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 | 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
[
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2011-10-23T16:15:22Z | Algorithm MONSA for All Closed Sets Finding. MONSA is an exact depth-first search algorithm extracting only frequent closed sets using several new very effective pruning techniques to be free from repetitive and empty patterns. | [
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 0 | 2,631,335 | null | C++ | null | [
"neural-network-pruning",
"pruning",
"structured-pruning"
] | mikksoone/monsa | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-09-29T17:30:08Z | [] | 2011-10-23T16:20:23Z | [
"efficiency.pruning"
] | null | 2026-09-29T18:47:14Z | gh-ml-readme-evidence-v2 | null | [] | [] | missing | 2026-09-29T18:47:14Z | mikksoone/monsa | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 1 | [] | 2013-12-14T17:24:37Z | https://github.com/mikksoone/monsa | null |
[
"general-ml",
"representation-learning"
] | [
"fine-tuning",
"transfer-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.transfer-learning"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2015-06-14T12:40:31Z | Code for paper "A Machine Learning Approach for Instance Matching Based on Similarity Metrics" ISWC2012. | [
"general-ml",
"representation-learning"
] | [
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 0 | 37,411,816 | null | Java | null | [
"fine-tuning",
"transfer-learning"
] | FicusRong/Instance-Matching-by-Transfer-Learning | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-09-27T16:09:59Z | [] | 2015-06-15T07:12:24Z | [
"general.transfer-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-context-only",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 5 | [] | 2018-05-26T17:43:38Z | https://github.com/FicusRong/Instance-Matching-by-Transfer-Learning | null |
[
"classical-ml",
"tabular-ml"
] | [
"ensemble-learning",
"random-forest"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.random-forest"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2015-10-21T02:26:09Z | code for paper "Feature-Budgeted Random Forest" ICML 2015 | [
"classical-ml",
"tabular-ml"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T16:52:35Z | false | 5 | 44,647,664 | null | C++ | MIT | [
"ensemble-learning",
"random-forest"
] | fnan/FeatureBudgetedRandomForest | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-09-28T18:50:00Z | [] | 2017-05-10T13:37:36Z | [
"general.random-forest"
] | 1b62dc8ab6b7f250ecd2b20fca7cee1ad83fb256 | 2026-09-26T11:23:39Z | gh-ml-readme-evidence-v1 | "1b62dc8ab6b7f250ecd2b20fca7cee1ad83fb256" | [
"installation",
"other",
"usage"
] | [
"course-cue",
"ml-method-context",
"paper-reference",
"survey-cue"
] | ok | 2026-09-26T11:23:39Z | fnan/FeatureBudgetedRandomForest | official-paper-method-implementation | [
"course-cue",
"ml-method-context",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue",
"paper-reference",
"survey-cue"
] | include | ml-contribution-v5 | 11 | [] | 2025-01-03T15:50:41Z | https://github.com/fnan/FeatureBudgetedRandomForest | null |
[
"computational-neuroscience",
"control",
"machine-learning",
"robotics",
"robotics-and-control"
] | [
"control",
"neural-network",
"neuromorphic-computing",
"spiking-neural-network"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"robotics.robot-control",
"specialized.spiking-neural-network"
] | true | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2016-01-15T14:16:57Z | Diverse, Noisy and Parallel: a New Spiking Neural Network Approach for Humanoid Robot Control | [
"computational-neuroscience",
"machine-learning",
"robotics-and-control"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T16:52:35Z | false | 5 | 49,722,881 | http://ieeexplore.ieee.org/document/7727325/ | Jupyter Notebook | null | [
"neural-network",
"neuromorphic-computing",
"spiking-neural-network"
] | ricardodeazambuja/IJCNN2016 | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 3 | 2026-09-26T14:50:46Z | [] | 2021-07-14T09:07:50Z | [
"specialized.spiking-neural-network"
] | 30cf7d12c3b0a6869d26ee10cd474a8c21bb0bdd | 2026-09-26T15:53:33Z | gh-ml-readme-evidence-v2 | "30cf7d12c3b0a6869d26ee10cd474a8c21bb0bdd" | [
"abstract",
"citation",
"method",
"other"
] | [
"ml-method-context"
] | ok | 2026-09-26T15:53:33Z | ricardodeazambuja/IJCNN2016 | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue"
] | include | ml-contribution-v5 | 17 | [
"baxter-robot",
"liquid-state-machines",
"lsm",
"robot",
"snn",
"spiking-neural-networks",
"vrep-simulator"
] | 2026-05-05T13:38:49Z | https://github.com/ricardodeazambuja/IJCNN2016 | null |
[
"classical-ml",
"health-and-biomedicine",
"medical-imaging"
] | [
"image-analysis",
"kernel-methods",
"support-vector-machine"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.support-vector-machine",
"medical.medical-imaging"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2016-01-15T17:45:31Z | k-support regularized Support Vector Machine (ksup-SVM) is a novel regularization method that extends the L1 regularized SVM to a mixed norm of both L1 and L2 norms. This enables the use of a correlated sparsity regularization with the power of the SVM framework. | [
"health-and-biomedicine",
"medical-imaging"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 1 | 49,734,905 | null | Matlab | GPL-3.0 | [
"image-analysis",
"support-vector-machine"
] | gkirtzou/ksup_svm | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-02T16:59:40Z | [] | 2016-01-15T17:46:26Z | [
"medical.medical-imaging"
] | 1bea0d6a666fdb0f5db3638e5f57fe9e8d0da40f | 2026-09-28T20:05:39Z | gh-ml-readme-evidence-v2 | "1bea0d6a666fdb0f5db3638e5f57fe9e8d0da40f" | [] | [
"ml-method-context"
] | ok | 2026-09-28T20:05:39Z | gkirtzou/ksup_svm | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue"
] | include | ml-contribution-v5 | 1 | [] | 2017-09-16T12:34:24Z | https://github.com/gkirtzou/ksup_svm | null |
[
"general-ml",
"reinforcement-learning"
] | [
"deep-reinforcement-learning",
"paper-implementation",
"policy-learning",
"reinforcement-learning"
] | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"general.arxiv",
"rl.deep"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2016-05-09T12:59:18Z | A Tensorflow based implementation of "Asynchronous Methods for Deep Reinforcement Learning": https://arxiv.org/abs/1602.01783 | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T16:52:35Z | false | 23 | 58,376,719 | null | Python | Apache-2.0 | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | traai/async-deep-rl | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 2 | 2026-09-26T10:28:13Z | [] | 2016-10-28T11:29:21Z | [
"rl.deep"
] | 2f4599ca9126ba8b0c1aeed9e61dd5c58f68eb5a | 2026-09-26T15:53:33Z | gh-ml-readme-evidence-v2 | "2f4599ca9126ba8b0c1aeed9e61dd5c58f68eb5a" | [
"other"
] | [
"method-contribution",
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-09-26T15:53:33Z | traai/async-deep-rl | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-method-context",
"ml-method-cue",
"paper-and-code-cue",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 68 | [] | 2025-09-12T07:50:25Z | https://github.com/traai/async-deep-rl | null |
[
"computer-vision",
"information-retrieval"
] | [
"image-retrieval",
"metric-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"vision.image-retrieval"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2016-05-20T21:40:55Z | Code for paper Sketch Me That Shoe | [
"computer-vision",
"information-retrieval"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T16:52:35Z | false | 26 | 59,328,028 | null | Jupyter Notebook | null | [
"image-retrieval",
"metric-learning"
] | seuliufeng/DeepSBIR | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-09-26T10:28:13Z | [] | 2018-04-27T16:40:53Z | [
"vision.image-retrieval"
] | df29b346cf7aaa48bfea796efb36ddc3c40ec56e | 2026-09-26T20:25:07Z | gh-ml-readme-evidence-v2 | "df29b346cf7aaa48bfea796efb36ddc3c40ec56e" | [
"other"
] | [
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-09-26T20:25:07Z | seuliufeng/DeepSBIR | readme-supported-paper-method-implementation | [
"ml-method-context",
"paper-and-code-cue",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 64 | [] | 2026-03-31T03:09:51Z | https://github.com/seuliufeng/DeepSBIR | null |
[
"audio",
"computational-neuroscience",
"machine-learning",
"speech-and-audio"
] | [
"audio-classification",
"neural-network",
"neuromorphic-computing",
"representation-learning",
"spiking-neural-network"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"audio-audio-classification",
"specialized.spiking-neural-network"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2016-06-17T09:07:33Z | This is the PyNN code used in the paper titled "Multilayer Spiking Neural Network for audio samples classification using SpiNNaker", which is already accepted for publication. | [
"computational-neuroscience",
"machine-learning",
"speech-and-audio"
] | [
"neural-network",
"classifier"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T16:52:35Z | false | 10 | 61,360,870 | null | Python | GPL-3.0 | [
"neural-network",
"neuromorphic-computing",
"spiking-neural-network"
] | jpdominguez/Multilayer-SNN-for-audio-samples-classification-using-SpiNNaker | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-09-26T14:50:46Z | [] | 2021-12-07T10:07:17Z | [
"specialized.spiking-neural-network"
] | c4278e768503576fb2f0cd78e54d16cf5351258d | 2026-09-26T20:25:07Z | gh-ml-readme-evidence-v2 | "c4278e768503576fb2f0cd78e54d16cf5351258d" | [
"abstract",
"citation",
"other"
] | [
"method-contribution",
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-09-26T20:25:07Z | jpdominguez/Multilayer-SNN-for-audio-samples-classification-using-SpiNNaker | readme-supported-paper-method-implementation | [
"method-contribution",
"ml-context-only",
"ml-method-context",
"paper-and-code-cue",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 32 | [] | 2026-03-11T19:49:03Z | https://github.com/jpdominguez/Multilayer-SNN-for-audio-samples-classification-using-SpiNNaker | null |
[
"deep-learning",
"general-ml",
"generative-modeling",
"health-and-biomedicine",
"medical-imaging",
"probabilistic-ml"
] | [
"bayesian-deep-learning",
"deep-learning",
"image-analysis",
"uncertainty-estimation"
] | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"general.bayesian-deep-learning",
"general.topic-deep-learning",
"medical.medical-imaging"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2016-11-30T12:09:59Z | Code for the paper "Improving Variational Auto-Encoders using Householder Flow" (https://arxiv.org/abs/1611.09630) | [
"general-ml",
"generative-modeling"
] | [
"deep-learning",
"generative-model",
"representation-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T15:29:21Z | false | 12 | 75,183,533 | https://jmtomczak.github.io/deebmed.html | Python | null | [
"deep-learning"
] | jmtomczak/vae_householder_flow | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | 6 | 2026-10-03T15:45:52Z | [] | 2017-01-26T09:18:13Z | [
"general.topic-deep-learning"
] | 528d3fffa7692dbfc08a6fe8290dd5b40cb18ccf | 2026-09-26T20:42:46Z | gh-ml-readme-evidence-v2 | "528d3fffa7692dbfc08a6fe8290dd5b40cb18ccf" | [
"citation",
"other",
"results"
] | [
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-09-26T20:42:46Z | jmtomczak/vae_householder_flow | readme-supported-paper-method-implementation | [
"ml-context-only",
"ml-method-context",
"paper-and-code-cue",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 74 | [
"deep-learning",
"generative-model",
"normalizing-flows",
"representation-learning",
"variational-autoencoders"
] | 2025-12-09T13:18:20Z | https://github.com/jmtomczak/vae_householder_flow | null |
[
"general-ml"
] | [
"deep-learning",
"neural-network"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.topic-deep-learning"
] | true | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2016-12-27T12:24:05Z | This is the official implementation of the paper "A Neural Network Approach to Missing Marker Reconstruction in Human Motion Capture" | [
"general-ml"
] | [
"deep-learning",
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 8 | 77,453,660 | null | Python | Apache-2.0 | [
"deep-learning",
"neural-network"
] | Svito-zar/NN-for-Missing-Marker-Reconstruction | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-04T15:58:58Z | [] | 2021-01-22T09:21:10Z | [
"general.topic-deep-learning"
] | 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 | 39 | [
"autoencoder",
"deep-learning",
"missing-data",
"missing-markers",
"motion-capture",
"motion-capture-processing",
"neural-networks",
"tensorflow"
] | 2026-03-04T11:05:13Z | https://github.com/Svito-zar/NN-for-Missing-Marker-Reconstruction | null |
[
"general-ml",
"generative-modeling"
] | [
"paper-implementation"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"general.arxiv"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2017-02-12T12:20:04Z | Tensorflow implementation of Wasserstein GAN - arxiv: https://arxiv.org/abs/1701.07875 | [
"general-ml",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 129 | 81,723,773 | null | Python | MIT | [
"paper-implementation"
] | shekkizh/WassersteinGAN.tensorflow | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 3 | 2026-09-24T16:52:35Z | [] | 2017-02-13T20:49:15Z | [
"general.arxiv"
] | 0a905a6db044bf0afed3788acf136f2d6625965b | 2026-09-26T21:03:54Z | gh-ml-readme-evidence-v2 | "0a905a6db044bf0afed3788acf136f2d6625965b" | [
"other",
"references"
] | [
"method-contribution",
"ml-method-context",
"model-training-artifact",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-09-26T21:03:54Z | shekkizh/WassersteinGAN.tensorflow | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-method-context",
"model-training-artifact",
"paper-and-code-cue",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 412 | [
"gan",
"generative-adversarial-network",
"tensorflow",
"wasserstein"
] | 2026-07-15T06:37:30Z | https://github.com/shekkizh/WassersteinGAN.tensorflow | null |
[
"generative-ai",
"generative-modeling"
] | [
"diffusion"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"recall.name.diffusion-model"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2017-03-03T05:41:00Z | Python code for paper "Model-free Inference of Diffusion Networks using RKHS Embeddings" | [
"generative-ai",
"generative-modeling"
] | [
"embedding"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-02T16:59:40Z | false | 1 | 83,761,833 | null | Python | MIT | [
"diffusion"
] | amber0309/KEBC | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-02T16:59:40Z | [] | 2020-08-11T04:08:11Z | [
"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 | [] | 2023-09-28T00:33:47Z | https://github.com/amber0309/KEBC | null |
[
"earth-observation",
"earth-science",
"environmental-science",
"geospatial",
"geospatial-science",
"remote-sensing",
"science-and-engineering"
] | [
"deep-learning",
"foundation-model",
"geospatial-learning",
"land-cover-classification",
"machine-learning",
"remote-sensing",
"semantic-segmentation"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"geo.land-cover",
"geo.topic-remote-sensing",
"science.remote-sensing"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2017-03-05T19:49:26Z | Data and code for the paper "Remote Sensing-Based Measurement of Living Environment Deprivation - Improving Classical Approaches with Machine Learning", by Dani Arribas-Bel, Jorge Patiño and Juanca Duque | [
"earth-observation",
"earth-science",
"environmental-science",
"geospatial",
"geospatial-science",
"remote-sensing",
"science-and-engineering"
] | [
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T16:52:35Z | false | 9 | 83,997,489 | null | Jupyter Notebook | null | [
"deep-learning",
"foundation-model",
"geospatial-learning",
"land-cover-classification",
"machine-learning",
"remote-sensing",
"semantic-segmentation"
] | darribas/satellite_led_liverpool | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 3 | 2026-09-26T10:28:13Z | [] | 2019-03-13T10:53:44Z | [
"geo.land-cover",
"geo.topic-remote-sensing",
"science.remote-sensing"
] | 1852140aef0a9d7900bae77e88b08a5e0cce01e1 | 2026-09-26T21:03:54Z | gh-ml-readme-evidence-v2 | "1852140aef0a9d7900bae77e88b08a5e0cce01e1" | [
"citation",
"other"
] | [
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-09-26T21:03:54Z | darribas/satellite_led_liverpool | readme-supported-paper-method-implementation | [
"ml-context-only",
"ml-method-context",
"paper-and-code-cue",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 14 | [
"data",
"machine-learning",
"paper",
"remote-sensing",
"reproducibility",
"socio-economic-indicators"
] | 2025-05-31T01:13:07Z | https://github.com/darribas/satellite_led_liverpool | null |
[
"general-ml",
"generative-ai",
"language"
] | [
"language-model",
"transformer"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"llm.transformers",
"recall.name.transformer-model"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2017-03-20T05:34:33Z | Implement a novel Dense Transformer Networks by Tensorflow | [
"general-ml"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 1 | 85,538,937 | null | Python | null | [
"transformer"
] | YongjunChen93/DSN_Tensorflow_Code | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-10-04T15:58:58Z | [] | 2017-10-22T22:24:43Z | [
"recall.name.transformer-model"
] | 8e13d5dccfaebab1609bb5c298550cc9c8093dd4 | 2026-09-30T18:16:34Z | gh-ml-readme-evidence-v2 | "8e13d5dccfaebab1609bb5c298550cc9c8093dd4" | [
"citation",
"method",
"other",
"overview"
] | [
"method-contribution",
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-09-30T18:16:34Z | YongjunChen93/DSN_Tensorflow_Code | 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 | 6 | [] | 2020-09-16T10:24:29Z | https://github.com/YongjunChen93/DSN_Tensorflow_Code | null |
[
"general-ml"
] | [
"paper-implementation"
] | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"general.arxiv"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2017-04-13T17:44:53Z | Hybrid Code Networks https://arxiv.org/abs/1702.03274 | [
"general-ml"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T16:52:35Z | false | 21 | 88,191,165 | null | Python | null | [
"paper-implementation"
] | johndpope/hcn | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-09-24T16:52:35Z | [] | 2017-04-13T17:43:32Z | [
"general.arxiv"
] | d555b918e3ddc52656ec9434dee20fd5c5c0d678 | 2026-09-27T17:16:20Z | gh-ml-readme-evidence-v2 | "d555b918e3ddc52656ec9434dee20fd5c5c0d678" | [
"installation",
"other"
] | [
"method-contribution",
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-09-27T17:16:20Z | johndpope/hcn | readme-supported-paper-method-implementation | [
"method-contribution",
"ml-method-context",
"paper-and-code-cue",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 80 | [
"bag-of-words",
"dialog",
"entitytracker",
"gensim",
"hybrid-code-networks",
"lstm",
"rnn",
"tensorflow",
"utterance"
] | 2025-01-17T13:05:41Z | https://github.com/johndpope/hcn | null |
[
"classical-ml",
"probabilistic-ml",
"representation-learning"
] | [
"gaussian-process",
"kernel-methods",
"metric-learning",
"representation-learning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.gaussian-process",
"general.metric-learning"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2017-04-14T11:49:57Z | Code for the paper "Gaussian Process Classification as Metric Learning for Forensic Writer Identification", published at DAS 2018 | [
"classical-ml",
"probabilistic-ml"
] | [
"classifier"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 0 | 88,263,462 | null | Python | MIT | [
"gaussian-process",
"kernel-methods"
] | fredrikwahlberg/das2018 | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-09-27T16:09:59Z | [] | 2018-05-03T13:28:29Z | [
"general.gaussian-process"
] | 2a92a732848d696c61433274e2dd39196d2519ed | 2026-09-27T17:16:20Z | gh-ml-readme-evidence-v2 | "2a92a732848d696c61433274e2dd39196d2519ed" | [] | [
"method-contribution",
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-09-27T17:16:20Z | fredrikwahlberg/das2018 | readme-supported-paper-method-implementation | [
"method-contribution",
"ml-method-context",
"paper-and-code-cue",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 2 | [
"document-analysis",
"gaussian-processes",
"multi-class-classification",
"unsupervised-feature-learning",
"writer-indentification"
] | 2020-09-24T16:59:04Z | https://github.com/fredrikwahlberg/das2018 | null |
[
"general-ml"
] | [
"paper-implementation"
] | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"general.arxiv"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2017-04-17T10:00:17Z | Source code for the EMNLP'17 paper "Deep Joint Entity Disambiguation with Local Neural Attention", https://arxiv.org/abs/1704.04920 | [
"general-ml"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T16:52:35Z | false | 50 | 88,495,676 | null | Lua | Apache-2.0 | [
"paper-implementation"
] | dalab/deep-ed | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-09-24T16:52:35Z | [] | 2018-01-12T12:11:19Z | [
"general.arxiv"
] | 856f857497ed2a61103bd5346cc9f43cbf0f9baf | 2026-09-27T17:16:20Z | gh-ml-readme-evidence-v2 | "856f857497ed2a61103bd5346cc9f43cbf0f9baf" | [
"other"
] | [
"method-contribution",
"ml-method-context",
"model-training-artifact",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-09-27T17:16:20Z | dalab/deep-ed | readme-supported-paper-method-implementation | [
"method-contribution",
"ml-context-only",
"ml-method-context",
"model-training-artifact",
"paper-and-code-cue",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 222 | [] | 2026-06-18T07:56:43Z | https://github.com/dalab/deep-ed | null |
[
"multimodal",
"multimodal-learning"
] | [
"convolutional-neural-network",
"multimodal-learning",
"neural-network"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"recall.name.multimodal-model"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2017-04-18T02:51:06Z | Code for paper "Musical Instrument Recognition in User-generated Videos using a Multimodal Convolutional Neural Network Architecture" by Olga Slizovskaia, Emilia Gomez, Gloria Haro. ICMR 2017 | [
"multimodal",
"multimodal-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T14:50:46Z | false | 1 | 88,574,254 | null | Python | GPL-3.0 | [
"convolutional-neural-network",
"multimodal-learning",
"neural-network"
] | Veleslavia/ICMR2017 | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-09-26T14:50:46Z | [] | 2017-12-19T14:59:59Z | [
"recall.name.multimodal-model"
] | 26bc02c9abc5f6be9bd8374ff5d183a273f2a08a | 2026-09-27T17:16:20Z | gh-ml-readme-evidence-v2 | "26bc02c9abc5f6be9bd8374ff5d183a273f2a08a" | [
"installation",
"other",
"usage"
] | [
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-09-27T17:16:20Z | Veleslavia/ICMR2017 | readme-supported-paper-method-implementation | [
"ml-context-only",
"ml-method-context",
"paper-and-code-cue",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 8 | [] | 2022-12-18T12:23:11Z | https://github.com/Veleslavia/ICMR2017 | null |
[
"general-ml",
"multimodal",
"multimodal-learning",
"reinforcement-learning",
"robotics"
] | [
"model-based-reinforcement-learning",
"multimodal-learning",
"paper-implementation",
"reinforcement-learning",
"world-model"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.arxiv",
"recall.name.multimodal-model",
"rl.model-based"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2017-04-20T17:40:30Z | Code for paper "Learning Multimodal Transition Dynamics for Model-Based Reinforcement Learning". | [
"general-ml",
"multimodal-learning",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 7 | 88,893,609 | null | Python | MIT | [
"paper-implementation",
"reinforcement-learning"
] | tmoer/multimodal_varinf | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-09-26T14:50:46Z | [] | 2018-05-24T11:17:50Z | [
"general.arxiv"
] | 041b494035a7fcc5e6bb26ea6d91b06e34abebe1 | 2026-09-27T17:16:20Z | gh-ml-readme-evidence-v2 | "041b494035a7fcc5e6bb26ea6d91b06e34abebe1" | [
"citation",
"method",
"other"
] | [
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-09-27T17:16:20Z | tmoer/multimodal_varinf | official-paper-method-implementation | [
"ml-method-context",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 34 | [] | 2026-09-13T06:40:15Z | https://github.com/tmoer/multimodal_varinf | null |
[
"general-ml",
"representation-learning"
] | [
"representation-learning",
"semi-supervised-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.semi-supervised-learning"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2017-04-24T19:06:03Z | Code for paper "Projected Estimators for Robust Semi-supervised Classification" | [
"general-ml",
"representation-learning"
] | [
"classifier"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-26T14:50:46Z | false | 1 | 89,277,125 | null | TeX | null | [
"representation-learning",
"semi-supervised-learning"
] | jkrijthe/ProjectedEstimators | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-09-26T14:50:46Z | [] | 2017-04-24T19:10:41Z | [
"general.semi-supervised-learning"
] | f9bbdaa45824478abc3fa192945a07d614fa3304 | 2026-09-27T17:16:20Z | gh-ml-readme-evidence-v2 | "f9bbdaa45824478abc3fa192945a07d614fa3304" | [
"abstract",
"other"
] | [
"method-contribution",
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-09-27T17:16:20Z | jkrijthe/ProjectedEstimators | readme-supported-paper-method-implementation | [
"method-contribution",
"ml-method-context",
"paper-and-code-cue",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 2 | [] | 2018-05-30T03:41:33Z | https://github.com/jkrijthe/ProjectedEstimators | null |
[
"reinforcement-learning"
] | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"rl.deep"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2017-05-28T13:14:59Z | Official implementation for the paper: "Shallow Updates for Deep Reinforcement Learning" | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 5 | 92,662,121 | null | Lua | null | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | Shallow-Updates-for-Deep-RL/Shallow_Updates_for_Deep_RL | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-09-29T17:30:08Z | [] | 2017-11-02T18:52:18Z | [
"rl.deep"
] | 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 | [] | 2024-01-04T16:14:26Z | https://github.com/Shallow-Updates-for-Deep-RL/Shallow_Updates_for_Deep_RL | null |
[
"recommender-systems"
] | [
"collaborative-filtering",
"neural-network"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"applied.collaborative-filtering"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2017-05-29T17:57:45Z | Code for paper "On Sampling Strategies for Neural Network-based Collaborative Filtering" | [
"recommender-systems"
] | [
"deep-learning",
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 17 | 92,763,174 | null | Python | MIT | [
"collaborative-filtering",
"neural-network"
] | chentingpc/NNCF | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 7 | 2026-10-01T17:50:46Z | [] | 2017-10-01T17:11:52Z | [
"applied.collaborative-filtering"
] | 33a5e2fa64c5f43b025f85d81baac5cf605955f0 | 2026-09-28T20:05:39Z | gh-ml-readme-evidence-v2 | "33a5e2fa64c5f43b025f85d81baac5cf605955f0" | [
"other"
] | [
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-09-28T20:05:39Z | chentingpc/NNCF | readme-supported-paper-method-implementation | [
"ml-context-only",
"ml-method-context",
"paper-and-code-cue",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 39 | [
"collaborative-filtering",
"deep-learning",
"neural-networks",
"recommender-system",
"sgd"
] | 2025-01-20T10:37:11Z | https://github.com/chentingpc/NNCF | null |
[
"imitation-learning",
"reinforcement-learning"
] | [
"imitation-learning",
"inverse-reinforcement-learning",
"reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"rl.inverse"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2017-06-29T22:47:01Z | Implementations of Inverse Reinforcement Learning and new algorithms | [
"imitation-learning",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 3 | 95,826,414 | null | Python | null | [
"imitation-learning",
"inverse-reinforcement-learning",
"reinforcement-learning"
] | siddharthanpr/irl | [
"description",
"query-match",
"repository-metadata"
] | 3 | 2026-09-25T16:01:25Z | [] | 2017-06-29T22:55:35Z | [
"rl.inverse"
] | 2ed058c5809522125cd20d9862ddeda5dc5bf65d | 2026-09-26T15:53:33Z | gh-ml-readme-evidence-v2 | "2ed058c5809522125cd20d9862ddeda5dc5bf65d" | [
"other"
] | [
"ml-method-context"
] | ok | 2026-09-26T15:53:33Z | siddharthanpr/irl | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue"
] | include | ml-contribution-v5 | 8 | [] | 2023-04-19T19:16:30Z | https://github.com/siddharthanpr/irl | null |
[
"deep-learning",
"embodied-ai",
"multimodal",
"representation-learning",
"robotics"
] | [
"continual-learning",
"lifelong-learning",
"multi-task-learning",
"robot-foundation-model",
"robot-learning",
"transfer-learning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.continual-learning",
"general.multi-task-learning",
"robotics.robot-foundation-model"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2017-07-13T21:07:15Z | Numenta published papers code and data | [
"embodied-ai",
"multimodal",
"robotics"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T15:29:21Z | false | 94 | 97,165,046 | null | Jupyter Notebook | AGPL-3.0 | [
"robot-foundation-model",
"robot-learning"
] | numenta/htmpapers | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 8 | 2026-09-29T17:30:08Z | [] | 2022-03-30T23:59:13Z | [
"robotics.robot-foundation-model"
] | 8d5c5efb658a9955d0b5a51303a706cda36321b4 | 2026-09-28T20:05:39Z | gh-ml-readme-evidence-v2 | "8d5c5efb658a9955d0b5a51303a706cda36321b4" | [
"dataset",
"other"
] | [
"method-contribution",
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-09-28T20:05:39Z | numenta/htmpapers | readme-supported-paper-method-implementation | [
"method-contribution",
"ml-method-context",
"paper-and-code-cue",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 341 | [
"htm",
"neocortex",
"numenta",
"paper",
"theory"
] | 2026-07-20T07:37:32Z | https://github.com/numenta/htmpapers | null |
[
"natural-language-processing",
"reinforcement-learning"
] | [
"information-extraction",
"reinforcement-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"nlp.information-extraction"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2017-07-20T16:27:23Z | Code for the paper 'Speeding up Reinforcement Learning-based Information Extraction Training using Asynchronous Methods' - EMNLP 2017 | [
"natural-language-processing",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 4 | 97,856,513 | null | Roff | MIT | [
"information-extraction",
"reinforcement-learning"
] | adi-sharma/RLIE_A3C | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-09-27T16:09:59Z | [] | 2018-09-10T09:17:07Z | [
"nlp.information-extraction"
] | 46e10caf20928c362886819429c47d943971d5f1 | 2026-09-28T20:05:39Z | gh-ml-readme-evidence-v2 | "46e10caf20928c362886819429c47d943971d5f1" | [
"citation",
"method",
"other"
] | [
"method-contribution",
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-09-28T20:05:39Z | adi-sharma/RLIE_A3C | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-method-context",
"ml-method-cue",
"paper-and-code-cue",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 8 | [] | 2025-08-25T16:38:44Z | https://github.com/adi-sharma/RLIE_A3C | null |
[
"graph-learning"
] | [
"convolutional-neural-network",
"graph-classification",
"graph-representation-learning",
"neural-network"
] | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"graph.graph-classification"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2017-07-28T14:29:49Z | Code and data for the paper 'Classifying Graphs as Images with Convolutional Neural Networks' (new title: 'Graph Classification with 2D Convolutional Neural Networks') | [
"graph-learning"
] | [
"deep-learning",
"neural-network",
"representation-learning",
"classifier",
"embedding"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 23 | 98,657,591 | https://arxiv.org/abs/1708.02218 | Python | null | [
"convolutional-neural-network",
"graph-classification",
"graph-representation-learning",
"neural-network"
] | Tixierae/graph_2D_CNN | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | 4 | 2026-09-25T16:01:25Z | [] | 2019-12-13T08:46:13Z | [
"graph.graph-classification"
] | 3282c32987b3da87fa6d3e7bcd1c32dfc3b8c75e | 2026-09-28T20:05:39Z | gh-ml-readme-evidence-v2 | "3282c32987b3da87fa6d3e7bcd1c32dfc3b8c75e" | [
"citation",
"installation",
"other"
] | [
"method-contribution",
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-09-28T20:05:39Z | Tixierae/graph_2D_CNN | readme-supported-paper-method-implementation | [
"method-contribution",
"ml-context-only",
"ml-method-context",
"paper-and-code-cue",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 77 | [
"2d-cnn",
"artificial-intelligence",
"classification",
"convolutional-neural-networks",
"deep-learning",
"embeddings",
"graph-2d-cnn",
"graph-kernels",
"graph-theory",
"keras",
"neural-networks",
"representation-learning",
"tensorflow"
] | 2026-02-09T21:43:11Z | https://github.com/Tixierae/graph_2D_CNN | null |
[
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"convolutional-neural-network",
"neural-network",
"neural-network-pruning",
"pruning",
"structured-pruning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2017-09-02T09:17:28Z | [IJCNN'19, IEEE JSTSP'19] Caffe code for our paper "Structured Pruning for Efficient ConvNets via Incremental Regularization"; [BMVC'18] "Structured Probabilistic Pruning for Convolutional Neural Network Acceleration" | [
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 6 | 102,185,768 | null | Makefile | NOASSERTION | [
"convolutional-neural-network",
"neural-network",
"neural-network-pruning",
"pruning",
"structured-pruning"
] | MingSun-Tse/Caffe_IncReg | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-01T17:50:46Z | [] | 2020-02-14T18:34:13Z | [
"efficiency.pruning"
] | 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 | [
"model-acceleration",
"model-compression",
"pruning"
] | 2023-05-30T09:45:27Z | https://github.com/MingSun-Tse/Caffe_IncReg | null |
[
"deep-learning",
"representation-learning"
] | [
"multi-task-learning",
"transfer-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.multi-task-learning"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2017-09-13T13:53:42Z | Code to replicate Experiments from the paper 'End-to-End Supervised Lobe Segmentation' | [
"deep-learning",
"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 12 | 103,406,163 | null | Python | null | [
"multi-task-learning",
"transfer-learning"
] | filipetrocadoferreira/end2endlobesegmentation | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-09-28T18:50:00Z | [] | 2018-03-29T10:39:29Z | [
"general.multi-task-learning"
] | 182d6d00ded65e0a2f7b43a948f5989ab63ae25e | 2026-09-29T18:47:14Z | gh-ml-readme-evidence-v2 | "182d6d00ded65e0a2f7b43a948f5989ab63ae25e" | [
"method",
"other"
] | [
"method-contribution",
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-09-29T18:47:14Z | filipetrocadoferreira/end2endlobesegmentation | readme-supported-paper-method-implementation | [
"method-contribution",
"ml-method-context",
"paper-and-code-cue",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 38 | [] | 2025-12-04T01:54:52Z | https://github.com/filipetrocadoferreira/end2endlobesegmentation | null |
[
"general-ml",
"generative-modeling",
"multimodal",
"representation-learning"
] | [
"multimodal-learning",
"paper-implementation",
"representation-learning",
"semi-supervised-learning"
] | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"general.arxiv",
"general.semi-supervised-learning",
"recall.name.multimodal-model"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2017-11-04T01:29:54Z | Tensorflow code for the Bayesian GAN (https://arxiv.org/abs/1705.09558) (NIPS 2017) | [
"generative-modeling",
"multimodal"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 171 | 109,457,615 | null | Jupyter Notebook | NOASSERTION | [
"multimodal-learning"
] | andrewgordonwilson/bayesgan | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 5 | 2026-09-30T16:47:43Z | [] | 2018-07-30T20:50:23Z | [
"recall.name.multimodal-model"
] | c3f71ee104e26b50be5f09c343f8cd7b7cec9de0 | 2026-09-30T18:16:34Z | gh-ml-readme-evidence-v2 | "c3f71ee104e26b50be5f09c343f8cd7b7cec9de0" | [
"dataset",
"installation",
"other",
"overview",
"usage"
] | [
"method-contribution",
"ml-method-context",
"model-training-artifact",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-09-30T18:16:34Z | andrewgordonwilson/bayesgan | readme-supported-paper-method-implementation | [
"method-contribution",
"ml-method-context",
"model-training-artifact",
"paper-and-code-cue",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 1,013 | [] | 2026-07-15T02:09:25Z | https://github.com/andrewgordonwilson/bayesgan | null |
[
"3d",
"computer-vision",
"multimodal"
] | [
"3d-reconstruction",
"neural-reconstruction"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.3d-reconstruction"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2017-11-14T21:42:24Z | Code for paper: "3D Reconstruction of Incomplete Archaeological Objects Using a Generative Adversarial Network" | [
"3d",
"computer-vision",
"multimodal"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 7 | 110,748,171 | null | Jupyter Notebook | MIT | [
"3d-reconstruction",
"neural-reconstruction"
] | renato145/3D-ORGAN | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-09-27T16:09:59Z | [] | 2021-12-29T09:55:33Z | [
"multimodal.3d-reconstruction"
] | 084dc33b8c37d1bf2defaa47607442e13e5d7129 | 2026-09-30T18:16:34Z | gh-ml-readme-evidence-v2 | "084dc33b8c37d1bf2defaa47607442e13e5d7129" | [
"citation",
"other"
] | [
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-09-30T18:16:34Z | renato145/3D-ORGAN | readme-supported-paper-method-implementation | [
"ml-method-context",
"paper-and-code-cue",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 16 | [] | 2024-11-17T07:49:19Z | https://github.com/renato145/3D-ORGAN | null |
[
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"generative-modeling",
"image-to-image-translation"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.image-to-image-translation"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2017-11-27T01:43:01Z | StarGAN - Official PyTorch Implementation (CVPR 2018) | [
"computer-vision",
"generative-ai",
"generative-modeling"
] | [
"generative-model"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 953 | 112,133,243 | null | Python | MIT | [
"generative-modeling",
"image-to-image-translation"
] | yunjey/stargan | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-09-28T18:50:00Z | [] | 2021-01-23T15:09:58Z | [
"vision.image-to-image-translation"
] | bdd147fb2fff356e072dbae29550ea79cef44eb7 | 2026-09-25T22:08:11Z | gh-ml-readme-evidence-v1 | "bdd147fb2fff356e072dbae29550ea79cef44eb7" | [
"citation",
"other"
] | [
"method-contribution",
"ml-method-context",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-09-25T22:08:11Z | yunjey/stargan | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-method-context",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 5,295 | [
"cvpr2018",
"generative-models",
"image-to-image-translation",
"pytorch",
"stargan"
] | 2026-09-22T20:17:59Z | https://github.com/yunjey/stargan | null |
[
"reinforcement-learning"
] | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"rl.deep"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2018-01-03T12:53:15Z | Source code for paper Classification with Costly Features using Deep Reinforcement Learning. | [
"reinforcement-learning"
] | [
"reinforcement-learning",
"classifier"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 24 | 116,136,597 | https://arxiv.org/abs/1711.07364 | Python | MIT | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | jaromiru/cwcf | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-09-29T17:30:08Z | [] | 2021-10-05T03:02:39Z | [
"rl.deep"
] | dd46e60b15eda09803a6fd956e249f092f9db0b6 | 2026-10-01T19:11:32Z | gh-ml-readme-evidence-v2 | "dd46e60b15eda09803a6fd956e249f092f9db0b6" | [] | [
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-10-01T19:11:32Z | jaromiru/cwcf | official-paper-method-implementation | [
"ml-method-context",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 58 | [
"classification",
"costly-features",
"deep-reinforcement-learning"
] | 2026-07-28T10:46:51Z | https://github.com/jaromiru/cwcf | null |
[
"deep-learning",
"representation-learning"
] | [
"representation-learning",
"self-supervised-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.self-supervised-learning"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2018-01-07T13:28:45Z | An pytorch implementation of time-contrastive networks as presented in the paper "Time-Contrastive Networks: Self-Supervised Learning from Multi-View Observation". | [
"deep-learning",
"representation-learning"
] | [
"self-supervised-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 14 | 116,567,651 | null | Python | MIT | [
"representation-learning",
"self-supervised-learning"
] | kekeblom/tcn | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-09-27T16:09:59Z | [] | 2018-01-07T13:46:12Z | [
"general.self-supervised-learning"
] | 72dedfdb92a91de766e31abecbec2f9a54f3a9cf | 2026-10-01T19:11:32Z | gh-ml-readme-evidence-v2 | "72dedfdb92a91de766e31abecbec2f9a54f3a9cf" | [
"other"
] | [
"method-contribution",
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-10-01T19:11:32Z | kekeblom/tcn | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-method-context",
"ml-method-cue",
"paper-and-code-cue",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 58 | [] | 2025-11-13T01:14:49Z | https://github.com/kekeblom/tcn | null |
[
"computer-vision",
"health-and-biomedicine",
"medical-imaging"
] | [
"image-analysis",
"segmentation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"medical.medical-image-segmentation",
"medical.medical-imaging",
"vision.segmentation"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2018-01-09T08:12:19Z | Code for the paper 'Generative Adversarial Network based Synthesis for Supervised Medical Image Segmentation' | [
"computer-vision",
"health-and-biomedicine",
"medical-imaging"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T14:50:46Z | false | 13 | 116,786,201 | null | Python | MIT | [
"image-analysis"
] | thomasneff/Generative-Adversarial-Network-based-Synthesis-for-Supervised-Medical-Image-Segmentation | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-04T15:58:58Z | [] | 2018-01-09T08:54:49Z | [
"medical.medical-imaging"
] | eb5b8f414a005f5e3eb37778036c6201e48b1606 | 2026-10-01T19:11:32Z | gh-ml-readme-evidence-v2 | "eb5b8f414a005f5e3eb37778036c6201e48b1606" | [
"other"
] | [
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-10-01T19:11:32Z | thomasneff/Generative-Adversarial-Network-based-Synthesis-for-Supervised-Medical-Image-Segmentation | readme-supported-paper-method-implementation | [
"ml-method-context",
"paper-and-code-cue",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 17 | [] | 2025-04-13T14:07:39Z | https://github.com/thomasneff/Generative-Adversarial-Network-based-Synthesis-for-Supervised-Medical-Image-Segmentation | null |
[
"representation-learning",
"transfer-learning"
] | [
"domain-adaptation",
"transfer-learning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.domain-adaptation"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2018-01-28T21:33:54Z | Code for the paper "Transductive Adversarial Networks (TAN)" by Sean Rowan (2018). | [
"representation-learning",
"transfer-learning"
] | [
"deep-learning",
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 0 | 119,299,839 | null | TeX | MIT | [
"domain-adaptation",
"transfer-learning"
] | seanrowan/tan | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-09-28T18:50:00Z | [] | 2018-09-13T09:13:19Z | [
"general.domain-adaptation"
] | 74adc3df78b0da075e9562eaed967815d3b047b2 | 2026-10-02T18:21:54Z | gh-ml-readme-evidence-v2 | "74adc3df78b0da075e9562eaed967815d3b047b2" | [
"other"
] | [
"method-contribution",
"ml-method-context",
"paper-reference"
] | ok | 2026-10-02T18:21:54Z | seanrowan/tan | readme-supported-paper-method-implementation | [
"method-contribution",
"ml-context-only",
"ml-method-context",
"paper-and-code-cue",
"paper-reference"
] | include | ml-contribution-v5 | 4 | [
"deep-learning",
"generative-adversarial-network",
"machine-learning-algorithms",
"transfer-learning"
] | 2024-01-04T16:20:20Z | https://github.com/seanrowan/tan | null |
[
"classical-ml"
] | [
"kernel-methods",
"support-vector-machine"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.support-vector-machine"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2018-02-07T16:03:03Z | In all the previous Support Vector Machine implementations, points which lie very close to the dividing hyperplane are not taken care of and are usually ignored. If we run the prediction function, there are high chances that these points can randomly lie either side of the hyperplane and affect the accuracy of the algo... | [
"classical-ml"
] | [
"classifier"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-03T15:45:52Z | false | 0 | 120,637,455 | null | Jupyter Notebook | null | [
"kernel-methods",
"support-vector-machine"
] | rajat708/BreastCancerPredictionthroughSVM | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-10-03T15:45:52Z | [] | 2018-02-07T16:04:54Z | [
"general.support-vector-machine"
] | a1887435c4c0d1687ee9771573141f0dfe636d79 | 2026-10-03T17:01:43Z | gh-ml-readme-evidence-v2 | "a1887435c4c0d1687ee9771573141f0dfe636d79" | [
"other"
] | [] | ok | 2026-10-03T17:01:43Z | rajat708/BreastCancerPredictionthroughSVM | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 0 | [] | 2018-02-07T16:04:55Z | https://github.com/rajat708/BreastCancerPredictionthroughSVM | null |
[
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"distillation",
"model-compression",
"quantization"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.model-compression",
"efficiency.quantization"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2018-02-15T17:06:17Z | Implements quantized distillation. Code for our paper "Model compression via distillation and quantization" | [
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:27:52Z | false | 76 | 121,656,522 | null | Python | MIT | [
"distillation",
"quantization"
] | antspy/quantized_distillation | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 7 | 2026-09-28T18:50:00Z | [] | 2024-07-25T10:12:38Z | [
"efficiency.quantization"
] | 24ff75f43cc3711a1f513ea4c0a9d18deb2c6dd9 | 2026-10-03T17:01:43Z | gh-ml-readme-evidence-v2 | "24ff75f43cc3711a1f513ea4c0a9d18deb2c6dd9" | [
"method",
"other",
"usage"
] | [
"paper-reference"
] | ok | 2026-10-03T17:01:43Z | antspy/quantized_distillation | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue",
"paper-reference"
] | include | ml-contribution-v5 | 335 | [] | 2026-09-07T07:57:04Z | https://github.com/antspy/quantized_distillation | null |
[
"deep-learning",
"representation-learning"
] | [
"representation-learning",
"self-supervised-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.self-supervised-learning"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2018-02-15T20:02:58Z | Code for Paper: Self-supervised Learning of Motion Capture | [
"deep-learning",
"representation-learning"
] | [
"self-supervised-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 13 | 121,676,445 | null | Python | null | [
"representation-learning",
"self-supervised-learning"
] | htung0101/3d_smpl | [
"description",
"query-match",
"repository-metadata"
] | 6 | 2026-09-26T14:50:46Z | [] | 2018-02-15T20:15:37Z | [
"general.self-supervised-learning"
] | 08fb111d7f9393543f085a413cf08101907349f3 | 2026-10-03T17:01:43Z | gh-ml-readme-evidence-v2 | "08fb111d7f9393543f085a413cf08101907349f3" | [
"citation",
"installation",
"method",
"other"
] | [
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-10-03T17:01:43Z | htung0101/3d_smpl | official-paper-method-implementation | [
"ml-method-context",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 90 | [] | 2025-04-05T22:43:41Z | https://github.com/htung0101/3d_smpl | null |
[
"general-ml",
"reinforcement-learning"
] | [
"meta-learning",
"paper-implementation",
"policy-gradient",
"policy-learning",
"policy-optimization",
"reinforcement-learning"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"general.arxiv",
"rl.core",
"rl.policy-gradient"
] | true | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2018-02-23T00:54:49Z | Code for the paper "Evolved Policy Gradients" | [
"reinforcement-learning"
] | [
"machine-learning",
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 55 | 122,554,700 | https://arxiv.org/abs/1802.04821 | Python | MIT | [
"meta-learning",
"policy-learning",
"reinforcement-learning"
] | openai/EPG | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 7 | 2026-10-04T15:58:58Z | [] | 2018-11-22T06:05:34Z | [
"rl.core"
] | 9528d2962b78debb49c1343f7c8e19e33f45681c | 2026-10-03T17:01:43Z | gh-ml-readme-evidence-v2 | "9528d2962b78debb49c1343f7c8e19e33f45681c" | [
"citation",
"installation",
"other",
"results"
] | [
"ml-method-context",
"paper-reference"
] | ok | 2026-10-03T17:01:43Z | openai/EPG | readme-supported-paper-method-implementation | [
"ml-method-context",
"ml-method-cue",
"paper-and-code-cue",
"paper-reference"
] | include | ml-contribution-v5 | 253 | [
"continuous-control",
"evolutionary-strategy",
"machine-learning",
"meta-learning",
"paper",
"reinforcement-learning"
] | 2026-09-18T11:30:01Z | https://github.com/openai/EPG | null |
[
"applied-mathematics",
"applied-physics",
"computational-science",
"generative-modeling"
] | [
"data-assimilation",
"inverse-problems",
"reconstruction"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"science.inverse-problems"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2018-02-26T11:21:34Z | Code for the paper: Solving Linear Inverse Problems using GAN priors | [
"applied-mathematics",
"applied-physics",
"computational-science",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 8 | 122,959,548 | null | Python | MIT | [
"data-assimilation",
"inverse-problems",
"reconstruction"
] | shahviraj/pgdgan | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-09-26T14:50:46Z | [] | 2021-09-28T23:45:13Z | [
"science.inverse-problems"
] | e113bfb250ba1abad50404224f145e7e37c3c8e2 | 2026-10-03T17:01:43Z | gh-ml-readme-evidence-v2 | "e113bfb250ba1abad50404224f145e7e37c3c8e2" | [
"installation",
"other"
] | [
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-10-03T17:01:43Z | shahviraj/pgdgan | readme-supported-paper-method-implementation | [
"ml-method-context",
"paper-and-code-cue",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 10 | [] | 2024-05-15T14:59:37Z | https://github.com/shahviraj/pgdgan | null |
[
"general-ml"
] | [
"neural-network",
"paper-implementation",
"self-attention"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"general.arxiv"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2018-03-02T10:20:26Z | Pytorch implementation of the Graph Attention Network model by Veličković et. al (2017, https://arxiv.org/abs/1710.10903) | [
"general-ml"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 701 | 123,565,023 | null | Python | MIT | [
"neural network",
"paper-implementation",
"self attention"
] | diegoantognini/pyGAT | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 2 | 2026-09-24T15:29:21Z | [] | 2023-07-06T21:23:03Z | [
"general.arxiv"
] | 602dc9063b875c9a329d89f6395f927045b3f10e | 2026-10-03T17:01:43Z | gh-ml-readme-evidence-v2 | "602dc9063b875c9a329d89f6395f927045b3f10e" | [
"installation",
"other"
] | [
"method-contribution",
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-10-03T17:01:43Z | diegoantognini/pyGAT | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-method-context",
"ml-method-cue",
"paper-and-code-cue",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 3,122 | [
"attention-mechanism",
"graph-attention-networks",
"neural-networks",
"python",
"pytorch",
"self-attention"
] | 2026-09-03T09:14:15Z | https://github.com/diegoantognini/pyGAT | null |
[
"natural-language-processing"
] | [
"named-entity-recognition"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"nlp.named-entity-recognition"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2018-03-02T19:45:57Z | TensorFlow Implementation For [Neural Architecture for Named Entity Recognition](https://arxiv.org/abs/1603.01360) | [
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-03T15:45:52Z | false | 0 | 123,623,364 | null | Jupyter Notebook | Apache-2.0 | [
"named-entity-recognition"
] | shengc/tf-lstm-crf-tagger | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-10-03T15:45:52Z | [] | 2018-03-04T05:03:50Z | [
"nlp.named-entity-recognition"
] | 8461d99d9e45e818ebc4ebc986b95ff2d659ef5e | 2026-10-03T17:01:43Z | gh-ml-readme-evidence-v2 | "8461d99d9e45e818ebc4ebc986b95ff2d659ef5e" | [] | [
"method-contribution",
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-10-03T17:01:43Z | shengc/tf-lstm-crf-tagger | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-context-only",
"ml-method-context",
"paper-and-code-cue",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 12 | [
"crf-rnn-tensorflow",
"named-entity-recognition",
"nlp",
"sequence-labeling",
"tensorflow"
] | 2024-04-22T02:43:35Z | https://github.com/shengc/tf-lstm-crf-tagger | null |
[
"audio",
"speech",
"speech-and-audio"
] | [
"architecture",
"convolutional-neural-network",
"evaluation",
"inference",
"neural-network",
"training"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"audio-asr"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2018-03-22T20:51:31Z | This is the code for the paper 'Quaternion Convolutional Neural Networks for End-to-End Automatic Speech Recognition'. It provides all the Keras classes to run a Quaternion Convolutional NN. | [
"audio",
"speech",
"speech-and-audio"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:27:52Z | false | 17 | 126,393,620 | null | Python | GPL-3.0 | [
"architecture",
"convolutional-neural-network",
"evaluation",
"inference",
"neural-network",
"training"
] | Orkis-Research/Quaternion-Convolutional-Neural-Networks-for-End-to-End-Automatic-Speech-Recognition | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-09-28T18:50:00Z | [] | 2019-01-24T10:43:03Z | [
"audio-asr"
] | 1a7c832a5f8748c837718aa0e08803b4f306622a | 2026-10-04T17:18:56Z | gh-ml-readme-evidence-v2 | "1a7c832a5f8748c837718aa0e08803b4f306622a" | [] | [
"method-contribution",
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-10-04T17:18:56Z | Orkis-Research/Quaternion-Convolutional-Neural-Networks-for-End-to-End-Automatic-Speech-Recognition | readme-supported-paper-method-implementation | [
"method-contribution",
"ml-context-only",
"ml-method-context",
"paper-and-code-cue",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 67 | [] | 2026-03-03T14:16:34Z | https://github.com/Orkis-Research/Quaternion-Convolutional-Neural-Networks-for-End-to-End-Automatic-Speech-Recognition | null |
[
"program-analysis",
"software-engineering"
] | [
"machine-learning",
"program-repair"
] | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"growth26.software-automated-program-repair"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2018-03-23T15:42:25Z | 58069 Java source code diffs. http://arxiv.org/pdf/1807.03200 | [
"program-analysis",
"software-engineering"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T22:51:24Z | false | 15 | 126,506,099 | http://arxiv.org/pdf/1807.03200 | null | null | [
"machine-learning",
"program-repair"
] | ASSERT-KTH/CodRep | [
"description",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-09-26T22:51:24Z | [] | 2019-07-21T17:57:27Z | [
"growth26.software-automated-program-repair"
] | 4f2c79e6c22be90299caa74689bbaa8b06e41548 | 2026-10-04T17:18:56Z | gh-ml-readme-evidence-v2 | "4f2c79e6c22be90299caa74689bbaa8b06e41548" | [
"other"
] | [
"method-contribution",
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-10-04T17:18:56Z | ASSERT-KTH/CodRep | readme-supported-paper-method-implementation | [
"method-contribution",
"ml-method-context",
"paper-and-code-cue",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 94 | [] | 2025-11-06T02:01:55Z | https://github.com/ASSERT-KTH/CodRep | null |
[
"general-ml",
"representation-learning"
] | [
"representation-learning",
"semi-supervised-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.semi-supervised-learning"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2018-03-24T05:40:13Z | PyTorch implementation of the Mask-X-RCNN network proposed in the 'Learning to Segment Everything' paper by Facebook AI Research | [
"general-ml",
"representation-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 34 | 126,569,627 | null | Python | null | [
"representation-learning",
"semi-supervised-learning"
] | skrish13/PyTorch-mask-x-rcnn | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-09-29T17:30:08Z | [] | 2018-06-08T10:50:50Z | [
"general.semi-supervised-learning"
] | 30036d56cbe42a843e4b6b3c72643ce28b7bae30 | 2026-10-04T17:18:56Z | gh-ml-readme-evidence-v2 | "30036d56cbe42a843e4b6b3c72643ce28b7bae30" | [
"method",
"other",
"overview",
"references"
] | [
"method-contribution",
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-10-04T17:18:56Z | skrish13/PyTorch-mask-x-rcnn | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-context-only",
"ml-method-context",
"paper-and-code-cue",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 156 | [] | 2025-03-31T09:19:18Z | https://github.com/skrish13/PyTorch-mask-x-rcnn | null |
[] | [
"meta-learning"
] | [
"description",
"github-topics",
"license-metadata",
"repository-metadata"
] | [] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2018-03-28T12:14:14Z | PyTorch code for CVPR 2018 paper: Learning to Compare: Relation Network for Few-Shot Learning (Few-Shot Learning part) | [] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T20:57:28.489717Z | false | 263 | 127,135,121 | null | Python | MIT | [
"meta-learning"
] | floodsung/LearningToCompare_FSL | [
"description",
"github-topics",
"license-metadata",
"repository-metadata"
] | 1 | 2026-09-26T20:57:28.489717Z | [] | 2019-10-22T03:19:44Z | [] | ba561e0371776e641be1557a586964e24b1a8025 | 2026-10-04T17:18:56Z | gh-ml-readme-evidence-v2 | "ba561e0371776e641be1557a586964e24b1a8025" | [
"installation",
"other"
] | [
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-10-04T17:18:56Z | floodsung/LearningToCompare_FSL | official-paper-method-implementation | [
"ml-method-context",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 1,076 | [
"few-shot-learning",
"meta-learning"
] | 2026-09-05T07:28:58Z | https://github.com/floodsung/LearningToCompare_FSL | {"candidate_evidence":["census-rule:liberal-ml-ai-v1","classifier-method:meta-learning"],"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["meta-learning"]} |
[
"natural-language-processing",
"reinforcement-learning"
] | [
"machine-translation",
"neural-machine-translation",
"reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"nlp.machine-translation"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2018-05-03T08:24:03Z | Code for our paper "A Reinforcement Learning Approach to Interactive-Predictive Neural Machine Translation" | [
"natural-language-processing",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 4 | 131,964,452 | null | Python | null | [
"machine-translation",
"neural-machine-translation",
"reinforcement-learning"
] | heidelkin/BIPNMT | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-09-30T16:47:43Z | [] | 2018-07-15T18:36:07Z | [
"nlp.machine-translation"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 7 | [] | 2024-01-04T16:22:46Z | https://github.com/heidelkin/BIPNMT | null |
[
"linguistics",
"natural-language-processing",
"reinforcement-learning"
] | [
"language-modeling",
"policy-learning",
"reinforcement-learning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"recall.computational-linguistics",
"rl.core"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2018-05-08T16:04:53Z | Code for paper "End-to-End Reinforcement Learning for Automatic Taxonomy Induction", ACL 2018 | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 12 | 132,633,365 | null | Python | MIT | [
"policy-learning",
"reinforcement-learning"
] | morningmoni/TaxoRL | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-04T15:58:58Z | [] | 2018-10-10T15:36:06Z | [
"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 | 65 | [
"dynet",
"reinforcement-learning",
"taxonomy",
"taxonomy-construction",
"taxonomy-induction",
"wordnet"
] | 2025-08-07T12:22:48Z | https://github.com/morningmoni/TaxoRL | null |
[
"data-centric-ai",
"machine-learning"
] | [
"active-learning",
"convolutional-neural-network",
"neural-network",
"sample-selection"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.active-learning"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2018-06-12T11:47:04Z | Source code for ICLR 2018 Paper: Active Learning for Convolutional Neural Networks: A Core-Set Approach | [
"data-centric-ai",
"machine-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 47 | 137,063,829 | null | Python | MIT | [
"active-learning",
"convolutional-neural-network",
"neural-network",
"sample-selection"
] | ozansener/active_learning_coreset | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-09-28T18:50:00Z | [] | 2018-10-23T13:57:25Z | [
"general.active-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-context-only",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 282 | [] | 2026-08-24T20:31:26Z | https://github.com/ozansener/active_learning_coreset | null |
[
"deep-learning",
"graph-learning",
"information-retrieval",
"linguistics",
"natural-language-processing"
] | [
"graph-neural-network",
"language-modeling",
"message-passing",
"neural-network",
"question-answering",
"reading-comprehension"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description",
"nlp.question-answering",
"recall.computational-linguistics"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2018-06-12T14:15:16Z | Accompanying code for our COLING 2018 paper "Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering" | [
"graph-learning",
"information-retrieval",
"natural-language-processing"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 26 | 137,081,892 | null | Python | Apache-2.0 | [
"graph-neural-network",
"neural-network",
"question-answering",
"reading-comprehension"
] | UKPLab/coling2018-graph-neural-networks-question-answering | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-02T16:59:40Z | [] | 2020-02-12T13:06:17Z | [
"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 | 175 | [] | 2026-04-27T14:30:42Z | https://github.com/UKPLab/coling2018-graph-neural-networks-question-answering | null |
[
"reinforcement-learning"
] | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.deep"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2018-06-26T11:53:36Z | Official Tensorflow implementation of drl-RPN: Deep Reinforcement Learning of Region Proposal Networks (CVPR 2018 paper) | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 21 | 138,731,040 | null | Jupyter Notebook | MIT | [
"deep-reinforcement-learning",
"policy-learning",
"reinforcement-learning"
] | aleksispi/drl-rpn-tf | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-09-29T17:30:08Z | [] | 2024-02-22T13:54:19Z | [
"rl.deep"
] | 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 | 78 | [] | 2025-10-08T12:41:18Z | https://github.com/aleksispi/drl-rpn-tf | null |
[
"natural-language-processing",
"reinforcement-learning"
] | [
"natural-language-inference",
"reinforcement-learning",
"textual-entailment"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"nlp-natural-language-inference"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2018-08-05T08:39:57Z | Code for ACL 2018 paper "Discourse Marker Augmented Network with Reinforcement Learning for Natural Language Inference". | [
"natural-language-processing",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T14:50:46Z | false | 4 | 143,596,688 | null | Python | null | [
"natural-language-inference",
"reinforcement-learning",
"textual-entailment"
] | ZJULearning/DMP | [
"description",
"query-match",
"repository-metadata"
] | 2 | 2026-09-27T16:09:59Z | [] | 2018-08-05T09:21:45Z | [
"nlp-natural-language-inference"
] | 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 | 17 | [] | 2022-02-24T07:13:19Z | https://github.com/ZJULearning/DMP | null |
[
"automl",
"general-ml"
] | [
"few-shot-learning",
"meta-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.meta-learning"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2018-08-18T04:36:49Z | code for our IJCAI 2018 paper : "Lifelong Domain Word Embedding via Meta-Learning" | [
"automl",
"general-ml"
] | [
"embedding"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 4 | 145,190,284 | null | C | MIT | [
"few-shot-learning",
"meta-learning"
] | howardhsu/L-DEM | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-09-28T18:50:00Z | [] | 2019-04-02T21:40:48Z | [
"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 | 11 | [] | 2020-02-18T03:55:57Z | https://github.com/howardhsu/L-DEM | null |
[
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"neural-network",
"neural-network-pruning",
"pruning",
"structured-pruning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.pruning"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2018-09-22T19:20:00Z | This repository provides the implementation of the method proposed in our paper "Pruning Deep Neural Networks using Partial Least Squares" | [
"efficient-ml",
"machine-learning-systems",
"model-compression"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 7 | 149,909,429 | null | Python | MIT | [
"neural-network",
"neural-network-pruning",
"pruning",
"structured-pruning"
] | arturjordao/PruningNeuralNetworks | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-04T15:58:58Z | [] | 2020-08-21T19:11:16Z | [
"efficiency.pruning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 26 | [] | 2025-10-08T12:36:38Z | https://github.com/arturjordao/PruningNeuralNetworks | null |
[
"deep-learning",
"representation-learning"
] | [
"continual-learning",
"lifelong-learning",
"neural-network"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"general.continual-learning"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2018-10-12T15:47:58Z | This code refers to all experiments in our paper "Autonomous Deep Learning: Continual Learning Approach for Dynamic Environments" | [
"deep-learning",
"representation-learning"
] | [
"deep-learning",
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 1 | 152,773,989 | https://epubs.siam.org/doi/abs/10.1137/1.9781611975673.75 | MATLAB | NOASSERTION | [
"continual-learning",
"lifelong-learning",
"neural-network"
] | andriash001/ADL | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 1 | 2026-09-27T16:09:59Z | [] | 2019-11-23T14:54:51Z | [
"general.continual-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-context-only",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 2 | [
"autonomous-deep-learning",
"continual-learning",
"data-streams",
"deep-learning",
"deep-neural-networks"
] | 2022-01-25T09:12:32Z | https://github.com/andriash001/ADL | null |
[
"classical-ml",
"tabular-and-structured-data",
"tabular-ml"
] | [
"ensemble-learning",
"gradient-boosting"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"general.gradient-boosting"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2018-10-26T13:12:25Z | This is the official clone for the implementation of the NIPS18 paper Multi-Layered Gradient Boosting Decision Trees (mGBDT) . | [
"classical-ml",
"tabular-and-structured-data",
"tabular-ml"
] | [
"representation-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 25 | 154,836,868 | null | Python | null | [
"ensemble-learning",
"gradient-boosting"
] | kingfengji/mGBDT | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 5 | 2026-09-27T16:09:59Z | [] | 2018-11-19T07:28:10Z | [
"general.gradient-boosting"
] | 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 | 103 | [
"gbdt",
"gradient-boosting-decision-trees",
"mgbdt",
"representation-learning",
"target-propagation"
] | 2026-07-08T19:07:27Z | https://github.com/kingfengji/mGBDT | 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-v3 | true | selected-by-current-rule | 2018-11-05T15:16:38Z | Source code for our AAAI paper "Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks". | [
"deep-learning",
"graph-learning"
] | [
"deep-learning",
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T20:57:28.489717Z | false | 44 | 156,237,794 | null | C++ | null | [
"graph-neural-network",
"message-passing",
"neural-network"
] | chrsmrrs/k-gnn | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 2 | 2026-09-27T16:09:59Z | [] | 2022-03-22T12:39:40Z | [
"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 | 191 | [
"deep-learning",
"end-to-end",
"graph-algorithms",
"graph-neural-networks",
"graphs",
"higher-order",
"pytorch",
"weisfeier-leman",
"weisfeiler-lehman"
] | 2026-04-08T10:37:08Z | https://github.com/chrsmrrs/k-gnn | null |
[
"classical-ml",
"computer-vision",
"efficient-ml",
"model-compression",
"representation-learning"
] | [
"distillation",
"knowledge-distillation",
"metric-learning",
"neural-network",
"representation-learning"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation",
"general.metric-learning"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2018-11-24T13:25:12Z | Official pytorch Implementation of Relational Knowledge Distillation, CVPR 2019 | [
"classical-ml",
"computer-vision",
"representation-learning"
] | [
"deep-learning",
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 51 | 158,938,672 | null | Python | null | [
"distillation",
"knowledge-distillation",
"metric-learning",
"neural-network",
"representation-learning"
] | lenscloth/RKD | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 9 | 2026-09-28T18:50:00Z | [] | 2021-05-17T04:00:24Z | [
"general.metric-learning"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 420 | [
"computer-vision",
"deep-learning",
"deep-neural-networks",
"knowledge-distillation",
"metric-learning"
] | 2026-09-09T01:48:58Z | https://github.com/lenscloth/RKD | null |
[
"distributed-ml",
"privacy-and-federated-learning"
] | [
"collaborative-learning",
"federated-learning"
] | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2018-12-12T20:57:32Z | Source code for paper "How to Backdoor Federated Learning" (https://arxiv.org/abs/1807.00459) | [
"distributed-ml",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 63 | 161,544,036 | null | Python | MIT | [
"collaborative-learning",
"federated-learning"
] | ebagdasa/backdoor_federated_learning | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 5 | 2026-09-27T16:09:59Z | [] | 2024-07-25T10:14:50Z | [
"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 | 316 | [] | 2026-09-21T05:09:04Z | https://github.com/ebagdasa/backdoor_federated_learning | null |
[
"reinforcement-learning"
] | [
"distributional-reinforcement-learning",
"reinforcement-learning",
"value-based-reinforcement-learning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"rl.distributional"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2019-01-03T05:26:26Z | Rlee is a research framework built on top of PyTorch 1.0 for fast prototyping of novel reinforcement learning algorithms. | [
"reinforcement-learning"
] | [
"deep-learning",
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T10:28:13Z | false | 0 | 163,927,063 | https://www.endtoend.ai | Python | MIT | [
"distributional-reinforcement-learning",
"reinforcement-learning",
"value-based-reinforcement-learning"
] | seungjaeryanlee/rlee | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-09-26T10:28:13Z | [] | 2023-07-06T21:31:51Z | [
"rl.distributional"
] | 43eb8512d46971fe41345d02069e6d31ecd8a6ad | 2026-09-26T15:53:33Z | gh-ml-readme-evidence-v2 | "43eb8512d46971fe41345d02069e6d31ecd8a6ad" | [
"other"
] | [
"ml-method-context",
"paper-reference"
] | ok | 2026-09-26T15:53:33Z | seungjaeryanlee/rlee | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue",
"paper-reference"
] | include | ml-contribution-v5 | 2 | [
"deep-learning",
"deep-reinforcement-learning",
"python",
"pytorch",
"reinforcement-learning"
] | 2024-01-09T11:17:53Z | https://github.com/seungjaeryanlee/rlee | null |
[
"interpretability-and-safety",
"language",
"natural-language-processing"
] | [
"language-model",
"language-modeling",
"transformer"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.language-model",
"nlp.language-modeling"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2019-01-22T15:41:03Z | Official Pytorch implementation of (Roles and Utilization of Attention Heads in Transformer-based Neural Language Models), ACL 2020 | [
"interpretability-and-safety",
"language",
"natural-language-processing"
] | [
"deep-learning",
"transformer"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 4 | 167,019,640 | null | Python | MIT | [
"language-model",
"language-modeling",
"transformer"
] | heartcored98/transformer_anatomy | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-04T15:58:58Z | [] | 2025-03-21T21:57:18Z | [
"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 | 16 | [
"attention-head",
"interpretability",
"interpretable-deep-learning",
"transformer-encoder"
] | 2024-11-06T14:39:53Z | https://github.com/heartcored98/transformer_anatomy | null |
[
"automl",
"general-ml"
] | [
"few-shot-learning",
"meta-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.meta-learning"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2019-01-28T15:00:04Z | Official code for the paper titled "Meta Learning Deep Visual Words for Fast Video Object Segmentation" | [
"automl",
"general-ml"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 3 | 167,980,836 | null | Python | MIT | [
"few-shot-learning",
"meta-learning"
] | harkiratbehl/MetaVOS | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-09-28T18:50:00Z | [] | 2020-04-13T10:58:10Z | [
"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 | 23 | [] | 2024-04-09T09:03:15Z | https://github.com/harkiratbehl/MetaVOS | null |
[
"reinforcement-learning"
] | [
"batch-reinforcement-learning",
"offline-reinforcement-learning",
"reinforcement-learning"
] | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | [
"rl.offline"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2019-02-02T09:18:31Z | [AAAI 2022] The official implementation of "DeepThermal: Combustion Optimization for Thermal Power Generating Units Using Offline Reinforcement Learning" | [
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 2 | 168,814,886 | null | Python | null | [
"batch-reinforcement-learning",
"offline-reinforcement-learning",
"reinforcement-learning"
] | ryanxhr/DeepThermal | [
"description",
"github-topics",
"query-match",
"repository-metadata"
] | 7 | 2026-09-25T16:01:25Z | [] | 2022-07-21T07:40:15Z | [
"rl.offline"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"contribution-language",
"ml-method-cue",
"official-paper-implementation-cue"
] | include | ml-contribution-v5 | 21 | [
"model-based-reinforcement-learning",
"offline-reinforcement-learning",
"tensorflow"
] | 2026-07-20T07:41:57Z | https://github.com/ryanxhr/DeepThermal | null |
[
"language",
"natural-language-processing"
] | [
"language-model",
"language-modeling"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.language-model",
"nlp.language-modeling"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2019-02-21T07:01:20Z | R Package for exploring Samples of generated texts from Open AI's new GPT-2 language model | [
"language",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 3 | 171,818,191 | null | R | NOASSERTION | [
"language-model",
"language-modeling"
] | kanishkamisra/gpt2samples | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-04T15:58:58Z | [] | 2019-02-21T19:54:49Z | [
"llm.language-model",
"nlp.language-modeling"
] | cd5f93b18b4317a4c3493dad9ef1a06b67de26da | 2026-10-04T17:18:56Z | gh-ml-readme-evidence-v2 | "cd5f93b18b4317a4c3493dad9ef1a06b67de26da" | [
"installation",
"other",
"usage"
] | [
"ml-method-context"
] | ok | 2026-10-04T17:18:56Z | kanishkamisra/gpt2samples | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue"
] | include | ml-contribution-v5 | 8 | [] | 2019-11-10T23:50:33Z | https://github.com/kanishkamisra/gpt2samples | null |
[
"linguistics",
"natural-language-processing"
] | [
"language-modeling"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"recall.computational-linguistics"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2019-02-24T20:52:37Z | code for our NAACL 2019 paper: "BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis" | [
"linguistics",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-25T16:01:25Z | false | 110 | 172,388,988 | null | Python | Apache-2.0 | [
"language-modeling"
] | howardhsu/BERT-for-RRC-ABSA | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-10-01T17:50:46Z | [] | 2021-02-05T05:58:43Z | [
"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 | 461 | [
"bert",
"reading-comprehension",
"sentiment-analysis"
] | 2026-09-08T02:37:55Z | https://github.com/howardhsu/BERT-for-RRC-ABSA | null |
[
"computer-vision",
"interpretability-and-safety",
"trustworthy-ml"
] | [
"explainable-ai",
"interpretability"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"trust.explainable-ai"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2019-03-11T12:28:20Z | Official PyTorch implementation of "Visualizing the Decision-making Process in Deep Neural Decision Forest", CVPR 2019 Workshops on Explainable AI | [
"computer-vision",
"interpretability-and-safety",
"trustworthy-ml"
] | [
"deep-learning",
"machine-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 19 | 174,995,387 | null | Python | MIT | [
"explainable-ai",
"interpretability"
] | Nicholasli1995/VisualizingNDF | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-09-26T14:50:46Z | [] | 2022-03-12T06:32:59Z | [
"trust.explainable-ai"
] | b74f875c209061e18922f285e544a37f7b731b34 | 2026-09-25T22:08:11Z | gh-ml-readme-evidence-v1 | "b74f875c209061e18922f285e544a37f7b731b34" | [
"citation",
"other",
"results",
"usage"
] | [
"method-contribution",
"ml-method-context",
"official-implementation-claim",
"paper-reference"
] | ok | 2026-09-25T22:08:11Z | Nicholasli1995/VisualizingNDF | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-context-only",
"ml-method-context",
"official-implementation-claim",
"paper-reference"
] | include | ml-contribution-v5 | 73 | [
"age-estimation",
"cifar10",
"computer-vision",
"deep-learning",
"imageclassification",
"machine-learning",
"mnist",
"visualization"
] | 2026-03-26T17:25:26Z | https://github.com/Nicholasli1995/VisualizingNDF | null |
[
"generative-ai",
"language"
] | [
"fine-tuning",
"parameter-efficient-fine-tuning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.finetuning"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2019-03-25T02:05:03Z | Code for paper Fine-tune BERT for Extractive Summarization | [
"generative-ai",
"language"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 410 | 177,497,186 | null | Python | Apache-2.0 | [
"fine-tuning",
"parameter-efficient-fine-tuning"
] | nlpyang/BertSum | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-09-28T18:50:00Z | [] | 2022-01-11T07:58:23Z | [
"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 | 1,504 | [] | 2026-09-24T17:44:57Z | https://github.com/nlpyang/BertSum | null |
[
"automl",
"general-ml"
] | [
"few-shot-learning",
"meta-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.meta-learning"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2019-04-15T06:50:49Z | Code for paper "Learning to Guide: Guidance Law Based on Deep Meta-learning and Model Predictive Path Integral Control" | [
"automl",
"general-ml"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 2 | 181,428,954 | null | Python | MIT | [
"few-shot-learning",
"meta-learning"
] | tccliangchen/deep_meta-learning_guidance_law | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-09-28T18:50:00Z | [] | 2019-05-26T10:52:29Z | [
"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 | 12 | [] | 2026-09-26T16:45:43Z | https://github.com/tccliangchen/deep_meta-learning_guidance_law | null |
[
"distributed-ml",
"privacy-and-federated-learning"
] | [
"collaborative-learning",
"federated-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"trust.federated-learning"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2019-05-09T02:34:51Z | Code for paper "Interpret Federated Learning with Shapley Values" | [
"distributed-ml",
"privacy-and-federated-learning"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 5 | 185,712,679 | null | Jupyter Notebook | Apache-2.0 | [
"collaborative-learning",
"federated-learning"
] | crownpku/federated_shap | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-09-27T16:09:59Z | [] | 2019-05-18T01:58:52Z | [
"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 | 39 | [] | 2026-08-10T20:20:16Z | https://github.com/crownpku/federated_shap | null |
[
"reinforcement-learning"
] | [
"meta-learning",
"reinforcement-learning"
] | [
"description",
"github-topics",
"license-metadata",
"repository-metadata"
] | [] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2019-05-17T02:30:00Z | Implementation of our paper "Meta Reinforcement Learning with Task Embedding and Shared Policy" | [
"reinforcement-learning"
] | [
"machine-learning",
"reinforcement-learning",
"embedding"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-26T20:57:28.489717Z | false | 7 | 187,133,156 | null | Python | NOASSERTION | [
"meta-learning",
"reinforcement-learning"
] | llan-ml/tesp | [
"description",
"github-topics",
"license-metadata",
"repository-metadata"
] | 1 | 2026-09-26T20:57:28.489717Z | [] | 2019-05-17T11:21:17Z | [] | 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 | 35 | [
"meta-learning",
"meta-reinforcement",
"meta-rl",
"reinforcement-learning",
"tesp"
] | 2025-11-16T07:43:02Z | https://github.com/llan-ml/tesp | {"candidate_evidence":["census-rule:liberal-ml-ai-v1","classifier-domain:reinforcement-learning","classifier-method:meta-learning","classifier-method:reinforcement-learning","generic-ml-ai-term"],"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["meta-learning"]} |
[] | [
"meta-learning"
] | [
"description",
"github-topics",
"repository-metadata"
] | [] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2019-05-17T04:22:53Z | The code for paper "CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning" | [] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T20:57:28.489717Z | false | 33 | 187,146,048 | null | Python | null | [
"meta-learning"
] | icoz69/CaNet | [
"description",
"github-topics",
"repository-metadata"
] | 4 | 2026-10-02T18:08:49.004055Z | [] | 2020-06-06T10:55:56Z | [] | 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 | 197 | [
"meta-learning"
] | 2026-09-29T07:02:29Z | https://github.com/icoz69/CaNet | {"candidate_evidence":["census-rule:liberal-ml-ai-v1","classifier-method:meta-learning"],"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["meta-learning"]} |
[] | [
"transformer"
] | [
"description",
"github-topics",
"repository-metadata"
] | [] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2019-05-26T11:55:39Z | Official code for semantic parsing model "HSP" in paper "Complex Question Decomposition for Semantic Parsing"(ACL'19). | [] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-02T18:08:49.004055Z | false | 7 | 188,676,934 | null | Python | null | [
"transformer"
] | cairoHy/HSP | [
"description",
"github-topics",
"repository-metadata"
] | 1 | 2026-10-02T18:08:49.004055Z | [] | 2023-07-06T21:22:42Z | [] | 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 | 31 | [
"paper-implementations",
"semantic-parsing",
"sequence-to-sequence",
"tensorflow",
"transformer"
] | 2026-05-17T04:12:07Z | https://github.com/cairoHy/HSP | {"candidate_evidence":["census-rule:liberal-ml-ai-v1","classifier-method:transformer","generic-ml-ai-term"],"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["transformer"]} |
[
"3d-vision",
"computer-vision",
"efficient-ml",
"model-compression"
] | [
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"depth-estimation",
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"knowledge-distillation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.knowledge-distillation",
"vision.depth-estimation"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2019-05-27T05:31:22Z | The official code for the paper 'Structured Knowledge Distillation for Semantic Segmentation'. (CVPR 2019 ORAL) and extension to other tasks. | [
"3d-vision",
"computer-vision"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 106 | 188,776,740 | null | Python | BSD-2-Clause | [
"3d-perception",
"depth-estimation",
"distillation",
"knowledge-distillation"
] | irfanICMLL/structure_knowledge_distillation | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 8 | 2026-09-28T18:50:00Z | [] | 2020-04-20T06:49:03Z | [
"vision.depth-estimation"
] | 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 | 739 | [] | 2026-08-20T21:02:24Z | https://github.com/irfanICMLL/structure_knowledge_distillation | null |
[
"automl",
"general-ml"
] | [
"few-shot-learning",
"meta-learning"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.meta-learning"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2019-05-28T12:48:11Z | Code for paper "Learning to Guide: Guidance Law Based on Deep Meta-learning and Model Predictive Path Integral Control" | [
"automl",
"general-ml"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-28T18:50:00Z | false | 5 | 189,027,100 | null | Python | MIT | [
"few-shot-learning",
"meta-learning"
] | BenjyWP/deep_meta-learning_guidance_law | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-09-28T18:50:00Z | [] | 2019-05-26T10:52:29Z | [
"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 | 19 | [] | 2026-05-17T03:17:56Z | https://github.com/BenjyWP/deep_meta-learning_guidance_law | null |
[
"multi-agent-systems",
"reinforcement-learning"
] | [
"centralized-training",
"multi-agent-reinforcement-learning",
"reinforcement-learning"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"rl.multiagent"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2019-05-29T02:29:00Z | Source code for paper:Multi-agent reinforcement learning for liquidation strategy analysis | [
"multi-agent-systems",
"reinforcement-learning"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 16 | 189,136,065 | null | Jupyter Notebook | null | [
"centralized-training",
"multi-agent-reinforcement-learning",
"reinforcement-learning"
] | WenhangBao/Multi-Agent-RL-for-Liquidation | [
"description",
"query-match",
"repository-metadata"
] | 4 | 2026-09-26T14:50:46Z | [] | 2019-05-30T00:02:59Z | [
"rl.multiagent"
] | 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 | 60 | [] | 2026-06-05T11:59:57Z | https://github.com/WenhangBao/Multi-Agent-RL-for-Liquidation | null |
[
"generative-ai",
"language"
] | [
"language-model",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.transformers"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2019-05-30T16:38:49Z | Code for paper Hierarchical Transformers for Multi-Document Summarization in ACL2019 | [
"generative-ai",
"language"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-04T15:58:58Z | false | 41 | 189,448,473 | null | Python | Apache-2.0 | [
"language-model",
"transformer"
] | nlpyang/hiersumm | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-04T15:58:58Z | [] | 2019-08-20T14:17:33Z | [
"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 | 236 | [] | 2026-09-21T02:03:40Z | https://github.com/nlpyang/hiersumm | null |
[
"computer-vision",
"generative-ai",
"generative-modeling",
"multimodal",
"video"
] | [
"generative-modeling",
"text-to-video",
"video-generation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"multimodal.text-to-video",
"vision.video-generation"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2019-05-31T17:00:00Z | Code for our IJCAI 2019 paper entitled "Conditional GAN with Discriminative Filter Generation for Text-to-Video Synthesis" | [
"computer-vision",
"generative-ai",
"generative-modeling"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T14:50:46Z | false | 3 | 189,629,698 | null | Python | BSD-2-Clause | [
"generative-modeling",
"video-generation"
] | minrq/CGAN_Text2Video | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-09-28T18:50:00Z | [] | 2022-03-29T15:31:48Z | [
"vision.video-generation"
] | 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 | [] | 2024-01-02T17:14:26Z | https://github.com/minrq/CGAN_Text2Video | null |
[
"generative-modeling"
] | [
"transformer"
] | [
"description",
"github-topics",
"repository-metadata"
] | [] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2019-06-02T15:34:38Z | New Transformer network-based GAN for video generation. | [
"generative-modeling"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-26T20:57:28.489717Z | false | 1 | 189,863,981 | null | Jupyter Notebook | null | [
"transformer"
] | Nilanshrajput/Video_Generation_Transformer | [
"description",
"github-topics",
"repository-metadata"
] | 1 | 2026-09-26T20:57:28.489717Z | [] | 2020-06-01T05:57:24Z | [] | null | 2026-09-26T21:03:54Z | gh-ml-readme-evidence-v2 | null | [] | [] | missing | 2026-09-26T21:03:54Z | Nilanshrajput/Video_Generation_Transformer | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 3 | [
"gan",
"pytorch",
"singan",
"video-generation"
] | 2023-08-28T11:07:04Z | https://github.com/Nilanshrajput/Video_Generation_Transformer | {"candidate_evidence":["census-rule:liberal-ml-ai-v1","classifier-domain:generative-modeling","classifier-method:transformer","generic-ml-ai-term"],"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["video-generation"]} |
[
"generative-ai",
"graph-learning",
"language",
"linguistics",
"natural-language-processing"
] | [
"language-model",
"language-modeling",
"transformer"
] | [
"description",
"license-metadata",
"paper-reference",
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"repository-metadata"
] | [
"llm.transformers",
"recall.computational-linguistics"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2019-06-03T16:07:26Z | Code for ACL 2019 Paper: "COMET: Commonsense Transformers for Automatic Knowledge Graph Construction" https://arxiv.org/abs/1906.05317 | [
"generative-ai",
"graph-learning",
"language"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 125 | 190,039,121 | null | Python | Apache-2.0 | [
"language-model",
"transformer"
] | atcbosselut/comet-commonsense | [
"description",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 2 | 2026-10-04T15:58:58Z | [] | 2022-11-17T17:14:24Z | [
"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 | 688 | [] | 2026-08-30T14:41:34Z | https://github.com/atcbosselut/comet-commonsense | null |
[
"linguistics",
"natural-language-processing"
] | [
"abstractive-summarization",
"coreference-resolution",
"discourse-understanding",
"language-modeling",
"text-summarization"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"nlp.coreference-resolution",
"nlp.text-summarization",
"recall.computational-linguistics"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2019-06-17T20:39:02Z | Code for paper "Discourse-Aware Neural Extractive Text Summarization" (ACL20) | [
"linguistics",
"natural-language-processing"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 30 | 192,415,533 | null | Python | MIT | [
"language-modeling"
] | jiacheng-xu/DiscoBERT | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-10-01T17:50:46Z | [] | 2020-04-25T03:44:47Z | [
"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 | 166 | [
"acl2020",
"bert-model",
"microsoft-dynamics-365",
"natural-language-processing",
"text-summarization"
] | 2026-03-05T05:06:13Z | https://github.com/jiacheng-xu/DiscoBERT | null |
[
"language",
"natural-language-processing"
] | [
"language-model",
"language-modeling"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"llm.language-model",
"nlp.language-modeling"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2019-07-23T14:14:16Z | code for our 2019 paper: "Adapt or Get Left Behind: Domain Adaptation through BERT Language Model Finetuning for Aspect-Target Sentiment Classification" | [
"language",
"natural-language-processing"
] | [
"classifier"
] | ml_related_text | gh-ml-relevance-v1 | 2026-09-30T16:47:43Z | false | 46 | 198,444,756 | null | Python | MIT | [
"language-model",
"language-modeling"
] | deepopinion/domain-adapted-atsc | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-10-03T15:45:52Z | [] | 2023-08-14T22:06:22Z | [
"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",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 187 | [] | 2026-05-21T06:40:03Z | https://github.com/deepopinion/domain-adapted-atsc | null |
[
"computer-vision",
"generative-ai"
] | [
"generative-modeling",
"image-to-image-translation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.image-to-image-translation"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2019-07-26T00:33:54Z | Official Tensorflow implementation of U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation (ICLR 2020) | [
"computer-vision",
"generative-ai"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 1,007 | 198,919,091 | null | Python | MIT | [
"generative-modeling",
"image-to-image-translation"
] | taki0112/UGATIT | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 7 | 2026-10-01T17:50:46Z | [] | 2021-05-20T03:23:05Z | [
"vision.image-to-image-translation"
] | 8705566f45d96a473d27a6b38ac2108592220fa3 | 2026-09-25T22:08:11Z | gh-ml-readme-evidence-v1 | "8705566f45d96a473d27a6b38ac2108592220fa3" | [
"citation",
"dataset",
"installation",
"method",
"other",
"usage"
] | [
"method-contribution",
"ml-method-context",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-09-25T22:08:11Z | taki0112/UGATIT | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-method-context",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 6,113 | [] | 2026-09-17T05:00:39Z | https://github.com/taki0112/UGATIT | null |
[
"computer-vision",
"deep-learning",
"graph-learning",
"science-and-engineering"
] | [
"graph-neural-network",
"graph-representation-learning",
"message-passing",
"neural-network"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description",
"graph.representation-learning"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2019-07-29T06:36:04Z | Many underlying relationships among data in several areas of science and engineering, e.g., computer vision, molecular chemistry, molecular biology, pattern recognition, and data mining, can be represented in terms of graphs. In this paper ,a new neural network model has propose which called graph neural network (GNN) | [
"computer-vision",
"deep-learning",
"graph-learning",
"science-and-engineering"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 9 | 199,393,193 | null | Jupyter Notebook | null | [
"graph-neural-network",
"graph-representation-learning",
"message-passing",
"neural-network"
] | Erfaan-Rostami/Hypergraph-and-Graph-Neural-Network-HGNN-GNN-- | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-09-27T16:09:59Z | [] | 2026-05-21T14:24:22Z | [
"graph.gnn-description",
"graph.representation-learning"
] | 2aa544589c2b902b837d2c56e9a1d57a5e7af014 | 2026-09-27T17:16:20Z | gh-ml-readme-evidence-v2 | "2aa544589c2b902b837d2c56e9a1d57a5e7af014" | [
"other"
] | [
"ml-method-context",
"paper-reference",
"survey-cue"
] | ok | 2026-09-27T17:16:20Z | Erfaan-Rostami/Hypergraph-and-Graph-Neural-Network-HGNN-GNN-- | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue",
"paper-reference",
"survey-cue"
] | include | ml-contribution-v5 | 58 | [] | 2026-06-02T14:37:23Z | https://github.com/Erfaan-Rostami/Hypergraph-and-Graph-Neural-Network-HGNN-GNN-- | null |
[
"computer-vision",
"generative-ai"
] | [
"generative-modeling",
"image-to-image-translation"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"vision.image-to-image-translation"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2019-07-29T07:44:56Z | Official PyTorch implementation of U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation | [
"computer-vision",
"generative-ai"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 460 | 199,404,030 | null | Python | MIT | [
"generative-modeling",
"image-to-image-translation"
] | znxlwm/UGATIT-pytorch | [
"description",
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"query-match",
"repository-metadata"
] | 7 | 2026-10-01T17:50:46Z | [] | 2023-03-16T02:38:05Z | [
"vision.image-to-image-translation"
] | 3ad2faea9f204973dc0c11ccee261656a3dd9b14 | 2026-09-25T22:08:11Z | gh-ml-readme-evidence-v1 | "3ad2faea9f204973dc0c11ccee261656a3dd9b14" | [
"method",
"other",
"usage"
] | [
"method-contribution",
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-09-25T22:08:11Z | znxlwm/UGATIT-pytorch | readme-supported-paper-method-implementation | [
"contribution-language",
"method-contribution",
"ml-method-context",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 2,533 | [] | 2026-09-24T17:45:47Z | https://github.com/znxlwm/UGATIT-pytorch | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2019-08-05T07:56:32Z | a novel DTA predition method using graph neural network | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 42 | 200,609,566 | null | Python | null | [
"graph-neural-network",
"message-passing",
"neural-network"
] | 595693085/DGraphDTA | [
"description",
"query-match",
"repository-metadata"
] | 7 | 2026-09-27T16:09:59Z | [] | 2023-07-12T16:23:50Z | [
"graph.gnn-description"
] | 1b63165b56276c5dd5805578ecb1ab77705e45e6 | 2026-09-26T15:53:33Z | gh-ml-readme-evidence-v2 | "1b63165b56276c5dd5805578ecb1ab77705e45e6" | [
"other"
] | [
"method-contribution",
"ml-method-context"
] | ok | 2026-09-26T15:53:33Z | 595693085/DGraphDTA | specific-method-with-novelty-claim | [
"method-contribution",
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue"
] | include | ml-contribution-v5 | 77 | [] | 2026-07-10T04:59:29Z | https://github.com/595693085/DGraphDTA | 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-v3 | true | selected-by-current-rule | 2019-08-11T09:13:23Z | DrBC: A novel graph neural network approach to identify high Betweenness Centraliy (BC) nodes ( CIKM'19 ) | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 7 | 201,742,627 | null | Python | MIT | [
"graph-neural-network",
"message-passing",
"neural-network"
] | fanchangjun/DrBC | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-09-27T16:09:59Z | [] | 2020-12-20T13:46:08Z | [
"graph.gnn-description"
] | 77539734a953855117f31d9a065580a9741bcd10 | 2026-09-27T17:16:20Z | gh-ml-readme-evidence-v2 | "77539734a953855117f31d9a065580a9741bcd10" | [
"other"
] | [
"ml-method-context",
"paper-reference"
] | ok | 2026-09-27T17:16:20Z | fanchangjun/DrBC | specific-method-with-novelty-claim | [
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue",
"paper-reference"
] | include | ml-contribution-v5 | 33 | [] | 2025-09-09T17:13:17Z | https://github.com/fanchangjun/DrBC | null |
[
"computer-vision"
] | [
"distillation",
"keypoint-detection",
"knowledge-distillation",
"pose-estimation"
] | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"vision.pose-estimation"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2019-08-14T12:28:56Z | Official pytorch Code for CVPR2019 paper "Fast Human Pose Estimation" https://arxiv.org/abs/1811.05419 | [
"computer-vision"
] | [
"deep-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 67 | 202,346,299 | null | Cuda | MIT | [
"distillation",
"keypoint-detection",
"knowledge-distillation",
"pose-estimation"
] | ilovepose/fast-human-pose-estimation.pytorch | [
"description",
"github-topics",
"license-metadata",
"paper-reference",
"query-match",
"repository-metadata"
] | 7 | 2026-09-28T18:50:00Z | [] | 2022-09-16T07:27:38Z | [
"vision.pose-estimation"
] | 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 | 399 | [
"coco-keypoints-detection",
"deep-learning",
"fast-pose-distillation",
"human-pose-estimation",
"knowledge-distillation",
"mpii-dataset",
"mscoco-keypoint"
] | 2026-08-17T13:40:21Z | https://github.com/ilovepose/fast-human-pose-estimation.pytorch | null |
[
"linguistics",
"natural-language-processing"
] | [
"language-modeling",
"transformer"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"recall.computational-linguistics"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2019-08-14T12:59:59Z | The PyTorch code for paper "Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations" | [
"linguistics",
"natural-language-processing"
] | [
"transformer"
] | ml_related_text | gh-ml-relevance-v1 | 2026-10-01T17:50:46Z | false | 27 | 202,351,503 | null | Python | GPL-3.0 | [
"language-modeling",
"transformer"
] | zhongpeixiang/KET | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 1 | 2026-10-01T17:50:46Z | [] | 2019-11-25T08:56:09Z | [
"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 | 80 | [] | 2025-09-15T02:06:52Z | https://github.com/zhongpeixiang/KET | null |
[
"automl",
"deep-learning",
"general-ml",
"graph-learning",
"representation-learning"
] | [
"few-shot-learning",
"graph-neural-network",
"message-passing",
"meta-learning",
"neural-network",
"representation-learning",
"semi-supervised-learning"
] | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.meta-learning",
"general.semi-supervised-learning",
"graph.gnn-description"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2019-08-15T03:04:58Z | official PyTorch implementation of paper "Continual Meta-Learning with Bayesian Graph Neural Networks" (AAAI2020) | [
"general-ml",
"graph-learning",
"representation-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 17 | 202,464,515 | null | Python | MIT | [
"graph-neural-network",
"meta-learning",
"neural-network",
"representation-learning",
"semi-supervised-learning"
] | Luoyadan/BGNN-AAAI | [
"description",
"github-topics",
"license-metadata",
"query-match",
"repository-metadata"
] | 5 | 2026-10-03T15:45:52Z | [] | 2020-06-18T09:26:50Z | [
"general.semi-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 | 65 | [
"few-shot-learning",
"graph-neural-networks",
"meta-learning"
] | 2026-09-27T07:45:00Z | https://github.com/Luoyadan/BGNN-AAAI | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2019-09-06T06:19:32Z | Source code for paper "Knowledge-aware Heterogeneous Graph Neural Networks for Inferring Substitutable and Complementary Items" | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-27T16:09:59Z | false | 0 | 206,726,521 | null | null | null | [
"graph-neural-network",
"message-passing",
"neural-network"
] | fanyubupt/KHGNN | [
"description",
"query-match",
"repository-metadata"
] | 1 | 2026-09-27T16:09:59Z | [] | 2019-09-06T06:19:33Z | [
"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 | [] | 2019-09-06T06:19:32Z | https://github.com/fanyubupt/KHGNN | null |
[] | [
"distillation",
"knowledge-distillation"
] | [
"description",
"github-topics",
"license-metadata",
"repository-metadata"
] | [] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2019-09-10T03:26:42Z | Official PyTorch implementation of "A Comprehensive Overhaul of Feature Distillation" (ICCV 2019) | [] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-26T20:57:28.489717Z | false | 74 | 207,457,047 | null | Python | MIT | [
"distillation",
"knowledge-distillation"
] | clovaai/overhaul-distillation | [
"description",
"github-topics",
"license-metadata",
"repository-metadata"
] | 1 | 2026-09-26T20:57:28.489717Z | [] | 2020-06-23T09:33:49Z | [] | 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 | 421 | [
"iccv2019",
"knowledge-distillation",
"knowledge-transfer",
"network-compression",
"teacher-student"
] | 2026-09-19T05:38:46Z | https://github.com/clovaai/overhaul-distillation | {"candidate_evidence":["census-rule:liberal-ml-ai-v1","classifier-method:distillation","classifier-method:knowledge-distillation"],"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["knowledge-distillation"]} |
[
"automl",
"deep-learning",
"efficient-ml",
"graph-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-architecture-search",
"neural-network"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"efficiency.neural-architecture-search",
"graph.gnn-description"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2019-09-16T17:20:34Z | Code for paper: Neural Architecture Search in Graph Neural Networks (BRACIS 2020) | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-25T16:01:25Z | false | 3 | 208,856,717 | null | Jupyter Notebook | Apache-2.0 | [
"graph-neural-network",
"message-passing",
"neural-network"
] | mhnnunes/nas_gnn | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 2 | 2026-09-27T16:09:59Z | [] | 2023-07-06T21:27:58Z | [
"graph.gnn-description"
] | null | null | null | null | null | null | null | null | null | official-paper-method-implementation | [
"ml-method-cue",
"official-paper-implementation-cue",
"paper-and-code-cue"
] | include | ml-contribution-v5 | 19 | [] | 2025-08-06T10:51:36Z | https://github.com/mhnnunes/nas_gnn | null |
[
"computer-vision",
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [
"change-detection",
"forecasting",
"image-analysis",
"sequence-modeling",
"time-series-forecasting"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"applied.time-series-forecasting",
"timeseries.forecasting",
"vision.change-detection"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2019-09-17T11:23:55Z | Code for our NeurIPS 2019 paper "Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models" | [
"computer-vision",
"forecasting",
"time-series",
"time-series-and-forecasting"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 77 | 209,034,747 | null | Python | NOASSERTION | [
"change-detection",
"forecasting",
"image-analysis",
"sequence-modeling"
] | vincent-leguen/DILATE | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 6 | 2026-10-01T17:50:46Z | [] | 2020-10-14T12:33:08Z | [
"timeseries.forecasting",
"vision.change-detection"
] | 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 | 400 | [] | 2026-09-10T14:36:50Z | https://github.com/vincent-leguen/DILATE | null |
[
"classical-ml"
] | [
"kernel-methods",
"support-vector-machine"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"general.support-vector-machine"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2019-10-02T17:09:43Z | Code for paper: "Support Vector Machines, Wasserstein's distance and gradient-penalty GANs maximize a margin" | [
"classical-ml"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 24 | 212,397,158 | null | Python | MIT | [
"kernel-methods",
"support-vector-machine"
] | AlexiaJM/MaximumMarginGANs | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 4 | 2026-09-29T17:30:08Z | [] | 2020-03-12T14:52:28Z | [
"general.support-vector-machine"
] | 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 | 180 | [] | 2026-09-23T15:32:20Z | https://github.com/AlexiaJM/MaximumMarginGANs | null |
[
"general-ml"
] | [
"novel-method",
"random-forest"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"general.novel-method"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2019-10-05T10:16:30Z | The code implements a novel method for converting random forest into a single decision tree | [
"general-ml"
] | [] | no_text_signal | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 4 | 212,979,949 | null | Python | null | [
"novel-method",
"random-forest"
] | sagyome/forest_based_tree | [
"description",
"query-match",
"repository-metadata"
] | 4 | 2026-09-25T16:01:25Z | [] | 2020-01-24T21:55:20Z | [
"general.novel-method"
] | dcba2ed3ddb2180c4260f3dac349e1a398adce38 | 2026-09-26T15:53:33Z | gh-ml-readme-evidence-v2 | "dcba2ed3ddb2180c4260f3dac349e1a398adce38" | [
"other"
] | [] | ok | 2026-09-26T15:53:33Z | sagyome/forest_based_tree | specific-method-with-novelty-claim | [
"contribution-language",
"method-tied-novelty-claim",
"ml-method-cue"
] | include | ml-contribution-v5 | 10 | [] | 2026-07-06T18:12:51Z | https://github.com/sagyome/forest_based_tree | null |
[
"reinforcement-learning"
] | [
"meta-learning",
"reinforcement-learning"
] | [
"description",
"github-topics",
"repository-metadata"
] | [] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2019-10-09T16:47:32Z | MetaGenRL, a novel meta reinforcement learning algorithm. Unlike prior work, MetaGenRL can generalize to new environments that are entirely different from those used for meta-training. | [
"reinforcement-learning"
] | [
"machine-learning",
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T18:03:22.392067Z | false | 14 | 213,972,367 | null | Python | null | [
"meta-learning",
"reinforcement-learning"
] | louiskirsch/metagenrl | [
"description",
"github-topics",
"repository-metadata"
] | 1 | 2026-09-30T18:03:22.392067Z | [] | 2020-06-05T14:29:31Z | [] | 8900c20fe30e2db939d4caa95f138301a3c33f67 | 2026-09-30T18:16:34Z | gh-ml-readme-evidence-v2 | "8900c20fe30e2db939d4caa95f138301a3c33f67" | [
"installation",
"other"
] | [
"ml-method-context",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | ok | 2026-09-30T18:16:34Z | louiskirsch/metagenrl | specific-method-with-novelty-claim | [
"contribution-language",
"method-tied-novelty-claim",
"ml-method-context",
"ml-method-cue",
"official-implementation-claim",
"paper-code-relationship",
"paper-reference"
] | include | ml-contribution-v5 | 71 | [
"machine-learning",
"meta-learning",
"reinforcement-learning"
] | 2026-04-30T06:33:45Z | https://github.com/louiskirsch/metagenrl | {"candidate_evidence":["census-rule:liberal-ml-ai-v1","classifier-domain:reinforcement-learning","classifier-method:meta-learning","classifier-method:reinforcement-learning","generic-ml-ai-term"],"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["meta-learning"]} |
[
"automl",
"general-ml"
] | [
"few-shot-learning",
"meta-learning"
] | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | [
"general.meta-learning"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2019-10-10T13:13:56Z | Official pytorch implementation of the paper "Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels" (NeurIPS 2020) | [
"automl",
"general-ml"
] | [
"deep-learning",
"classifier"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-30T18:03:22.392067Z | false | 31 | 214,187,045 | https://arxiv.org/abs/1910.05199 | Python | null | [
"few-shot-learning",
"meta-learning"
] | BayesWatch/deep-kernel-transfer | [
"description",
"github-topics",
"paper-reference",
"query-match",
"repository-metadata"
] | 3 | 2026-10-03T15:45:52Z | [] | 2022-01-19T23:32:46Z | [
"general.meta-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 | 208 | [
"bayesian-methods",
"classification",
"deep-kernel-learning",
"deep-learning",
"few-shot-learning",
"gaussian-processes",
"gpytorch",
"kernel",
"kernel-methods",
"kernels",
"meta-learning",
"one-shot-learning",
"paper",
"regression",
"shot-learning",
"uncertainty",
"uncertainty-quant... | 2026-07-03T19:31:18Z | https://github.com/BayesWatch/deep-kernel-transfer | null |
[
"reinforcement-learning",
"robotics"
] | [
"domain-randomization",
"reinforcement-learning",
"sim-to-real"
] | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | [
"robotics.domain-randomization"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2019-10-18T10:52:12Z | Code associated with our paper "Robust Domain Randomization for Reinforcement Learning" | [
"reinforcement-learning",
"robotics"
] | [
"reinforcement-learning"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-24T10:49:00Z | false | 3 | 216,003,313 | null | Python | MIT | [
"domain-randomization",
"reinforcement-learning",
"sim-to-real"
] | uncharted-technologies/robust-domain-randomization | [
"description",
"license-metadata",
"query-match",
"repository-metadata"
] | 3 | 2026-09-25T16:01:25Z | [] | 2022-11-22T04:35:32Z | [
"robotics.domain-randomization"
] | 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 | 12 | [] | 2025-06-18T07:06:45Z | https://github.com/uncharted-technologies/robust-domain-randomization | null |
[
"deep-learning",
"graph-learning"
] | [
"graph-neural-network",
"message-passing",
"neural-network"
] | [
"description",
"query-match",
"repository-metadata"
] | [
"graph.gnn-description"
] | false | candidate | ml-candidate-v3 | true | selected-by-current-rule | 2019-10-30T19:06:39Z | Official PyTorch Implementation for "GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking with 2D-3D Multi-Feature Learning", CVPR 2020 | [
"deep-learning",
"graph-learning"
] | [
"neural-network"
] | direct_ml_text | gh-ml-relevance-v1 | 2026-09-29T17:30:08Z | false | 6 | 218,602,535 | http://www.xinshuoweng.com/ | null | null | [
"graph-neural-network",
"message-passing",
"neural-network"
] | xinshuoweng/GNN3DMOT | [
"description",
"query-match",
"repository-metadata"
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[
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"description",
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