all_domains list | all_methods list | all_novelty_signals list | all_query_ids list | archived bool | candidate_status string | candidate_rule_version string | candidate_eligible bool | candidate_reason string | candidate_evidence list | created_at string | description string | domains list | evidence_signals list | evidence_tier string | evidence_version string | first_observed_at string | fork bool | forks int64 | github_id int64 | homepage string | language string | license string | methods list | name string | novelty_signals list | observation_count int64 | observed_at string | paper_ids list | pushed_at string | query_ids list | readme_blob_sha string | readme_checked_at string | readme_evidence_version string | readme_etag string | readme_sections list | readme_signals list | readme_status string | readme_observed_at string | readme_repository_name_at_fetch string | selection_reason string | selection_signals list | selection_status string | selection_version string | stars int64 | topics list | updated_at string | url string | extra_json string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
[
"deep-learning",
"information-retrieval",
"language",
"multimodal",
"natural-language-processing",
"representation-learning",
"vision"
] | [
"question-answering",
"reading-comprehension",
"representation-learning",
"self-supervised-learning",
"vision-language-model",
"visual-question-answering"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.self-supervised-learning",
"multimodal.visual-question-answering",
"nlp.question-answering"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-04-29T10:30:11Z | Code for our IJCAI2020 paper: Overcoming Language Priors with Self-supervised Learning for Visual Question Answering | [
"information-retrieval",
"natural-language-processing"
] | [
"self-supervised-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 11 | 259,895,108 | null | Python | null | [
"question-answering",
"reading-comprehension",
"self-supervised-learning"
] | CrossmodalGroup/SSL-VQA | [
"description",
"query-match",
"repository-metadata"
] | 9 | 2026-10-14T16:10:46Z | [] | 2020-08-21T08:49:44Z | [
"nlp.question-answering"
] | 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 | 52 | [] | 2025-08-29T11:01:30Z | https://github.com/CrossmodalGroup/SSL-VQA | null |
[
"linguistics",
"natural-language-processing",
"tabular-and-structured-data"
] | [
"language-modeling",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"recall.computational-linguistics"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-05-01T18:06:27Z | Implementation of the semi-structured inference model in our ACL 2020 paper, INFOTABS: Inference on Tables as Semi-structured Data. | [
"linguistics",
"natural-language-processing",
"tabular-and-structured-data"
] | [
"machine-learning",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-15T15:20:52Z | false | 9 | 260,525,805 | https://infotabs.github.io/ | Python | Apache-2.0 | [
"language-modeling",
"transformer"
] | utahnlp/infotabs-code | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-15T15:20:52Z | [] | 2021-12-07T17:08:13Z | [
"recall.computational-linguistics"
] | 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 | 17 | [
"acl2020",
"inference",
"infotabs",
"nli",
"nlp",
"nlp-datasets",
"nlp-machine-learning",
"roberta",
"semi-structured-data",
"svm",
"tables",
"transformer",
"wikipedia"
] | 2026-03-20T13:17:31Z | https://github.com/utahnlp/infotabs-code | null |
[
"automl",
"general-ml"
] | [
"few-shot-learning",
"meta-learning",
"novel-method"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.meta-learning",
"general.novel-method"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-05-02T02:54:05Z | Useful implementation of Meta-Learning models in Pytorch Novel method to use the soft-parameter-sharing scheme on MAML to keep hard-learned knowledge in a non end-to-end fashion | [
"general-ml"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 0 | 260,602,105 | null | Jupyter Notebook | null | [
"meta-learning",
"novel-method"
] | domzhaomathematics/Meta-Learning | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-10-14T15:56:14Z | [] | 2020-06-11T15:55:42Z | [
"general.novel-method"
] | 01d9883c86021eb9fb536f6f2bc253dadb283c2d | 2026-09-27T17:16:20Z | gh-ml-readme-evidence-v2 | "01d9883c86021eb9fb536f6f2bc253dadb283c2d" | [
"other"
] | [
"method-contribution",
"ml-method-context"
] | ok | 2026-09-27T17:16:20Z | domzhaomathematics/Meta-Learning | specific-method-with-novelty-claim | [
"contribution-language",
"method-contribution",
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue"
] | include | ml-contribution-v5 | 3 | [] | 2026-05-28T20:19:58Z | https://github.com/domzhaomathematics/Meta-Learning | null |
[
"efficient-ml",
"model-compression",
"speech-and-audio"
] | [
"distillation",
"knowledge-distillation"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-05-10T12:14:37Z | [INTERSPEECH 2021] Official Keras Implementation of "Knowledge Distillation for Singing Voice Detection" | [
"efficient-ml",
"model-compression",
"speech-and-audio"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T20:57:28.489717Z | false | 1 | 262,779,806 | null | Python | MIT | [
"distillation",
"knowledge-distillation"
] | mvp18/KD-SVD | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-09-28T18:50:00Z | [] | 2021-09-02T21:37:38Z | [
"efficiency.knowledge-distillation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 1 | [
"ensemble-knowledge-distillation",
"knowledge-distillation",
"melody-extraction",
"singing-voice-detection"
] | 2022-07-20T06:54:12Z | https://github.com/mvp18/KD-SVD | null |
[
"automl",
"computer-vision",
"general-ml"
] | [
"few-shot-learning",
"meta-learning"
] | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"general.meta-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-05-13T00:47:52Z | Official code for the paper "Task2Vec: Task Embedding for Meta-Learning" (https://arxiv.org/abs/1902.03545, ICCV 2019) | [
"automl",
"computer-vision",
"general-ml"
] | [
"embedding"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-24T14:28:44Z | false | 31 | 263,487,494 | null | Python | Apache-2.0 | [
"few-shot-learning",
"meta-learning"
] | awslabs/aws-cv-task2vec | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 4 | 2026-10-14T15:56:14Z | [] | 2023-07-13T20:29:52Z | [
"general.meta-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 | 126 | [] | 2026-03-19T11:33:45Z | https://github.com/awslabs/aws-cv-task2vec | null |
[
"automl",
"computer-vision",
"general-ml",
"reinforcement-learning"
] | [
"few-shot-learning",
"meta-learning",
"policy-learning",
"reinforcement-learning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.meta-learning",
"rl.core"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-05-13T13:12:01Z | Source code for CVPR 2020 paper "Learning to Forget for Meta-Learning" | [
"computer-vision",
"reinforcement-learning"
] | [
"deep-learning",
"machine-learning",
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T14:28:44Z | false | 5 | 263,633,646 | https://github.com/baiksung/L2F | Python | MIT | [
"meta-learning",
"policy-learning",
"reinforcement-learning"
] | baiksung/L2F | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-10-19T17:46:36Z | [] | 2020-10-26T03:54:45Z | [
"rl.core"
] | 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 | 37 | [
"computer-vision",
"cvpr2020",
"deep-learning",
"few-shot-learning",
"machine-learning",
"maml",
"meta-learning",
"pytorch",
"reinforcement-learning"
] | 2026-07-25T08:18:20Z | https://github.com/baiksung/L2F | null |
[
"distributed-ml",
"efficient-ml",
"model-compression",
"privacy-and-federated-learning"
] | [
"collaborative-learning",
"federated-learning",
"quantization"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.quantization",
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-05-13T20:12:39Z | Python code for paper "Lazily Aggregated Quantized Gradient Innovation for Communication-Efficient Federated Learning" | [
"efficient-ml",
"model-compression",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 2 | 263,733,153 | null | null | null | [
"federated-learning",
"quantization"
] | sunjunaimer/TPAMI-LAQ | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-03T15:45:52Z | [] | 2024-01-20T03:03:45Z | [
"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 | 9 | [] | 2025-11-22T08:20:12Z | https://github.com/sunjunaimer/TPAMI-LAQ | null |
[
"autonomous-driving",
"reinforcement-learning",
"robotics",
"robotics-and-control"
] | [
"perception",
"planning",
"policy-learning",
"reinforcement-learning"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"rl.core",
"robotics.autonomous-driving"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-05-21T03:53:25Z | Source code for paper "Safe, Efficient, and Comfortable Velocity Control based on Reinforcement Learning for Autonomous Driving" | [
"reinforcement-learning",
"robotics-and-control"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T14:50:46Z | false | 27 | 265,749,415 | https://scholar.google.com/citations?user=5Ysgg7AAAAAJ&hl=en | Jupyter Notebook | null | [
"policy-learning",
"reinforcement-learning"
] | MeixinZhu/Velocity_control | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 5 | 2026-10-19T17:46:36Z | [] | 2023-03-24T22:33:57Z | [
"rl.core"
] | 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 | 122 | [
"adaptive-cruise-control",
"autonomous-driving",
"car-following",
"ddpg",
"model-predictive-control",
"reinforcement-learning",
"velocity-control"
] | 2026-09-01T08:21:14Z | https://github.com/MeixinZhu/Velocity_control | null |
[
"information-retrieval",
"software-engineering"
] | [
"code-retrieval",
"representation-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"growth26.software-neural-code-search"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-05-21T10:57:18Z | Code for paper "PSCS: A Path-based Neural Model for SemanticCode Search" | [
"information-retrieval",
"software-engineering"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T22:51:24Z | false | 3 | 265,825,316 | null | Python | null | [
"code-retrieval",
"representation-learning"
] | v587su/PSCS_public | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-09-26T22:51:24Z | [] | 2020-08-07T06:39:37Z | [
"growth26.software-neural-code-search"
] | 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 | 6 | [] | 2024-04-26T15:51:11Z | https://github.com/v587su/PSCS_public | null |
[
"reinforcement-learning"
] | [
"policy-learning",
"reinforcement-learning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.core"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-05-28T07:37:40Z | Lifelong Reinforcement Learning codes. Python implementation for the SR-LLRL Algorithm, proposed in our 2021 IEEE SMC Conference Paper "Accelerating lifelong reinforcement learning via reshaping rewards". | [
"reinforcement-learning"
] | [
"machine-learning",
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-19T17:46:36Z | false | 4 | 267,524,684 | null | Python | Apache-2.0 | [
"policy-learning",
"reinforcement-learning"
] | Kchu/LifelongRL | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-19T17:46:36Z | [] | 2022-03-30T04:02:15Z | [
"rl.core"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 24 | [
"agent",
"algorithm",
"lifelong-reinforcement-learning",
"lifelong-rl",
"machine-learning",
"python",
"reinforcement-learning",
"sr-llrl",
"transfer-learning"
] | 2026-04-05T23:50:00Z | https://github.com/Kchu/LifelongRL | null |
[
"computer-vision",
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"neural-network",
"neural-network-pruning",
"pruning",
"structured-pruning"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-05-30T17:40:56Z | This repository is the official implementation of the paper Pruning via Iterative Ranking of Sensitivity Statistics and implements novel pruning / compression algorithms for deep learning / neural networks. Amongst others it implements structured pruning before training, its actual parameter shrinking and unstructured ... | [
"computer-vision",
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"deep-learning",
"machine-learning",
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-07T18:07:52Z | false | 5 | 268,130,192 | https://arxiv.org/abs/2006.00896 | Python | MIT | [
"neural-network",
"neural-network-pruning",
"pruning",
"structured-pruning"
] | StijnVerdenius/SNIP-it | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 2 | 2026-10-14T15:56:14Z | [] | 2023-05-22T23:19:18Z | [
"efficiency.pruning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"method-tied-novelty-claim",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 34 | [
"artificial-intelligence",
"computer-vision",
"deep-learning",
"machine-learning",
"model-compression",
"neural-networks",
"pruning",
"publication",
"science",
"science-research",
"speedup",
"structured-pruning",
"structured-pruning-before-training",
"unstructured-pruning"
] | 2026-09-22T08:19:46Z | https://github.com/StijnVerdenius/SNIP-it | null |
[
"deep-learning",
"geometric-learning",
"graph-learning"
] | [
"geometric-deep-learning",
"graph-neural-network",
"manifold-learning",
"message-passing",
"neural-network"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"graph.geometric-learning",
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-06-01T06:10:26Z | Official PyTorch implementation of "Towards Deeper Graph Neural Networks" [KDD2020] | [
"deep-learning",
"geometric-learning",
"graph-learning"
] | [
"deep-learning",
"neural-network",
"classifier"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 31 | 268,439,607 | https://arxiv.org/abs/2007.09296 | Python | GPL-3.0 | [
"geometric-deep-learning",
"graph-neural-network",
"manifold-learning",
"message-passing",
"neural-network"
] | mengliu1998/DeeperGNN | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 8 | 2026-10-12T20:17:59Z | [] | 2022-10-11T04:01:57Z | [
"graph.geometric-learning",
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 154 | [
"geometric-deep-learning",
"graph-neural-networks",
"node-classification",
"semi-supervised-learning"
] | 2026-09-07T07:58:17Z | https://github.com/mengliu1998/DeeperGNN | null |
[
"general-ml",
"health-and-biomedicine",
"representation-learning"
] | [
"k-means",
"representation-learning",
"semi-supervised-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.semi-supervised-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-06-01T14:59:43Z | Description: This package implements the SSKM_Succ algorithm incorporating K-means clustering with a new semi-supervised learning algorithm for predicting of the succinylation sites. 1.Code (1) peptide.cpp: This file divides the sequence.txt into the name+position.txt and window21.txt. (2) AAindex.cpp: This file comp... | [
"general-ml",
"health-and-biomedicine",
"representation-learning"
] | [
"model-training"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-12T20:31:19Z | false | 0 | 268,553,054 | null | C++ | null | [
"k-means",
"representation-learning",
"semi-supervised-learning"
] | yangyq505/SSKM_Succ | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-12T20:31:19Z | [] | 2020-06-01T15:16:02Z | [
"general.semi-supervised-learning"
] | null | null | null | null | null | null | null | null | null | specific-method-with-novelty-claim | [
"contribution-language",
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [] | 2020-06-01T15:16:05Z | https://github.com/yangyq505/SSKM_Succ | null |
[
"computer-vision",
"multimodal"
] | [
"distillation",
"image-captioning",
"vision-language"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.image-captioning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-06-05T06:08:16Z | Official code for the paper "Self-Distillation for Few-Shot Image Captioning" | [
"computer-vision",
"multimodal"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-12T20:17:59Z | false | 5 | 269,544,675 | null | Python | MIT | [
"distillation",
"image-captioning",
"vision-language"
] | chenxy99/SD-FSIC | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-12T20:17:59Z | [] | 2021-03-15T09:25:22Z | [
"vision.image-captioning"
] | 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 | 18 | [] | 2026-03-17T16:45:46Z | https://github.com/chenxy99/SD-FSIC | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion",
"neural-network"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"recall.name.diffusion-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-06-05T08:23:15Z | code for paper "Anomalous diffusion dynamics of learning in deep neural networks" | [
"generative-ai",
"generative-modeling"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-21T15:48:06Z | false | 0 | 269,572,184 | null | MATLAB | MIT | [
"diffusion",
"neural-network"
] | ifgovh/Anomalous-diffusion-dynamics-of-SGD | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-21T15:48:06Z | [] | 2021-11-30T20:46:31Z | [
"recall.name.diffusion-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 6 | [] | 2024-11-26T15:40:00Z | https://github.com/ifgovh/Anomalous-diffusion-dynamics-of-SGD | null |
[
"interpretability-and-safety",
"natural-language-processing",
"trustworthy-ml"
] | [
"explainable-ai",
"interpretability",
"text-generation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"nlp.text-generation",
"trust.explainable-ai"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-06-06T11:50:36Z | A Generic and Model-Agnostic Exemplar Synthetization Framework for Explainable AI - Official implementation of the ECML 2020 paper | [
"interpretability-and-safety",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 0 | 269,965,347 | null | Python | null | [
"text-generation"
] | antoniobarbalau/exemplar | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-10-18T14:44:29Z | [] | 2022-12-08T04:26:03Z | [
"nlp.text-generation"
] | 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 | 3 | [] | 2022-10-03T04:59:07Z | https://github.com/antoniobarbalau/exemplar | null |
[
"computer-vision",
"document-understanding",
"generative-ai",
"language"
] | [
"language-model",
"ocr",
"transformer"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"llm.transformers",
"vision.ocr"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-06-09T07:50:07Z | PyTorch implementation of my new method for Scene Text Recognition (STR) based on Transformer,Equipped with Transformer, this method outperforms the best model of the aforementioned deep-text-recognition-benchmark by 7.6% on CUTE80. | [
"generative-ai",
"language"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 51 | 270,937,597 | https://mp.weixin.qq.com/s/a_ahIwxiCaO7Bxmj81HUTw | Python | null | [
"language-model",
"transformer"
] | opconty/Transformer_STR | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 3 | 2026-10-17T13:17:44Z | [] | 2023-10-03T21:58:20Z | [
"llm.transformers"
] | e04d471fc3511a0c203bf28add5d579e94ece433 | 2026-10-04T17:18:56Z | gh-ml-readme-evidence-v2 | "e04d471fc3511a0c203bf28add5d579e94ece433" | [
"other",
"references",
"results"
] | [
"method-contribution",
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-10-04T17:18:56Z | opconty/Transformer_STR | specific-method-with-novelty-claim | [
"contribution-language",
"method-contribution",
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 224 | [
"ocr",
"text-recognition",
"transformer-str"
] | 2026-08-07T14:14:54Z | https://github.com/opconty/Transformer_STR | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-06-11T11:17:37Z | The implementation of our ICDM 2020 paper "Exploratory Adversarial Attacks on Graph Neural Networks". | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 1 | 271,526,782 | null | Python | null | [
"graph-neural-network",
"message-passing",
"neural-network"
] | EpoAtk/EpoAtk | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 1 | 2026-09-30T16:47:43Z | [] | 2020-10-21T01:48:56Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 3 | [
"adversarial-attacks"
] | 2025-08-27T17:18:05Z | https://github.com/EpoAtk/EpoAtk | null |
[
"computer-vision"
] | [
"distillation",
"knowledge-distillation"
] | [
"description",
"github-topics",
"license-metadata",
"repository-metadata"
] | [] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-06-15T12:24:19Z | The code for paper "Distilling the Long-Range Knowledge for Real-Time Semantic Segmentation". | [
"computer-vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T20:57:28.489717Z | false | 2 | 272,431,841 | null | Python | MIT | [
"distillation",
"knowledge-distillation"
] | wangyunnan/Real-Time-Semantic-Segmentation | [
"description",
"github-topics",
"license-metadata",
"repository-metadata"
] | 1 | 2026-09-26T20:57:28.489717Z | [] | 2021-04-18T13:35:53Z | [] | 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 | 8 | [
"knowledge-distillation",
"opencv3",
"pyqt5",
"pytorch",
"semantic-segmentation"
] | 2024-07-16T06:10:13Z | https://github.com/wangyunnan/Real-Time-Semantic-Segmentation | {"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["knowledge-distillation"]} |
[
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"github-topics",
"license-metadata",
"repository-metadata"
] | [] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-06-17T08:50:11Z | The official PyTorch implementation for NCSNv2 (NeurIPS 2020) | [
"generative-modeling"
] | [
"generative-model",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T20:57:28.489717Z | false | 59 | 272,924,197 | null | Python | MIT | [
"diffusion"
] | ermongroup/ncsnv2 | [
"description",
"github-topics",
"license-metadata",
"repository-metadata"
] | 1 | 2026-09-26T20:57:28.489717Z | [] | 2021-06-12T17:43:10Z | [] | 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 | 330 | [
"diffusion-models",
"generative-models",
"neurips-2020",
"score-based-generative-modeling",
"score-matching"
] | 2026-09-23T15:38:52Z | https://github.com/ermongroup/ncsnv2 | {"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["diffusion-models"]} |
[
"audio",
"generative-ai",
"language",
"multimodal",
"multimodal-learning",
"speech"
] | [
"emotion-recognition",
"fine-tuning",
"multimodal-learning",
"parameter-efficient-fine-tuning",
"self-supervised-learning",
"speech-understanding"
] | [
"description",
"github-topics",
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"query-match",
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] | [
"audio-speech-emotion-recognition",
"llm.finetuning",
"recall.name.multimodal-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-06-19T07:48:29Z | The code for our INTERSPEECH 2020 paper - Jointly Fine-Tuning "BERT-like'" Self Supervised Models to Improve Multimodal Speech Emotion Recognition | [
"multimodal",
"multimodal-learning"
] | [
"self-supervised-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T14:50:46Z | false | 11 | 273,436,106 | null | Python | MIT | [
"multimodal-learning",
"self-supervised-learning"
] | shamanez/BERT-like-is-All-You-Need | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-10-14T16:10:46Z | [] | 2021-02-26T22:22:45Z | [
"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 | 121 | [
"bert-model",
"fine-tuning",
"multimodal-emotion-recognition",
"multimodal-representation",
"pretrained-models",
"self-supervised-learning",
"sentiment-analysis",
"speech-emotion-recognition"
] | 2026-09-16T11:04:25Z | https://github.com/shamanez/BERT-like-is-All-You-Need | null |
[
"generative-ai",
"language"
] | [
"fine-tuning",
"parameter-efficient-fine-tuning"
] | [
"description",
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"paper-reference",
"query-match",
"repository-metadata"
] | [
"llm.finetuning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-06-22T15:48:36Z | For the code release of our arXiv paper "Revisiting Few-sample BERT Fine-tuning" (https://arxiv.org/abs/2006.05987). | [
"generative-ai",
"language"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 14 | 274,179,950 | null | Python | NOASSERTION | [
"fine-tuning",
"parameter-efficient-fine-tuning"
] | asappresearch/revisit-bert-finetuning | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 2 | 2026-10-12T20:17:59Z | [] | 2023-06-12T21:27:55Z | [
"llm.finetuning"
] | 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 | 185 | [] | 2025-12-31T18:27:32Z | https://github.com/asappresearch/revisit-bert-finetuning | null |
[
"generative-ai",
"health-and-biomedicine",
"language"
] | [
"language-model",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"llm.transformers"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-06-23T17:52:03Z | This is the implementation of the CDGPT2 model mentioned in our paper 'Automated Radiology Report Generation using Conditioned Transformers' | [
"generative-ai",
"health-and-biomedicine",
"language"
] | [
"deep-learning",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-17T13:17:44Z | false | 28 | 274,472,759 | https://doi.org/10.1016/j.imu.2021.100557 | Python | NOASSERTION | [
"language-model",
"transformer"
] | omar-mohamed/GPT2-Chest-X-Ray-Report-Generation | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-10-17T13:17:44Z | [] | 2024-07-25T12:18:10Z | [
"llm.transformers"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 92 | [
"chest-xrays",
"deep-learning",
"gpt2",
"medical-imaging",
"radiology",
"report-generation",
"transfer-learning",
"transformer"
] | 2026-08-02T11:36:12Z | https://github.com/omar-mohamed/GPT2-Chest-X-Ray-Report-Generation | null |
[
"reinforcement-learning"
] | [
"policy-gradient",
"policy-learning",
"reinforcement-learning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.core"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-06-25T22:38:18Z | Official implementation of the AAAI 2021 paper Deep Bayesian Quadrature Policy Optimization. | [
"reinforcement-learning"
] | [
"deep-learning",
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-19T17:46:36Z | false | 6 | 275,028,559 | null | Python | MIT | [
"policy-gradient",
"policy-learning",
"reinforcement-learning"
] | Akella17/Deep-Bayesian-Quadrature-Policy-Optimization | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-19T17:46:36Z | [] | 2021-02-17T20:15:11Z | [
"rl.core"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 18 | [
"actor-critic",
"advantage-actor-critic",
"bayesian-quadrature",
"continuous-control",
"deep-learning",
"deep-reinforcement-learning",
"gaussian-processes",
"monte-carlo",
"mujoco",
"natural-policy-gradient",
"policy-gradient",
"probablistic-numerics",
"pytorch",
"reinforcement-learning",
... | 2026-09-29T11:19:55Z | https://github.com/Akella17/Deep-Bayesian-Quadrature-Policy-Optimization | null |
[
"general-ml",
"representation-learning"
] | [
"representation-learning",
"semi-supervised-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.semi-supervised-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-06-29T08:06:15Z | Belief matching framework official implementation | [
"general-ml",
"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T14:28:44Z | false | 4 | 275,769,739 | null | Python | MIT | [
"representation-learning",
"semi-supervised-learning"
] | tjoo512/belief-matching-framework | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-10-14T15:56:14Z | [] | 2023-03-24T22:16:48Z | [
"general.semi-supervised-learning"
] | 6cd30a8e686d64de4f806febab702cbb8a35fca4 | 2026-09-26T11:23:39Z | gh-ml-readme-evidence-v1 | "6cd30a8e686d64de4f806febab702cbb8a35fca4" | [
"other",
"overview"
] | [
"method-contribution",
"ml-method-context",
"paper-reference"
] | ok | 2026-09-26T11:23:39Z | tjoo512/belief-matching-framework | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-method-context",
"paper-reference"
] | include | ml-contribution-v5 | 41 | [] | 2025-10-19T07:29:36Z | https://github.com/tjoo512/belief-matching-framework | null |
[
"deep-learning",
"representation-learning"
] | [
"continual-learning",
"lifelong-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.continual-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-06-30T16:10:17Z | A novel BERT model with online Elastic Weight Loss that learns and consolidates information to mimic memory while minimizing catastrophic forgetting. | [
"deep-learning",
"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 3 | 276,149,045 | null | Python | null | [
"continual-learning",
"lifelong-learning"
] | jdubpark/continual-bert | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-13T17:52:02Z | [] | 2024-07-25T11:09:29Z | [
"general.continual-learning"
] | cdc37e83800c7c9e30e7d972769b7f1887a4f42f | 2026-09-29T18:47:14Z | gh-ml-readme-evidence-v2 | "cdc37e83800c7c9e30e7d972769b7f1887a4f42f" | [
"method",
"other",
"overview"
] | [] | ok | 2026-09-29T18:47:14Z | jdubpark/continual-bert | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 10 | [] | 2024-06-18T08:14:18Z | https://github.com/jdubpark/continual-bert | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-07-03T10:25:41Z | Code for paper ‘Towards Parallelism Prediction of Sequential Programs with Graph Neural Network’. | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 0 | 276,872,337 | null | Python | MIT | [
"graph-neural-network",
"message-passing",
"neural-network"
] | wsl071134/Parallelismprediction-1 | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-09-30T16:47:43Z | [] | 2020-08-17T07:42:26Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 0 | [] | 2020-10-13T06:13:14Z | https://github.com/wsl071134/Parallelismprediction-1 | null |
[
"efficient-ml",
"model-compression"
] | [
"distillation",
"knowledge-distillation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-07-04T03:22:47Z | [MICCAI2020] Code for paper : Deep Semi-supervised Knowledge Distillation for Overlapping Cervical Cell Instance Segmentation | [
"efficient-ml",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T14:50:46Z | false | 8 | 277,030,535 | null | Python | MIT | [
"distillation",
"knowledge-distillation"
] | Amandaynzhou/MMT-PSM | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-13T17:52:02Z | [] | 2020-11-18T03:40:33Z | [
"efficiency.knowledge-distillation"
] | 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 | 54 | [] | 2025-06-13T08:21:31Z | https://github.com/Amandaynzhou/MMT-PSM | null |
[] | [
"distillation",
"knowledge-distillation"
] | [
"description",
"github-topics",
"repository-metadata"
] | [] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-07-04T05:20:30Z | [AAAI-2021, TKDE-2023] Official implementation for "Cross-Layer Distillation with Semantic Calibration". | [] | [
"deep-learning",
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T20:57:28.489717Z | false | 15 | 277,043,998 | null | Jupyter Notebook | null | [
"distillation",
"knowledge-distillation"
] | DefangChen/SemCKD | [
"description",
"github-topics",
"repository-metadata"
] | 1 | 2026-09-26T20:57:28.489717Z | [] | 2024-07-29T19:30:23Z | [] | 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 | 78 | [
"aaai2021",
"deep-learning",
"knowledge-distillation",
"machine-learning"
] | 2026-01-25T23:15:28Z | https://github.com/DefangChen/SemCKD | {"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["knowledge-distillation"]} |
[
"generative-ai",
"language"
] | [
"language-model",
"transformer"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.transformers"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-07-05T23:20:47Z | Open source code for Paper "A Co-Interactive Transformer for Joint Slot Filling and Intent Detection" | [
"generative-ai",
"language"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-17T13:17:44Z | false | 21 | 277,399,491 | null | Python | null | [
"language-model",
"transformer"
] | kangbrilliant/DCA-Net | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-17T13:17:44Z | [] | 2021-12-13T11:53:57Z | [
"llm.transformers"
] | 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 | 77 | [] | 2026-03-20T06:52:20Z | https://github.com/kangbrilliant/DCA-Net | null |
[
"linguistics",
"natural-language-processing"
] | [
"language-modeling"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"recall.computational-linguistics"
] | true | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-07-06T07:54:34Z | This is the code for loading the SenseBERT model, described in our paper from ACL 2020. | [
"linguistics",
"natural-language-processing"
] | [
"machine-learning",
"embedding"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-14T16:10:46Z | false | 11 | 277,478,009 | null | Python | Apache-2.0 | [
"language-modeling"
] | AI21Labs/sense-bert | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-14T16:10:46Z | [] | 2023-03-24T23:47:37Z | [
"recall.computational-linguistics"
] | 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 | 48 | [
"bert",
"bert-embeddings",
"bert-model",
"bert-models",
"natural-language-processing",
"natural-language-understanding",
"nlp",
"nlp-machine-learning"
] | 2026-05-04T12:15:40Z | https://github.com/AI21Labs/sense-bert | null |
[
"language",
"linguistics",
"natural-language-processing"
] | [
"language-model",
"language-modeling"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"llm.language-model",
"nlp.language-modeling",
"recall.computational-linguistics"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-07-06T10:04:48Z | Source code for ACL 2020 paper "Learning Spoken Language Representations with Neural Lattice Language Modeling" | [
"linguistics",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 4 | 277,506,975 | null | Python | null | [
"language-modeling"
] | MiuLab/Lattice-ELMo | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 2 | 2026-10-15T15:20:52Z | [] | 2023-02-11T01:07:29Z | [
"recall.computational-linguistics"
] | 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 | 18 | [
"elmo",
"lattice",
"spoken-language-understanding"
] | 2026-07-02T14:26:45Z | https://github.com/MiuLab/Lattice-ELMo | null |
[
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"neural-network",
"quantization"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.quantization"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-07-09T03:02:37Z | Code for paper "Automatic Neural Network Compression by Sparsity-Quantization Joint Learning: A Constrained Optimization-based Approach" | [
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 4 | 278,248,522 | null | Python | MIT | [
"neural-network",
"quantization"
] | hyang1990/sparsity_quantization_joint | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-14T15:56:14Z | [] | 2020-07-09T03:08:48Z | [
"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 | 21 | [] | 2026-09-17T08:43:16Z | https://github.com/hyang1990/sparsity_quantization_joint | null |
[
"classical-ml",
"tabular-ml"
] | [
"ensemble-learning",
"random-forest"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.random-forest"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-07-11T17:39:27Z | random forests from scratch with new class imbalance technique | [
"classical-ml",
"tabular-ml"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-28T17:27:44Z | false | 0 | 278,911,712 | null | Jupyter Notebook | null | [
"ensemble-learning",
"random-forest"
] | denisdunn/biased_random_forest | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-28T17:27:44Z | [] | 2020-07-17T15:11:31Z | [
"general.random-forest"
] | 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 | [] | 2020-07-17T15:11:33Z | https://github.com/denisdunn/biased_random_forest | null |
[
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"neural-network",
"quantization"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.quantization"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-07-16T20:37:27Z | Code for our paper at ECCV 2020: Post-Training Piecewise Linear Quantization for Deep Neural Networks | [
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 14 | 280,253,325 | null | Python | NOASSERTION | [
"neural-network",
"quantization"
] | jun-fang/PWLQ | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-14T15:56:14Z | [] | 2021-11-04T07:11:22Z | [
"efficiency.quantization"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 68 | [] | 2025-10-08T12:35:14Z | https://github.com/jun-fang/PWLQ | null |
[
"automl",
"deep-learning",
"efficient-ml",
"representation-learning"
] | [
"neural-architecture-search",
"representation-learning",
"self-supervised-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.neural-architecture-search",
"general.self-supervised-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-07-18T06:47:45Z | Official implementation of the paper "Pretraining Neural Architecture Search Controllers with Locality-based Self-Supervised Learning" (NeurIPSW 2020) | [
"automl",
"deep-learning",
"efficient-ml",
"representation-learning"
] | [
"self-supervised-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T14:50:46Z | false | 0 | 280,599,637 | null | Python | null | [
"neural-architecture-search",
"representation-learning",
"self-supervised-learning"
] | Multi-Objective-NAS/self-supervised-nas | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-10-14T15:56:14Z | [] | 2021-11-05T04:06:45Z | [
"efficiency.neural-architecture-search",
"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 | 5 | [] | 2022-08-25T02:26:04Z | https://github.com/Multi-Objective-NAS/self-supervised-nas | null |
[
"reinforcement-learning",
"robotics"
] | [
"behavior-cloning",
"imitation-learning",
"reinforcement-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.imitation-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-07-18T14:41:45Z | This project aims to use a combination of imitation learning and reinforcement learning in order to play Asseto Corsa by learning new policies from expert samples and reward function. These policies are a combination of a function of minimizing the difference between expert moves and the maximizing the reward. | [
"reinforcement-learning",
"robotics"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 1 | 280,675,470 | null | Python | MIT | [
"behavior-cloning",
"imitation-learning",
"reinforcement-learning"
] | abhimasand/AI-Assetto-Corsa | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-13T17:52:02Z | [] | 2020-09-10T11:29:35Z | [
"rl.imitation-learning"
] | 468b6f47d7ee3a0cfc896e0ce2d7c54b0cc8bff0 | 2026-09-28T20:05:39Z | gh-ml-readme-evidence-v2 | "468b6f47d7ee3a0cfc896e0ce2d7c54b0cc8bff0" | [
"other",
"references",
"usage"
] | [
"ml-method-context"
] | ok | 2026-09-28T20:05:39Z | abhimasand/AI-Assetto-Corsa | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue"
] | include | ml-contribution-v5 | 26 | [] | 2026-08-05T10:19:56Z | https://github.com/abhimasand/AI-Assetto-Corsa | null |
[] | [
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"repository-metadata"
] | [] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-07-23T01:07:04Z | [NeurIPS 2020] Official code for the paper "DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation". Includes a PyTorch library for deep learning with SVG data. | [] | [
"deep-learning",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-01T19:00:27.900159Z | false | 114 | 281,817,032 | null | Jupyter Notebook | MIT | [
"transformer"
] | alexandre01/deepsvg | [
"description",
"github-topics",
"license-metadata",
"repository-metadata"
] | 1 | 2026-10-01T19:00:27.900159Z | [] | 2026-09-04T13:21:24Z | [] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 1,165 | [
"transformer"
] | 2026-10-01T17:44:37Z | https://github.com/alexandre01/deepsvg | {"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["transformer"]} |
[
"generative-ai",
"graph-learning",
"information-retrieval",
"language",
"linguistics",
"natural-language-processing"
] | [
"language-model",
"language-modeling",
"question-answering",
"reading-comprehension",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.transformers",
"nlp.question-answering",
"recall.computational-linguistics"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-07-23T16:11:29Z | Source code for paper "Conversational Question Answering over Knowledge Graphs with Transformer and Graph Attention Networks" | [
"generative-ai",
"graph-learning",
"language",
"natural-language-processing"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-11T16:48:52Z | false | 7 | 281,998,798 | null | Python | MIT | [
"language-model",
"transformer"
] | endrikacupaj/LASAGNE | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-17T13:17:44Z | [] | 2023-07-23T23:56:20Z | [
"llm.transformers"
] | 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 | 33 | [
"conversational-question-answering",
"graph-attention-networks",
"multitask",
"semantic-parsing",
"transformer"
] | 2025-05-13T08:28:21Z | https://github.com/endrikacupaj/LASAGNE | null |
[] | [
"meta-learning"
] | [
"description",
"github-topics",
"repository-metadata"
] | [] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-08-03T12:08:43Z | A PyTorch implementation of our INTERSPEECH2020 paper 'An inverstigation of few-shot learning in spoken term classification.' | [] | [
"classifier"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-02T18:08:49.004055Z | false | 0 | 284,689,925 | null | Python | null | [
"meta-learning"
] | Codelegant92/STC-MAML-PyTorch | [
"description",
"github-topics",
"repository-metadata"
] | 1 | 2026-10-02T18:08:49.004055Z | [] | 2022-07-06T06:14:16Z | [] | 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 | [
"few-shot-learning",
"keyword-spotting",
"meta-learning",
"pytorch"
] | 2026-04-15T12:51:21Z | https://github.com/Codelegant92/STC-MAML-PyTorch | {"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["meta-learning"]} |
[
"distributed-ml",
"privacy-and-federated-learning"
] | [
"collaborative-learning",
"federated-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-08-14T15:09:10Z | Code for Paper "Adaptive Vertical Federated Learning on Unbalanced Features" | [
"distributed-ml",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 0 | 287,560,953 | null | Python | null | [
"collaborative-learning",
"federated-learning"
] | cugzj/Adaptive-VFL-Tensorflow | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-09-30T16:47:43Z | [] | 2020-08-14T15:26:46Z | [
"trust.federated-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 4 | [] | 2026-05-16T10:31:42Z | https://github.com/cugzj/Adaptive-VFL-Tensorflow | null |
[
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"neural-network-pruning",
"pruning",
"quantization",
"structured-pruning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning",
"efficiency.quantization"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-08-18T19:43:05Z | Pruning and new algorithm to quantize different models | [
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 0 | 288,548,843 | null | Python | null | [
"neural-network-pruning",
"pruning",
"quantization",
"structured-pruning"
] | lxuan94-pp/Quantization | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-08T18:15:14Z | [] | 2020-08-18T19:48:38Z | [
"efficiency.pruning"
] | null | 2026-10-04T17:18:56Z | gh-ml-readme-evidence-v2 | null | [] | [] | missing | 2026-10-04T17:18:56Z | lxuan94-pp/Quantization | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [] | 2020-08-18T19:48:48Z | https://github.com/lxuan94-pp/Quantization | null |
[
"information-retrieval",
"linguistics",
"natural-language-processing"
] | [
"language-modeling",
"question-answering",
"reading-comprehension"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"nlp.question-answering",
"recall.computational-linguistics"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-08-19T18:34:23Z | PyTorch code for paper, 'Split-NER: Named Entity Recognition via Two Question-Answering-based Classifications', ACL'23 | [
"linguistics",
"natural-language-processing"
] | [
"deep-learning",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-14T16:10:46Z | false | 0 | 288,808,805 | null | Python | null | [
"language-modeling"
] | c3sr/split-ner | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 2 | 2026-10-17T13:17:44Z | [] | 2023-11-07T21:45:22Z | [
"recall.computational-linguistics"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 18 | [
"acl",
"acl2023",
"bert-fine-tuning",
"deep-learning",
"huggingface-transformers",
"information-extraction",
"named-entity-recognition",
"natural-language-processing",
"ner",
"ontonotes",
"pytorch",
"question-answering",
"span-detection",
"split-ner"
] | 2026-03-14T08:15:26Z | https://github.com/c3sr/split-ner | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
"graph-representation-learning",
"message-passing",
"neural-network"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description",
"graph.representation-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-08-23T06:22:51Z | MGN-Net: A novel Graph Neural Network for integrating heterogenous graph population derived from multiple sources. | [
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 4 | 289,628,966 | null | Python | null | [
"graph-neural-network",
"graph-representation-learning",
"neural-network"
] | basiralab/MGN-Net | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 3 | 2026-10-14T15:56:14Z | [] | 2022-06-06T14:37:53Z | [
"graph.representation-learning"
] | bcfcc2cd0b8c5c6ef500c141646672767d44bb3b | 2026-09-27T17:16:20Z | gh-ml-readme-evidence-v2 | "bcfcc2cd0b8c5c6ef500c141646672767d44bb3b" | [
"citation",
"dataset",
"installation",
"other",
"overview",
"references",
"results"
] | [
"method-contribution",
"ml-method-context"
] | ok | 2026-09-27T17:16:20Z | basiralab/MGN-Net | specific-method-with-novelty-claim | [
"method-contribution",
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue"
] | include | ml-contribution-v5 | 12 | [
"biological-networks",
"brain-connectivity",
"brain-networks",
"connectomes",
"data-fusion",
"data-integration",
"graph-integration",
"graph-neural-networks",
"graph-normalizer",
"multigraph",
"multiview-networks",
"network-neuroscience"
] | 2026-02-21T04:39:54Z | https://github.com/basiralab/MGN-Net | null |
[
"efficient-ml",
"model-compression",
"privacy-and-federated-learning",
"representation-learning",
"transfer-learning"
] | [
"distillation",
"domain-adaptation",
"federated-learning",
"knowledge-distillation",
"transfer-learning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation",
"general.domain-adaptation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-08-31T05:14:03Z | Here is the official implementation of the model KD3A in paper "KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation". | [
"efficient-ml",
"model-compression",
"privacy-and-federated-learning",
"representation-learning",
"transfer-learning"
] | [
"deep-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T15:29:21Z | false | 14 | 291,620,892 | null | Python | MIT | [
"distillation",
"domain-adaptation",
"federated-learning",
"knowledge-distillation",
"transfer-learning"
] | FengHZ/KD3A | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-10-13T17:52:02Z | [] | 2022-08-30T05:15:10Z | [
"efficiency.knowledge-distillation",
"general.domain-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 | 119 | [
"deep-learning",
"domain-adaptation",
"federated-learning",
"transfer-learning",
"unsupervised-learning"
] | 2026-09-29T07:00:19Z | https://github.com/FengHZ/KD3A | null |
[
"reinforcement-learning"
] | [
"convolutional-neural-network",
"deep-reinforcement-learning",
"neural-network",
"policy-learning",
"reinforcement-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-09-12T05:09:51Z | code for paper "Automatic Trading in Futures Market via Convolutional Neural Network and Deep Reinforcement Learning" | [
"reinforcement-learning"
] | [
"neural-network",
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-13T18:05:26Z | false | 3 | 294,874,774 | null | null | GPL-3.0 | [
"convolutional-neural-network",
"deep-reinforcement-learning",
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] | Azrulfy/automatic-trading | [
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] | 1 | 2026-10-13T18:05:26Z | [] | 2020-09-06T08:32:39Z | [
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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 | [] | 2025-03-21T16:09:41Z | https://github.com/Azrulfy/automatic-trading | null |
[
"classical-ml",
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] | [
"general.contrastive-learning",
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-09-14T02:15:05Z | Code and pretrained models for our paper "Domain-invariant Similarity Activation Map Contrastive Learning for Retrieval-based Long-term Visual Localization" | [
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"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T14:50:46Z | false | 2 | 295,280,134 | null | Python | MIT | [
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] | HanjiangHu/DISAM | [
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] | 4 | 2026-10-14T15:56:14Z | [] | 2021-10-24T01:15:31Z | [
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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 | 26 | [] | 2025-05-06T12:35:44Z | https://github.com/HanjiangHu/DISAM | null |
[
"deep-learning",
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] | [
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] | [
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"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-09-16T13:11:57Z | [ICML 2021] GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training (official implementation) | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 22 | 296,036,643 | null | Python | MIT | [
"graph-neural-network",
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] | lsj2408/GraphNorm | [
"description",
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"query-match",
"repository-metadata"
] | 2 | 2026-10-12T20:17:59Z | [] | 2022-12-19T09:18:40Z | [
"graph.gnn-description"
] | 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 | 106 | [
"graph-neural-networks",
"normalization"
] | 2026-03-20T06:43:59Z | https://github.com/lsj2408/GraphNorm | null |
[
"reinforcement-learning"
] | [
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] | [
"description",
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] | [
"rl.core"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-09-17T01:17:04Z | (TG'2021) Code for paper "Efficient Reinforcement Learning for StarCraft by Abstract Forward Models and Transfer Learning". TG = Transactions on Games. | [
"reinforcement-learning"
] | [
"neural-network",
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-21T15:48:06Z | false | 3 | 296,185,180 | null | Python | MIT | [
"neural-network",
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"reinforcement-learning"
] | liuruoze/Thought-SC2 | [
"description",
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"query-match",
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] | 1 | 2026-10-21T15:48:06Z | [] | 2023-04-24T09:06:07Z | [
"rl.core"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 11 | [
"deep-neural-networks",
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"starcraft-ii",
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"starcraft2",
"starcraft2-ai",
"tensorflow",
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] | 2026-06-16T19:38:08Z | https://github.com/liuruoze/Thought-SC2 | null |
[
"reinforcement-learning",
"robotics",
"robotics-and-control"
] | [
"policy-learning",
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"robot-learning",
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] | [
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] | [
"rl.core",
"robotics.robotics-topic",
"robotics.sim2real"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-09-18T14:15:17Z | Source code for our paper "Sim-to-real reinforcement learning applied to end-to-end vehicle control" | [
"reinforcement-learning",
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] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 11 | 296,640,873 | null | Python | MIT | [
"policy-learning",
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] | kaland313/Duckietown-RL | [
"description",
"github-topics",
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"query-match",
"repository-metadata"
] | 6 | 2026-10-21T15:48:06Z | [] | 2021-12-09T14:37:06Z | [
"rl.core"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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"paper-and-code-cue"
] | include | ml-contribution-v5 | 34 | [
"duckietown",
"reinforcement-learning",
"robotics"
] | 2026-01-10T14:32:02Z | https://github.com/kaland313/Duckietown-RL | null |
[
"generative-ai",
"language",
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] | [
"language-model",
"transformer"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.transformers"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-09-20T15:06:35Z | Official implementation code of the paper: "TENT: Tensorized Encoder Transformer for temperature forecasting". | [
"generative-ai",
"language",
"time-series-and-forecasting"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-19T17:46:36Z | false | 8 | 297,101,495 | null | Python | null | [
"language-model",
"transformer"
] | onurbil/TENT | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-19T17:46:36Z | [] | 2024-01-21T08:01:45Z | [
"llm.transformers"
] | 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 | 21 | [] | 2026-09-01T06:58:30Z | https://github.com/onurbil/TENT | null |
[
"3d",
"computer-vision",
"embodied-ai",
"multimodal",
"robotics",
"robotics-and-control"
] | [
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] | [
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"query-match",
"repository-metadata"
] | [
"multimodal.3d-reconstruction",
"robotics.manipulation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-09-28T06:22:42Z | Code for paper Amodal 3D Reconstruction for Robotic Manipulation via Stability and Connectivity | [
"embodied-ai",
"robotics",
"robotics-and-control"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T14:50:46Z | false | 2 | 299,209,027 | null | Python | NOASSERTION | [
"manipulation"
] | wagnew3/ARM | [
"description",
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"query-match",
"repository-metadata"
] | 4 | 2026-10-13T17:52:02Z | [] | 2021-04-27T19:04:30Z | [
"robotics.manipulation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 27 | [] | 2026-07-04T09:31:16Z | https://github.com/wagnew3/ARM | null |
[
"distributed-ml",
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] | [
"collaborative-learning",
"federated-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-09-29T07:59:34Z | Code for Paper "Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption" | [
"distributed-ml",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 4 | 299,545,859 | null | Python | null | [
"collaborative-learning",
"federated-learning"
] | grasses/Vertical-Federated-Learning | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-14T15:56:14Z | [] | 2022-11-17T15:17:47Z | [
"trust.federated-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 34 | [] | 2025-12-04T08:49:31Z | https://github.com/grasses/Vertical-Federated-Learning | null |
[
"deep-learning",
"generative-ai",
"language",
"multimodal",
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] | [
"language-model",
"multi-task-learning",
"multimodal-learning",
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] | [
"description",
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] | [
"general.multi-task-learning",
"llm.transformers",
"recall.name.multimodal-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-09-30T12:47:56Z | The PyTorch implementation of our paper "Password-conditioned Anonymization and Deanonymization with Face Identity Transformers" in ECCV 2020. | [
"generative-ai",
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] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-07T18:07:52Z | false | 12 | 299,916,592 | null | Python | Apache-2.0 | [
"language-model",
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] | laoreja/face-identity-transformer | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-19T17:46:36Z | [] | 2021-03-04T08:40:15Z | [
"llm.transformers"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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] | include | ml-contribution-v5 | 35 | [] | 2026-03-17T08:38:40Z | https://github.com/laoreja/face-identity-transformer | null |
[
"generative-ai",
"language",
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] | [
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] | [
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] | [
"llm.finetuning",
"llm.language-model",
"nlp.language-modeling"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-10-02T05:43:26Z | Code for Paper: Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data | [
"generative-ai",
"language",
"natural-language-processing"
] | [
"deep-learning",
"machine-learning",
"classifier"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 3 | 300,513,121 | null | Python | Apache-2.0 | [
"fine-tuning",
"parameter-efficient-fine-tuning"
] | Lingkai-Kong/Calibrated-BERT-Fine-Tuning | [
"description",
"github-topics",
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"query-match",
"repository-metadata"
] | 3 | 2026-10-12T20:17:59Z | [] | 2020-11-16T09:02:17Z | [
"llm.finetuning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
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] | include | ml-contribution-v5 | 35 | [
"bert",
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"nlp",
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"ood-detection",
"open-world-classification",
"robustness",
"text-classification",
"uncertainty-estimation",
"uncertainty-quantification"
] | 2026-09-24T17:47:24Z | https://github.com/Lingkai-Kong/Calibrated-BERT-Fine-Tuning | null |
[
"linguistics",
"natural-language-processing"
] | [
"language-modeling",
"transformer"
] | [
"description",
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"repository-metadata"
] | [
"recall.computational-linguistics"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-10-04T22:00:42Z | Python Code for paper Attention is All You Need For Chinese Word Segmentation. | [
"linguistics",
"natural-language-processing"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-17T13:17:44Z | false | 3 | 301,235,947 | https://arxiv.org/abs/1910.14537 | Python | MIT | [
"language-modeling",
"transformer"
] | akibcmi/SAMS | [
"description",
"github-topics",
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"query-match",
"repository-metadata"
] | 1 | 2026-10-17T13:17:44Z | [] | 2020-11-26T11:35:49Z | [
"recall.computational-linguistics"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 9 | [
"chinese-word-segmentation",
"natural-language-processing",
"transformer"
] | 2025-11-20T15:16:15Z | https://github.com/akibcmi/SAMS | null |
[
"reinforcement-learning",
"robotics",
"robotics-and-control"
] | [
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] | [
"description",
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] | [
"robotics.robotics-topic"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-10-05T08:07:30Z | Code accompanying our SIGGRAPH 2020 paper "CARL: Controllable Agent with Reinforcement Learning for Quadruped Locomotion" | [
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"robotics",
"robotics-and-control"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-19T17:46:36Z | false | 9 | 301,335,866 | https://arxiv.org/abs/2005.03288 | C++ | MIT | [
"reinforcement-learning",
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] | inventec-ai-center/carl-siggraph2020 | [
"description",
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"repository-metadata"
] | 1 | 2026-10-19T17:46:36Z | [] | 2021-01-13T05:51:38Z | [
"robotics.robotics-topic"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 61 | [
"robotics",
"robotics-simulation",
"simulation"
] | 2026-01-20T07:50:58Z | https://github.com/inventec-ai-center/carl-siggraph2020 | null |
[
"computer-vision",
"generative-ai",
"language",
"multimodal",
"multimodal-learning"
] | [
"language-model",
"multimodal-learning",
"transformer"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"llm.transformers",
"recall.name.multimodal-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-10-05T21:43:53Z | Official code for paper "Spatially Aware Multimodal Transformers for TextVQA" published at ECCV, 2020. | [
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"generative-ai",
"language",
"multimodal-learning"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-13T18:05:26Z | false | 13 | 301,547,198 | https://yashkant.github.io/projects/sam-textvqa | Python | null | [
"language-model",
"transformer"
] | yashkant/sam-textvqa | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 3 | 2026-10-19T17:46:36Z | [] | 2021-09-15T14:48:42Z | [
"llm.transformers"
] | 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 | 65 | [
"eccv",
"language",
"textvqa",
"vision"
] | 2026-09-14T09:14:00Z | https://github.com/yashkant/sam-textvqa | null |
[
"linguistics",
"natural-language-processing"
] | [
"language-modeling"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"recall.computational-linguistics"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-10-06T02:29:53Z | Data and code for our paper "Exploring and Predicting Transferability across NLP Tasks", to appear at EMNLP 2020. | [
"linguistics",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-17T13:17:44Z | false | 9 | 301,593,351 | null | Python | Apache-2.0 | [
"language-modeling"
] | tuvuumass/task-transferability | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-17T13:17:44Z | [] | 2021-03-08T07:10:46Z | [
"recall.computational-linguistics"
] | 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 | 48 | [
"bert",
"emnlp2020",
"natural-language-processing",
"nlp-tasks",
"task-transferability",
"transfer-learning"
] | 2025-12-28T11:13:20Z | https://github.com/tuvuumass/task-transferability | null |
[
"language",
"natural-language-processing"
] | [
"language-model",
"language-modeling"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.language-model",
"nlp.language-modeling"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-10-06T09:54:48Z | Official Code Repository for the paper "Neural Mask Generator: Learning to Generate Adaptive Word Maskings for Language Model Adaptation (EMNLP 2020)" | [
"language",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 2 | 301,682,451 | null | Python | null | [
"language-model",
"language-modeling"
] | Nardien/NMG | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-08T18:15:14Z | [] | 2023-06-19T08:52:48Z | [
"llm.language-model",
"nlp.language-modeling"
] | 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 | 20 | [] | 2024-05-22T11:58:21Z | https://github.com/Nardien/NMG | null |
[
"language",
"natural-language-processing"
] | [
"language-model",
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] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"llm.language-model",
"nlp.language-modeling"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-10-06T11:35:29Z | Official implementation of PKM-augmented language models (Findings of EMNLP 2020) | [
"language",
"natural-language-processing"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-08T18:15:14Z | false | 0 | 301,705,898 | null | null | null | [
"language-model",
"language-modeling"
] | clovaai/pkm-transformers | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 1 | 2026-10-08T18:15:14Z | [] | 2020-10-09T01:49:33Z | [
"llm.language-model",
"nlp.language-modeling"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 10 | [
"memory",
"transformers"
] | 2025-04-16T05:06:06Z | https://github.com/clovaai/pkm-transformers | null |
[
"reinforcement-learning"
] | [
"deep-reinforcement-learning",
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] | [
"description",
"query-match",
"repository-metadata"
] | [
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-10-10T02:44:09Z | Official implementation of paper "Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning" | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 114 | 302,799,738 | null | Python | null | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | zcaicaros/L2D | [
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] | 5 | 2026-10-12T20:31:19Z | [] | 2022-08-31T06:18:26Z | [
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] | include | ml-contribution-v5 | 392 | [] | 2026-10-08T11:57:27Z | https://github.com/zcaicaros/L2D | null |
[
"linguistics",
"natural-language-processing"
] | [
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"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"recall.computational-linguistics"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-10-13T14:26:34Z | Complimentary code for our paper Automatic punctuation restoration with BERT models | [
"linguistics",
"natural-language-processing"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-16T15:36:56Z | false | 7 | 303,729,549 | null | Jupyter Notebook | MIT | [
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] | attilanagy234/neural-punctuator | [
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"repository-metadata"
] | 1 | 2026-10-16T15:36:56Z | [] | 2023-11-06T08:11:56Z | [
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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 | 49 | [
"bert",
"punctuation-restoration",
"transformer"
] | 2026-05-08T04:15:59Z | https://github.com/attilanagy234/neural-punctuator | null |
[
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] | [
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] | [
"description",
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] | [
"llm.transformers"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-10-15T00:22:33Z | Official Pytorch Implementation of Length-Adaptive Transformer (ACL 2021) | [
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] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-19T17:46:36Z | false | 13 | 304,166,905 | null | Python | Apache-2.0 | [
"language-model",
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] | clovaai/length-adaptive-transformer | [
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] | 1 | 2026-10-19T17:46:36Z | [] | 2020-11-02T12:22:07Z | [
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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 | 102 | [
"efficiency",
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"transformers"
] | 2025-08-15T04:43:18Z | https://github.com/clovaai/length-adaptive-transformer | null |
[
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] | [
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-10-15T14:12:29Z | This repository provides the implementation of the method proposed in our paper "Stage-Wise Neural Architecture Search" | [
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 1 | 304,347,471 | null | Python | null | [
"neural-architecture-search"
] | arturjordao/StageWiseArchitectureSearch | [
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] | 2 | 2026-10-14T15:56:14Z | [] | 2021-10-30T21:23:28Z | [
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] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 3 | [] | 2021-10-30T21:23:32Z | https://github.com/arturjordao/StageWiseArchitectureSearch | null |
[] | [
"meta-learning"
] | [
"description",
"github-topics",
"license-metadata",
"repository-metadata"
] | [] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-10-20T07:47:01Z | Source code for NeurIPS 2020 paper "Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding" | [] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-02T18:08:49.004055Z | false | 0 | 305,629,966 | null | Python | Apache-2.0 | [
"meta-learning"
] | llan-ml/MetaTNE | [
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"repository-metadata"
] | 1 | 2026-10-02T18:08:49.004055Z | [] | 2020-11-17T12:55:00Z | [] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 10 | [
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] | 2023-12-28T07:57:27Z | https://github.com/llan-ml/MetaTNE | {"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["meta-learning"]} |
[
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-10-20T07:51:34Z | Official Code Repository for the paper "Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction" (NeurIPS 2020) | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:27:52Z | false | 11 | 305,631,080 | https://arxiv.org/abs/2006.06648 | Python | null | [
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] | JinheonBaek/GEN | [
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] | 12 | 2026-10-14T15:56:14Z | [] | 2021-03-26T18:08:18Z | [
"graph.topic-gnn"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 60 | [
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"graph-neural-network",
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] | 2025-12-08T08:15:43Z | https://github.com/JinheonBaek/GEN | null |
[
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] | [
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] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-10-21T11:48:46Z | Official code for "Throughput-Optimal Topology Design for Cross-Silo Federated Learning" (NeurIPS'20) | [
"distributed-ml",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 7 | 306,007,155 | null | Python | Apache-2.0 | [
"collaborative-learning",
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] | omarfoq/communication-in-cross-silo-fl | [
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] | 3 | 2026-10-14T15:56:14Z | [] | 2022-10-10T09:05:44Z | [
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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 | 33 | [
"federated-learning",
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"training-inaturalist"
] | 2026-06-26T20:24:02Z | https://github.com/omarfoq/communication-in-cross-silo-fl | null |
[
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] | [
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] | [
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] | [
"general.metric-learning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-10-21T13:42:54Z | The source code for paper--MORE: A Metric learning based framework for Open-domain Relation Extraction. | [
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"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 1 | 306,037,827 | null | Python | null | [
"metric-learning",
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] | RenzeLou/MORE | [
"description",
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] | 1 | 2026-10-04T15:58:58Z | [] | 2021-01-15T03:26:50Z | [
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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 | 12 | [
"bert",
"metric-learning",
"relation-extraction"
] | 2024-07-11T09:23:50Z | https://github.com/RenzeLou/MORE | null |
[
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] | [
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] | [
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] | [
"rl.deep"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-10-22T17:05:19Z | Code for paper "Bridging Imagination and Reality for Model-Based Deep Reinforcement Learning". | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-13T18:05:26Z | false | 2 | 306,405,778 | null | Python | null | [
"deep-reinforcement-learning",
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] | Mehooz/BIRD_code | [
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] | 1 | 2026-10-13T18:05:26Z | [] | 2021-05-23T07:11:43Z | [
"rl.deep"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 14 | [] | 2024-04-07T17:50:58Z | https://github.com/Mehooz/BIRD_code | null |
[
"generative-ai",
"language"
] | [
"language-model",
"transformer"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.transformers"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-10-22T18:06:32Z | Source code for ICLR 2021 paper : Pre-training Text-to-Text Transformers for Concept-Centric Common Sense | [
"generative-ai",
"language"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-20T15:59:06Z | false | 1 | 306,420,877 | https://inklab.usc.edu/calm-project | Python | null | [
"language-model",
"transformer"
] | INK-USC/CALM | [
"description",
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"repository-metadata"
] | 1 | 2026-10-20T15:59:06Z | [] | 2021-09-16T07:36:36Z | [
"llm.transformers"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 25 | [] | 2026-07-03T19:32:34Z | https://github.com/INK-USC/CALM | null |
[
"automl",
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] | [
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] | [
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] | [
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] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-10-23T08:28:54Z | Source code for NeurIPS 2020 paper "Meta-Learning with Adaptive Hyperparameters" | [
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] | [
"deep-learning",
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T14:28:44Z | false | 16 | 306,574,876 | null | Python | null | [
"few-shot-learning",
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] | baiksung/ALFA | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 6 | 2026-10-14T15:56:14Z | [] | 2022-07-20T12:34:22Z | [
"general.meta-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
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] | include | ml-contribution-v5 | 89 | [
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"maml",
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"neurips-2020",
"pytorch"
] | 2026-10-05T08:06:46Z | https://github.com/baiksung/ALFA | null |
[
"deep-learning",
"graph-learning",
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] | [
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] | [
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"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-10-27T15:11:02Z | This repository contains the official implementation of the paper "Reliable Graph Neural Networks via Robust Aggregation" (NeurIPS, 2020). | [
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] | [
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 4 | 307,739,496 | https://www.in.tum.de/daml/reliable-gnn-via-robust-aggregation/ | Python | MIT | [
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"neural-network"
] | sigeisler/reliable_gnn_via_robust_aggregation | [
"description",
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] | 1 | 2026-09-29T17:30:08Z | [] | 2022-03-16T09:51:22Z | [
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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 | [
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"graph",
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] | 2026-03-20T11:50:13Z | https://github.com/sigeisler/reliable_gnn_via_robust_aggregation | null |
[
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] | [
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] | [
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] | [
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"nlp.language-modeling"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-10-28T05:04:51Z | Code for paper: Probing for Multilingual Numerical Understanding in Transformer-Based Language Models | [
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] | [
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] | ml_related_text | gh-ml-relevance-v1 | 2026-10-10T16:53:26Z | false | 0 | 307,912,069 | https://arxiv.org/abs/2010.06666 | Python | null | [
"language-model",
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] | devinj1121/llm_num_probe | [
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] | 2 | 2026-10-21T15:48:06Z | [] | 2020-10-28T05:05:57Z | [
"llm.transformers"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2024-04-28T01:51:58Z | https://github.com/devinj1121/llm_num_probe | null |
[
"efficient-ml",
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] | [
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"knowledge-distillation"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-10-30T13:05:38Z | Pytorch implementation of our paper accepted by IEEE TNNLS, 2022 -- Distilling a Powerful Student Model via Online Knowledge Distillation | [
"efficient-ml",
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] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 9 | 308,632,871 | null | Python | null | [
"distillation",
"knowledge-distillation"
] | SJLeo/FFSD | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-13T17:52:02Z | [] | 2021-11-11T08:05:24Z | [
"efficiency.knowledge-distillation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 31 | [] | 2026-01-03T07:45:12Z | https://github.com/SJLeo/FFSD | null |
[
"computer-vision",
"image-processing",
"low-level-vision"
] | [
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] | [
"description",
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"repository-metadata"
] | [
"recall26.image-denoising"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-11-02T12:46:28Z | The code for paper-ENHANCED NON-LOCAL CASCADING NETWORK WITH ATTENTION MECHANISM FOR HYPERSPECTRAL IMAGE DENOISING | [
"computer-vision",
"image-processing",
"low-level-vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-05T20:10:04Z | false | 0 | 309,367,423 | null | null | null | [
"image-denoising",
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] | OPTIMAL-HSIG/ENCAM | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-05T20:10:04Z | [] | 2020-11-02T12:46:30Z | [
"recall26.image-denoising"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
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] | include | ml-contribution-v5 | 0 | [] | 2020-11-02T12:46:32Z | https://github.com/OPTIMAL-HSIG/ENCAM | null |
[
"computer-vision",
"earth-observation",
"geospatial",
"remote-sensing",
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"license-metadata",
"query-match",
"repository-metadata"
] | [
"geo.topic-remote-sensing",
"vision.change-detection"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-11-03T06:41:37Z | [MultiTemp 2019] Official Tensorflow implementation for Change Detection in Multi-temporal VHR Images Based on Deep Siamese Multi-scale Convolutional Neural Networks. | [
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"geospatial",
"remote-sensing",
"science-and-engineering"
] | [
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"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 16 | 309,597,161 | null | Python | MIT | [
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"machine-learning",
"neural-network"
] | ChenHongruixuan/DSMSCN | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 9 | 2026-10-14T15:56:14Z | [] | 2023-11-23T07:30:56Z | [
"geo.topic-remote-sensing"
] | 0064e563a4d4d9c7d27a535d1bccde918c01a24f | 2026-09-26T11:23:39Z | gh-ml-readme-evidence-v1 | "0064e563a4d4d9c7d27a535d1bccde918c01a24f" | [
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] | [
"method-contribution",
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-09-26T11:23:39Z | ChenHongruixuan/DSMSCN | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-context-only",
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 79 | [
"change-detection",
"convolutional-neural-network",
"deep-learning",
"remote-sensing",
"tensorflow"
] | 2026-02-25T19:04:26Z | https://github.com/ChenHongruixuan/DSMSCN | null |
[
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"repository-metadata"
] | [
"bio.microscopy-cell-analysis",
"llm.transformers"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-11-05T16:29:50Z | Official and maintained implementation of the paper "Attention-Based Transformers for Instance Segmentation of Cells in Microstructures" [BIBM 2020]. | [
"generative-ai",
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] | [
"deep-learning",
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] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T14:28:44Z | false | 25 | 310,357,344 | https://arxiv.org/pdf/2011.09763 | Python | MIT | [
"language-model",
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] | ChristophReich1996/Cell-DETR | [
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"rl.deep"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 8 | [] | 2026-02-10T12:09:56Z | https://github.com/DongqinZhou/C-D-RL | null |
[
"linguistics",
"natural-language-processing"
] | [
"language-modeling",
"text-generation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"nlp.text-generation",
"recall.computational-linguistics"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2020-12-29T08:22:50Z | Code for paper "Diversifying Neural Text Generation with Part-of-Speech Guided Softmax and Sampling" by Zhixian Yang, Pengxuan Xu, and Xiaojun Wan. This paper is accepted by COLING 2022. | [
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-19T17:46:36Z | false | 0 | 325,229,194 | null | Python | Apache-2.0 | [
"text-generation"
] | FadedCosine/POS-Guided-Neural-Text-Generation | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-23T15:59:01Z | [] | 2022-11-21T12:31:04Z | [
"nlp.text-generation"
] | 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 | 7 | [] | 2024-09-26T05:32:35Z | https://github.com/FadedCosine/POS-Guided-Neural-Text-Generation | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"recall.name.diffusion-model"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-01-03T19:00:16Z | Official code for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral) | [
"generative-ai",
"generative-modeling"
] | [
"generative-model",
"diffusion-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T20:57:28.489717Z | false | 232 | 326,479,996 | https://arxiv.org/abs/2011.13456 | Jupyter Notebook | Apache-2.0 | [
"diffusion"
] | yang-song/score_sde | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 3 | 2026-10-11T16:35:57Z | [] | 2022-11-29T23:42:42Z | [
"recall.name.diffusion-model"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 1,849 | [
"controllable-generation",
"diffusion-models",
"flax",
"generative-models",
"iclr-2021",
"inverse-problems",
"jax",
"score-based-generative-modeling",
"score-matching",
"stochastic-differential-equations"
] | 2026-10-10T00:58:32Z | https://github.com/yang-song/score_sde | null |
[
"reinforcement-learning"
] | [
"policy-learning",
"reinforcement-learning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.core"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-01-09T10:23:54Z | PyTorch implementation of our paper Reinforcement Learning with Random Delays (ICLR 2020) | [
"reinforcement-learning"
] | [
"deep-learning",
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-23T15:59:01Z | false | 11 | 328,128,347 | null | Python | MIT | [
"policy-learning",
"reinforcement-learning"
] | rmst/rlrd | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-23T15:59:01Z | [] | 2022-05-25T19:45:36Z | [
"rl.core"
] | 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 | 45 | [
"deep-learning",
"deep-reinforcement-learning",
"pytorch",
"reinforcement-learning"
] | 2026-09-28T06:38:49Z | https://github.com/rmst/rlrd | null |
[
"computer-vision",
"generative-ai",
"language",
"multimodal"
] | [
"image-captioning",
"language-model",
"transformer",
"vision-language"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.transformers",
"vision.image-captioning"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-01-11T14:33:31Z | Official pytorch implementation of paper "Dual-Level Collaborative Transformer for Image Captioning" (AAAI 2021). | [
"generative-ai",
"language"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-07T18:07:52Z | false | 30 | 328,691,685 | null | Jupyter Notebook | BSD-3-Clause | [
"language-model",
"transformer"
] | luo3300612/image-captioning-DLCT | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-22T16:09:07Z | [] | 2022-06-08T03:14:23Z | [
"llm.transformers"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 203 | [
"image-captioning"
] | 2026-03-26T11:58:27Z | https://github.com/luo3300612/image-captioning-DLCT | null |
[
"deep-learning",
"efficient-ml",
"graph-learning",
"model-compression"
] | [
"distillation",
"graph-neural-network",
"knowledge-distillation",
"message-passing",
"neural-network"
] | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation",
"graph.gnn-description"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-01-16T06:41:03Z | The official code of WWW2021 paper: Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework | [
"efficient-ml",
"graph-learning",
"model-compression"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T14:50:46Z | false | 19 | 330,101,580 | https://arxiv.org/pdf/2103.02885.pdf | Python | null | [
"distillation",
"graph-neural-network",
"knowledge-distillation",
"neural-network"
] | BUPT-GAMMA/CPF | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | 5 | 2026-10-10T16:40:02Z | [] | 2021-07-09T09:38:14Z | [
"efficiency.knowledge-distillation"
] | 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 | 75 | [
"graph-neural-networks",
"knowledge-distillation"
] | 2026-07-24T08:29:46Z | https://github.com/BUPT-GAMMA/CPF | null |
[
"multi-agent-systems",
"reinforcement-learning"
] | [
"centralized-training",
"multi-agent-reinforcement-learning",
"reinforcement-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.multiagent"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-01-20T04:51:19Z | Official Implementation of 'UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling with Transformers' ICLR 2021(spotlight) | [
"multi-agent-systems",
"reinforcement-learning"
] | [
"reinforcement-learning",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 17 | 331,196,113 | null | Python | MIT | [
"centralized-training",
"multi-agent-reinforcement-learning",
"reinforcement-learning"
] | Theohhhu/UPDeT | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-10T16:40:02Z | [] | 2021-02-03T04:43:50Z | [
"rl.multiagent"
] | 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 | 139 | [] | 2026-06-28T17:24:31Z | https://github.com/Theohhhu/UPDeT | null |
[
"applied-mathematics",
"applied-physics",
"computational-science"
] | [
"data-assimilation",
"inverse-problems",
"reconstruction"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"science.inverse-problems"
] | false | candidate | ml-candidate-v4 | true | selected-by-current-rule | [] | 2021-01-26T09:44:32Z | The official code of JSAC paper "Solving Sparse Linear Inverse Problems in Communication Systems: A Deep Learning Approach With Adaptive Depth" | [
"applied-mathematics",
"applied-physics",
"computational-science"
] | [
"deep-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T14:50:46Z | false | 2 | 333,038,121 | null | Python | MIT | [
"data-assimilation",
"inverse-problems",
"reconstruction"
] | wc253/HaltingNetwork | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-10T16:40:02Z | [] | 2022-09-30T00:48:29Z | [
"science.inverse-problems"
] | 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 | 3 | [] | 2025-11-08T13:07:23Z | https://github.com/wc253/HaltingNetwork | null |
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