modelomics/gh-ml · Datasets at Hugging Face

[ "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" ]

3

2026-09-26T14:50:46Z

[]

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

[ "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" ]

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", "generative-modeling", "probabilistic-ml" ]

[ "bayesian-deep-learning", "uncertainty-estimation" ]

[ "description", "github-topics", "paper-reference", "query-match", "repository-metadata" ]

[ "general.bayesian-deep-learning" ]

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)

[ "deep-learning", "generative-modeling", "probabilistic-ml" ]

[ "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

[ "bayesian-deep-learning", "uncertainty-estimation" ]

jmtomczak/vae_householder_flow

[ "description", "github-topics", "paper-reference", "query-match", "repository-metadata" ]

4

2026-09-26T10:28:13Z

[]

2017-01-26T09:18:13Z

[ "general.bayesian-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", "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

[ "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", "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" ]

null

official-paper-method-implementation

[ "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]

include

ml-contribution-v5

34

[]

2026-09-13T06:40:15Z

https://github.com/tmoer/multimodal_varinf

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

[ "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" ]

4

2026-09-26T14:50:46Z

[]

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

[ "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", "model-compression", "quantization" ]

antspy/quantized_distillation

[ "description", "license-metadata", "query-match", "repository-metadata" ]

5

2026-09-25T16:01:25Z

[]

2024-07-25T10:12:38Z

[ "efficiency.model-compression", "efficiency.quantization" ]

null

official-paper-method-implementation

[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]

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" ]

null

official-paper-method-implementation

[ "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]

include

ml-contribution-v5

90

[]

2025-04-05T22:43:41Z

https://github.com/htung0101/3d_smpl

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

[]

null

official-paper-method-implementation

[ "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]

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"]}

[ "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" ]

3

2026-09-25T16:01:25Z

[]

2018-10-23T13:57:25Z

[ "general.active-learning" ]

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

[ "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" ]

1

2026-09-26T14:50:46Z

[]

2018-08-05T09:21:45Z

[ "nlp-natural-language-inference" ]

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

[ "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" ]

4

2026-09-26T14:50:46Z

[]

2018-11-19T07:28:10Z

[ "general.gradient-boosting" ]

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

[ "graph-learning" ]

[ "graph-neural-network", "neural-network" ]

[ "description", "github-topics", "repository-metadata" ]

[]

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".

[ "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", "neural-network" ]

chrsmrrs/k-gnn

[ "description", "github-topics", "repository-metadata" ]

1

2026-09-26T20:57:28.489717Z

[]

2022-03-22T12:39:40Z

[]

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

{"candidate_evidence":["census-rule:liberal-ml-ai-v1","classifier-domain:graph-learning","classifier-method:graph-neural-network","classifier-method:neural-network","generic-ml-ai-term"],"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["graph-neural-networks"]}

[ "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

[ "computer-vision", "efficient-ml", "model-compression" ]

[ "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", "neural-network" ]

lenscloth/RKD

[ "description", "github-topics", "query-match", "repository-metadata" ]

7

2026-09-26T14:50:46Z

[]

2021-05-17T04:00:24Z

[ "efficiency.knowledge-distillation" ]

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" ]

4

2026-09-26T14:50:46Z

[]

2024-07-25T10:14:50Z

[ "trust.federated-learning" ]

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

[ "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" ]

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" ]

4

2026-09-25T16:01:25Z

[]

2022-07-21T07:40:15Z

[ "rl.offline" ]

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

[ "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" ]

1

2026-09-25T16:01:25Z

[]

2021-02-05T05:58:43Z

[ "recall.computational-linguistics" ]

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" ]

3

2026-09-25T16:01:25Z

[]

2022-01-11T07:58:23Z

[ "llm.finetuning" ]

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

[ "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

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" ]

1

2026-09-26T20:57:28.489717Z

[]

2020-06-06T10:55:56Z

[]

null

official-paper-method-implementation

[ "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]

include

ml-contribution-v5

198

[ "cvpr2019", "few-shot-learning", "meta-learning", "segmentation" ]

2026-06-16T07:14:08Z

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"]}

[ "3d-vision", "computer-vision", "efficient-ml", "model-compression" ]

[ "3d-perception", "depth-estimation", "distillation", "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", "efficient-ml", "model-compression" ]

[]

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" ]

6

2026-09-26T14:50:46Z

[]

2020-04-20T06:49:03Z

[ "efficiency.knowledge-distillation", "vision.depth-estimation" ]

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

[ "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

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", "generative-modeling", "multimodal", "video" ]

[ "text-to-video", "video-generation" ]

[ "description", "license-metadata", "query-match", "repository-metadata" ]

[ "multimodal.text-to-video" ]

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"

[ "generative-ai", "generative-modeling", "multimodal", "video" ]

[]

no_text_signal

gh-ml-relevance-v1

2026-09-26T14:50:46Z

false

3

189,629,698

null

Python

BSD-2-Clause

[ "text-to-video", "video-generation" ]

minrq/CGAN_Text2Video

[ "description", "license-metadata", "query-match", "repository-metadata" ]

1

2026-09-26T14:50:46Z

[]

2022-03-29T15:31:48Z

[ "multimodal.text-to-video" ]

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"]}

[ "natural-language-processing" ]

[ "abstractive-summarization", "coreference-resolution", "discourse-understanding", "text-summarization" ]

[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]

[ "nlp.coreference-resolution", "nlp.text-summarization" ]

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)

[ "natural-language-processing" ]

[]

no_text_signal

gh-ml-relevance-v1

2026-09-24T10:49:00Z

false

30

192,415,533

null

Python

MIT

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jiacheng-xu/DiscoBERT

[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]

4

2026-09-26T14:50:46Z

[]

2020-04-25T03:44:47Z

[ "nlp.coreference-resolution", "nlp.text-summarization" ]

null

official-paper-method-implementation

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ml-contribution-v5

166

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2026-03-05T05:06:13Z

https://github.com/jiacheng-xu/DiscoBERT

null

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[ "generative-modeling", "image-to-image-translation" ]

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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)

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[]

no_text_signal

gh-ml-relevance-v1

2026-09-24T10:49:00Z

false

1,007

198,919,091

null

Python

MIT

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taki0112/UGATIT

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4

2026-09-26T14:50:46Z

[]

2021-05-20T03:23:05Z

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8705566f45d96a473d27a6b38ac2108592220fa3

2026-09-25T22:08:11Z

gh-ml-readme-evidence-v1

"8705566f45d96a473d27a6b38ac2108592220fa3"

[ "citation", "dataset", "installation", "method", "other", "usage" ]

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ok

2026-09-25T22:08:11Z

taki0112/UGATIT

readme-supported-paper-method-implementation

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include

ml-contribution-v5

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[]

2026-09-17T05:00:39Z

https://github.com/taki0112/UGATIT

null

[ "computer-vision", "generative-ai" ]

[ "generative-modeling", "image-to-image-translation" ]

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[ "vision.image-to-image-translation" ]

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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

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4

2026-09-26T14:50:46Z

[]

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" ]

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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

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[ "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" ]

6

2026-09-26T14:50:46Z

[]

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

[ "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" ]

5

2026-09-26T14:50:46Z

[]

2022-09-16T07:27:38Z

[ "vision.pose-estimation" ]

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

[]

[ "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

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", "efficient-ml", "graph-learning" ]

[ "graph-neural-network", "neural-architecture-search", "neural-network" ]

[ "description", "license-metadata", "query-match", "repository-metadata" ]

[ "efficiency.neural-architecture-search" ]

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)

[ "automl", "efficient-ml", "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", "neural-architecture-search", "neural-network" ]

mhnnunes/nas_gnn

[ "description", "license-metadata", "query-match", "repository-metadata" ]

1

2026-09-25T16:01:25Z

[]

2023-07-06T21:27:58Z

[ "efficiency.neural-architecture-search" ]

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

[ "forecasting", "time-series", "time-series-and-forecasting" ]

[ "forecasting", "sequence-modeling", "time-series-forecasting" ]

[ "description", "license-metadata", "query-match", "repository-metadata" ]

[ "applied.time-series-forecasting", "timeseries.forecasting" ]

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"

[ "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

[ "forecasting", "sequence-modeling" ]

vincent-leguen/DILATE

[ "description", "license-metadata", "query-match", "repository-metadata" ]

4

2026-09-26T14:50:46Z

[]

2020-10-14T12:33:08Z

[ "timeseries.forecasting" ]

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" ]

3

2026-09-25T16:01:25Z

[]

2020-03-12T14:52:28Z

[ "general.support-vector-machine" ]

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", "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

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

[ "efficient-ml", "machine-learning-systems", "model-compression" ]

[ "neural-network-pruning", "pruning", "structured-pruning" ]

[ "description", "github-topics", "query-match", "repository-metadata" ]

[ "efficiency.pruning" ]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2019-11-17T10:35:12Z

Pytorch implementation of our paper accepted by CVPR 2020 (Oral) -- HRank: Filter Pruning using High-Rank Feature Map

[ "efficient-ml", "machine-learning-systems", "model-compression" ]

[]

no_text_signal

gh-ml-relevance-v1

2026-09-24T10:49:00Z

false

50

222,231,732

https://128.84.21.199/abs/2002.10179

Python

null

[ "neural-network-pruning", "pruning", "structured-pruning" ]

lmbxmu/HRank

[ "description", "github-topics", "query-match", "repository-metadata" ]

3

2026-09-25T16:01:25Z

[]

2021-02-11T16:48:26Z

[ "efficiency.pruning" ]

null

official-paper-method-implementation

[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]

include

ml-contribution-v5

257

[ "acceleration", "compression", "pruning" ]

2026-01-30T12:38:52Z

https://github.com/lmbxmu/HRank

null

[ "computer-vision" ]

[ "self-supervised-learning" ]

[ "description", "github-topics", "license-metadata", "repository-metadata" ]

[]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2019-11-22T06:08:58Z

Code for ICLR 2020 paper "VL-BERT: Pre-training of Generic Visual-Linguistic Representations".

[ "computer-vision" ]

[ "representation-learning", "self-supervised-learning" ]

direct_ml_text

gh-ml-relevance-v1

2026-09-26T20:57:28.489717Z

false

109

223,335,609

null

Jupyter Notebook

MIT

[ "self-supervised-learning" ]

jackroos/VL-BERT

[ "description", "github-topics", "license-metadata", "repository-metadata" ]

1

2026-09-26T20:57:28.489717Z

[]

2023-05-22T22:33:35Z

[]

null

official-paper-method-implementation

[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]

include

ml-contribution-v5

740

[ "bert", "iclr2020", "pre-training", "pytorch", "representation-learning", "self-supervised-learning", "vision-and-language", "vl-bert" ]

2026-08-07T14:17:29Z

https://github.com/jackroos/VL-BERT

{"candidate_evidence":["census-rule:liberal-ml-ai-v1","classifier-domain:computer-vision","classifier-method:self-supervised-learning"],"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["self-supervised-learning"]}

[ "multimodal", "reinforcement-learning", "robotics" ]

[ "environment-modeling", "reinforcement-learning", "world-model" ]

[ "description", "github-topics", "query-match", "repository-metadata" ]

[ "multimodal.world-models" ]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2019-11-26T12:08:03Z

A new version of world models using Echo-state networks and random weight-fixed CNNs

[ "multimodal", "reinforcement-learning", "robotics" ]

[ "reinforcement-learning" ]

direct_ml_text

gh-ml-relevance-v1

2026-09-26T14:50:46Z

false

1

224,183,627

null

Python

null

[ "environment-modeling", "reinforcement-learning", "world-model" ]

Shahdsaf/Semi-Supervised-World-Models

[ "description", "github-topics", "query-match", "repository-metadata" ]

2

2026-09-26T14:50:46Z

[]

2020-06-01T23:12:22Z

[ "multimodal.world-models" ]

9deeb85227d7695efc1e56d4f574db208e1f1b54

2026-09-26T15:53:33Z

gh-ml-readme-evidence-v2

"9deeb85227d7695efc1e56d4f574db208e1f1b54"

[ "other", "results" ]

[ "ml-method-context", "model-training-artifact", "paper-reference" ]

ok

2026-09-26T15:53:33Z

Shahdsaf/Semi-Supervised-World-Models

specific-method-with-novelty-claim

[ "method-tied-novelty-claim", "ml-method-context", "ml-method-cue", "model-training-artifact", "paper-reference" ]

include

ml-contribution-v5

5

[ "car-racing", "cma-es", "echo-state-networks", "gym", "mdnrnn", "ppo", "proximal-policy-optimization", "rcrc", "reinforcement-learning", "reservoir-computing", "rnn", "vae-pytorch", "variational-autoencoder", "world-models" ]

2025-05-28T04:16:27Z

https://github.com/Shahdsaf/Semi-Supervised-World-Models

null

[ "natural-language-processing" ]

[ "contrastive-learning", "self-supervised-learning" ]

[ "description", "github-topics", "license-metadata", "repository-metadata" ]

[]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2019-11-26T18:36:43Z

The corresponding code from our paper "DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations". Do not hesitate to open an issue if you run into any trouble!

[ "natural-language-processing" ]

[ "representation-learning", "self-supervised-learning", "embedding", "transformer" ]

direct_ml_text

gh-ml-relevance-v1

2026-09-26T20:57:28.489717Z

false

33

224,261,160

https://aclanthology.org/2021.acl-long.72/

Python

Apache-2.0

[ "contrastive-learning", "self-supervised-learning" ]

JohnGiorgi/DeCLUTR

[ "description", "github-topics", "license-metadata", "repository-metadata" ]

1

2026-09-26T20:57:28.489717Z

[]

2023-04-21T01:57:07Z

[]

null

official-paper-method-implementation

[ "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]

include

ml-contribution-v5

376

[ "allennlp", "contrastive-learning", "metric-learning", "natural-language-processing", "pytorch", "representation-learning", "self-supervised-learning", "semantic-search", "semantic-text-similarity", "sentence-embeddings", "sentence-similarity", "transformers" ]

2026-08-20T05:59:37Z

https://github.com/JohnGiorgi/DeCLUTR

{"candidate_evidence":["census-rule:liberal-ml-ai-v1","classifier-domain:natural-language-processing","classifier-method:contrastive-learning","classifier-method:self-supervised-learning","generic-ml-ai-term"],"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["self-supervise...

[ "general-ml" ]

[ "novel-method", "support-vector-machine" ]

[ "description", "query-match", "repository-metadata" ]

[ "general.novel-method" ]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2019-11-27T01:18:51Z

SGL-SVM: a novel method for tumor classification via support vector machine with sparse group Lasso

[ "general-ml" ]

[ "classifier" ]

ml_related_text

gh-ml-relevance-v1

2026-09-24T10:49:00Z

false

2

224,317,097

null

R

null

[ "novel-method", "support-vector-machine" ]

QUST-AIBBDRC/SGL-SVM

[ "description", "query-match", "repository-metadata" ]

4

2026-09-25T16:01:25Z

[]

2019-11-27T01:26:51Z

[ "general.novel-method" ]

null

2026-09-26T15:53:33Z

gh-ml-readme-evidence-v2

null

[]

missing

2026-09-26T15:53:33Z

QUST-AIBBDRC/SGL-SVM

specific-method-with-novelty-claim

[ "method-tied-novelty-claim", "ml-method-cue" ]

include

ml-contribution-v5

3

[]

2026-08-16T01:25:53Z

https://github.com/QUST-AIBBDRC/SGL-SVM

null

[ "efficient-ml", "machine-learning-systems", "model-compression" ]

[ "neural-network-pruning", "pruning", "structured-pruning" ]

[ "description", "paper-reference", "query-match", "repository-metadata" ]

[ "efficiency.pruning" ]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2019-12-04T02:19:55Z

Pytorch implementation of our paper accepted by IJCAI 2020 -- Channel Pruning via Automatic Structure Search

[ "efficient-ml", "machine-learning-systems", "model-compression" ]

[]

no_text_signal

gh-ml-relevance-v1

2026-09-24T10:49:00Z

false

27

225,759,924

https://arxiv.org/abs/2001.08565

Python

null

[ "neural-network-pruning", "pruning", "structured-pruning" ]

lmbxmu/ABCPruner

[ "description", "paper-reference", "query-match", "repository-metadata" ]

3

2026-09-25T16:01:25Z

[]

2021-02-11T16:54:47Z

[ "efficiency.pruning" ]

null

official-paper-method-implementation

[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]

include

ml-contribution-v5

147

[]

2026-02-10T08:00:53Z

https://github.com/lmbxmu/ABCPruner

null

[ "efficient-ml", "model-compression" ]

[ "distillation", "knowledge-distillation" ]

[ "description", "github-topics", "query-match", "repository-metadata" ]

[ "efficiency.knowledge-distillation" ]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2019-12-05T09:14:18Z

[AAAI-2020] Official implementation for "Online Knowledge Distillation with Diverse Peers".

[ "efficient-ml", "model-compression" ]

[ "deep-learning", "machine-learning" ]

direct_ml_text

gh-ml-relevance-v1

2026-09-24T10:49:00Z

false

14

226,061,776

null

Python

null

[ "distillation", "knowledge-distillation" ]

DefangChen/OKDDip

[ "description", "github-topics", "query-match", "repository-metadata" ]

7

2026-09-26T14:50:46Z

[]

2023-07-06T21:27:36Z

[ "efficiency.knowledge-distillation" ]

null

official-paper-method-implementation

[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue" ]

include

ml-contribution-v5

76

[ "deep-learning", "knowledge-distillation", "machine-learning" ]

2026-01-01T04:42:01Z

https://github.com/DefangChen/OKDDip

null

[ "deep-learning", "graph-learning" ]

[ "graph-classification", "graph-representation-learning", "graph-transformer", "message-passing", "transformer" ]

[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]

[ "graph.graph-classification", "graph.graph-transformer" ]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2019-12-16T11:27:15Z

IEEE TNNLS 2021, transformer, multi-graph transformer, graph, graph classification, sketch recognition, sketch classification, free-hand sketch, official code of the paper "Multi-Graph Transformer for Free-Hand Sketch Recognition"

[ "deep-learning", "graph-learning" ]

[ "classifier", "transformer" ]

ml_related_text

gh-ml-relevance-v1

2026-09-24T10:49:00Z

false

32

228,371,818

null

Python

MIT

[ "graph-classification", "graph-representation-learning", "graph-transformer", "message-passing", "transformer" ]

PengBoXiangShang/multigraph_transformer

[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]

4

2026-09-25T16:01:25Z

[]

2021-05-10T07:25:14Z

[ "graph.graph-classification", "graph.graph-transformer" ]

null

official-paper-method-implementation

[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]

include

ml-contribution-v5

305

[ "graph", "multi-graph-transformer", "pytorch", "pytorch-implementation", "sketch", "sketch-recognition", "sparse-graphs", "transformer", "transformer-architecture" ]

2026-08-18T13:39:26Z

https://github.com/PengBoXiangShang/multigraph_transformer

null

[ "deep-learning", "representation-learning" ]

[ "continual-learning", "lifelong-learning" ]

[ "description", "license-metadata", "query-match", "repository-metadata" ]

[ "general.continual-learning" ]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2019-12-30T07:03:51Z

Official code for ICLR 2020 paper "A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning."

[ "deep-learning", "representation-learning" ]

[]

no_text_signal

gh-ml-relevance-v1

2026-09-26T14:50:46Z

false

16

230,865,834

null

Python

MIT

[ "continual-learning", "lifelong-learning" ]

soochan-lee/CN-DPM

[ "description", "license-metadata", "query-match", "repository-metadata" ]

1

2026-09-26T14:50:46Z

[]

2020-08-22T01:05:18Z

[ "general.continual-learning" ]

null

official-paper-method-implementation

[ "ml-context-only", "official-paper-implementation-cue", "paper-and-code-cue" ]

include

ml-contribution-v5

100

[]

2026-09-07T07:58:02Z

https://github.com/soochan-lee/CN-DPM

null

[ "biology", "computational-biology", "microscopy", "robotics-and-control" ]

[ "cell-segmentation", "computer-vision", "image-analysis" ]

[ "description", "query-match", "repository-metadata" ]

[ "bio.microscopy-cell-analysis" ]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2020-01-06T17:30:48Z

Python code for recurrent fully convolutional network (RFCN) models from our paper "Deep learning robotic guidance for autonomous vascular access"

[ "biology", "computational-biology", "microscopy", "robotics-and-control" ]

[ "deep-learning" ]

direct_ml_text

gh-ml-relevance-v1

2026-09-26T14:50:46Z

false

0

232,151,079

null

Python

null

[ "cell-segmentation", "computer-vision", "image-analysis" ]

alvchn/nmi-vasc-robot

[ "description", "query-match", "repository-metadata" ]

1

2026-09-26T14:50:46Z

[]

2025-02-04T03:31:12Z

[ "bio.microscopy-cell-analysis" ]

null

official-paper-method-implementation

[ "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]

include

ml-contribution-v5

1

[]

2025-02-04T17:25:34Z

https://github.com/alvchn/nmi-vasc-robot

null

[]

[ "distillation", "knowledge-distillation", "self-supervised-learning" ]

[ "description", "github-topics", "repository-metadata" ]

[]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2020-02-02T10:23:51Z

The source code of our IJCAI 2018 paper "Better and Faster: Knowledge Transfer from Multiple Self-supervised Learning Tasks via Graph Distillation for Video Classification".

[]

[ "self-supervised-learning", "classifier" ]

direct_ml_text

gh-ml-relevance-v1

2026-09-26T20:57:28.489717Z

false

1

237,753,556

null

[ "distillation", "knowledge-distillation", "self-supervised-learning" ]

zcrwind/ss-graph-distillation

[ "description", "github-topics", "repository-metadata" ]

1

2026-09-26T20:57:28.489717Z

[]

2020-02-02T10:23:52Z

[]

null

official-paper-method-implementation

[ "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]

include

ml-contribution-v5

1

[ "graph", "ijcai-18", "knowledge-distillation", "pytorch", "self-supervised-learning" ]

2021-09-29T18:44:34Z

https://github.com/zcrwind/ss-graph-distillation

{"candidate_evidence":["census-rule:liberal-ml-ai-v1","classifier-method:distillation","classifier-method:knowledge-distillation","classifier-method:self-supervised-learning"],"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["knowledge-distillation","self-supervised-learnin...

[]

[ "self-supervised-learning" ]

[ "description", "github-topics", "license-metadata", "repository-metadata" ]

[]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2020-02-03T03:47:53Z

self-supervised learning, deep learning, representation learning, RotNet, temporal convolutional network(TCN), deformation transformation, sketch pre-train, sketch classification, sketch retrieval, free-hand sketch, official code of paper "Deep Self-Supervised Representation Learning for Free-Hand Sketch"

[]

[ "deep-learning", "representation-learning", "self-supervised-learning", "classifier" ]

direct_ml_text

gh-ml-relevance-v1

2026-09-26T20:57:28.489717Z

false

13

237,879,732

null

Python

MIT

[ "self-supervised-learning" ]

zzz1515151/self-supervised_learning_sketch

[ "description", "github-topics", "license-metadata", "repository-metadata" ]

1

2026-09-26T20:57:28.489717Z

[]

2020-02-24T01:29:56Z

[]

null

official-paper-method-implementation

[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]

include

ml-contribution-v5

48

[ "deep-learning", "free-hand-sketch", "representation-learning", "rotnet", "self-supervised", "self-supervised-learning", "sketch-classificaton", "sketch-recognition", "sketch-retrieval", "temporal-convolutional-network", "temporal-convolutions" ]

2025-05-27T18:12:18Z

https://github.com/zzz1515151/self-supervised_learning_sketch

{"candidate_evidence":["census-rule:liberal-ml-ai-v1","classifier-method:self-supervised-learning","generic-ml-ai-term"],"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["self-supervised-learning"]}

[ "classification", "time-series", "time-series-and-forecasting" ]

[ "classification", "self-supervised-learning", "sequence-modeling" ]

[ "description", "query-match", "repository-metadata" ]

[ "timeseries.classification" ]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2020-02-13T16:07:27Z

Code for our paper Self Supervised Learning for Semi Supervised Time Series Classification PAKDD 2020

[ "classification", "time-series", "time-series-and-forecasting" ]

[ "self-supervised-learning", "classifier" ]

direct_ml_text

gh-ml-relevance-v1

2026-09-24T10:49:00Z

false

7

240,302,639

null

Python

null

[ "classification", "self-supervised-learning", "sequence-modeling" ]

super-shayan/semi-super-ts-clf

[ "description", "query-match", "repository-metadata" ]

3

2026-09-25T16:01:25Z

[]

2020-09-21T18:25:18Z

[ "timeseries.classification" ]

null

official-paper-method-implementation

[ "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]

include

ml-contribution-v5

16

[]

2024-06-25T11:45:14Z

https://github.com/super-shayan/semi-super-ts-clf

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

2020-02-14T10:48:54Z

Code associated with our paper "Robust Visual Domain Randomization for Reinforcement Learning"

[ "reinforcement-learning", "robotics" ]

[ "reinforcement-learning" ]

direct_ml_text

gh-ml-relevance-v1

2026-09-24T10:49:00Z

false

1

240,486,813

null

Python

MIT

[ "domain-randomization", "reinforcement-learning", "sim-to-real" ]

IndustAI/visual-domain-randomization

[ "description", "license-metadata", "query-match", "repository-metadata" ]

3

2026-09-25T16:01:25Z

[]

2022-11-22T04:39:24Z

[ "robotics.domain-randomization" ]

null

official-paper-method-implementation

[ "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]

include

ml-contribution-v5

5

[]

2021-03-08T08:59:04Z

https://github.com/IndustAI/visual-domain-randomization

null

[ "automl", "computer-vision", "general-ml" ]

[ "few-shot-learning", "meta-learning" ]

[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]

[ "general.meta-learning" ]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2020-03-31T04:09:48Z

Source code for CVPR 2020 paper "Scene-Adaptive Video Frame Interpolation via Meta-Learning"

[ "automl", "computer-vision", "general-ml" ]

[ "deep-learning" ]

direct_ml_text

gh-ml-relevance-v1

2026-09-24T14:28:44Z

false

12

251,497,675

null

Python

MIT

[ "few-shot-learning", "meta learning", "meta-learning" ]

myungsub/meta-interpolation

[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]

1

2026-09-24T14:28:44Z

[]

2020-08-14T07:54:30Z

[ "general.meta-learning" ]

null

official-paper-method-implementation

[ "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]

include

ml-contribution-v5

80

[ "computer-vision", "cvpr2020", "deep-learning", "frame-interpolation", "meta-learning", "pytorch", "slow-motion", "video-frame-interpolation" ]

2025-03-01T03:57:23Z

https://github.com/myungsub/meta-interpolation

null

[ "classical-ml", "computer-vision", "information-retrieval", "representation-learning" ]

[ "image-retrieval", "metric-learning", "representation-learning" ]

[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]

[ "general.metric-learning", "vision.image-retrieval" ]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2020-04-07T15:09:10Z

Official PyTorch Implementation of Proxy Anchor Loss for Deep Metric Learning, CVPR 2020

[ "classical-ml", "representation-learning" ]

[]

no_text_signal

gh-ml-relevance-v1

2026-09-24T14:28:44Z

false

63

253,829,710

null

Python

MIT

[ "metric-learning", "representation-learning" ]

sung-yeon-kim/Proxy-Anchor-CVPR2020

[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]

3

2026-09-25T16:01:25Z

[]

2022-05-17T01:45:29Z

[ "general.metric-learning" ]

7816b341d5c42f1ce99be28f29551e51c43d6b07

2026-09-25T22:08:11Z

gh-ml-readme-evidence-v1

"7816b341d5c42f1ce99be28f29551e51c43d6b07"

[ "citation", "installation", "method", "other" ]

[ "method-contribution", "ml-method-context", "paper-code-relationship", "paper-reference" ]

ok

2026-09-25T22:08:11Z

sung-yeon-kim/Proxy-Anchor-CVPR2020

readme-supported-paper-method-implementation

[ "contribution-language", "method-contribution", "ml-method-context", "paper-code-relationship", "paper-reference" ]

include

ml-contribution-v5

326

[ "cvpr2020", "deep-metric-learning", "image-retrieval", "proxy-anchor-loss", "pytorch" ]

2026-09-23T18:01:13Z

https://github.com/sung-yeon-kim/Proxy-Anchor-CVPR2020

null

[ "distributed-ml", "privacy-and-federated-learning" ]

[ "collaborative-learning", "federated-learning" ]

[ "description", "license-metadata", "query-match", "repository-metadata" ]

[ "trust.federated-learning" ]

true

candidate

ml-candidate-v3

true

selected-by-current-rule

2020-04-11T23:12:32Z

Code for paper "Adaptive Federated Learning in Resource Constrained Edge Computing Systems"

[ "distributed-ml", "privacy-and-federated-learning" ]

[]

no_text_signal

gh-ml-relevance-v1

2026-09-24T10:49:00Z

false

83

254,971,503

null

Python

MIT

[ "collaborative-learning", "federated-learning" ]

IBM/adaptive-federated-learning

[ "description", "license-metadata", "query-match", "repository-metadata" ]

4

2026-09-26T14:50:46Z

[]

2025-05-07T23:59:52Z

[ "trust.federated-learning" ]

null

official-paper-method-implementation

[ "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]

include

ml-contribution-v5

292

[]

2026-09-25T08:12:29Z

https://github.com/IBM/adaptive-federated-learning

null

[ "general-ml", "natural-language-processing" ]

[ "distillation", "knowledge-distillation", "research-implementation" ]

[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]

[ "general.research-code" ]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2020-04-17T22:56:57Z

Research code for ACL 2020 paper: "Distilling Knowledge Learned in BERT for Text Generation".

[ "general-ml", "natural-language-processing" ]

[]

no_text_signal

gh-ml-relevance-v1

2026-09-24T10:49:00Z

false

20

256,626,656

null

Python

MIT

[ "distillation", "knowledge distillation", "research-implementation" ]

ChenRocks/Distill-BERT-Textgen

[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]

3

2026-09-24T15:29:21Z

[]

2021-06-30T21:50:43Z

[ "general.research-code" ]

null

official-paper-method-implementation

[ "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]

include

ml-contribution-v5

128

[ "bert-model", "knowledge-distillation", "machine-translation", "natural-language-processing" ]

2026-07-03T19:32:24Z

https://github.com/ChenRocks/Distill-BERT-Textgen

null

[ "deep-learning", "language", "multimodal", "natural-language-processing", "representation-learning", "vision" ]

[ "representation-learning", "self-supervised-learning", "vision-language-model", "visual-question-answering" ]

[ "description", "query-match", "repository-metadata" ]

[ "general.self-supervised-learning", "multimodal.visual-question-answering" ]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2020-04-29T10:30:11Z

Code for our IJCAI2020 paper: Overcoming Language Priors with Self-supervised Learning for Visual Question Answering

[ "deep-learning", "language", "multimodal", "natural-language-processing", "representation-learning", "vision" ]

[ "self-supervised-learning" ]

direct_ml_text

gh-ml-relevance-v1

2026-09-24T10:49:00Z

false

11

259,895,108

null

Python

null

[ "representation-learning", "self-supervised-learning", "vision-language-model", "visual-question-answering" ]

CrossmodalGroup/SSL-VQA

[ "description", "query-match", "repository-metadata" ]

5

2026-09-26T14:50:46Z

[]

2020-08-21T08:49:44Z

[ "general.self-supervised-learning", "multimodal.visual-question-answering" ]

null

official-paper-method-implementation

[ "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]

include

ml-contribution-v5

52

[]

2025-08-29T11:01:30Z

https://github.com/CrossmodalGroup/SSL-VQA

null

[ "speech-and-audio" ]

[ "distillation", "knowledge-distillation" ]

[ "description", "github-topics", "license-metadata", "repository-metadata" ]

[]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2020-05-10T12:14:37Z

[INTERSPEECH 2021] Official Keras Implementation of "Knowledge Distillation for Singing Voice Detection"

[ "speech-and-audio" ]

[]

no_text_signal

gh-ml-relevance-v1

2026-09-26T20:57:28.489717Z

false

1

262,779,806

null

Python

MIT

[ "distillation", "knowledge-distillation" ]

mvp18/KD-SVD

[ "description", "github-topics", "license-metadata", "repository-metadata" ]

1

2026-09-26T20:57:28.489717Z

[]

2021-09-02T21:37:38Z

[]

null

official-paper-method-implementation

[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue" ]

include

ml-contribution-v5

1

[ "ensemble-knowledge-distillation", "knowledge-distillation", "melody-extraction", "singing-voice-detection" ]

2022-07-20T06:54:12Z

https://github.com/mvp18/KD-SVD

{"candidate_evidence":["census-rule:liberal-ml-ai-v1","classifier-domain:speech-and-audio","classifier-method:distillation","classifier-method:knowledge-distillation"],"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["knowledge-distillation"]}

[ "automl", "computer-vision", "general-ml" ]

[ "few-shot-learning", "meta-learning" ]

[ "description", "license-metadata", "paper-reference", "query-match", "repository-metadata" ]

[ "general.meta-learning" ]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2020-05-13T00:47:52Z

Official code for the paper "Task2Vec: Task Embedding for Meta-Learning" (https://arxiv.org/abs/1902.03545, ICCV 2019)

[ "automl", "computer-vision", "general-ml" ]

[ "embedding" ]

ml_related_text

gh-ml-relevance-v1

2026-09-24T14:28:44Z

false

31

263,487,494

null

Python

Apache-2.0

[ "few-shot-learning", "meta learning", "meta-learning" ]

awslabs/aws-cv-task2vec

[ "description", "license-metadata", "paper-reference", "query-match", "repository-metadata" ]

1

2026-09-24T14:28:44Z

[]

2023-07-13T20:29:52Z

[ "general.meta-learning" ]

null

official-paper-method-implementation

[ "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]

include

ml-contribution-v5

126

[]

2026-03-19T11:33:45Z

https://github.com/awslabs/aws-cv-task2vec

null

[ "automl", "computer-vision", "general-ml", "reinforcement-learning" ]

[ "few-shot-learning", "meta-learning", "reinforcement-learning" ]

[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]

[ "general.meta-learning" ]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2020-05-13T13:12:01Z

Source code for CVPR 2020 paper "Learning to Forget for Meta-Learning"

[ "automl", "computer-vision", "general-ml", "reinforcement-learning" ]

[ "deep-learning", "machine-learning", "reinforcement-learning" ]

direct_ml_text

gh-ml-relevance-v1

2026-09-24T14:28:44Z

false

5

263,633,646

https://github.com/baiksung/L2F

Python

MIT

[ "few-shot-learning", "meta learning", "meta-learning", "reinforcement learning" ]

baiksung/L2F

[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]

1

2026-09-24T14:28:44Z

[]

2020-10-26T03:54:45Z

[ "general.meta-learning" ]

null

official-paper-method-implementation

[ "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]

include

ml-contribution-v5

37

[ "computer-vision", "cvpr2020", "deep-learning", "few-shot-learning", "machine-learning", "maml", "meta-learning", "pytorch", "reinforcement-learning" ]

2026-07-25T08:18:20Z

https://github.com/baiksung/L2F

null

[ "autonomous-driving", "reinforcement-learning", "robotics", "robotics-and-control" ]

[ "perception", "planning", "reinforcement-learning" ]

[ "description", "github-topics", "query-match", "repository-metadata" ]

[ "robotics.autonomous-driving" ]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2020-05-21T03:53:25Z

Source code for paper "Safe, Efficient, and Comfortable Velocity Control based on Reinforcement Learning for Autonomous Driving"

[ "autonomous-driving", "reinforcement-learning", "robotics", "robotics-and-control" ]

[ "reinforcement-learning" ]

direct_ml_text

gh-ml-relevance-v1

2026-09-26T14:50:46Z

false

27

265,749,415

https://scholar.google.com/citations?user=5Ysgg7AAAAAJ&hl=en

Jupyter Notebook

null

[ "perception", "planning", "reinforcement-learning" ]

MeixinZhu/Velocity_control

[ "description", "github-topics", "query-match", "repository-metadata" ]

1

2026-09-26T14:50:46Z

[]

2023-03-24T22:33:57Z

[ "robotics.autonomous-driving" ]

null

official-paper-method-implementation

[ "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]

include

ml-contribution-v5

122

[ "adaptive-cruise-control", "autonomous-driving", "car-following", "ddpg", "model-predictive-control", "reinforcement-learning", "velocity-control" ]

2026-09-01T08:21:14Z

https://github.com/MeixinZhu/Velocity_control

null

[ "deep-learning", "geometric-learning", "graph-learning" ]

[ "geometric-deep-learning", "graph-neural-network", "manifold-learning", "message-passing", "neural-network" ]

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[ "graph.geometric-learning", "graph.gnn-description" ]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2020-06-01T06:10:26Z

Official PyTorch implementation of "Towards Deeper Graph Neural Networks" [KDD2020]

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[ "deep-learning", "neural-network", "classifier" ]

direct_ml_text

gh-ml-relevance-v1

2026-09-24T10:49:00Z

false

31

268,439,607

https://arxiv.org/abs/2007.09296

Python

GPL-3.0

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mengliu1998/DeeperGNN

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6

2026-09-26T14:50:46Z

[]

2022-10-11T04:01:57Z

[ "graph.geometric-learning", "graph.gnn-description" ]

null

official-paper-method-implementation

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include

ml-contribution-v5

154

[ "geometric-deep-learning", "graph-neural-networks", "node-classification", "semi-supervised-learning" ]

2026-09-07T07:58:17Z

https://github.com/mengliu1998/DeeperGNN

null

[ "computer-vision" ]

[ "distillation", "knowledge-distillation" ]

[ "description", "github-topics", "license-metadata", "repository-metadata" ]

[]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2020-06-15T12:24:19Z

The code for paper "Distilling the Long-Range Knowledge for Real-Time Semantic Segmentation".

[ "computer-vision" ]

[]

no_text_signal

gh-ml-relevance-v1

2026-09-26T20:57:28.489717Z

false

2

272,431,841

null

Python

MIT

[ "distillation", "knowledge-distillation" ]

wangyunnan/Real-Time-Semantic-Segmentation

[ "description", "github-topics", "license-metadata", "repository-metadata" ]

1

2026-09-26T20:57:28.489717Z

[]

2021-04-18T13:35:53Z

[]

null

official-paper-method-implementation

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include

ml-contribution-v5

8

[ "knowledge-distillation", "opencv3", "pyqt5", "pytorch", "semantic-segmentation" ]

2024-07-16T06:10:13Z

https://github.com/wangyunnan/Real-Time-Semantic-Segmentation

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[ "diffusion" ]

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[]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2020-06-17T08:50:11Z

The official PyTorch implementation for NCSNv2 (NeurIPS 2020)

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[ "generative-model", "diffusion-model" ]

direct_ml_text

gh-ml-relevance-v1

2026-09-26T20:57:28.489717Z

false

59

272,924,197

null

Python

MIT

[ "diffusion" ]

ermongroup/ncsnv2

[ "description", "github-topics", "license-metadata", "repository-metadata" ]

1

2026-09-26T20:57:28.489717Z

[]

2021-06-12T17:43:10Z

[]

null

official-paper-method-implementation

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include

ml-contribution-v5

330

[ "diffusion-models", "generative-models", "neurips-2020", "score-based-generative-modeling", "score-matching" ]

2026-09-23T15:38:52Z

https://github.com/ermongroup/ncsnv2

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[ "audio", "multimodal-learning", "speech" ]

[ "emotion-recognition", "self-supervised-learning", "speech-understanding" ]

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[ "audio-speech-emotion-recognition" ]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2020-06-19T07:48:29Z

The code for our INTERSPEECH 2020 paper - Jointly Fine-Tuning "BERT-like'" Self Supervised Models to Improve Multimodal Speech Emotion Recognition

[ "audio", "multimodal-learning", "speech" ]

[ "self-supervised-learning" ]

direct_ml_text

gh-ml-relevance-v1

2026-09-26T14:50:46Z

false

11

273,436,106

null

Python

MIT

[ "emotion-recognition", "self-supervised-learning", "speech-understanding" ]

shamanez/BERT-like-is-All-You-Need

[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]

1

2026-09-26T14:50:46Z

[]

2021-02-26T22:22:45Z

[ "audio-speech-emotion-recognition" ]

null

official-paper-method-implementation

[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]

include

ml-contribution-v5

121

[ "bert-model", "fine-tuning", "multimodal-emotion-recognition", "multimodal-representation", "pretrained-models", "self-supervised-learning", "sentiment-analysis", "speech-emotion-recognition" ]

2026-09-16T11:04:25Z

https://github.com/shamanez/BERT-like-is-All-You-Need

null

[ "general-ml", "representation-learning" ]

[ "representation-learning", "semi-supervised-learning" ]

[ "description", "license-metadata", "query-match", "repository-metadata" ]

[ "general.semi-supervised-learning" ]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2020-06-29T08:06:15Z

Belief matching framework official implementation

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[]

no_text_signal

gh-ml-relevance-v1

2026-09-24T14:28:44Z

false

4

275,769,739

null

Python

MIT

[ "representation-learning", "semi-supervised-learning" ]

tjoo512/belief-matching-framework

[ "description", "license-metadata", "query-match", "repository-metadata" ]

1

2026-09-24T14:28:44Z

[]

2023-03-24T22:16:48Z

[ "general.semi-supervised-learning" ]

6cd30a8e686d64de4f806febab702cbb8a35fca4

2026-09-26T11:23:39Z

gh-ml-readme-evidence-v1

"6cd30a8e686d64de4f806febab702cbb8a35fca4"

[ "other", "overview" ]

[ "method-contribution", "ml-method-context", "paper-reference" ]

ok

2026-09-26T11:23:39Z

tjoo512/belief-matching-framework

readme-supported-paper-method-implementation

[ "contribution-language", "method-contribution", "ml-method-context", "paper-reference" ]

include

ml-contribution-v5

41

[]

2025-10-19T07:29:36Z

https://github.com/tjoo512/belief-matching-framework

null

[ "efficient-ml", "model-compression" ]

[ "distillation", "knowledge-distillation" ]

[ "description", "license-metadata", "query-match", "repository-metadata" ]

[ "efficiency.knowledge-distillation" ]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2020-07-04T03:22:47Z

[MICCAI2020] Code for paper : Deep Semi-supervised Knowledge Distillation for Overlapping Cervical Cell Instance Segmentation

[ "efficient-ml", "model-compression" ]

[]

no_text_signal

gh-ml-relevance-v1

2026-09-26T14:50:46Z

false

8

277,030,535

null

Python

MIT

[ "distillation", "knowledge-distillation" ]

Amandaynzhou/MMT-PSM

[ "description", "license-metadata", "query-match", "repository-metadata" ]

1

2026-09-26T14:50:46Z

[]

2020-11-18T03:40:33Z

[ "efficiency.knowledge-distillation" ]

null

official-paper-method-implementation

[ "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]

include

ml-contribution-v5

54

[]

2025-06-13T08:21:31Z

https://github.com/Amandaynzhou/MMT-PSM

null

[]

[ "distillation", "knowledge-distillation" ]

[ "description", "github-topics", "repository-metadata" ]

[]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2020-07-04T05:20:30Z

[AAAI-2021, TKDE-2023] Official implementation for "Cross-Layer Distillation with Semantic Calibration".

[]

[ "deep-learning", "machine-learning" ]

direct_ml_text

gh-ml-relevance-v1

2026-09-26T20:57:28.489717Z

false

15

277,043,998

null

Jupyter Notebook

null

[ "distillation", "knowledge-distillation" ]

DefangChen/SemCKD

[ "description", "github-topics", "repository-metadata" ]

1

2026-09-26T20:57:28.489717Z

[]

2024-07-29T19:30:23Z

[]

null

official-paper-method-implementation

[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue" ]

include

ml-contribution-v5

78

[ "aaai2021", "deep-learning", "knowledge-distillation", "machine-learning" ]

2026-01-25T23:15:28Z

https://github.com/DefangChen/SemCKD

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[ "automl", "efficient-ml" ]

[ "neural-architecture-search", "self-supervised-learning" ]

[ "description", "query-match", "repository-metadata" ]

[ "efficiency.neural-architecture-search" ]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2020-07-18T06:47:45Z

Official implementation of the paper "Pretraining Neural Architecture Search Controllers with Locality-based Self-Supervised Learning" (NeurIPSW 2020)

[ "automl", "efficient-ml" ]

[ "self-supervised-learning" ]

direct_ml_text

gh-ml-relevance-v1

2026-09-26T14:50:46Z

false

0

280,599,637

null

Python

null

[ "neural-architecture-search", "self-supervised-learning" ]

Multi-Objective-NAS/self-supervised-nas

[ "description", "query-match", "repository-metadata" ]

1

2026-09-26T14:50:46Z

[]

2021-11-05T04:06:45Z

[ "efficiency.neural-architecture-search" ]

null

official-paper-method-implementation

[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]

include

ml-contribution-v5

5

[]

2022-08-25T02:26:04Z

https://github.com/Multi-Objective-NAS/self-supervised-nas

null

[ "efficient-ml", "model-compression", "privacy-and-federated-learning", "representation-learning", "transfer-learning" ]

[ "distillation", "domain-adaptation", "federated-learning", "knowledge-distillation", "transfer-learning" ]

[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]

[ "efficiency.knowledge-distillation", "general.domain-adaptation" ]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2020-08-31T05:14:03Z

Here is the official implementation of the model KD3A in paper "KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation".

[ "efficient-ml", "model-compression", "privacy-and-federated-learning", "representation-learning", "transfer-learning" ]

[ "deep-learning" ]

direct_ml_text

gh-ml-relevance-v1

2026-09-24T15:29:21Z

false

14

291,620,892

null

Python

MIT

[ "distillation", "domain-adaptation", "federated-learning", "knowledge-distillation", "transfer-learning" ]

FengHZ/KD3A

[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]

4

2026-09-26T14:50:46Z

[]

2022-08-30T05:15:10Z

[ "efficiency.knowledge-distillation", "general.domain-adaptation" ]

null

official-paper-method-implementation

[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]

include

ml-contribution-v5

120

[ "deep-learning", "domain-adaptation", "federated-learning", "transfer-learning", "unsupervised-learning" ]

2026-03-31T03:26:17Z

https://github.com/FengHZ/KD3A

null

[ "computer-vision", "robotics" ]

[ "contrastive-learning", "visual-localization", "visual-place-recognition" ]

[ "description", "license-metadata", "query-match", "repository-metadata" ]

[ "vision.visual-localization" ]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2020-09-14T02:15:05Z

Code and pretrained models for our paper "Domain-invariant Similarity Activation Map Contrastive Learning for Retrieval-based Long-term Visual Localization"

[ "computer-vision", "robotics" ]

[]

no_text_signal

gh-ml-relevance-v1

2026-09-26T14:50:46Z

false

2

295,280,134

null

Python

MIT

[ "contrastive-learning", "visual-localization", "visual-place-recognition" ]

HanjiangHu/DISAM

[ "description", "license-metadata", "query-match", "repository-metadata" ]

1

2026-09-26T14:50:46Z

[]

2021-10-24T01:15:31Z

[ "vision.visual-localization" ]

null

official-paper-method-implementation

[ "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]

include

ml-contribution-v5

26

[]

2025-05-06T12:35:44Z

https://github.com/HanjiangHu/DISAM

null

[ "reinforcement-learning", "robotics", "robotics-and-control" ]

[ "reinforcement-learning", "sim-to-real" ]

[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]

[ "robotics.sim2real" ]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2020-09-18T14:15:17Z

Source code for our paper "Sim-to-real reinforcement learning applied to end-to-end vehicle control"

[ "reinforcement-learning", "robotics", "robotics-and-control" ]

[ "reinforcement-learning" ]

direct_ml_text

gh-ml-relevance-v1

2026-09-24T10:49:00Z

false

11

296,640,873

null

Python

MIT

[ "reinforcement-learning", "sim-to-real" ]

kaland313/Duckietown-RL

[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]

3

2026-09-25T16:01:25Z

[]

2021-12-09T14:37:06Z

[ "robotics.sim2real" ]

null

official-paper-method-implementation

[ "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]

include

ml-contribution-v5

34

[ "duckietown", "reinforcement-learning", "robotics" ]

2026-01-10T14:32:02Z

https://github.com/kaland313/Duckietown-RL

null

[ "embodied-ai", "robotics", "robotics-and-control" ]

[ "manipulation" ]

[ "description", "license-metadata", "query-match", "repository-metadata" ]

[ "robotics.manipulation" ]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2020-09-28T06:22:42Z

Code for paper Amodal 3D Reconstruction for Robotic Manipulation via Stability and Connectivity

[ "embodied-ai", "robotics", "robotics-and-control" ]

[]

no_text_signal

gh-ml-relevance-v1

2026-09-26T14:50:46Z

false

2

299,209,027

null

Python

NOASSERTION

[ "manipulation" ]

wagnew3/ARM

[ "description", "license-metadata", "query-match", "repository-metadata" ]

1

2026-09-26T14:50:46Z

[]

2021-04-27T19:04:30Z

[ "robotics.manipulation" ]

null

official-paper-method-implementation

[ "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]

include

ml-contribution-v5

27

[]

2026-07-04T09:31:16Z

https://github.com/wagnew3/ARM

null

[ "deep-learning", "graph-learning", "knowledge-graphs" ]

[ "graph-neural-network", "graph-representation-learning", "link-prediction", "message-passing", "meta-learning", "neural-network" ]

[ "description", "github-topics", "paper-reference", "query-match", "repository-metadata" ]

[ "graph.link-prediction", "graph.topic-gnn" ]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2020-10-20T07:51:34Z

Official Code Repository for the paper "Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction" (NeurIPS 2020)

[ "graph-learning", "knowledge-graphs" ]

[ "deep-learning", "neural-network" ]

direct_ml_text

gh-ml-relevance-v1

2026-09-24T10:27:52Z

false

11

305,631,080

https://arxiv.org/abs/2006.06648

Python

null

[ "graph-neural-network", "graph-representation-learning", "link-prediction", "meta-learning", "neural-network" ]

JinheonBaek/GEN

[ "description", "github-topics", "paper-reference", "query-match", "repository-metadata" ]

7

2026-09-26T14:50:46Z

[]

2021-03-26T18:08:18Z

[ "graph.link-prediction" ]

null

official-paper-method-implementation

[ "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]

include

ml-contribution-v5

60

[ "deep-learning", "few-shot-learning", "graph-link-prediction", "graph-neural-network", "graph-neural-networks", "knowledge-graph", "link-prediction", "meta-learning" ]

2025-12-08T08:15:43Z

https://github.com/JinheonBaek/GEN

null

[ "automl", "general-ml" ]

[ "few-shot-learning", "meta-learning" ]

[ "description", "github-topics", "query-match", "repository-metadata" ]

[ "general.meta-learning" ]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2020-10-23T08:28:54Z

Source code for NeurIPS 2020 paper "Meta-Learning with Adaptive Hyperparameters"

[ "automl", "general-ml" ]

[ "deep-learning", "machine-learning" ]

direct_ml_text

gh-ml-relevance-v1

2026-09-24T14:28:44Z

false

16

306,574,876

null

Python

null

[ "few-shot-learning", "meta learning", "meta-learning" ]

baiksung/ALFA

[ "description", "github-topics", "query-match", "repository-metadata" ]

1

2026-09-24T14:28:44Z

[]

2022-07-20T12:34:22Z

[ "general.meta-learning" ]

null

official-paper-method-implementation

[ "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]

include

ml-contribution-v5

90

[ "deep-learning", "few-shot-learning", "machine-learning", "maml", "meta-learning", "neurips", "neurips-2020", "pytorch" ]

2026-07-22T08:32:00Z

https://github.com/baiksung/ALFA

null

[ "computer-vision", "science-and-engineering" ]

[ "change-detection", "convolutional-neural-network", "image-analysis", "neural-network" ]

[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]

[ "vision.change-detection" ]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2020-11-03T06:41:37Z

[MultiTemp 2019] Official Tensorflow implementation for Change Detection in Multi-temporal VHR Images Based on Deep Siamese Multi-scale Convolutional Neural Networks.

[ "computer-vision", "science-and-engineering" ]

[ "deep-learning", "neural-network" ]

direct_ml_text

gh-ml-relevance-v1

2026-09-24T10:49:00Z

false

16

309,597,161

null

Python

MIT

[ "change-detection", "convolutional-neural-network", "image-analysis", "neural-network" ]

ChenHongruixuan/DSMSCN

[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]

3

2026-09-25T16:01:25Z

[]

2023-11-23T07:30:56Z

[ "vision.change-detection" ]

0064e563a4d4d9c7d27a535d1bccde918c01a24f

2026-09-26T11:23:39Z

gh-ml-readme-evidence-v1

"0064e563a4d4d9c7d27a535d1bccde918c01a24f"

[ "abstract", "citation", "dataset", "installation", "method", "other" ]

[ "method-contribution", "ml-method-context", "paper-code-relationship", "paper-reference" ]

ok

2026-09-26T11:23:39Z

ChenHongruixuan/DSMSCN

readme-supported-paper-method-implementation

[ "contribution-language", "method-contribution", "ml-context-only", "ml-method-context", "paper-code-relationship", "paper-reference" ]

include

ml-contribution-v5

79

[ "change-detection", "convolutional-neural-network", "deep-learning", "remote-sensing", "tensorflow" ]

2026-02-25T19:04:26Z

https://github.com/ChenHongruixuan/DSMSCN

null

[ "biology", "computational-biology", "microscopy" ]

[ "cell-segmentation", "computer-vision", "image-analysis", "transformer" ]

[ "description", "github-topics", "license-metadata", "paper-reference", "query-match", "repository-metadata" ]

[ "bio.microscopy-cell-analysis" ]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2020-11-05T16:29:50Z

Official and maintained implementation of the paper "Attention-Based Transformers for Instance Segmentation of Cells in Microstructures" [BIBM 2020].

[ "biology", "computational-biology", "microscopy" ]

[ "deep-learning", "transformer" ]

direct_ml_text

gh-ml-relevance-v1

2026-09-24T14:28:44Z

false

25

310,357,344

https://arxiv.org/pdf/2011.09763

Python

MIT

[ "cell-segmentation", "computer-vision", "image-analysis", "transformer" ]

ChristophReich1996/Cell-DETR

[ "description", "github-topics", "license-metadata", "paper-reference", "query-match", "repository-metadata" ]

6

2026-09-26T14:50:46Z

[]

2022-03-28T00:36:03Z

[ "bio.microscopy-cell-analysis" ]

null

official-paper-method-implementation

[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]

include

ml-contribution-v5

108

[ "attention", "bibm-2020", "cell-detr", "cell-segmentation", "deep-learning", "instance-segmentation", "synthetic-biology", "system-biology", "transformer" ]

2026-09-14T13:30:53Z

https://github.com/ChristophReich1996/Cell-DETR

null

[ "computer-vision", "general-ml", "generative-ai", "interpretability-and-safety", "language" ]

[ "language-model", "novel-method", "transformer" ]

[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]

[ "general.novel-method", "llm.transformers" ]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2020-11-23T21:00:00Z

[CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.

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[ "deep-learning", "transformer" ]

direct_ml_text

gh-ml-relevance-v1

2026-09-24T10:49:00Z

false

260

315,439,501

null

Jupyter Notebook

MIT

[ "language-model", "novel-method", "transformer" ]

hila-chefer/Transformer-Explainability

[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]

4

2026-09-25T16:01:25Z

[]

2024-01-24T05:59:39Z

[ "general.novel-method", "llm.transformers" ]

null

official-paper-method-implementation

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ml-contribution-v5

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[ "attention-matrix", "attention-visualization", "bert", "bert-model", "cvpr2021", "deep-learning", "explainability", "perturbation", "transformer-interpretability", "vision-transformer", "visualize-classifications", "vit" ]

2026-09-17T01:58:13Z

https://github.com/hila-chefer/Transformer-Explainability

null

[ "3d", "computer-vision", "robotics" ]

[ "3d-perception", "scene-understanding" ]

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[ "multimodal.scene-understanding" ]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2020-11-27T02:39:08Z

🕸️ [CVPR'21] Official PyTorch code of Holistic 3D Scene Understanding from a Single Image with Implicit Representation. Also includes a PyTorch implementation of the decoder of LDIF (from 3D Shape Representation with Local Deep Implicit Functions).

[ "3d", "computer-vision", "robotics" ]

[ "deep-learning" ]

direct_ml_text

gh-ml-relevance-v1

2026-09-24T10:49:00Z

false

37

316,382,831

https://chengzhag.github.io/publication/im3d/

Python

MIT

[ "3d-perception", "scene-understanding" ]

chengzhag/Implicit3DUnderstanding

[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]

3

2026-09-25T16:01:25Z

[]

2021-09-11T08:07:33Z

[ "multimodal.scene-understanding" ]

ea74ac7b23e863e3d77f3c5b5dbfd8f08f75582b

2026-09-26T11:23:39Z

gh-ml-readme-evidence-v1

"ea74ac7b23e863e3d77f3c5b5dbfd8f08f75582b"

[ "citation", "installation", "other", "overview", "usage" ]

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ok

2026-09-26T11:23:39Z

chengzhag/Implicit3DUnderstanding

readme-supported-paper-method-implementation

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ml-contribution-v5

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[ "coop", "deep-learning", "ldif", "pytorch", "scene-graph", "sgcn" ]

2025-09-28T21:47:46Z

https://github.com/chengzhag/Implicit3DUnderstanding

null

[ "forecasting", "time-series", "time-series-and-forecasting" ]

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[ "timeseries.probabilistic-forecasting" ]

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candidate

ml-candidate-v3

true

selected-by-current-rule

2020-12-10T09:41:27Z

Code for our NeurIPS 2020 paper "Probabilistic Time Series Forecasting with Structured Shape and Temporal Diversity"

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[]

no_text_signal

gh-ml-relevance-v1

2026-09-24T10:49:00Z

false

17

320,225,443

null

Python

null

[ "probabilistic-forecasting", "uncertainty-estimation" ]

vincent-leguen/STRIPE

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4

2026-09-26T14:50:46Z

[]

2021-04-16T20:18:57Z

[ "timeseries.probabilistic-forecasting" ]

null

official-paper-method-implementation

[ "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]

include

ml-contribution-v5

89

[]

2026-04-12T09:48:31Z

https://github.com/vincent-leguen/STRIPE

null

[ "generative-modeling" ]

[ "diffusion" ]

[ "description", "github-topics", "license-metadata", "paper-reference", "repository-metadata" ]

[]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2021-01-03T19:00:16Z

Official code for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral)

[ "generative-modeling" ]

[ "generative-model", "diffusion-model" ]

direct_ml_text

gh-ml-relevance-v1

2026-09-26T20:57:28.489717Z

false

232

326,479,996

https://arxiv.org/abs/2011.13456

Jupyter Notebook

Apache-2.0

[ "diffusion" ]

yang-song/score_sde

[ "description", "github-topics", "license-metadata", "paper-reference", "repository-metadata" ]

1

2026-09-26T20:57:28.489717Z

[]

2022-11-29T23:42:42Z

[]

null

official-paper-method-implementation

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include

ml-contribution-v5

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[ "controllable-generation", "diffusion-models", "flax", "generative-models", "iclr-2021", "inverse-problems", "jax", "score-based-generative-modeling", "score-matching", "stochastic-differential-equations" ]

2026-09-22T08:06:51Z

https://github.com/yang-song/score_sde

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[ "efficient-ml", "graph-learning", "model-compression" ]

[ "distillation", "graph-neural-network", "knowledge-distillation", "neural-network" ]

[ "description", "github-topics", "paper-reference", "query-match", "repository-metadata" ]

[ "efficiency.knowledge-distillation" ]

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candidate

ml-candidate-v3

true

selected-by-current-rule

2021-01-16T06:41:03Z

The official code of WWW2021 paper: Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework

[ "efficient-ml", "graph-learning", "model-compression" ]

[ "neural-network" ]

direct_ml_text

gh-ml-relevance-v1

2026-09-26T14:50:46Z

false

19

330,101,580

https://arxiv.org/pdf/2103.02885.pdf

Python

null

[ "distillation", "graph-neural-network", "knowledge-distillation", "neural-network" ]

BUPT-GAMMA/CPF

[ "description", "github-topics", "paper-reference", "query-match", "repository-metadata" ]

1

2026-09-26T14:50:46Z

[]

2021-07-09T09:38:14Z

[ "efficiency.knowledge-distillation" ]

null

official-paper-method-implementation

[ "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]

include

ml-contribution-v5

75

[ "graph-neural-networks", "knowledge-distillation" ]

2026-07-24T08:29:46Z

https://github.com/BUPT-GAMMA/CPF

null

[ "multi-agent-systems", "reinforcement-learning" ]

[ "centralized-training", "multi-agent-reinforcement-learning", "reinforcement-learning" ]

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[ "rl.multiagent" ]

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candidate

ml-candidate-v3

true

selected-by-current-rule

2021-01-20T04:51:19Z

Official Implementation of 'UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling with Transformers' ICLR 2021(spotlight)

[ "multi-agent-systems", "reinforcement-learning" ]

[ "reinforcement-learning", "transformer" ]

direct_ml_text

gh-ml-relevance-v1

2026-09-24T10:49:00Z

false

17

331,196,113

null

Python

MIT

[ "centralized-training", "multi-agent-reinforcement-learning", "reinforcement-learning" ]

Theohhhu/UPDeT

[ "description", "license-metadata", "query-match", "repository-metadata" ]

4

2026-09-26T14:50:46Z

[]

2021-02-03T04:43:50Z

[ "rl.multiagent" ]

null

official-paper-method-implementation

[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue" ]

include

ml-contribution-v5

139

[]

2026-06-28T17:24:31Z

https://github.com/Theohhhu/UPDeT

null

[ "applied-mathematics", "applied-physics", "computational-science" ]

[ "data-assimilation", "inverse-problems", "reconstruction" ]

[ "description", "license-metadata", "query-match", "repository-metadata" ]

[ "science.inverse-problems" ]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2021-01-26T09:44:32Z

The official code of JSAC paper "Solving Sparse Linear Inverse Problems in Communication Systems: A Deep Learning Approach With Adaptive Depth"

[ "applied-mathematics", "applied-physics", "computational-science" ]

[ "deep-learning" ]

direct_ml_text

gh-ml-relevance-v1

2026-09-26T14:50:46Z

false

2

333,038,121

null

Python

MIT

[ "data-assimilation", "inverse-problems", "reconstruction" ]

wc253/HaltingNetwork

[ "description", "license-metadata", "query-match", "repository-metadata" ]

1

2026-09-26T14:50:46Z

[]

2022-09-30T00:48:29Z

[ "science.inverse-problems" ]

null

official-paper-method-implementation

[ "ml-context-only", "official-paper-implementation-cue", "paper-and-code-cue" ]

include

ml-contribution-v5

3

[]

2025-11-08T13:07:23Z

https://github.com/wc253/HaltingNetwork

null

[ "computational-science", "computer-vision", "physics" ]

[ "neural-operator", "neural-operators", "surrogate-modeling" ]

[ "description", "github-topics", "license-metadata", "paper-reference", "query-match", "repository-metadata" ]

[ "science.neural-operators" ]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2021-01-29T15:16:07Z

[CVPR 2021] Involution: Inverting the Inherence of Convolution for Visual Recognition, a brand new neural operator

[ "computational-science", "computer-vision", "physics" ]

[ "classifier" ]

ml_related_text

gh-ml-relevance-v1

2026-09-24T10:49:00Z

false

175

334,181,506

https://arxiv.org/abs/2103.06255

Python

MIT

[ "neural-operator", "neural-operators", "surrogate-modeling" ]

d-li14/involution

[ "description", "github-topics", "license-metadata", "paper-reference", "query-match", "repository-metadata" ]

3

2026-09-25T16:01:25Z

[]

2021-07-16T06:01:08Z

[ "science.neural-operators" ]

e799f7498df0e116a931546972efb35fb822500d

2026-09-26T15:53:33Z

gh-ml-readme-evidence-v2

"e799f7498df0e116a931546972efb35fb822500d"

[ "method", "other", "usage" ]

[ "ml-method-context", "paper-reference", "reproduction-cue" ]

ok

2026-09-26T15:53:33Z

d-li14/involution

specific-method-with-novelty-claim

[ "contribution-language", "method-tied-novelty-claim", "ml-method-context", "ml-method-cue", "paper-reference", "reproduction-cue" ]

include

ml-contribution-v5

1,310

[ "cvpr2021", "image-classification", "instance-segmentation", "involution", "object-detection", "operator", "pre-trained-model", "pytorch", "semantic-segmentation" ]

2026-08-06T07:33:28Z

https://github.com/d-li14/involution

null

[ "distributed-ml", "privacy-and-federated-learning" ]

[ "collaborative-learning", "federated-learning" ]

[ "description", "query-match", "repository-metadata" ]

[ "trust.federated-learning" ]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2021-02-07T09:29:46Z

Official code implementation for "Personalized Federated Learning using Hypernetworks" [ICML 2021]

[ "distributed-ml", "privacy-and-federated-learning" ]

[]

no_text_signal

gh-ml-relevance-v1

2026-09-24T10:49:00Z

false

31

336,749,029

null

Python

null

[ "collaborative-learning", "federated-learning" ]

AvivSham/pFedHN

[ "description", "query-match", "repository-metadata" ]

4

2026-09-26T14:50:46Z

[]

2023-02-14T08:05:23Z

[ "trust.federated-learning" ]

null

official-paper-method-implementation

[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue" ]

include

ml-contribution-v5

199

[]

2026-08-26T19:40:10Z

https://github.com/AvivSham/pFedHN

null

[ "embodied-ai", "robotics", "robotics-and-control" ]

[ "manipulation" ]

[ "description", "query-match", "repository-metadata" ]

[ "robotics.manipulation" ]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2021-02-08T05:48:30Z

This package presents a novel dexterous robotic manipulation technique for picking thin objects called Scooping.

[ "embodied-ai", "robotics", "robotics-and-control" ]

[]

no_text_signal

gh-ml-relevance-v1

2026-09-26T14:50:46Z

false

0

336,973,413

null

Jupyter Notebook

null

[ "manipulation" ]

JS-RML/Scooping-Manipulation

[ "description", "query-match", "repository-metadata" ]

1

2026-09-26T14:50:46Z

[]

2021-08-27T02:25:38Z

[ "robotics.manipulation" ]

9f21fa9016058d8e2097e78d6338410c3dfcd443

2026-09-26T15:53:33Z

gh-ml-readme-evidence-v2

"9f21fa9016058d8e2097e78d6338410c3dfcd443"

[ "other", "overview" ]

[]

ok

2026-09-26T15:53:33Z

JS-RML/Scooping-Manipulation

specific-method-with-novelty-claim

[ "method-tied-novelty-claim", "ml-method-cue" ]

include

ml-contribution-v5

4

[]

2026-08-05T07:09:05Z

https://github.com/JS-RML/Scooping-Manipulation

null

[ "automl", "general-ml", "recommender-systems" ]

[ "few-shot-learning", "meta-learning" ]

[ "description", "github-topics", "query-match", "repository-metadata" ]

[ "general.meta-learning" ]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2021-02-08T06:25:46Z

This is an official implementation for "Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising"(KDD2021).

[ "automl", "general-ml", "recommender-systems" ]

[]

no_text_signal

gh-ml-relevance-v1

2026-09-24T14:28:44Z

false

10

336,980,673

null

Python

null

[ "few-shot-learning", "meta learning", "meta-learning" ]

easezyc/MetaHeac

[ "description", "github-topics", "query-match", "repository-metadata" ]

1

2026-09-24T14:28:44Z

[]

2022-02-22T12:36:47Z

[ "general.meta-learning" ]

null

official-paper-method-implementation

[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue" ]

include

ml-contribution-v5

60

[ "advertising", "campaign", "marketing", "meta-learning", "recommendation", "transfer-learning" ]

2026-08-01T12:09:42Z

https://github.com/easezyc/MetaHeac

null

[ "efficient-ml", "model-compression" ]

[ "distillation", "knowledge-distillation" ]

[ "description", "license-metadata", "query-match", "repository-metadata" ]

[ "efficiency.knowledge-distillation" ]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2021-02-09T07:27:55Z

Official implementation for (Show, Attend and Distill: Knowledge Distillation via Attention-based Feature Matching, AAAI-2021)

[ "efficient-ml", "model-compression" ]

[]

no_text_signal

gh-ml-relevance-v1

2026-09-24T10:49:00Z

false

21

337,325,366

null

Python

Apache-2.0

[ "distillation", "knowledge-distillation" ]

clovaai/attention-feature-distillation

[ "description", "license-metadata", "query-match", "repository-metadata" ]

6

2026-09-26T14:50:46Z

[]

2021-02-09T07:30:49Z

[ "efficiency.knowledge-distillation" ]

null

official-paper-method-implementation

[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue" ]

include

ml-contribution-v5

122

[]

2026-08-07T03:59:31Z

https://github.com/clovaai/attention-feature-distillation

null

[ "computer-vision", "health-and-biomedicine", "medical-imaging" ]

[ "image-analysis", "segmentation", "transformer" ]

[ "description", "github-topics", "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

2021-02-14T03:00:14Z

Official Pytorch Code for "Medical Transformer: Gated Axial-Attention for Medical Image Segmentation" - MICCAI 2021

[ "computer-vision", "health-and-biomedicine", "medical-imaging" ]

[ "deep-learning", "transformer" ]

direct_ml_text

gh-ml-relevance-v1

2026-09-24T10:27:52Z

false

174

338,717,688

null

Python

MIT

[ "segmentation", "transformer" ]

jeya-maria-jose/Medical-Transformer

[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]

6

2026-09-26T14:50:46Z

[]

2023-02-23T08:42:53Z

[ "medical.medical-image-segmentation" ]

null

official-paper-method-implementation

[ "ml-method-cue", "official-paper-implementation-cue" ]

include

ml-contribution-v5

861

[ "attention", "deep-learning", "medical-image-analysis", "medical-imaging", "pytorch", "segmentation", "transformer", "transformers" ]

2026-08-07T21:14:32Z

https://github.com/jeya-maria-jose/Medical-Transformer

null

[ "3d", "computer-vision", "robotics" ]

[ "3d-object-detection", "3d-perception" ]

[ "description", "license-metadata", "query-match", "repository-metadata" ]

[ "multimodal.3d-object-detection" ]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2021-02-20T09:21:17Z

The official code release of LidarMTL, a simple and efficient multi-task network for 3D object detection and road understanding

[ "3d", "computer-vision", "robotics" ]

[]

no_text_signal

gh-ml-relevance-v1

2026-09-24T14:28:44Z

false

13

340,612,863

null

Python

Apache-2.0

[ "3d-object-detection", "3d-perception" ]

frankfengdi/LidarMTL

[ "description", "license-metadata", "query-match", "repository-metadata" ]

1

2026-09-24T14:28:44Z

[]

2021-03-09T17:21:40Z

[ "multimodal.3d-object-detection" ]

61d5fc3d008e658bdea98403bd8cafb8dd0a9f91

2026-09-26T11:23:39Z

gh-ml-readme-evidence-v1

"61d5fc3d008e658bdea98403bd8cafb8dd0a9f91"

[ "citation", "installation", "method", "other", "results" ]

[ "method-contribution", "ml-method-context", "official-implementation-claim" ]

ok

2026-09-26T11:23:39Z

frankfengdi/LidarMTL

readme-supported-paper-method-implementation

[ "method-contribution", "ml-method-context", "official-implementation-claim" ]

include

ml-contribution-v5

110

[]

2026-05-29T02:58:03Z

https://github.com/frankfengdi/LidarMTL

null

[ "deep-learning", "graph-learning" ]

[ "graph-transformer", "message-passing", "transformer" ]

[ "description", "license-metadata", "query-match", "repository-metadata" ]

[ "graph.graph-transformer" ]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2021-02-22T14:11:39Z

Source code for paper "LET: Linguistic Knowledge Enhanced Graph Transformer for Chinese Short Text Matching", AAAI2021.

[ "deep-learning", "graph-learning" ]

[ "transformer" ]

ml_related_text

gh-ml-relevance-v1

2026-09-24T10:49:00Z

false

3

341,222,382

null

Python

MIT

[ "graph-transformer", "message-passing", "transformer" ]

lbe0613/LET

[ "description", "license-metadata", "query-match", "repository-metadata" ]

4

2026-09-25T16:01:25Z

[]

2021-06-04T08:47:32Z

[ "graph.graph-transformer" ]

null

official-paper-method-implementation

[ "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]

include

ml-contribution-v5

48

[]

2024-09-14T10:08:58Z

https://github.com/lbe0613/LET

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

2021-02-26T12:40:03Z

Code for CVPR 2021 paper: Revamping Cross-Modal Recipe Retrieval with Hierarchical Transformers and Self-supervised Learning

[ "deep-learning", "representation-learning" ]

[ "self-supervised-learning", "transformer" ]

direct_ml_text

gh-ml-relevance-v1

2026-09-26T14:50:46Z

false

25

342,571,959

null

Python

Apache-2.0

[ "representation-learning", "self-supervised-learning" ]

amzn/image-to-recipe-transformers

[ "description", "license-metadata", "query-match", "repository-metadata" ]

1

2026-09-26T14:50:46Z

[]

2021-03-24T09:05:25Z

[ "general.self-supervised-learning" ]

null

official-paper-method-implementation

[ "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]

include

ml-contribution-v5

88

[]

2026-08-05T03:21:26Z

https://github.com/amzn/image-to-recipe-transformers

null

[ "embodied-ai", "reinforcement-learning", "robotics", "robotics-and-control" ]

[ "manipulation", "sim-to-real" ]

[ "description", "paper-reference", "query-match", "repository-metadata" ]

[ "robotics.manipulation", "robotics.sim2real" ]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2021-03-01T13:10:44Z

Official code (simulation part) for paper Sim-to-Real Transfer for Robotic Manipulation with Tactile Sensory Zihan Ding, Ya-Yen Tsai, Wang Wei Lee, Bidan Huang International Conference on Intelligent Robots and Systems (IROS) 2021

[ "embodied-ai", "robotics", "robotics-and-control" ]

[]

no_text_signal

gh-ml-relevance-v1

2026-09-24T10:49:00Z

false

4

343,420,830

https://arxiv.org/abs/2103.00410

Python

null

[ "manipulation" ]

quantumiracle/Robotic_Door_Opening_with_Tactile_Simulation

[ "description", "paper-reference", "query-match", "repository-metadata" ]

4

2026-09-26T14:50:46Z

[]

2022-02-11T15:07:52Z

[ "robotics.manipulation" ]

null

official-paper-method-implementation

[ "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]

include

ml-contribution-v5

25

[]

2026-08-18T03:50:27Z

https://github.com/quantumiracle/Robotic_Door_Opening_with_Tactile_Simulation

null

[ "efficient-ml", "model-compression" ]

[ "distillation", "knowledge-distillation" ]

[ "description", "license-metadata", "query-match", "repository-metadata" ]

[ "efficiency.knowledge-distillation" ]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2021-03-05T05:13:47Z

Official implementation for (Refine Myself by Teaching Myself : Feature Refinement via Self-Knowledge Distillation, CVPR-2021)

[ "efficient-ml", "model-compression" ]

[]

no_text_signal

gh-ml-relevance-v1

2026-09-24T10:49:00Z

false

26

344,701,952

null

Python

Apache-2.0

[ "distillation", "knowledge-distillation" ]

MingiJi/FRSKD

[ "description", "license-metadata", "query-match", "repository-metadata" ]

6

2026-09-26T14:50:46Z

[]

2024-04-30T01:05:09Z

[ "efficiency.knowledge-distillation" ]

null

official-paper-method-implementation

[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue" ]

include

ml-contribution-v5

104

[]

2026-05-15T09:40:10Z

https://github.com/MingiJi/FRSKD

null

[ "computer-vision", "health-and-biomedicine", "medical-imaging", "multimodal-learning" ]

[ "image-analysis", "transformer" ]

[ "description", "github-topics", "license-metadata", "paper-reference", "query-match", "repository-metadata" ]

[ "medical.medical-imaging" ]

false

candidate

ml-candidate-v3

true

selected-by-current-rule

2021-03-05T10:43:04Z

This repo provides the official code for : 1) TransBTS: Multimodal Brain Tumor Segmentation Using Transformer (https://arxiv.org/abs/2103.04430) , accepted by MICCAI2021. 2) TransBTSV2: Towards Better and More Efficient Volumetric Segmentation of Medical Images(https://arxiv.org/abs/2201.12785).

[ "computer-vision", "health-and-biomedicine", "medical-imaging", "multimodal-learning" ]

[ "transformer" ]

ml_related_text

gh-ml-relevance-v1

2026-09-24T10:27:52Z

false

93

344,778,476

null

Python

Apache-2.0

[ "image-analysis", "transformer" ]

Rubics-Xuan/TransBTS

[ "description", "github-topics", "license-metadata", "paper-reference", "query-match", "repository-metadata" ]

4

2026-09-25T16:01:25Z

[]

2024-03-11T10:27:01Z

[ "medical.medical-imaging" ]

null

official-paper-method-implementation

[ "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]

include

ml-contribution-v5

452

[ "medical-image-segmentation", "pytorch", "transformer" ]

2026-09-24T00:49:15Z

https://github.com/Rubics-Xuan/TransBTS

null