiv.org/abs/2508.13434v2 | Python | Apache-2.0 | [
"sequence-modeling"
] | WinfredGe/EventTSF | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 13 | 2026-10-31T16:49:26Z | [] | 2026-07-20T11:24:02Z | [
"timeseries.topic"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 22 | [
"cross-modality",
"deep-learning",
"distribution-shift",
"event-driven",
"machine-learning",
"multimodal-deep-learning",
"time-series",
"time-series-analysis",
"time-series-forecasting"
] | 2026-10-08T03:05:24Z | https://github.com/WinfredGe/EventTSF | null |
[
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-20T11:47:50Z | This repository contains the official source code for the paper: "Paired Image Generation with Diffusion-Guided Diffusion Models" | [
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-29T17:49:51Z | false | 1 | 1,138,219,195 | null | Python | null | [
"diffusion"
] | zhanghx1320/PIG | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-29T17:49:51Z | [] | 2026-04-10T02:40:27Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 3 | [] | 2026-09-21T13:24:46Z | https://github.com/zhanghx1320/PIG | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"interpretability-and-safety",
"machine-learning",
"multimodal",
"vision"
] | [
"diffusion",
"generative-modeling",
"image-generation",
"machine-unlearning",
"privacy",
"text-to-image"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase",
"multimodal.text-to-image",
"specialized.machine-unlearning",
"vision.image-diffusion"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-20T19:43:42Z | Official implementation of "SPQR: A Standardized Benchmark for Modern Safety Alignment Methods in Text-to-Image Diffusion Models" [ECCV'26] | [
"generative-ai",
"generative-modeling",
"interpretability-and-safety"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 0 | 1,138,529,823 | null | Python | NOASSERTION | [
"diffusion"
] | talha-alam/spqr | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 8 | 2026-10-28T17:27:44Z | [] | 2026-06-30T17:12:10Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 21 | [] | 2026-07-02T20:34:13Z | https://github.com/talha-alam/spqr | null |
[
"graph-learning",
"time-series"
] | [
"dynamic-graph-learning",
"rag",
"temporal-graph-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.temporal-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-21T07:27:45Z | Official code repository for the ACL 2026 paper: "VideoStir: Understanding Long Videos via Spatio-Temporally Structured and Intent-Aware RAG" | [
"graph-learning",
"time-series"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T16:16:53Z | false | 1 | 1,138,842,655 | null | Jupyter Notebook | MIT | [
"dynamic-graph-learning",
"rag",
"temporal-graph-learning"
] | RomGai/VideoStir | [
"description",
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"query-match",
"repository-metadata"
] | 4 | 2026-10-16T15:36:56Z | [] | 2026-06-13T17:57:18Z | [
"graph.temporal-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 20 | [] | 2026-10-08T07:07:17Z | https://github.com/RomGai/VideoStir | null |
[
"machine-learning"
] | [
"curriculum-learning",
"retrieval-augmented-generation",
"training-strategy"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"specialized.curriculum-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-21T11:32:00Z | Official implementation of WWW2026 paper: SeleCom (Rethinking Soft Compression in Retrieval-Augmented Generation: A Query-Conditioned Selector Perspective) | [
"machine-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T10:12:50Z | false | 0 | 1,138,991,931 | null | Python | MIT | [
"curriculum-learning",
"retrieval-augmented-generation",
"training-strategy"
] | yhliu7458/SeleCom | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-23T15:59:01Z | [] | 2026-06-27T11:11:49Z | [
"specialized.curriculum-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 4 | [] | 2026-07-25T15:36:31Z | https://github.com/yhliu7458/SeleCom | null |
[
"computer-vision",
"generative-ai",
"language"
] | [
"language-model",
"representation-learning",
"self-supervised-learning",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.transformers",
"vision.self-supervised-vision"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-21T12:19:31Z | Official code for 'DAGA: Dynamic Attention-Guided Adaptation for Self-Supervised Vision Transformers' (NeurIPS 2026) | [
"computer-vision",
"generative-ai",
"language"
] | [
"self-supervised-learning",
"classifier",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T14:34:36Z | false | 0 | 1,139,020,197 | https://github.com/Jaywalk18/DAGA#readme | Python | MIT | [
"language-model",
"self-supervised-learning",
"transformer"
] | Jaywalk18/DAGA | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-10-04T14:53:29Z | [] | 2026-09-29T07:03:47Z | [
"llm.transformers"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 0 | [
"dino",
"dinov3",
"image-classification",
"neurips2026",
"object-detection",
"parameter-efficient-fine-tuning",
"self-supervised-learning",
"semantic-segmentation",
"vision-transformer"
] | 2026-10-02T08:19:16Z | https://github.com/Jaywalk18/DAGA | null |
[
"computer-vision",
"embodied-ai",
"language",
"multimodal",
"multimodal-learning",
"robotics",
"vision"
] | [
"contrastive-learning",
"robot-learning",
"vision-language-action",
"vision-language-model"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.vision-language",
"robotics.visual-language-action"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-21T16:31:31Z | [ICML 2026] This repo is the official implementation of "LangForce : Bayesian Decomposition of Vision Language Action Models via Latent Action Queries" | [
"computer-vision",
"language",
"multimodal",
"multimodal-learning",
"vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T16:16:53Z | false | 4 | 1,139,189,558 | null | Python | null | [
"contrastive-learning",
"vision-language-model"
] | ZGC-EmbodyAI/LangForce | [
"description",
"query-match",
"repository-metadata"
] | 5 | 2026-10-16T15:36:56Z | [] | 2026-07-29T07:12:06Z | [
"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 | 82 | [] | 2026-10-06T08:04:52Z | https://github.com/ZGC-EmbodyAI/LangForce | null |
[
"efficient-ml",
"general-ml",
"machine-learning",
"machine-learning-systems",
"model-compression"
] | [
"machine-unlearning",
"neural-network-pruning",
"novel-method",
"privacy",
"pruning",
"structured-pruning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning",
"general.novel-method",
"specialized.machine-unlearning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-22T11:21:45Z | Evaluation of various ML unlearning approaches, ranging from established techniques like finetuning and poisoning to novel methods like Pruning Complex, in an ablation study. | [
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 0 | 1,139,754,650 | null | Jupyter Notebook | null | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | vstrozzi/assessing-machine-unlearning-approaches | [
"description",
"query-match",
"repository-metadata"
] | 5 | 2026-11-01T17:09:41Z | [] | 2026-01-22T15:40:02Z | [
"efficiency.pruning"
] | d8d303b5898177cb543c7e632f29a72109d2569c | 2026-09-26T15:53:33Z | gh-ml-readme-evidence-v2 | "d8d303b5898177cb543c7e632f29a72109d2569c" | [
"abstract",
"other",
"overview"
] | [
"method-contribution",
"ml-method-context"
] | ok | 2026-09-26T15:53:33Z | vstrozzi/assessing-machine-unlearning-approaches | specific-method-with-novelty-claim | [
"contribution-language",
"method-contribution",
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue"
] | include | ml-contribution-v5 | 1 | [] | 2026-01-22T15:46:06Z | https://github.com/vstrozzi/assessing-machine-unlearning-approaches | null |
[
"deep-learning",
"multimodal-learning",
"representation-learning"
] | [
"representation-learning",
"self-supervised-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.self-supervised-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-23T13:40:27Z | Official implementation of the paper "FairSSL: Fair Multimodal Self-Supervised Learning", published at ICML 2026. | [
"deep-learning",
"multimodal-learning",
"representation-learning"
] | [
"self-supervised-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-06T17:10:23Z | false | 0 | 1,140,586,950 | null | Python | MIT | [
"representation-learning",
"self-supervised-learning"
] | abtinmU/FairSSL | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-06T17:10:23Z | [] | 2026-07-08T23:00:59Z | [
"general.self-supervised-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1 | [] | 2026-07-24T21:26:32Z | https://github.com/abtinmU/FairSSL | null |
[
"efficient-ml",
"machine-learning-systems",
"model-compression",
"natural-language-processing"
] | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-24T09:03:17Z | [EMNLP 2026 findings] Official PyTorch implementation of SPSR: Achieving Superior Large Language Model Layer Pruning Performance by Super-fast Recovery | [
"efficient-ml",
"machine-learning-systems",
"model-compression",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-11-01T17:09:41Z | false | 0 | 1,141,107,928 | null | Python | null | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | BeiYazi0/SPSR | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-11-01T17:09:41Z | [] | 2026-10-29T03:57:59Z | [
"efficiency.pruning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-10-29T03:58:03Z | https://github.com/BeiYazi0/SPSR | null |
[
"embodied-ai",
"robotics",
"robotics-and-control"
] | [
"imitation-learning",
"manipulation",
"teleoperation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"robotics.manipulation",
"robotics.teleoperation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-25T07:09:06Z | Trustworthy Evaluation of Robotic Manipulation: A New Benchmark and AutoEval Methods | [
"embodied-ai",
"robotics",
"robotics-and-control"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 0 | 1,141,654,235 | null | Python | Apache-2.0 | [
"imitation-learning",
"teleoperation"
] | LogSSim/TERM-Bench | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-04T15:58:58Z | [] | 2026-01-27T06:58:32Z | [
"robotics.teleoperation"
] | 31529575d08f30c9cfd1aad805e27ba4a85279fa | 2026-09-26T15:53:33Z | gh-ml-readme-evidence-v2 | "31529575d08f30c9cfd1aad805e27ba4a85279fa" | [
"installation",
"other",
"overview",
"results"
] | [
"paper-reference"
] | ok | 2026-09-26T15:53:33Z | LogSSim/TERM-Bench | specific-method-with-novelty-claim | [
"contribution-language",
"method-tied-novelty-claim",
"ml-method-cue",
"paper-reference"
] | include | ml-contribution-v5 | 11 | [] | 2026-08-17T20:10:16Z | https://github.com/LogSSim/TERM-Bench | null |
[
"machine-learning"
] | [
"adaptation",
"test-time-adaptation"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"specialized.test-time-adaptation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-25T09:52:13Z | [ICML 2026] The official code repo for our paper "Neural QAOA²: Differentiable Joint Graph Partitioning and Parameter Initialization for Quantum Combinatorial Optimization". | [
"machine-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T10:12:50Z | false | 2 | 1,141,715,995 | https://proceedings.mlr.press/v306/zheng26x.html | Python | MIT | [
"adaptation",
"test-time-adaptation"
] | 0SliverBullet/Neural-QAOA-Squared | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-05T20:10:04Z | [] | 2026-05-18T03:46:16Z | [
"specialized.test-time-adaptation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 19 | [
"differentiable-programming",
"quantum-combinatorial-optimization"
] | 2026-10-05T03:38:14Z | https://github.com/0SliverBullet/Neural-QAOA-Squared | null |
[
"generative-ai",
"generative-modeling",
"health-and-biomedicine",
"language",
"safety"
] | [
"alignment",
"diffusion",
"preference-optimization"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.preference-optimization"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-26T05:08:31Z | Official implementation of "The Hallucination Gap: Dissecting Structural Collapse and Symmetrical Deception in Discrete Protein Diffusion" submitted to KDD 2026. | [
"generative-ai",
"generative-modeling",
"health-and-biomedicine",
"language",
"safety"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-15T15:20:52Z | false | 0 | 1,142,210,157 | null | Python | null | [
"alignment",
"diffusion",
"preference-optimization"
] | Wu-Junqi/DPO-Protein-Diffusion | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-15T15:20:52Z | [] | 2026-02-09T12:00:07Z | [
"llm.preference-optimization"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 1 | [] | 2026-04-04T12:47:44Z | https://github.com/Wu-Junqi/DPO-Protein-Diffusion | null |
[
"computer-vision",
"generative-modeling"
] | [
"diffusion",
"restoration",
"super-resolution"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"vision.image-super-resolution"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-26T07:36:39Z | The official PyTorch implementation of the paper "Diffusion-Based Arbitrary-Scale Magnetic Resonance Image Super-Resolution via Progressive K-space Reconstruction and Denoising" | [
"computer-vision",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 0 | 1,142,277,636 | null | Python | null | [
"diffusion",
"restoration",
"super-resolution"
] | Jiazhen-Wang/PRDDiff-main | [
"description",
"query-match",
"repository-metadata"
] | 4 | 2026-11-01T17:09:41Z | [] | 2026-02-06T15:01:23Z | [
"vision.image-super-resolution"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 4 | [] | 2026-08-04T02:42:00Z | https://github.com/Jiazhen-Wang/PRDDiff-main | null |
[
"foundation-models",
"generative-ai",
"graph-learning"
] | [
"graph-foundation-model",
"graph-pretraining",
"multimodal",
"pretraining",
"rag",
"retrieval-augmented-generation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.graph-foundation-model",
"llm.foundation-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-26T08:00:55Z | This repository is the official implementation of "RAG-GFM: Overcoming In-Memory Bottlenecks in Graph Foundation Models via Retrieval-Augmented Generation" accepted by WWW 2026. | [
"foundation-models",
"generative-ai"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 1 | 1,142,290,663 | null | Python | MIT | [
"multimodal",
"pretraining",
"rag",
"retrieval-augmented-generation"
] | haonan-yuan/RAG-GFM | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-15T15:20:52Z | [] | 2026-02-04T07:26:18Z | [
"llm.foundation-models"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 1 | [] | 2026-03-15T07:03:01Z | https://github.com/haonan-yuan/RAG-GFM | null |
[
"efficient-ml",
"generative-ai",
"inference-systems",
"language",
"machine-learning-systems",
"model-compression",
"natural-language-processing"
] | [
"efficient-inference",
"inference-optimization",
"language-model",
"neural-network-pruning",
"pretraining",
"pruning",
"speculative-decoding",
"structured-pruning"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning",
"efficiency.speculative-decoding",
"llm.language-models",
"llm.speculative-decoding"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-26T08:06:05Z | Official Implementation of DART (DART: Low-Latency Parallel Drafting with Continuity-Aware Tree Pruning for Speculative Decoding, EMNLP26). | [
"efficient-ml",
"machine-learning-systems",
"model-compression",
"natural-language-processing"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 3 | 1,142,293,513 | https://arxiv.org/abs/2601.19278 | Python | Apache-2.0 | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | fvliang/DART | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 19 | 2026-10-31T16:49:26Z | [] | 2026-08-25T14:20:41Z | [
"efficiency.pruning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 72 | [
"large-language-models",
"llm-inference",
"speculative-decoding"
] | 2026-10-16T10:26:45Z | https://github.com/fvliang/DART | null |
[
"computational-science",
"physics"
] | [
"neural-operator",
"neural-operators",
"surrogate-modeling"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"science.neural-operators"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-26T09:49:31Z | The official source code for "EqGINO: Equivariant Geometry-Informed Fourier Neural Operator for 3D PDEs" at ICML 2026 | [
"computational-science",
"physics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 1 | 1,142,353,772 | null | Python | null | [
"neural-operator",
"neural-operators",
"surrogate-modeling"
] | sung-won-kim/EqGINO | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-09-26T10:28:13Z | [] | 2026-06-21T08:23:26Z | [
"science.neural-operators"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 9 | [] | 2026-07-31T21:32:40Z | https://github.com/sung-won-kim/EqGINO | null |
[
"clinical-informatics",
"health-and-biomedicine",
"reinforcement-learning"
] | [
"decision-support",
"reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"medical.clinical-decision-support"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-26T13:28:32Z | Official implementation of the paper: Language Agents for Hypothesis-driven Clinical Decision Making with Reinforcement Learning (ICLR 2026) | [
"clinical-informatics",
"health-and-biomedicine",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-29T17:49:51Z | false | 0 | 1,142,485,288 | null | Python | null | [
"decision-support",
"reinforcement-learning"
] | dharouni/LA-CDM | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-31T16:49:26Z | [] | 2026-08-24T08:18:24Z | [
"medical.clinical-decision-support"
] | 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 | 8 | [] | 2026-10-08T02:15:31Z | https://github.com/dharouni/LA-CDM | null |
[
"efficient-ml",
"generative-modeling",
"machine-learning-systems",
"model-compression"
] | [
"diffusion",
"quantization"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"efficiency.quantization"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-26T13:47:58Z | [ICLR 2026] This is the official PyTorch implementation of "QVGen: Pushing the Limit of Quantized Video Generative Models". | [
"efficient-ml",
"generative-modeling",
"machine-learning-systems",
"model-compression"
] | [
"generative-model",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-25T15:11:36Z | false | 1 | 1,142,498,573 | https://arxiv.org/pdf/2505.11497 | Python | Apache-2.0 | [
"diffusion",
"quantization"
] | ModelTC/QVGen | [
"description",
"github-topics",
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"query-match",
"repository-metadata"
] | 2 | 2026-10-28T17:27:44Z | [] | 2026-02-11T21:05:19Z | [
"efficiency.quantization"
] | 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 | 34 | [
"cogvideox",
"diffusion-models",
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"quantization-aware-training",
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"wan21"
] | 2026-10-03T03:27:25Z | https://github.com/ModelTC/QVGen | null |
[
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] | [
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] | [
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] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-27T05:12:48Z | Official implementation of the ICLR 2026 paper, DeRaDiff: Denoising Time Realignment of Diffusion Models. | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-29T17:49:51Z | false | 0 | 1,143,030,342 | null | Python | null | [
"diffusion"
] | itsShahain/DeRaDiff | [
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] | 1 | 2026-10-29T17:49:51Z | [] | 2026-02-28T04:53:00Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-02-28T04:53:03Z | https://github.com/itsShahain/DeRaDiff | null |
[
"general-ml",
"generative-ai",
"generative-modeling",
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] | [
"description",
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] | [
"general.novel-method",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-27T05:29:48Z | A novel method to deploy federated learning with diffusion model | [
"generative-ai",
"generative-modeling",
"privacy-and-federated-learning"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 0 | 1,143,039,570 | null | Python | null | [
"diffusion",
"federated-learning"
] | suxas/DMFL | [
"description",
"query-match",
"repository-metadata"
] | 4 | 2026-10-29T17:49:51Z | [] | 2026-09-04T04:30:24Z | [
"llm.diffusion-phrase"
] | b6b912528e5d123025f3dee62b121be2677413c0 | 2026-09-26T15:53:33Z | gh-ml-readme-evidence-v2 | "b6b912528e5d123025f3dee62b121be2677413c0" | [] | [] | ok | 2026-09-26T15:53:33Z | suxas/DMFL | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-04-08T07:53:10Z | https://github.com/suxas/DMFL | null |
[
"3d",
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] | [
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] | [
"multimodal.3d-reconstruction",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-27T08:49:54Z | [ICLR 2026] Official implementation of "Splat and Distill: Augmenting Teachers with Feed-Forward 3D Reconstruction For 3D-Aware Distillation" | [
"3d",
"computer-vision",
"multimodal"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 0 | 1,143,169,880 | https://davidshavin4.github.io/Splat-and-Distill/ | Python | MIT | [
"3d-reconstruction",
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] | davidshavin4/Splat-and-Distill | [
"description",
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"query-match",
"repository-metadata"
] | 4 | 2026-10-07T18:07:52Z | [] | 2026-05-04T03:51:39Z | [
"multimodal.3d-reconstruction"
] | 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 | 30 | [] | 2026-09-16T20:24:39Z | https://github.com/davidshavin4/Splat-and-Distill | null |
[
"audio",
"general-ml",
"speech"
] | [
"neural-network",
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] | [
"description",
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"repository-metadata"
] | [
"cross26.audio-neural-vocoder",
"general.neural-network"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-28T08:39:49Z | [ICLR 2026] Official implementation of Toward Complex-Valued Neural Networks for Waveform Generation | [
"audio",
"speech"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 4 | 1,144,059,093 | https://hs-oh-prml.github.io/ComVo/ | Python | MIT | [
"neural-network",
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] | hs-oh-prml/ComVo | [
"description",
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"query-match",
"repository-metadata"
] | 4 | 2026-09-26T22:51:24Z | [] | 2026-04-10T06:13:13Z | [
"cross26.audio-neural-vocoder"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 21 | [] | 2026-09-21T12:56:28Z | https://github.com/hs-oh-prml/ComVo | null |
[
"health-and-biomedicine",
"multimodal-learning",
"natural-language-processing"
] | [] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"repository-metadata"
] | [] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-29T01:09:47Z | Official Code Repository of Clinical EEG Language Model (CELM): "Neural Signals Generate Clinical Notes in the Wild" NeurIPS 2026 | [
"health-and-biomedicine",
"multimodal-learning",
"natural-language-processing"
] | [
"deep-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T18:03:22.392067Z | false | 3 | 1,144,717,632 | https://arxiv.org/abs/2601.22197 | Python | MIT | [] | Jathurshan0330/CELM | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"repository-metadata"
] | 3 | 2026-10-02T18:08:49.004055Z | [] | 2026-09-17T15:54:42Z | [] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-context-only",
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] | include | ml-contribution-v5 | 18 | [
"multimodal-deep-learning"
] | 2026-09-28T16:30:57Z | https://github.com/Jathurshan0330/CELM | {"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["multimodal-deep-learning"]} |
[
"computer-vision",
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] | [
"diffusion",
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] | [
"description",
"query-match",
"repository-metadata"
] | [
"vision.image-restoration"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-29T06:57:04Z | Official code for ICML2026-Uncertainty-Aware Diffusion Bridge Model (UDBM). | [
"computer-vision",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-26T17:25:59Z | false | 1 | 1,144,891,704 | null | Python | null | [
"diffusion",
"restoration"
] | Jabruson/UDBM | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-26T17:25:59Z | [] | 2026-06-14T08:23:36Z | [
"vision.image-restoration"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 15 | [] | 2026-10-21T06:03:08Z | https://github.com/Jabruson/UDBM | null |
[
"computer-vision",
"language",
"machine-learning",
"multimodal",
"multimodal-learning",
"natural-language-processing",
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"vision"
] | [
"adaptation",
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] | [
"description",
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] | [
"multimodal.vision-language",
"multimodal.vlm",
"probable.adaptation-domain-generalization-readme",
"specialized.test-time-adaptation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-29T10:39:09Z | The official code for "Prototype-Based Test-Time Adaptation of Vision-Language Models" (ICML 2026) | [
"computer-vision",
"machine-learning",
"multimodal-learning",
"natural-language-processing",
"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T10:12:50Z | false | 0 | 1,145,031,528 | null | Python | null | [
"domain-generalization",
"test-time-adaptation",
"transfer-learning"
] | hzhxmu/PTA | [
"description",
"query-match",
"repository-metadata"
] | 9 | 2026-10-23T15:59:01Z | [] | 2026-05-02T10:14:06Z | [
"probable.adaptation-domain-generalization-readme"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 15 | [] | 2026-10-13T18:35:53Z | https://github.com/hzhxmu/PTA | null |
[
"earth-science",
"geospatial-science",
"remote-sensing",
"science-and-engineering",
"sequence-modeling",
"time-series"
] | [
"foundation-model",
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"sequence-modeling",
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] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"science.remote-sensing",
"timeseries.state-space-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-29T12:19:49Z | The official implementation of the paper “OSDMamba: Enhancing Oil Spill Detection from Remote Sensing Images Using Selective State Space Model”. | [
"earth-science",
"geospatial-science",
"remote-sensing",
"science-and-engineering"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 1,145,094,308 | null | null | MIT | [
"foundation-model",
"geospatial-learning",
"remote-sensing",
"state-space-model"
] | Chenshuaiyu1120/Oil-Spill-detection | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-26T17:25:59Z | [] | 2026-01-29T12:19:49Z | [
"science.remote-sensing"
] | 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 | [] | 2026-04-01T16:22:41Z | https://github.com/Chenshuaiyu1120/Oil-Spill-detection | null |
[
"earth-science",
"geospatial-science",
"remote-sensing",
"science-and-engineering"
] | [
"foundation-model",
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"mamba",
"remote-sensing"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"science.remote-sensing"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-29T12:23:53Z | The official implementation of the paper “MambaMPD: A Mamba-Driven Segmentation Framework for Marine Pollution Detection from Remote Sensing Imagery" | [
"earth-science",
"geospatial-science",
"remote-sensing",
"science-and-engineering"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-26T17:25:59Z | false | 0 | 1,145,097,041 | null | null | MIT | [
"foundation-model",
"geospatial-learning",
"mamba",
"remote-sensing"
] | Chenshuaiyu1120/MambaMPD | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-26T17:25:59Z | [] | 2026-03-23T16:26:47Z | [
"science.remote-sensing"
] | 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 | [] | 2026-04-01T16:22:40Z | https://github.com/Chenshuaiyu1120/MambaMPD | null |
[
"earth-science",
"geospatial-science",
"multimodal",
"multimodal-learning",
"remote-sensing",
"science-and-engineering"
] | [
"foundation-model",
"geospatial-learning",
"mamba",
"multimodal-learning",
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] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"recall.name.multimodal-model",
"science.remote-sensing"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-29T12:28:45Z | The official implementation of the paper “MambaMPD: A Mamba-Driven Segmentation Framework for Marine Pollution Detection from Remote Sensing Imagery" | [
"multimodal",
"multimodal-learning",
"science-and-engineering"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-19T16:36:56Z | false | 0 | 1,145,100,165 | null | Python | CC0-1.0 | [
"mamba",
"multimodal-learning"
] | Multimodal-Intelligence-Lab-MIL/MambaMPD | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-30T16:05:33Z | [] | 2026-10-09T21:36:09Z | [
"recall.name.multimodal-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 3 | [] | 2026-10-11T10:09:44Z | https://github.com/Multimodal-Intelligence-Lab-MIL/MambaMPD | null |
[
"earth-science",
"geospatial-science",
"multimodal-learning",
"remote-sensing",
"science-and-engineering",
"sequence-modeling",
"time-series"
] | [
"foundation-model",
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"sequence-modeling",
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] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"science.remote-sensing",
"timeseries.state-space-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-29T12:29:30Z | The official implementation of the paper “OSDMamba: Enhancing Oil Spill Detection from Remote Sensing Images Using Selective State Space Model”. | [
"earth-science",
"geospatial-science",
"multimodal-learning",
"remote-sensing",
"science-and-engineering"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 1,145,100,604 | null | Python | MIT | [
"foundation-model",
"geospatial-learning",
"remote-sensing",
"state-space-model"
] | Multimodal-Intelligence-Lab-MIL/OSDMamba | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-26T17:25:59Z | [] | 2026-02-06T12:50:55Z | [
"science.remote-sensing"
] | 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 | [] | 2026-09-24T09:19:41Z | https://github.com/Multimodal-Intelligence-Lab-MIL/OSDMamba | null |
[
"machine-learning",
"multimodal",
"multimodal-learning"
] | [
"fine-tuning",
"model-adaptation",
"multimodal-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"probable.adaptation-finetuning-readme",
"recall.name.multimodal-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-30T03:22:23Z | [ICML 2026] The official implementation of paper "Generation Enhances Understanding in Unified Multimodal Models via Multi-Representation Generation" | [
"machine-learning",
"multimodal-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-28T16:14:12Z | false | 8 | 1,145,645,695 | null | Python | null | [
"fine-tuning",
"model-adaptation"
] | Zihan-Su/UniMRG | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-29T16:41:07Z | [] | 2026-10-06T05:41:20Z | [
"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",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 101 | [] | 2026-10-11T02:34:01Z | https://github.com/Zihan-Su/UniMRG | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"vision"
] | [
"diffusion"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.diffusion",
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-30T05:10:23Z | Official implementation of "TransNormal: Dense Visual Semantics for Diffusion-based Transparent Object Normal Estimation" (ICML 2026). Single-step diffusion model for accurate surface normal prediction of transparent objects. | [
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 0 | 1,145,694,038 | https://longxiang-ai.github.io/TransNormal/ | Python | NOASSERTION | [
"diffusion"
] | longxiang-ai/TransNormal | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-10-28T17:27:44Z | [] | 2026-09-09T06:45:03Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 23 | [
"3d-perception",
"computer-vision",
"diffusion-model",
"dinov3",
"normal-estimation",
"surface-normal-estimation",
"transparent-objects"
] | 2026-09-11T04:31:44Z | https://github.com/longxiang-ai/TransNormal | null |
[
"deep-learning",
"efficient-ml",
"language"
] | [
"conditional-computation",
"mamba",
"mixture-of-experts",
"sparse-activation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.mixture-of-experts"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-30T08:57:58Z | The official implementation for the paper [TR-MoE: Mamba-based Temporal Reliability-Aware Mixture-of-Experts for Robust Tracking Fusion] | [
"deep-learning",
"efficient-ml",
"language"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 0 | 1,145,820,790 | null | null | null | [
"conditional-computation",
"mamba",
"mixture-of-experts",
"sparse-activation"
] | unknown5263/TR-MoE | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-02T16:59:40Z | [] | 2026-01-30T09:02:18Z | [
"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",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-01-30T09:02:21Z | https://github.com/unknown5263/TR-MoE | null |
[
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-30T13:35:49Z | Official implementation of the paper "TOKENTRIM: INFERENCE-TIME TOKEN PRUNING FOR AUTOREGRESSIVE LONG VIDEO GENERATION" | [
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T16:16:53Z | false | 0 | 1,145,992,667 | null | null | null | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | arielshaulov/TokenTrim | [
"description",
"query-match",
"repository-metadata"
] | 4 | 2026-10-31T16:49:26Z | [] | 2026-02-08T20:19:47Z | [
"efficiency.pruning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 15 | [] | 2026-03-17T14:51:40Z | https://github.com/arielshaulov/TokenTrim | null |
[
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"forecasting",
"multivariate-forecasting"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"timeseries.multivariate"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-30T14:55:30Z | The repo is the official implementation for the paper: PARCNet :Phase-Aware Residual Correction Network for Efficient Multivariate Time Series Forecasting | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-03T15:45:52Z | false | 1 | 1,146,046,337 | null | Python | MIT | [
"forecasting",
"multivariate-forecasting"
] | 9527dandelion/PARCNet | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-03T15:45:52Z | [] | 2026-01-30T15:00:31Z | [
"timeseries.multivariate"
] | 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 | [] | 2026-07-14T10:16:33Z | https://github.com/9527dandelion/PARCNet | null |
[
"foundation-models",
"generative-ai"
] | [
"multimodal",
"pretraining"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.foundation-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-30T19:37:49Z | Official code implementation of our paper CAT: Curvature-Adaptive Transformers for Geometry-Aware Learning (Non-Euclidean Foundation Models and Geometric Learning Workshop @ NeurIPS 2025). | [
"foundation-models",
"generative-ai"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-15T15:20:52Z | false | 0 | 1,146,224,318 | null | Python | null | [
"multimodal",
"pretraining"
] | smojha/curvature-adaptive-transformers | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-15T15:20:52Z | [] | 2026-01-30T19:41:21Z | [
"llm.foundation-models"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-01-30T19:41:25Z | https://github.com/smojha/curvature-adaptive-transformers | null |
[
"clinical-informatics",
"health-and-biomedicine"
] | [
"distillation",
"outcome-prediction",
"risk-prediction",
"survival-analysis"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"medical.survival-analysis"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-01-31T01:01:53Z | This repository contains the official implementation of the paper: “TGBD-Net: Tumor-Guided Joint Learning for EGFR Mutation Prediction and Survival Estimation in Lung CT via Bridging Distillation” (Submitted to MICCAI 2026) | [
"clinical-informatics",
"health-and-biomedicine"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-20T15:59:06Z | false | 0 | 1,146,357,514 | null | Python | null | [
"distillation",
"outcome-prediction",
"risk-prediction",
"survival-analysis"
] | MenchYoung/TGBD-Net | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-20T15:59:06Z | [] | 2026-05-24T02:30:44Z | [
"medical.survival-analysis"
] | 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 | [] | 2026-10-19T14:30:45Z | https://github.com/MenchYoung/TGBD-Net | null |
[
"earth-observation",
"general-ml",
"generative-ai",
"generative-modeling",
"geospatial",
"remote-sensing",
"science-and-engineering"
] | [
"deep-learning",
"diffusion",
"machine-learning",
"paper-implementation"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"general.arxiv",
"geo.topic-remote-sensing",
"recall.name.diffusion-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-01T14:30:04Z | 🤗 Official implementation for "CC-Pan: Channel-wise Compression based Diffusion for Efficient Pan-Sharpening" https://arxiv.org/abs/2602.04473 | [
"earth-observation",
"generative-modeling",
"geospatial",
"remote-sensing",
"science-and-engineering"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 24 | 1,147,297,776 | https://salad-pan.github.io | Python | MIT | [
"deep-learning",
"diffusion",
"machine-learning"
] | JJLibra/CC-Pan | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 9 | 2026-10-10T16:53:26Z | [] | 2026-05-21T12:29:21Z | [
"geo.topic-remote-sensing"
] | 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 | 247 | [
"fine-tuning",
"image-fusion",
"pansharpening",
"remote-sensing",
"stable-diffusion"
] | 2026-09-25T22:13:11Z | https://github.com/JJLibra/CC-Pan | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | true | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-01T21:17:41Z | Code for paper "EVODMs: variational learning of PDEs for stochastic systems via diffusion models with quantified epistemic uncertainty" | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-29T17:49:51Z | false | 0 | 1,147,510,095 | null | Jupyter Notebook | null | [
"diffusion"
] | celiareina/EVODMs | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-29T17:49:51Z | [] | 2026-02-04T19:53:05Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-07-06T03:08:58Z | https://github.com/celiareina/EVODMs | null |
[
"efficient-ml",
"generative-modeling",
"machine-learning-systems",
"model-compression"
] | [
"diffusion",
"quantization"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.quantization"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-02T02:37:42Z | PyTorch code for our paper "LSGQuant: Layer-Sensitivity Guided Quantization for One-Step Diffusion Real-World Video Super-Resolution" | [
"efficient-ml",
"generative-modeling",
"machine-learning-systems",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-25T15:11:36Z | false | 1 | 1,147,629,621 | null | Python | Apache-2.0 | [
"diffusion",
"quantization"
] | zhengchen1999/LSGQuant | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-28T17:27:44Z | [] | 2026-10-21T07:07:03Z | [
"efficiency.quantization"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 16 | [] | 2026-10-21T07:07:07Z | https://github.com/zhengchen1999/LSGQuant | null |
[
"general-ml",
"generative-ai",
"language",
"natural-language-processing"
] | [
"distillation",
"language-model",
"long-context-modeling",
"paper-implementation",
"reward-modeling"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"general.arxiv",
"growth26.llm-process-reward-model",
"llm.long-context"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-02T16:47:21Z | [NeurIPS'26] Official Implementation for AgentArk: Distilling Multi-Agent Intelligence into a Single LLM Agent(https://arxiv.org/abs/2602.03955) | [
"generative-ai",
"language",
"natural-language-processing"
] | [
"large-language-model",
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 30 | 1,148,164,419 | https://aifrontierlab.github.io/AgentArk/ | Python | Apache-2.0 | [
"distillation",
"language-model",
"long-context-modeling"
] | AIFrontierLab/AgentArk | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 4 | 2026-10-20T15:59:06Z | [] | 2026-05-09T18:45:07Z | [
"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",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 242 | [
"agent",
"ai",
"cmu",
"distillation",
"georgia-tech",
"llm",
"llm-agent",
"ml",
"multi-agent-systems",
"nlp"
] | 2026-10-19T02:48:28Z | https://github.com/AIFrontierLab/AgentArk | null |
[
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"forecasting",
"sequence-modeling"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"timeseries.forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-03T09:19:30Z | Official implementation for AAAI 2026 paper: FreDN: Frequency Domain Decomposition Network for Long-term Time Series Forecasting | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 0 | 1,148,707,618 | null | Python | MIT | [
"forecasting",
"sequence-modeling"
] | An-z-d/FreDN | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-30T17:16:29Z | [] | 2026-04-01T07:47:03Z | [
"timeseries.forecasting"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 15 | [] | 2026-10-21T03:11:02Z | https://github.com/An-z-d/FreDN | null |
[
"computer-vision",
"generative-ai",
"language",
"multimodal-learning",
"natural-language-processing"
] | [
"language-model",
"pretraining"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"llm.language-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-03T11:12:34Z | [ICLR 2026] Official code of PPE: Positional Preservation Embedding for Token Compression in Multimodal Large Language Models. | [
"computer-vision",
"generative-ai",
"language",
"multimodal-learning",
"natural-language-processing"
] | [
"large-language-model",
"embedding"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-22T16:09:07Z | false | 1 | 1,148,785,955 | https://arxiv.org/abs/2510.22936 | Python | Apache-2.0 | [
"language-model",
"pretraining"
] | MouxiaoHuang/PPE | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-10-22T16:09:07Z | [] | 2026-03-16T13:13:48Z | [
"llm.language-models"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 10 | [
"iclr2026",
"large-language-models",
"multimodal",
"positional-encoding",
"token-clustering",
"token-compression",
"token-merging",
"vision-language-model"
] | 2026-09-12T05:24:55Z | https://github.com/MouxiaoHuang/PPE | null |
[
"efficient-ml",
"machine-learning-systems",
"model-compression",
"recommender-systems"
] | [
"quantization"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.quantization"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-03T11:56:51Z | Official implementation of the paper "Rethinking Generative Recommender Tokenizer: Recsys-Native Encoding and Semantic Quantization Beyond LLMs" | [
"efficient-ml",
"machine-learning-systems",
"model-compression",
"recommender-systems"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T16:16:53Z | false | 5 | 1,148,815,224 | null | Python | MIT | [
"quantization"
] | FuCongResearchSquad/ReSID | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-28T17:27:44Z | [] | 2026-02-07T09:29:15Z | [
"efficiency.quantization"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 41 | [] | 2026-10-22T13:36:34Z | https://github.com/FuCongResearchSquad/ReSID | null |
[
"natural-language-processing",
"reinforcement-learning"
] | [
"machine-translation",
"neural-machine-translation",
"reinforcement-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"nlp.machine-translation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-03T12:21:42Z | This is the official implementation of the paper PEGRL: Improving Machine Translation by Post-Editing Guided Reinforcement Learning. | [
"natural-language-processing",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 1 | 1,148,832,119 | null | Python | MIT | [
"machine-translation",
"neural-machine-translation",
"reinforcement-learning"
] | NJUNLP/peg-rl | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-21T15:48:06Z | [] | 2026-04-17T10:13:06Z | [
"nlp.machine-translation"
] | 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 | [] | 2026-09-21T13:07:58Z | https://github.com/NJUNLP/peg-rl | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-04T04:49:02Z | AI pipeline that understands an image, rewrites it into creative prompts with LLMs, and generates new visuals using diffusion models. | [
"generative-ai",
"generative-modeling"
] | [
"large-language-model",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-29T17:49:51Z | false | 0 | 1,149,414,453 | null | Jupyter Notebook | null | [
"diffusion"
] | Damn-cod3r/image-to-imagination-ai | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-29T17:49:51Z | [] | 2026-02-04T04:52:56Z | [
"llm.diffusion-phrase"
] | 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 | [] | 2026-02-04T04:53:00Z | https://github.com/Damn-cod3r/image-to-imagination-ai | null |
[
"health-and-biomedicine",
"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 | [] | 2026-02-04T06:58:17Z | This repository contains the official reference implementation for the paper: Variational Inference for Pattern Extraction and Recognition in Genome Sequences using State Space Models for Cancer Detection | [
"health-and-biomedicine",
"sequence-modeling",
"time-series"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 0 | 1,149,485,592 | null | Python | null | [
"sequence-modeling",
"state-space-model"
] | SiddhanthBhat/viper-genomics | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2026-03-25T15:47:09Z | [
"timeseries.state-space-models"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2026-03-25T15:48:39Z | https://github.com/SiddhanthBhat/viper-genomics | null |
[
"computer-vision",
"document-understanding",
"information-retrieval",
"natural-language-processing"
] | [
"bm25",
"document-retrieval",
"information-retrieval",
"lexical-retrieval",
"ocr",
"rag"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"nlp-bm25-retrieval",
"nlp-document-retrieval",
"vision.ocr"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-04T07:14:53Z | [DeepRead] This is the official implementation of the DeepRead paper. | [
"information-retrieval"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 7 | 1,149,495,683 | https://arxiv.org/abs/2602.05014 | Python | Apache-2.0 | [
"bm25",
"lexical-retrieval",
"rag"
] | Zhanli-Li/DeepRead | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 6 | 2026-10-31T16:49:26Z | [] | 2026-06-03T08:26:40Z | [
"nlp-bm25-retrieval"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 64 | [
"agentic-rag",
"deepread",
"ocr",
"rag"
] | 2026-10-30T12:31:15Z | https://github.com/Zhanli-Li/DeepRead | null |
[
"computational-science",
"physics"
] | [
"neural-operator",
"neural-operators",
"surrogate-modeling"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"science.neural-operators"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-04T07:20:34Z | The official code for "Tucker-FNO: Tensor Tucker-Fourier Neural Operator and its Universal Approximation Theory" in ICLR 2026. | [
"computational-science",
"physics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 1 | 1,149,499,389 | null | Python | null | [
"neural-operator",
"neural-operators",
"surrogate-modeling"
] | GuanchengZhou/Tucker-FNO | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-09-26T14:50:46Z | [] | 2026-02-06T07:14:33Z | [
"science.neural-operators"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 6 | [] | 2026-07-23T18:59:58Z | https://github.com/GuanchengZhou/Tucker-FNO | null |
[
"generative-ai",
"language",
"natural-language-processing",
"time-series",
"time-series-and-forecasting"
] | [
"language-model",
"pretraining",
"sequence-modeling"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"llm.language-models",
"timeseries.topic"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-04T09:54:37Z | [ICLR 2026] Official implementation of SciTS: Scientific Time Series Understanding and Generation with LLMs | [
"natural-language-processing",
"time-series",
"time-series-and-forecasting"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T16:16:53Z | false | 0 | 1,149,601,440 | https://arxiv.org/abs/2510.03255 | Python | Apache-2.0 | [
"sequence-modeling"
] | OpenTSLab/TimeOmni | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 6 | 2026-10-31T16:49:26Z | [] | 2026-03-03T06:15:33Z | [
"timeseries.topic"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-context-only",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 19 | [
"large-language-models",
"scits",
"time-series",
"timeomni"
] | 2026-10-15T08:43:19Z | https://github.com/OpenTSLab/TimeOmni | null |
[
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"forecasting",
"multivariate-forecasting",
"sequence-modeling"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"timeseries.forecasting",
"timeseries.multivariate"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-04T10:07:57Z | [ICLR 2026] Official implementation for "Enhancing Multivariate Time Series Forecasting with Global Temporal Retrieval" | [
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 3 | 1,149,610,913 | null | Python | Apache-2.0 | [
"forecasting",
"sequence-modeling"
] | macovaseas/GTR | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 7 | 2026-10-30T17:16:29Z | [] | 2026-04-05T12:59:10Z | [
"timeseries.forecasting"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 29 | [] | 2026-10-25T06:33:46Z | https://github.com/macovaseas/GTR | null |
[
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-05T13:18:16Z | Official PyTorch implementation of the paper "Focus-Scan-Refine: From Human Visual Perception to Efficient Visual Token Pruning" | [
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-31T16:49:26Z | false | 0 | 1,150,596,303 | null | null | null | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | ILOT-code/FSR | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-31T16:49:26Z | [] | 2026-02-06T02:25:06Z | [
"efficiency.pruning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 4 | [] | 2026-08-26T16:19:33Z | https://github.com/ILOT-code/FSR | null |
[
"distributed-ml",
"privacy-and-federated-learning",
"privacy-ml"
] | [
"collaborative-learning",
"federated-learning",
"privacy-preserving-ml"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"growth26.privacy-vertical-federated",
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-07T06:42:42Z | [ICLR2026] Official implementation of the paper "Towards Privacy-Guaranteed Label Unlearning in Vertical Federated Learning: Few-Shot Forgetting Without Disclosure | [
"distributed-ml",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 0 | 1,151,971,604 | null | Python | NOASSERTION | [
"collaborative-learning",
"federated-learning"
] | bryanhx/Towards-Privacy-Guaranteed-Label-Unlearning-in-Vertical-Federated-Learning | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-21T15:48:06Z | [] | 2026-02-13T06:34:54Z | [
"trust.federated-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 8 | [] | 2026-08-13T10:03:19Z | https://github.com/bryanhx/Towards-Privacy-Guaranteed-Label-Unlearning-in-Vertical-Federated-Learning | null |
[
"deep-learning",
"efficient-ml",
"language",
"representation-learning"
] | [
"conditional-computation",
"continual-learning",
"lifelong-learning",
"mixture-of-experts",
"sparse-activation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.mixture-of-experts",
"general.continual-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-07T18:59:37Z | Official implementation of "One-Prompt Strikes Back: Sparse Mixture of Experts for Prompt-based Continual Learning " (ICLR 2026) | [
"deep-learning",
"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 2 | 1,152,360,328 | null | Python | MIT | [
"continual-learning",
"lifelong-learning",
"mixture-of-experts"
] | Minhchuyentoancbn/SMoPE | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 8 | 2026-10-29T17:49:51Z | [] | 2026-03-29T09:41:52Z | [
"general.continual-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 17 | [] | 2026-07-29T18:51:36Z | https://github.com/Minhchuyentoancbn/SMoPE | null |
[
"interpretability-and-safety",
"reinforcement-learning",
"safety"
] | [
"constrained-reinforcement-learning",
"reinforcement-learning",
"safe-reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"rl.safe"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-08T08:14:08Z | Official implementation and supplementary materials for the paper "Escaping the Alignment Trap: Physics-Guided Safe Reinforcement Learning for Power Grid Restoration". Featuring the Hybrid EV-CVaR objective and Broad Gini metric for safety-critical infrastructure control. | [
"interpretability-and-safety",
"reinforcement-learning",
"safety"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 0 | 1,152,662,495 | null | null | null | [
"constrained-reinforcement-learning",
"reinforcement-learning",
"safe-reinforcement-learning"
] | BaSO6-coder/Escaping-the-Alignment-Trap | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-09-26T14:34:36Z | [] | 2026-02-08T08:33:38Z | [
"rl.safe"
] | 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 | [] | 2026-02-08T08:33:41Z | https://github.com/BaSO6-coder/Escaping-the-Alignment-Trap | null |
[
"deep-learning",
"efficient-ml",
"graph-learning",
"language",
"natural-language-processing",
"recommender-systems"
] | [
"conditional-computation",
"mixture-of-experts",
"retrieval-augmented-generation",
"sparse-activation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.mixture-of-experts"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-08T18:47:53Z | This is the pytorch implementation for our KDD paper "Mixture-of-Experts Knowledge Graph Retrieval-Augmented Generation for Multi-Agent LLM-based Recommendation". | [
"deep-learning",
"efficient-ml",
"graph-learning",
"language",
"natural-language-processing",
"recommender-systems"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 0 | 1,153,000,290 | null | Python | null | [
"conditional-computation",
"mixture-of-experts",
"retrieval-augmented-generation",
"sparse-activation"
] | Sjay-Wang/MixRAGRec | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-10-01T17:50:46Z | [] | 2026-08-05T17:14:35Z | [
"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",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 10 | [] | 2026-09-22T02:30:00Z | https://github.com/Sjay-Wang/MixRAGRec | null |
[
"foundation-models",
"generative-ai"
] | [
"multimodal",
"pretraining"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.foundation-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-09T03:50:46Z | Official Code Repository for the ICLR 2026 Paper "Human Behavior Atlas: Benchmarking Unified Psychological and Social Behavior Understanding" & ICML 2026 Paper "Omnisapiens: A foundation model for social behavior processing via heterogeneity-aware relative policy optimization" | [
"foundation-models",
"generative-ai"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-13T18:05:26Z | false | 4 | 1,153,225,131 | null | Python | null | [
"multimodal",
"pretraining"
] | MIT-MI/human_behavior_atlas | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-13T18:05:26Z | [] | 2026-07-21T11:38:32Z | [
"llm.foundation-models"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 41 | [] | 2026-10-12T13:49:52Z | https://github.com/MIT-MI/human_behavior_atlas | null |
[
"deep-learning",
"distributed-ml",
"natural-language-processing",
"privacy-and-federated-learning",
"representation-learning"
] | [
"collaborative-learning",
"continual-learning",
"federated-learning",
"lifelong-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.continual-learning",
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-09T05:01:50Z | The official implementation of the paper "Helmsman: Autonomous Synthesis of Federated Learning Systems via Collaborative LLM Agents", published at ICLR 2026. | [
"deep-learning",
"natural-language-processing",
"privacy-and-federated-learning",
"representation-learning"
] | [
"large-language-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 2 | 1,153,259,005 | https://haoyuan-l.github.io/helmsman-web/ | Python | Apache-2.0 | [
"continual-learning",
"federated-learning",
"lifelong-learning"
] | haoyuan-l/Helmsman | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-30T17:16:29Z | [] | 2026-08-12T17:23:41Z | [
"general.continual-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 11 | [] | 2026-10-08T06:02:45Z | https://github.com/haoyuan-l/Helmsman | null |
[
"geospatial",
"remote-sensing",
"satellite-imagery",
"sequence-modeling",
"time-series"
] | [
"deep-learning",
"image-analysis",
"object-detection",
"sequence-modeling",
"state-space-model"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"geo.object-detection",
"timeseries.state-space-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-09T06:32:47Z | Official PyTorch implementation of “Enabling True Global Perception in State Space Models for Visual Tasks” (ICLR 2026). | [
"geospatial",
"remote-sensing",
"satellite-imagery"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 1 | 1,153,308,692 | null | Python | null | [
"deep-learning",
"image-analysis",
"object-detection"
] | Xinmu-Tantai/GMamba-GSSM | [
"description",
"query-match",
"repository-metadata"
] | 5 | 2026-10-09T17:31:36Z | [] | 2026-02-13T17:19:41Z | [
"geo.object-detection"
] | 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 | 12 | [] | 2026-09-02T11:36:05Z | https://github.com/Xinmu-Tantai/GMamba-GSSM | null |
[
"multimodal",
"natural-language-processing",
"reinforcement-learning",
"robotics"
] | [
"environment-modeling",
"world-model"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.world-models"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-09T13:34:00Z | Official code for the AAMAS 2026 paper "Translating Latent State World Model Representations into Natural Language" | [
"multimodal",
"natural-language-processing",
"reinforcement-learning",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 1,153,594,130 | null | Python | MIT | [
"environment-modeling",
"world-model"
] | m-barker/Translating-Latent-State-World-Model-Representations-into-Natural-Language-AAMAS-2026 | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-04T15:58:58Z | [] | 2026-02-13T11:57:17Z | [
"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 | 0 | [] | 2026-02-13T11:57:21Z | https://github.com/m-barker/Translating-Latent-State-World-Model-Representations-into-Natural-Language-AAMAS-2026 | null |
[
"embodied-ai",
"reinforcement-learning",
"robotics",
"robotics-and-control"
] | [
"manipulation",
"reinforcement-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"robotics.manipulation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-09T14:26:11Z | Official code of our paper "RM-RL: Role-Model Reinforcement Learning for Precise Robot Manipulation" (ICRA 2026) | [
"embodied-ai",
"reinforcement-learning",
"robotics",
"robotics-and-control"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 0 | 1,153,636,139 | null | Jupyter Notebook | MIT | [
"manipulation",
"reinforcement-learning"
] | NTUMARS/RMRL | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-02T16:59:40Z | [] | 2026-02-09T14:41:47Z | [
"robotics.manipulation"
] | 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 | 10 | [] | 2026-05-22T06:34:00Z | https://github.com/NTUMARS/RMRL | null |
[
"deep-learning",
"efficient-ml",
"language",
"natural-language-processing"
] | [
"conditional-computation",
"mixture-of-experts",
"sparse-activation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.mixture-of-experts"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-09T15:14:34Z | [ICLR'26] Official code for TD-MoE: tensor decomposition for compressing Mixture-of-Experts language models. | [
"deep-learning",
"efficient-ml",
"language",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 4 | 1,153,676,288 | null | Python | MIT | [
"conditional-computation",
"mixture-of-experts",
"sparse-activation"
] | ust-xu/TD-MoE | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-01T17:50:46Z | [] | 2026-04-23T13:19:31Z | [
"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 | 13 | [] | 2026-09-21T07:01:53Z | https://github.com/ust-xu/TD-MoE | null |
[
"graph-learning",
"time-series",
"time-series-and-forecasting"
] | [
"dynamic-graph-learning",
"neural-network",
"temporal-graph-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.temporal-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-09T15:35:32Z | Source code to our published paper which uses AI and deep learning model for wind power forecast. | [
"graph-learning",
"time-series",
"time-series-and-forecasting"
] | [
"deep-learning",
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-16T15:36:56Z | false | 0 | 1,153,693,231 | null | Python | null | [
"dynamic-graph-learning",
"neural-network",
"temporal-graph-learning"
] | Duntem/Wind-Power-Forecasting-Using-Multi-level-Adaptive-Graph-Convolution-Neural-Network- | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-16T15:36:56Z | [] | 2026-02-09T15:39:14Z | [
"graph.temporal-learning"
] | 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 | 2 | [] | 2026-02-19T15:25:55Z | https://github.com/Duntem/Wind-Power-Forecasting-Using-Multi-level-Adaptive-Graph-Convolution-Neural-Network- | null |
[
"geospatial",
"hydrology",
"meteorology"
] | [
"forecasting",
"scientific-machine-learning",
"spatiotemporal-modeling"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"geo.precipitation-forecasting"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-11T09:40:35Z | [ICLR 2026] Official implementation of "RainPro-8: An Efficient Deep Learning Model to Estimate Rainfall Probabilities Over 8 Hours" paper | [
"geospatial",
"hydrology",
"meteorology"
] | [
"deep-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 1 | 1,155,243,854 | null | Python | null | [
"forecasting",
"scientific-machine-learning",
"spatiotemporal-modeling"
] | rafapablos/RainPro | [
"description",
"query-match",
"repository-metadata"
] | 11 | 2026-10-03T15:45:52Z | [] | 2026-04-11T13:21:08Z | [
"geo.precipitation-forecasting"
] | 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 | 9 | [] | 2026-09-26T05:25:17Z | https://github.com/rafapablos/RainPro | null |
[
"computer-vision",
"efficient-ml",
"health-and-biomedicine",
"medical-imaging",
"model-compression"
] | [
"distillation",
"knowledge-distillation",
"segmentation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation",
"medical.medical-image-segmentation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-11T10:44:59Z | Official implementation of the IEEE TIM paper: Text-Driven Medical Image Segmentation With Text-Free Inference via Knowledge Distillation. | [
"computer-vision",
"efficient-ml",
"health-and-biomedicine",
"medical-imaging",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 0 | 1,155,289,370 | null | Python | null | [
"distillation",
"knowledge-distillation",
"segmentation"
] | Studentpengyu/TextFreeKD-TIM | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-08T18:15:14Z | [] | 2026-02-11T13:37:48Z | [
"efficiency.knowledge-distillation",
"medical.medical-image-segmentation"
] | 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 | [] | 2026-06-30T07:04:15Z | https://github.com/Studentpengyu/TextFreeKD-TIM | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-11T12:37:58Z | Official implementation of the paper "Guidance Watermarking for Diffusion Models" | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-28T17:27:44Z | false | 0 | 1,155,366,401 | null | Python | null | [
"diffusion"
] | EnoalG/Guidance-Watermarking-for-Diffusion-Models | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-28T17:27:44Z | [] | 2026-04-25T17:37:30Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 3 | [] | 2026-08-12T13:09:31Z | https://github.com/EnoalG/Guidance-Watermarking-for-Diffusion-Models | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"information-retrieval",
"multimodal",
"vision"
] | [
"diffusion",
"image-generation",
"image-retrieval",
"metric-learning",
"text-to-image"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"multimodal.text-to-image",
"vision.image-retrieval"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-11T17:00:06Z | Official PyTorch implementation for paper: ADaFuSE: Adaptive Diffusion-generated Image and Text Fusion for Interactive Text-to-Image Retrieval | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 0 | 1,155,574,036 | null | Python | null | [
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] | 3 | 2026-10-16T15:36:56Z | [] | 2026-02-13T19:17:16Z | [
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] | include | ml-contribution-v5 | 5 | [] | 2026-03-26T03:12:14Z | https://github.com/longkukuhi/ADaFuSE | null |
[
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] | [
"multimodal.vision-language",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-12T11:22:27Z | [ICML 2026] Official implementation of HALO: A Unified Vision-Language-Action Model for Embodied Multimodal Chain-of-Thought Reasoning. | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T16:16:53Z | false | 2 | 1,156,195,728 | null | Python | MIT | [
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] | qshou-coder/HALO | [
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] | 5 | 2026-10-16T15:36:56Z | [] | 2026-06-18T04:03:06Z | [
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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 | 17 | [] | 2026-10-12T03:41:25Z | https://github.com/qshou-coder/HALO | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-13T10:01:07Z | This repository contains the code from our paper "A Stochastic Gradient Descent Approach to Design Policy Gradient Methods for LQR" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 1,156,999,830 | null | MATLAB | MIT | [
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] | col-tasas/2026-SGDLQR | [
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] | 1 | 2026-10-04T15:58:58Z | [] | 2026-02-20T16:59:01Z | [
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] | include | ml-contribution-v5 | 0 | [] | 2026-02-21T13:46:18Z | https://github.com/col-tasas/2026-SGDLQR | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-14T07:49:22Z | This repository is the official code implementation for the paper "GREPO: A Benchmark for Graph Neural Networks on Repository-Level Bug Localization". | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 1 | 1,157,731,679 | null | Python | MIT | [
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] | 3 | 2026-10-20T15:59:06Z | [] | 2026-04-11T10:54:39Z | [
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] | include | ml-contribution-v5 | 7 | [] | 2026-10-04T15:03:23Z | https://github.com/qingpingmo/GREPO | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-14T14:35:47Z | Official Implementation of "An Iterative LLM Framework for SIBT utilizing RAG-based Adaptive Weight Optimization" arXiv:2509.08407 | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-30T16:05:33Z | false | 0 | 1,157,934,127 | null | Python | null | [
"rag",
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] | BUAAXZzz/LLMAutoPlan | [
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] | 2 | 2026-11-01T16:11:12Z | [] | 2026-10-11T02:40:47Z | [
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] | include | ml-contribution-v5 | 1 | [] | 2026-10-21T16:35:45Z | https://github.com/BUAAXZzz/LLMAutoPlan | null |
[
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] | [
"efficiency.speculative-decoding",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-15T11:00:26Z | Official implementation of "Learning To Draft: Adaptive Speculative Decoding with Reinforcement Learning" (ICLR 2026) | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 8 | 1,158,426,859 | null | Python | Apache-2.0 | [
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] | zhzihao/Learning-to-Draft | [
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] | 14 | 2026-10-13T18:05:26Z | [] | 2026-03-01T13:55:44Z | [
"llm.speculative-decoding"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 23 | [] | 2026-09-02T02:52:29Z | https://github.com/zhzihao/Learning-to-Draft | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-18T11:10:41Z | [NeurIPS 2026] Official implementation of "Multivariate Time Series Forecasting needs Cross Variable Loss" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T09:41:18Z | false | 0 | 1,160,779,830 | null | Python | null | [
"forecasting",
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] | Day333/CvLoss | [
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] | 4 | 2026-10-30T17:16:29Z | [] | 2026-10-27T03:56:42Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 10 | [] | 2026-10-27T03:56:45Z | https://github.com/Day333/CvLoss | null |
[
"deep-learning",
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] | [
"efficiency.mixture-of-experts",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-18T11:34:24Z | code for paper： Mixture-of-experts based Multi-Critic Deep Reinforcement Learning for Sustainable Management of Data Center Microgrids | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 6 | 1,160,795,479 | null | Python | null | [
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] | ikelq/Mixture-of-experts-based-Multi-Critic-DRL-for-Sustainable-Management-of-Data-Center-Microgrids | [
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] | 9 | 2026-10-30T17:16:29Z | [] | 2026-09-30T02:22:22Z | [
"rl.deep"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 24 | [] | 2026-10-21T08:34:09Z | https://github.com/ikelq/Mixture-of-experts-based-Multi-Critic-DRL-for-Sustainable-Management-of-Data-Center-Microgrids | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-19T15:12:41Z | [NeurIPS 2026] Official implementation of "TMPO: Trajectory Matching Policy Optimization for Diverse and Efficient Diffusion Alignment" | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-01T16:56:07Z | false | 0 | 1,161,832,315 | https://mair-lab-hust.github.io/TMPO/ | Python | null | [
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] | MAIR-Lab-HUST/TMPO | [
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] | 6 | 2026-10-09T16:23:55Z | [] | 2026-09-25T04:19:18Z | [
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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 | 19 | [
"diffusion-rl",
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] | 2026-09-25T04:19:22Z | https://github.com/MAIR-Lab-HUST/TMPO | null |
[
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] | [
"efficiency.pruning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-19T17:43:36Z | Official code for our paper "Sink-Aware Pruning for Diffusion Language Models" | [
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"generative-modeling",
"machine-learning-systems",
"model-compression",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T16:16:53Z | false | 1 | 1,161,946,274 | https://www.arxiv.org/abs/2602.17664 | Python | MIT | [
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] | VILA-Lab/Sink-Aware-Pruning | [
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] | 4 | 2026-10-31T16:49:26Z | [] | 2026-02-26T10:35:08Z | [
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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 | 13 | [] | 2026-09-27T13:04:41Z | https://github.com/VILA-Lab/Sink-Aware-Pruning | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-19T23:18:31Z | Code for paper "Optimizing corridor-level transit efficiency: multi-agent reinforcement learning with multi-discrete actions leveraging connected vehicle data for transit priority". | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 0 | 1,162,146,989 | https://doi.org/10.1016/j.ijtst.2025.07.010 | Python | null | [
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] | ytj254/TSP-MAPPO | [
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] | 1 | 2026-10-05T20:10:04Z | [] | 2026-02-19T23:41:00Z | [
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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 | [] | 2026-02-19T23:41:03Z | https://github.com/ytj254/TSP-MAPPO | null |
[
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-20T01:06:34Z | [ICLR 2026] Official Implementation of paper "Pixel-Level Residual Diffusion Transformer: Scalable 3D CT Volume Generation" | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-22T14:58:59Z | false | 1 | 1,162,195,572 | null | Python | Apache-2.0 | [
"diffusion",
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] | Fredy-Zhang/PRDiT | [
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"query-match",
"repository-metadata"
] | 2 | 2026-10-25T14:09:27Z | [] | 2026-10-06T23:09:27Z | [
"recall.name.diffusion-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 11 | [] | 2026-10-06T23:09:31Z | https://github.com/Fredy-Zhang/PRDiT | null |
[
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] | [
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] | [
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"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-20T01:57:45Z | [AAAI 2025] Official Implementation of paper "TCAM-Diff: Triplane-Aware Cross-Attention Medical Diffusion Model" | [
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"generative-modeling"
] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-28T17:27:44Z | false | 0 | 1,162,217,994 | null | null | Apache-2.0 | [
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] | Fredy-Zhang/TCAM-Diff | [
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] | 1 | 2026-10-28T17:27:44Z | [] | 2026-04-08T02:37:00Z | [
"llm.diffusion-phrase"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 3 | [] | 2026-04-08T02:37:08Z | https://github.com/Fredy-Zhang/TCAM-Diff | null |
[
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] | [
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] | [
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] | [
"efficiency.mixture-of-experts"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-20T05:05:30Z | [CVPR 2026] This is the official PyTorch implementation of "MoDES: Accelerating Mixture-of-Experts Multimodal Large Language Models via Dynamic Expert Skipping". | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 0 | 1,162,302,708 | https://arxiv.org/abs/2511.15690 | Python | Apache-2.0 | [
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] | ModelTC/MoDES | [
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] | 3 | 2026-10-01T17:50:46Z | [] | 2026-03-16T08:47:15Z | [
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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 | 34 | [
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"moe",
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] | 2026-09-23T01:29:50Z | https://github.com/ModelTC/MoDES | null |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-20T09:45:25Z | Official implementation of MMPD: Diverse Time Series Forecasting via Multi-Mode Patch Diffusion Loss (ICLR 2026). | [
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"time-series",
"time-series-and-forecasting"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 2 | 1,162,463,346 | null | Python | null | [
"diffusion",
"sequence-modeling"
] | Thinklab-SJTU/MMPD | [
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] | 8 | 2026-10-31T16:49:26Z | [] | 2026-04-03T08:19:31Z | [
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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 | 18 | [
"diffusion-models",
"loss-functions",
"time-series",
"time-series-forecasting"
] | 2026-10-17T12:09:27Z | https://github.com/Thinklab-SJTU/MMPD | null |
[
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-21T10:47:51Z | Official implementation for the CVPR2026 paper MaskDiME: Adaptive Masked Diffusion for Precise and Efficient Visual Counterfactual Explanations | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 0 | 1,163,256,440 | null | Python | MIT | [
"diffusion"
] | clguo/MaskDiME | [
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"repository-metadata"
] | 5 | 2026-10-25T14:09:27Z | [] | 2026-10-06T14:45:55Z | [
"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 | 4 | [] | 2026-10-11T08:57:58Z | https://github.com/clguo/MaskDiME | null |
[
"generative-modeling"
] | [
"flow-matching"
] | [
"description",
"github-topics",
"license-metadata",
"repository-metadata"
] | [] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-21T18:53:07Z | [ICLR 2026] Official Code of: "FlowCast: Advancing Precipitation Nowcasting with Conditional Flow Matching" | [
"generative-modeling"
] | [
"generative-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T18:03:22.392067Z | false | 2 | 1,163,519,050 | null | Python | Apache-2.0 | [
"flow-matching"
] | b-rbmp/FlowCast | [
"description",
"github-topics",
"license-metadata",
"repository-metadata"
] | 1 | 2026-09-30T18:03:22.392067Z | [] | 2026-08-03T20:29:10Z | [] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 26 | [
"conditional-flow-matching",
"flow-matching",
"generative-model",
"generative-models",
"nowcasting",
"precipitation-nowcasting"
] | 2026-09-17T07:16:00Z | https://github.com/b-rbmp/FlowCast | {"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["flow-matching"]} |
[
"efficient-ml",
"generative-ai",
"generative-modeling",
"machine-learning-systems",
"model-compression"
] | [
"diffusion",
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"pruning",
"structured-pruning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning",
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-22T02:30:23Z | Official implementation of CVPR 2026 "Roots Beneath the Cut: Uncovering the Risk of Concept Revival in Pruning-Based Unlearning for Diffusion Models" | [
"efficient-ml",
"generative-modeling",
"machine-learning-systems",
"model-compression"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-28T17:27:44Z | false | 0 | 1,163,704,589 | null | Python | MIT | [
"diffusion",
"neural-network-pruning",
"pruning",
"structured-pruning"
] | Brankozz/Roots-Beneath-the-Cut | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-31T16:49:26Z | [] | 2026-06-16T18:59:29Z | [
"efficiency.pruning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 8 | [] | 2026-09-21T13:52:56Z | https://github.com/Brankozz/Roots-Beneath-the-Cut | null |
[
"continual-learning",
"deep-learning",
"reinforcement-learning",
"representation-learning"
] | [
"continual-learning",
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"reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.continual-learning",
"rl.continual"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-22T02:38:31Z | Official Implementation of Principled Fast and Meta Knowledge Learners for Continual Reinforcement Learning (ICLR 2026) | [
"deep-learning",
"reinforcement-learning",
"representation-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T13:58:11Z | false | 3 | 1,163,707,488 | null | Python | null | [
"continual-learning",
"lifelong-learning",
"reinforcement-learning"
] | datake/FAME | [
"description",
"query-match",
"repository-metadata"
] | 6 | 2026-10-29T17:49:51Z | [] | 2026-03-01T02:16:44Z | [
"general.continual-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 14 | [] | 2026-09-20T08:08:59Z | https://github.com/datake/FAME | null |
[
"computer-vision",
"deep-learning",
"efficient-ml",
"health-and-biomedicine",
"language",
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] | [
"conditional-computation",
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"sparse-activation"
] | [
"description",
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"query-match",
"repository-metadata"
] | [
"efficiency.mixture-of-experts",
"medical.medical-image-segmentation",
"medical.medical-imaging",
"vision.segmentation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-22T13:22:02Z | Official implementation of SegMoTE: Token-Level Mixture of Experts for Medical Image Segmentation (CVPR 2026) | [
"computer-vision",
"health-and-biomedicine",
"medical-imaging"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 0 | 1,163,988,999 | null | Python | MIT | [
"mixture-of-experts",
"segmentation"
] | InMyDreammer/SegMoTE | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-10-06T17:10:23Z | [] | 2026-06-02T11:42:59Z | [
"medical.medical-image-segmentation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 29 | [
"segmote"
] | 2026-08-30T10:32:26Z | https://github.com/InMyDreammer/SegMoTE | null |
[
"computer-vision",
"embodied-ai",
"language",
"multimodal",
"multimodal-learning",
"robotics",
"robotics-and-control",
"vision"
] | [
"contrastive-learning",
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"vision-language-action",
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] | [
"description",
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"query-match",
"repository-metadata"
] | [
"multimodal.vision-language",
"robotics.manipulation",
"robotics.visual-language-action"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-22T13:57:07Z | [CVPR 2026] Official Implementation for Global Prior Meets Local Consistency: Dual-Memory Augmented Vision-Language-Action Model for Efficient Robotic Manipulation | [
"computer-vision",
"language",
"multimodal",
"multimodal-learning",
"robotics-and-control",
"vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:59:41Z | false | 1 | 1,164,008,682 | null | Python | MIT | [
"contrastive-learning",
"vision-language-model"
] | iLearn-Lab/CVPR26-OptimusVLA | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-16T15:36:56Z | [] | 2026-07-16T06:56:10Z | [
"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 | 35 | [] | 2026-10-15T13:00:46Z | https://github.com/iLearn-Lab/CVPR26-OptimusVLA | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.diffusion-phrase"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2026-02-22T18:58:00Z | This repository is a project to generate new data using diffusion model. | [
"generative-ai",
"generative-modeling"
] | [
"diffusion-model"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-29T17:49:51Z | false | 0 | 1,164,181,061 | null | Jupyter Notebook | null | [
"diffusion"
] | prakhosha/Diffusion-Model-for-Data-Generation | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-29T17:49:51Z | [] | 2026-02-25T07:44:04Z | [
"llm.diffusion-phrase"
] | 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 | [] | 2026-02-25T07:44:08Z | https://github.com/prakhosha/Diffusion-Model-for-Data-Generation | null |
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