Datasets:
modelomics
/
gh-ml

Dataset card Data Studio Files
xet
 Community
2
all_domains
list
all_methods
list
all_novelty_signals
list
all_query_ids
list
archived
bool
candidate_status
string
candidate_rule_version
string
candidate_eligible
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candidate_reason
string
candidate_evidence
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paper_ids
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query_ids
list
readme_blob_sha
string
readme_checked_at
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readme_evidence_version
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readme_etag
string
readme_sections
list
readme_signals
list
readme_status
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string
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string
selection_signals
list
selection_status
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stars
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topics
list
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extra_json
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[ "efficient-ml", "machine-learning-systems", "model-compression" ]
[ "neural-network-pruning", "pruning", "structured-pruning" ]
[ "description", "query-match", "repository-metadata" ]
[ "efficiency.pruning" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2011-10-23T16:15:22Z
Algorithm MONSA for All Closed Sets Finding. MONSA is an exact depth-first search algorithm extracting only frequent closed sets using several new very effective pruning techniques to be free from repetitive and empty patterns.
[ "efficient-ml", "machine-learning-systems", "model-compression" ]
[]
no_text_signal
gh-ml-relevance-v1
2026-09-29T17:30:08Z
false
0
2,631,335
null
C++
null
[ "neural-network-pruning", "pruning", "structured-pruning" ]
mikksoone/monsa
[ "description", "query-match", "repository-metadata" ]
1
2026-09-29T17:30:08Z
[]
2011-10-23T16:20:23Z
[ "efficiency.pruning" ]
null
2026-09-29T18:47:14Z
gh-ml-readme-evidence-v2
null
[]
[]
missing
2026-09-29T18:47:14Z
mikksoone/monsa
specific-method-with-novelty-claim
[ "method-tied-novelty-claim", "ml-method-cue" ]
include
ml-contribution-v5
1
[]
2013-12-14T17:24:37Z
https://github.com/mikksoone/monsa
null
[ "classical-ml", "general-ml", "representation-learning" ]
[ "kernel-methods", "machine-learning", "representation-learning", "semi-supervised-learning", "support-vector-machine" ]
[ "description", "license-metadata", "query-match", "repository-metadata" ]
[ "general.machine-learning-readme", "general.semi-supervised-learning", "general.support-vector-machine", "recall.name.machine-learning" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2013-07-26T18:59:43Z
Multiple-Instance Support Vector Machines
[ "general-ml" ]
[]
no_text_signal
gh-ml-relevance-v1
2026-09-24T10:49:00Z
false
83
11,692,651
null
Python
BSD-3-Clause
[ "machine-learning" ]
garydoranjr/misvm
[ "description", "license-metadata", "query-match", "repository-metadata" ]
11
2026-10-13T18:05:26Z
[]
2019-12-01T02:00:55Z
[ "recall.name.machine-learning" ]
52bebf5f592e0841be2726643c97282307c096d0
2026-10-05T21:36:53Z
gh-ml-readme-evidence-v2
"52bebf5f592e0841be2726643c97282307c096d0"
[ "installation", "other" ]
[ "method-contribution", "ml-method-context", "paper-code-relationship", "paper-reference" ]
ok
2026-10-05T21:36:53Z
garydoranjr/misvm
readme-supported-paper-method-implementation
[ "method-contribution", "ml-method-context", "ml-method-cue", "paper-code-relationship", "paper-reference" ]
include
ml-contribution-v5
248
[]
2026-09-20T08:02:24Z
https://github.com/garydoranjr/misvm
null
[ "general-ml", "representation-learning" ]
[ "fine-tuning", "transfer-learning" ]
[ "description", "query-match", "repository-metadata" ]
[ "general.transfer-learning" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2015-06-14T12:40:31Z
Code for paper "A Machine Learning Approach for Instance Matching Based on Similarity Metrics" ISWC2012.
[ "general-ml", "representation-learning" ]
[ "machine-learning" ]
direct_ml_text
gh-ml-relevance-v1
2026-09-27T16:09:59Z
false
0
37,411,816
null
Java
null
[ "fine-tuning", "transfer-learning" ]
FicusRong/Instance-Matching-by-Transfer-Learning
[ "description", "query-match", "repository-metadata" ]
2
2026-10-30T17:05:26Z
[]
2015-06-15T07:12:24Z
[ "general.transfer-learning" ]
null
null
null
null
null
null
null
null
null
official-paper-method-implementation
[ "ml-context-only", "official-paper-implementation-cue", "paper-and-code-cue" ]
include
ml-contribution-v5
5
[]
2018-05-26T17:43:38Z
https://github.com/FicusRong/Instance-Matching-by-Transfer-Learning
null
[ "classical-ml", "tabular-ml" ]
[ "ensemble-learning", "random-forest" ]
[ "description", "license-metadata", "query-match", "repository-metadata" ]
[ "general.random-forest" ]
false
candidate
ml-candidate-v4
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" ]
5
2026-10-30T17:05:26Z
[]
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", "general-ml", "machine-learning", "robotics", "robotics-and-control" ]
[ "control", "neural-network", "neuromorphic-computing", "spiking-neural-network" ]
[ "description", "github-topics", "query-match", "repository-metadata" ]
[ "general.neural-network", "robotics.robot-control", "specialized.spiking-neural-network" ]
true
candidate
ml-candidate-v4
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", "control", "machine-learning", "robotics", "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
[ "control", "neural-network", "neuromorphic-computing", "spiking-neural-network" ]
ricardodeazambuja/IJCNN2016
[ "description", "github-topics", "query-match", "repository-metadata" ]
5
2026-10-25T15:01:34Z
[]
2021-07-14T09:07:50Z
[ "robotics.robot-control", "specialized.spiking-neural-network" ]
30cf7d12c3b0a6869d26ee10cd474a8c21bb0bdd
2026-09-26T15:53:33Z
gh-ml-readme-evidence-v2
"30cf7d12c3b0a6869d26ee10cd474a8c21bb0bdd"
[ "abstract", "citation", "method", "other" ]
[ "ml-method-context" ]
ok
2026-09-26T15:53:33Z
ricardodeazambuja/IJCNN2016
specific-method-with-novelty-claim
[ "method-tied-novelty-claim", "ml-method-context", "ml-method-cue" ]
include
ml-contribution-v5
17
[ "baxter-robot", "liquid-state-machines", "lsm", "robot", "snn", "spiking-neural-networks", "vrep-simulator" ]
2026-05-05T13:38:49Z
https://github.com/ricardodeazambuja/IJCNN2016
null
[ "classical-ml", "health-and-biomedicine", "medical-imaging" ]
[ "image-analysis", "kernel-methods", "support-vector-machine" ]
[ "description", "license-metadata", "query-match", "repository-metadata" ]
[ "general.support-vector-machine", "medical.medical-imaging" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2016-01-15T17:45:31Z
k-support regularized Support Vector Machine (ksup-SVM) is a novel regularization method that extends the L1 regularized SVM to a mixed norm of both L1 and L2 norms. This enables the use of a correlated sparsity regularization with the power of the SVM framework.
[ "health-and-biomedicine", "medical-imaging" ]
[]
no_text_signal
gh-ml-relevance-v1
2026-09-28T18:50:00Z
false
1
49,734,905
null
Matlab
GPL-3.0
[ "image-analysis", "support-vector-machine" ]
gkirtzou/ksup_svm
[ "description", "license-metadata", "query-match", "repository-metadata" ]
2
2026-10-02T16:59:40Z
[]
2016-01-15T17:46:26Z
[ "medical.medical-imaging" ]
1bea0d6a666fdb0f5db3638e5f57fe9e8d0da40f
2026-09-28T20:05:39Z
gh-ml-readme-evidence-v2
"1bea0d6a666fdb0f5db3638e5f57fe9e8d0da40f"
[]
[ "ml-method-context" ]
ok
2026-09-28T20:05:39Z
gkirtzou/ksup_svm
specific-method-with-novelty-claim
[ "method-tied-novelty-claim", "ml-method-context", "ml-method-cue" ]
include
ml-contribution-v5
1
[]
2017-09-16T12:34:24Z
https://github.com/gkirtzou/ksup_svm
null
[ "general-ml", "reinforcement-learning" ]
[ "deep-reinforcement-learning", "paper-implementation", "policy-learning", "reinforcement-learning" ]
[ "description", "license-metadata", "paper-reference", "query-match", "repository-metadata" ]
[ "general.arxiv", "rl.deep" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2016-05-09T12:59:18Z
A Tensorflow based implementation of "Asynchronous Methods for Deep Reinforcement Learning": https://arxiv.org/abs/1602.01783
[ "reinforcement-learning" ]
[ "reinforcement-learning" ]
direct_ml_text
gh-ml-relevance-v1
2026-09-24T16:52:35Z
false
23
58,376,719
null
Python
Apache-2.0
[ "deep-reinforcement-learning", "policy-learning", "reinforcement-learning" ]
traai/async-deep-rl
[ "description", "license-metadata", "paper-reference", "query-match", "repository-metadata" ]
3
2026-10-24T14:20:46Z
[]
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", "general-ml", "information-retrieval" ]
[ "deep-learning", "image-retrieval", "metric-learning" ]
[ "description", "query-match", "repository-metadata" ]
[ "general.deep-learning-readme", "recall.name.deep-learning", "vision.image-retrieval" ]
false
candidate
ml-candidate-v4
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" ]
5
2026-10-25T15:01:34Z
[]
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-v4
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" ]
5
2026-10-25T15:01:34Z
[]
2021-12-07T10:07:17Z
[ "specialized.spiking-neural-network" ]
c4278e768503576fb2f0cd78e54d16cf5351258d
2026-09-26T20:25:07Z
gh-ml-readme-evidence-v2
"c4278e768503576fb2f0cd78e54d16cf5351258d"
[ "abstract", "citation", "other" ]
[ "method-contribution", "ml-method-context", "paper-code-relationship", "paper-reference" ]
ok
2026-09-26T20:25:07Z
jpdominguez/Multilayer-SNN-for-audio-samples-classification-using-SpiNNaker
readme-supported-paper-method-implementation
[ "method-contribution", "ml-context-only", "ml-method-context", "paper-and-code-cue", "paper-code-relationship", "paper-reference" ]
include
ml-contribution-v5
32
[]
2026-03-11T19:49:03Z
https://github.com/jpdominguez/Multilayer-SNN-for-audio-samples-classification-using-SpiNNaker
null
[ "deep-learning", "general-ml", "generative-modeling", "health-and-biomedicine", "medical-imaging", "probabilistic-ml" ]
[ "bayesian-deep-learning", "deep-learning", "image-analysis", "uncertainty-estimation" ]
[ "description", "github-topics", "paper-reference", "query-match", "repository-metadata" ]
[ "general.bayesian-deep-learning", "general.deep-learning-readme", "general.topic-deep-learning", "medical.medical-imaging" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2016-11-30T12:09:59Z
Code for the paper "Improving Variational Auto-Encoders using Householder Flow" (https://arxiv.org/abs/1611.09630)
[ "general-ml", "generative-modeling" ]
[ "deep-learning", "generative-model", "representation-learning" ]
direct_ml_text
gh-ml-relevance-v1
2026-09-24T15:29:21Z
false
12
75,183,533
https://jmtomczak.github.io/deebmed.html
Python
null
[ "deep-learning" ]
jmtomczak/vae_householder_flow
[ "description", "github-topics", "paper-reference", "query-match", "repository-metadata" ]
10
2026-10-25T15:11:36Z
[]
2017-01-26T09:18:13Z
[ "general.deep-learning-readme" ]
528d3fffa7692dbfc08a6fe8290dd5b40cb18ccf
2026-09-26T20:42:46Z
gh-ml-readme-evidence-v2
"528d3fffa7692dbfc08a6fe8290dd5b40cb18ccf"
[ "citation", "other", "results" ]
[ "ml-method-context", "paper-code-relationship", "paper-reference" ]
ok
2026-09-26T20:42:46Z
jmtomczak/vae_householder_flow
readme-supported-paper-method-implementation
[ "ml-context-only", "ml-method-context", "paper-and-code-cue", "paper-code-relationship", "paper-reference" ]
include
ml-contribution-v5
74
[ "deep-learning", "generative-model", "normalizing-flows", "representation-learning", "variational-autoencoders" ]
2025-12-09T13:18:20Z
https://github.com/jmtomczak/vae_householder_flow
null
[ "general-ml" ]
[ "deep-learning", "neural-network" ]
[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]
[ "general.neural-network", "general.topic-deep-learning" ]
true
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2016-12-27T12:24:05Z
This is the official implementation of the paper "A Neural Network Approach to Missing Marker Reconstruction in Human Motion Capture"
[ "general-ml" ]
[ "deep-learning", "neural-network" ]
direct_ml_text
gh-ml-relevance-v1
2026-10-04T15:58:58Z
false
8
77,453,660
null
Python
Apache-2.0
[ "neural-network" ]
Svito-zar/NN-for-Missing-Marker-Reconstruction
[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]
2
2026-10-26T17:25:59Z
[]
2021-01-22T09:21:10Z
[ "general.neural-network" ]
null
null
null
null
null
null
null
null
null
official-paper-method-implementation
[ "contribution-language", "ml-context-only", "official-paper-implementation-cue", "paper-and-code-cue" ]
include
ml-contribution-v5
39
[ "autoencoder", "deep-learning", "missing-data", "missing-markers", "motion-capture", "motion-capture-processing", "neural-networks", "tensorflow" ]
2026-03-04T11:05:13Z
https://github.com/Svito-zar/NN-for-Missing-Marker-Reconstruction
null
[ "general-ml", "generative-modeling" ]
[ "paper-implementation" ]
[ "description", "github-topics", "license-metadata", "paper-reference", "query-match", "repository-metadata" ]
[ "general.arxiv" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2017-02-12T12:20:04Z
Tensorflow implementation of Wasserstein GAN - arxiv: https://arxiv.org/abs/1701.07875
[ "general-ml", "generative-modeling" ]
[]
no_text_signal
gh-ml-relevance-v1
2026-09-24T10:49:00Z
false
129
81,723,773
null
Python
MIT
[ "paper-implementation" ]
shekkizh/WassersteinGAN.tensorflow
[ "description", "github-topics", "license-metadata", "paper-reference", "query-match", "repository-metadata" ]
3
2026-09-24T16:52:35Z
[]
2017-02-13T20:49:15Z
[ "general.arxiv" ]
0a905a6db044bf0afed3788acf136f2d6625965b
2026-09-26T21:03:54Z
gh-ml-readme-evidence-v2
"0a905a6db044bf0afed3788acf136f2d6625965b"
[ "other", "references" ]
[ "method-contribution", "ml-method-context", "model-training-artifact", "paper-code-relationship", "paper-reference" ]
ok
2026-09-26T21:03:54Z
shekkizh/WassersteinGAN.tensorflow
readme-supported-paper-method-implementation
[ "contribution-language", "method-contribution", "ml-method-context", "model-training-artifact", "paper-and-code-cue", "paper-code-relationship", "paper-reference" ]
include
ml-contribution-v5
412
[ "gan", "generative-adversarial-network", "tensorflow", "wasserstein" ]
2026-07-15T06:37:30Z
https://github.com/shekkizh/WassersteinGAN.tensorflow
null
[ "generative-ai", "generative-modeling" ]
[ "diffusion" ]
[ "description", "license-metadata", "query-match", "repository-metadata" ]
[ "recall.name.diffusion-model" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2017-03-03T05:41:00Z
Python code for paper "Model-free Inference of Diffusion Networks using RKHS Embeddings"
[ "generative-ai", "generative-modeling" ]
[ "embedding" ]
ml_related_text
gh-ml-relevance-v1
2026-10-02T16:59:40Z
false
1
83,761,833
null
Python
MIT
[ "diffusion" ]
amber0309/KEBC
[ "description", "license-metadata", "query-match", "repository-metadata" ]
1
2026-10-02T16:59:40Z
[]
2020-08-11T04:08:11Z
[ "recall.name.diffusion-model" ]
null
null
null
null
null
null
null
null
null
official-paper-method-implementation
[ "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]
include
ml-contribution-v5
6
[]
2023-09-28T00:33:47Z
https://github.com/amber0309/KEBC
null
[ "classical-ml", "earth-observation", "earth-science", "environmental-science", "general-ml", "geospatial", "geospatial-science", "remote-sensing", "science-and-engineering", "tabular-ml" ]
[ "deep-learning", "ensemble-learning", "foundation-model", "geospatial-learning", "gradient-boosting", "land-cover-classification", "machine-learning", "remote-sensing", "semantic-segmentation" ]
[ "description", "github-topics", "query-match", "repository-metadata" ]
[ "general.topic-machine-learning", "geo.land-cover", "geo.topic-remote-sensing", "probable.classical-gradient-boosting-readme", "science.remote-sensing" ]
false
candidate
ml-candidate-v4
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", "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", "machine-learning", "remote-sensing" ]
darribas/satellite_led_liverpool
[ "description", "github-topics", "query-match", "repository-metadata" ]
8
2026-10-23T15:47:21Z
[]
2019-03-13T10:53:44Z
[ "geo.topic-remote-sensing", "science.remote-sensing" ]
1852140aef0a9d7900bae77e88b08a5e0cce01e1
2026-09-26T21:03:54Z
gh-ml-readme-evidence-v2
"1852140aef0a9d7900bae77e88b08a5e0cce01e1"
[ "citation", "other" ]
[ "ml-method-context", "paper-code-relationship", "paper-reference" ]
ok
2026-09-26T21:03:54Z
darribas/satellite_led_liverpool
readme-supported-paper-method-implementation
[ "ml-context-only", "ml-method-context", "paper-and-code-cue", "paper-code-relationship", "paper-reference" ]
include
ml-contribution-v5
14
[ "data", "machine-learning", "paper", "remote-sensing", "reproducibility", "socio-economic-indicators" ]
2025-05-31T01:13:07Z
https://github.com/darribas/satellite_led_liverpool
null
[ "general-ml", "generative-ai", "language" ]
[ "language-model", "transformer" ]
[ "description", "query-match", "repository-metadata" ]
[ "llm.transformers", "recall.name.transformer-model" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2017-03-20T05:34:33Z
Implement a novel Dense Transformer Networks by Tensorflow
[ "general-ml" ]
[ "transformer" ]
ml_related_text
gh-ml-relevance-v1
2026-09-30T16:47:43Z
false
1
85,538,937
null
Python
null
[ "transformer" ]
YongjunChen93/DSN_Tensorflow_Code
[ "description", "query-match", "repository-metadata" ]
2
2026-10-04T15:58:58Z
[]
2017-10-22T22:24:43Z
[ "recall.name.transformer-model" ]
8e13d5dccfaebab1609bb5c298550cc9c8093dd4
2026-09-30T18:16:34Z
gh-ml-readme-evidence-v2
"8e13d5dccfaebab1609bb5c298550cc9c8093dd4"
[ "citation", "method", "other", "overview" ]
[ "method-contribution", "ml-method-context", "paper-code-relationship", "paper-reference" ]
ok
2026-09-30T18:16:34Z
YongjunChen93/DSN_Tensorflow_Code
specific-method-with-novelty-claim
[ "contribution-language", "method-contribution", "method-tied-novelty-claim", "ml-method-context", "ml-method-cue", "paper-code-relationship", "paper-reference" ]
include
ml-contribution-v5
6
[]
2020-09-16T10:24:29Z
https://github.com/YongjunChen93/DSN_Tensorflow_Code
null
[ "general-ml" ]
[ "paper-implementation" ]
[ "description", "github-topics", "paper-reference", "query-match", "repository-metadata" ]
[ "general.arxiv" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2017-04-13T17:44:53Z
Hybrid Code Networks https://arxiv.org/abs/1702.03274
[ "general-ml" ]
[]
no_text_signal
gh-ml-relevance-v1
2026-09-24T16:52:35Z
false
21
88,191,165
null
Python
null
[ "paper-implementation" ]
johndpope/hcn
[ "description", "github-topics", "paper-reference", "query-match", "repository-metadata" ]
1
2026-09-24T16:52:35Z
[]
2017-04-13T17:43:32Z
[ "general.arxiv" ]
d555b918e3ddc52656ec9434dee20fd5c5c0d678
2026-09-27T17:16:20Z
gh-ml-readme-evidence-v2
"d555b918e3ddc52656ec9434dee20fd5c5c0d678"
[ "installation", "other" ]
[ "method-contribution", "ml-method-context", "paper-code-relationship", "paper-reference" ]
ok
2026-09-27T17:16:20Z
johndpope/hcn
readme-supported-paper-method-implementation
[ "method-contribution", "ml-method-context", "paper-and-code-cue", "paper-code-relationship", "paper-reference" ]
include
ml-contribution-v5
80
[ "bag-of-words", "dialog", "entitytracker", "gensim", "hybrid-code-networks", "lstm", "rnn", "tensorflow", "utterance" ]
2025-01-17T13:05:41Z
https://github.com/johndpope/hcn
null
[ "classical-ml", "probabilistic-ml", "representation-learning" ]
[ "gaussian-process", "kernel-methods", "metric-learning", "representation-learning" ]
[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]
[ "general.gaussian-process", "general.metric-learning" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2017-04-14T11:49:57Z
Code for the paper "Gaussian Process Classification as Metric Learning for Forensic Writer Identification", published at DAS 2018
[ "classical-ml", "representation-learning" ]
[ "classifier" ]
ml_related_text
gh-ml-relevance-v1
2026-09-26T10:28:13Z
false
0
88,263,462
null
Python
MIT
[ "metric-learning", "representation-learning" ]
fredrikwahlberg/das2018
[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]
3
2026-10-23T15:47:21Z
[]
2018-05-03T13:28:29Z
[ "general.metric-learning" ]
2a92a732848d696c61433274e2dd39196d2519ed
2026-09-27T17:16:20Z
gh-ml-readme-evidence-v2
"2a92a732848d696c61433274e2dd39196d2519ed"
[]
[ "method-contribution", "ml-method-context", "paper-code-relationship", "paper-reference" ]
ok
2026-09-27T17:16:20Z
fredrikwahlberg/das2018
readme-supported-paper-method-implementation
[ "method-contribution", "ml-method-context", "paper-and-code-cue", "paper-code-relationship", "paper-reference" ]
include
ml-contribution-v5
2
[ "document-analysis", "gaussian-processes", "multi-class-classification", "unsupervised-feature-learning", "writer-indentification" ]
2020-09-24T16:59:04Z
https://github.com/fredrikwahlberg/das2018
null
[ "general-ml" ]
[ "paper-implementation" ]
[ "description", "license-metadata", "paper-reference", "query-match", "repository-metadata" ]
[ "general.arxiv" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2017-04-17T10:00:17Z
Source code for the EMNLP'17 paper "Deep Joint Entity Disambiguation with Local Neural Attention", https://arxiv.org/abs/1704.04920
[ "general-ml" ]
[]
no_text_signal
gh-ml-relevance-v1
2026-09-24T16:52:35Z
false
50
88,495,676
null
Lua
Apache-2.0
[ "paper-implementation" ]
dalab/deep-ed
[ "description", "license-metadata", "paper-reference", "query-match", "repository-metadata" ]
1
2026-09-24T16:52:35Z
[]
2018-01-12T12:11:19Z
[ "general.arxiv" ]
856f857497ed2a61103bd5346cc9f43cbf0f9baf
2026-09-27T17:16:20Z
gh-ml-readme-evidence-v2
"856f857497ed2a61103bd5346cc9f43cbf0f9baf"
[ "other" ]
[ "method-contribution", "ml-method-context", "model-training-artifact", "paper-code-relationship", "paper-reference" ]
ok
2026-09-27T17:16:20Z
dalab/deep-ed
readme-supported-paper-method-implementation
[ "method-contribution", "ml-context-only", "ml-method-context", "model-training-artifact", "paper-and-code-cue", "paper-code-relationship", "paper-reference" ]
include
ml-contribution-v5
222
[]
2026-06-18T07:56:43Z
https://github.com/dalab/deep-ed
null
[ "multimodal", "multimodal-learning" ]
[ "convolutional-neural-network", "multimodal-learning", "neural-network" ]
[ "description", "license-metadata", "query-match", "repository-metadata" ]
[ "recall.name.multimodal-model" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2017-04-18T02:51:06Z
Code for paper "Musical Instrument Recognition in User-generated Videos using a Multimodal Convolutional Neural Network Architecture" by Olga Slizovskaia, Emilia Gomez, Gloria Haro. ICMR 2017
[ "multimodal", "multimodal-learning" ]
[ "neural-network" ]
direct_ml_text
gh-ml-relevance-v1
2026-09-26T14:50:46Z
false
1
88,574,254
null
Python
GPL-3.0
[ "convolutional-neural-network", "multimodal-learning", "neural-network" ]
Veleslavia/ICMR2017
[ "description", "license-metadata", "query-match", "repository-metadata" ]
1
2026-09-26T14:50:46Z
[]
2017-12-19T14:59:59Z
[ "recall.name.multimodal-model" ]
26bc02c9abc5f6be9bd8374ff5d183a273f2a08a
2026-09-27T17:16:20Z
gh-ml-readme-evidence-v2
"26bc02c9abc5f6be9bd8374ff5d183a273f2a08a"
[ "installation", "other", "usage" ]
[ "ml-method-context", "paper-code-relationship", "paper-reference" ]
ok
2026-09-27T17:16:20Z
Veleslavia/ICMR2017
readme-supported-paper-method-implementation
[ "ml-context-only", "ml-method-context", "paper-and-code-cue", "paper-code-relationship", "paper-reference" ]
include
ml-contribution-v5
8
[]
2022-12-18T12:23:11Z
https://github.com/Veleslavia/ICMR2017
null
[ "general-ml", "multimodal", "multimodal-learning", "reinforcement-learning", "robotics" ]
[ "model-based-reinforcement-learning", "multimodal-learning", "paper-implementation", "reinforcement-learning", "world-model" ]
[ "description", "license-metadata", "query-match", "repository-metadata" ]
[ "general.arxiv", "recall.name.multimodal-model", "rl.model-based" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2017-04-20T17:40:30Z
Code for paper "Learning Multimodal Transition Dynamics for Model-Based Reinforcement Learning".
[ "multimodal", "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
[ "multimodal-learning", "reinforcement-learning" ]
tmoer/multimodal_varinf
[ "description", "license-metadata", "query-match", "repository-metadata" ]
7
2026-10-23T15:47:21Z
[]
2018-05-24T11:17:50Z
[ "recall.name.multimodal-model" ]
041b494035a7fcc5e6bb26ea6d91b06e34abebe1
2026-09-27T17:16:20Z
gh-ml-readme-evidence-v2
"041b494035a7fcc5e6bb26ea6d91b06e34abebe1"
[ "citation", "method", "other" ]
[ "ml-method-context", "paper-code-relationship", "paper-reference" ]
ok
2026-09-27T17:16:20Z
tmoer/multimodal_varinf
official-paper-method-implementation
[ "ml-method-context", "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue", "paper-code-relationship", "paper-reference" ]
include
ml-contribution-v5
33
[]
2026-10-20T18:23:34Z
https://github.com/tmoer/multimodal_varinf
null
[ "general-ml", "representation-learning" ]
[ "representation-learning", "semi-supervised-learning" ]
[ "description", "query-match", "repository-metadata" ]
[ "general.semi-supervised-learning" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2017-04-24T19:06:03Z
Code for paper "Projected Estimators for Robust Semi-supervised Classification"
[ "general-ml", "representation-learning" ]
[ "classifier" ]
ml_related_text
gh-ml-relevance-v1
2026-09-26T14:50:46Z
false
1
89,277,125
null
TeX
null
[ "representation-learning", "semi-supervised-learning" ]
jkrijthe/ProjectedEstimators
[ "description", "query-match", "repository-metadata" ]
1
2026-09-26T14:50:46Z
[]
2017-04-24T19:10:41Z
[ "general.semi-supervised-learning" ]
f9bbdaa45824478abc3fa192945a07d614fa3304
2026-09-27T17:16:20Z
gh-ml-readme-evidence-v2
"f9bbdaa45824478abc3fa192945a07d614fa3304"
[ "abstract", "other" ]
[ "method-contribution", "ml-method-context", "paper-code-relationship", "paper-reference" ]
ok
2026-09-27T17:16:20Z
jkrijthe/ProjectedEstimators
readme-supported-paper-method-implementation
[ "method-contribution", "ml-method-context", "paper-and-code-cue", "paper-code-relationship", "paper-reference" ]
include
ml-contribution-v5
2
[]
2018-05-30T03:41:33Z
https://github.com/jkrijthe/ProjectedEstimators
null
[ "reinforcement-learning" ]
[ "deep-reinforcement-learning", "policy-learning", "reinforcement-learning" ]
[ "description", "query-match", "repository-metadata" ]
[ "rl.deep" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2017-05-28T13:14:59Z
Official implementation for the paper: "Shallow Updates for Deep Reinforcement Learning"
[ "reinforcement-learning" ]
[ "reinforcement-learning" ]
direct_ml_text
gh-ml-relevance-v1
2026-09-29T17:30:08Z
false
5
92,662,121
null
Lua
null
[ "deep-reinforcement-learning", "policy-learning", "reinforcement-learning" ]
Shallow-Updates-for-Deep-RL/Shallow_Updates_for_Deep_RL
[ "description", "query-match", "repository-metadata" ]
2
2026-10-21T15:35:44Z
[]
2017-11-02T18:52:18Z
[ "rl.deep" ]
null
null
null
null
null
null
null
null
null
official-paper-method-implementation
[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]
include
ml-contribution-v5
18
[]
2024-01-04T16:14:26Z
https://github.com/Shallow-Updates-for-Deep-RL/Shallow_Updates_for_Deep_RL
null
[ "general-ml", "recommender-systems" ]
[ "collaborative-filtering", "deep-learning", "neural-network" ]
[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]
[ "applied.collaborative-filtering", "general.topic-deep-learning" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2017-05-29T17:57:45Z
Code for paper "On Sampling Strategies for Neural Network-based Collaborative Filtering"
[ "recommender-systems" ]
[ "deep-learning", "neural-network" ]
direct_ml_text
gh-ml-relevance-v1
2026-09-24T10:49:00Z
false
17
92,763,174
null
Python
MIT
[ "collaborative-filtering", "neural-network" ]
chentingpc/NNCF
[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]
9
2026-10-21T15:35:44Z
[]
2017-10-01T17:11:52Z
[ "applied.collaborative-filtering" ]
33a5e2fa64c5f43b025f85d81baac5cf605955f0
2026-09-28T20:05:39Z
gh-ml-readme-evidence-v2
"33a5e2fa64c5f43b025f85d81baac5cf605955f0"
[ "other" ]
[ "ml-method-context", "paper-code-relationship", "paper-reference" ]
ok
2026-09-28T20:05:39Z
chentingpc/NNCF
readme-supported-paper-method-implementation
[ "ml-context-only", "ml-method-context", "paper-and-code-cue", "paper-code-relationship", "paper-reference" ]
include
ml-contribution-v5
39
[ "collaborative-filtering", "deep-learning", "neural-networks", "recommender-system", "sgd" ]
2025-01-20T10:37:11Z
https://github.com/chentingpc/NNCF
null
[ "imitation-learning", "reinforcement-learning" ]
[ "imitation-learning", "inverse-reinforcement-learning", "reinforcement-learning" ]
[ "description", "query-match", "repository-metadata" ]
[ "rl.inverse" ]
false
candidate
ml-candidate-v4
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" ]
4
2026-10-22T15:57:46Z
[]
2017-06-29T22:55:35Z
[ "rl.inverse" ]
2ed058c5809522125cd20d9862ddeda5dc5bf65d
2026-09-26T15:53:33Z
gh-ml-readme-evidence-v2
"2ed058c5809522125cd20d9862ddeda5dc5bf65d"
[ "other" ]
[ "ml-method-context" ]
ok
2026-09-26T15:53:33Z
siddharthanpr/irl
specific-method-with-novelty-claim
[ "method-tied-novelty-claim", "ml-method-context", "ml-method-cue" ]
include
ml-contribution-v5
8
[]
2023-04-19T19:16:30Z
https://github.com/siddharthanpr/irl
null
[ "deep-learning", "embodied-ai", "multimodal", "representation-learning", "robotics" ]
[ "continual-learning", "lifelong-learning", "multi-task-learning", "robot-foundation-model", "robot-learning", "transfer-learning" ]
[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]
[ "general.continual-learning", "general.multi-task-learning", "robotics.robot-foundation-model" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2017-07-13T21:07:15Z
Numenta published papers code and data
[ "deep-learning", "representation-learning" ]
[]
no_text_signal
gh-ml-relevance-v1
2026-09-24T15:29:21Z
false
94
97,165,046
null
Jupyter Notebook
AGPL-3.0
[ "multi-task-learning", "transfer-learning" ]
numenta/htmpapers
[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]
10
2026-10-23T15:47:21Z
[]
2022-03-30T23:59:13Z
[ "general.multi-task-learning" ]
8d5c5efb658a9955d0b5a51303a706cda36321b4
2026-09-28T20:05:39Z
gh-ml-readme-evidence-v2
"8d5c5efb658a9955d0b5a51303a706cda36321b4"
[ "dataset", "other" ]
[ "method-contribution", "ml-method-context", "paper-code-relationship", "paper-reference" ]
ok
2026-09-28T20:05:39Z
numenta/htmpapers
readme-supported-paper-method-implementation
[ "method-contribution", "ml-method-context", "paper-and-code-cue", "paper-code-relationship", "paper-reference" ]
include
ml-contribution-v5
341
[ "htm", "neocortex", "numenta", "paper", "theory" ]
2026-07-20T07:37:32Z
https://github.com/numenta/htmpapers
null
[ "natural-language-processing", "reinforcement-learning" ]
[ "information-extraction", "reinforcement-learning" ]
[ "description", "license-metadata", "query-match", "repository-metadata" ]
[ "nlp.information-extraction" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2017-07-20T16:27:23Z
Code for the paper 'Speeding up Reinforcement Learning-based Information Extraction Training using Asynchronous Methods' - EMNLP 2017
[ "natural-language-processing", "reinforcement-learning" ]
[ "reinforcement-learning" ]
direct_ml_text
gh-ml-relevance-v1
2026-09-27T16:09:59Z
false
4
97,856,513
null
Roff
MIT
[ "information-extraction", "reinforcement-learning" ]
adi-sharma/RLIE_A3C
[ "description", "license-metadata", "query-match", "repository-metadata" ]
2
2026-10-21T15:35:44Z
[]
2018-09-10T09:17:07Z
[ "nlp.information-extraction" ]
46e10caf20928c362886819429c47d943971d5f1
2026-09-28T20:05:39Z
gh-ml-readme-evidence-v2
"46e10caf20928c362886819429c47d943971d5f1"
[ "citation", "method", "other" ]
[ "method-contribution", "ml-method-context", "paper-code-relationship", "paper-reference" ]
ok
2026-09-28T20:05:39Z
adi-sharma/RLIE_A3C
readme-supported-paper-method-implementation
[ "contribution-language", "method-contribution", "ml-method-context", "ml-method-cue", "paper-and-code-cue", "paper-code-relationship", "paper-reference" ]
include
ml-contribution-v5
8
[]
2025-08-25T16:38:44Z
https://github.com/adi-sharma/RLIE_A3C
null
[ "general-ml", "graph-learning" ]
[ "artificial-intelligence", "convolutional-neural-network", "deep-learning", "graph-classification", "graph-representation-learning", "neural-network" ]
[ "description", "github-topics", "paper-reference", "query-match", "repository-metadata" ]
[ "general.topic-artificial-intelligence", "general.topic-deep-learning", "graph.graph-classification" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2017-07-28T14:29:49Z
Code and data for the paper 'Classifying Graphs as Images with Convolutional Neural Networks' (new title: 'Graph Classification with 2D Convolutional Neural Networks')
[ "graph-learning" ]
[ "deep-learning", "neural-network", "representation-learning", "classifier", "embedding" ]
direct_ml_text
gh-ml-relevance-v1
2026-09-24T10:49:00Z
false
23
98,657,591
https://arxiv.org/abs/1708.02218
Python
null
[ "convolutional-neural-network", "graph-classification", "graph-representation-learning", "neural-network" ]
Tixierae/graph_2D_CNN
[ "description", "github-topics", "paper-reference", "query-match", "repository-metadata" ]
7
2026-10-21T15:35:44Z
[]
2019-12-13T08:46:13Z
[ "graph.graph-classification" ]
3282c32987b3da87fa6d3e7bcd1c32dfc3b8c75e
2026-09-28T20:05:39Z
gh-ml-readme-evidence-v2
"3282c32987b3da87fa6d3e7bcd1c32dfc3b8c75e"
[ "citation", "installation", "other" ]
[ "method-contribution", "ml-method-context", "paper-code-relationship", "paper-reference" ]
ok
2026-09-28T20:05:39Z
Tixierae/graph_2D_CNN
readme-supported-paper-method-implementation
[ "method-contribution", "ml-context-only", "ml-method-context", "paper-and-code-cue", "paper-code-relationship", "paper-reference" ]
include
ml-contribution-v5
77
[ "2d-cnn", "artificial-intelligence", "classification", "convolutional-neural-networks", "deep-learning", "embeddings", "graph-2d-cnn", "graph-kernels", "graph-theory", "keras", "neural-networks", "representation-learning", "tensorflow" ]
2026-02-09T21:43:11Z
https://github.com/Tixierae/graph_2D_CNN
null
[ "efficient-ml", "machine-learning-systems", "model-compression" ]
[ "convolutional-neural-network", "neural-network", "neural-network-pruning", "pruning", "structured-pruning" ]
[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]
[ "efficiency.pruning" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2017-09-02T09:17:28Z
[IJCNN'19, IEEE JSTSP'19] Caffe code for our paper "Structured Pruning for Efficient ConvNets via Incremental Regularization"; [BMVC'18] "Structured Probabilistic Pruning for Convolutional Neural Network Acceleration"
[ "efficient-ml", "machine-learning-systems", "model-compression" ]
[ "neural-network" ]
direct_ml_text
gh-ml-relevance-v1
2026-09-30T16:47:43Z
false
6
102,185,768
null
Makefile
NOASSERTION
[ "convolutional-neural-network", "neural-network", "neural-network-pruning", "pruning", "structured-pruning" ]
MingSun-Tse/Caffe_IncReg
[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]
3
2026-10-23T15:47:21Z
[]
2020-02-14T18:34:13Z
[ "efficiency.pruning" ]
null
null
null
null
null
null
null
null
null
official-paper-method-implementation
[ "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]
include
ml-contribution-v5
14
[ "model-acceleration", "model-compression", "pruning" ]
2023-05-30T09:45:27Z
https://github.com/MingSun-Tse/Caffe_IncReg
null
[ "deep-learning", "representation-learning" ]
[ "multi-task-learning", "transfer-learning" ]
[ "description", "query-match", "repository-metadata" ]
[ "general.multi-task-learning" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2017-09-13T13:53:42Z
Code to replicate Experiments from the paper 'End-to-End Supervised Lobe Segmentation'
[ "deep-learning", "representation-learning" ]
[]
no_text_signal
gh-ml-relevance-v1
2026-09-28T18:50:00Z
false
12
103,406,163
null
Python
null
[ "multi-task-learning", "transfer-learning" ]
filipetrocadoferreira/end2endlobesegmentation
[ "description", "query-match", "repository-metadata" ]
2
2026-10-23T15:47:21Z
[]
2018-03-29T10:39:29Z
[ "general.multi-task-learning" ]
182d6d00ded65e0a2f7b43a948f5989ab63ae25e
2026-09-29T18:47:14Z
gh-ml-readme-evidence-v2
"182d6d00ded65e0a2f7b43a948f5989ab63ae25e"
[ "method", "other" ]
[ "method-contribution", "ml-method-context", "paper-code-relationship", "paper-reference" ]
ok
2026-09-29T18:47:14Z
filipetrocadoferreira/end2endlobesegmentation
readme-supported-paper-method-implementation
[ "method-contribution", "ml-method-context", "paper-and-code-cue", "paper-code-relationship", "paper-reference" ]
include
ml-contribution-v5
39
[]
2026-10-10T02:27:38Z
https://github.com/filipetrocadoferreira/end2endlobesegmentation
null
[ "general-ml", "generative-modeling", "multimodal", "representation-learning" ]
[ "multimodal-learning", "paper-implementation", "representation-learning", "semi-supervised-learning" ]
[ "description", "license-metadata", "paper-reference", "query-match", "repository-metadata" ]
[ "general.arxiv", "general.semi-supervised-learning", "recall.name.multimodal-model" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2017-11-04T01:29:54Z
Tensorflow code for the Bayesian GAN (https://arxiv.org/abs/1705.09558) (NIPS 2017)
[ "general-ml", "generative-modeling", "multimodal", "representation-learning" ]
[]
no_text_signal
gh-ml-relevance-v1
2026-09-24T10:49:00Z
false
170
109,457,615
null
Jupyter Notebook
NOASSERTION
[ "multimodal-learning", "representation-learning", "semi-supervised-learning" ]
andrewgordonwilson/bayesgan
[ "description", "license-metadata", "paper-reference", "query-match", "repository-metadata" ]
6
2026-10-23T15:47:21Z
[]
2018-07-30T20:50:23Z
[ "general.semi-supervised-learning", "recall.name.multimodal-model" ]
c3f71ee104e26b50be5f09c343f8cd7b7cec9de0
2026-09-30T18:16:34Z
gh-ml-readme-evidence-v2
"c3f71ee104e26b50be5f09c343f8cd7b7cec9de0"
[ "dataset", "installation", "other", "overview", "usage" ]
[ "method-contribution", "ml-method-context", "model-training-artifact", "paper-code-relationship", "paper-reference" ]
ok
2026-09-30T18:16:34Z
andrewgordonwilson/bayesgan
readme-supported-paper-method-implementation
[ "method-contribution", "ml-method-context", "model-training-artifact", "paper-and-code-cue", "paper-code-relationship", "paper-reference" ]
include
ml-contribution-v5
1,014
[]
2026-10-13T17:36:35Z
https://github.com/andrewgordonwilson/bayesgan
null
[ "3d", "computer-vision", "multimodal" ]
[ "3d-reconstruction", "neural-reconstruction" ]
[ "description", "license-metadata", "query-match", "repository-metadata" ]
[ "multimodal.3d-reconstruction" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2017-11-14T21:42:24Z
Code for paper: "3D Reconstruction of Incomplete Archaeological Objects Using a Generative Adversarial Network"
[ "3d", "computer-vision", "multimodal" ]
[]
no_text_signal
gh-ml-relevance-v1
2026-09-27T16:09:59Z
false
7
110,748,171
null
Jupyter Notebook
MIT
[ "3d-reconstruction", "neural-reconstruction" ]
renato145/3D-ORGAN
[ "description", "license-metadata", "query-match", "repository-metadata" ]
2
2026-10-21T15:35:44Z
[]
2021-12-29T09:55:33Z
[ "multimodal.3d-reconstruction" ]
084dc33b8c37d1bf2defaa47607442e13e5d7129
2026-09-30T18:16:34Z
gh-ml-readme-evidence-v2
"084dc33b8c37d1bf2defaa47607442e13e5d7129"
[ "citation", "other" ]
[ "ml-method-context", "paper-code-relationship", "paper-reference" ]
ok
2026-09-30T18:16:34Z
renato145/3D-ORGAN
readme-supported-paper-method-implementation
[ "ml-method-context", "paper-and-code-cue", "paper-code-relationship", "paper-reference" ]
include
ml-contribution-v5
16
[]
2024-11-17T07:49:19Z
https://github.com/renato145/3D-ORGAN
null
[ "general-ml", "generative-ai", "language" ]
[ "language-model", "transformer" ]
[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]
[ "llm.transformers", "recall.name.transformer-model" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2017-11-18T19:48:01Z
Extends scikit-learn with new models, transformers, metrics, plotting.
[ "generative-ai", "language" ]
[ "machine-learning", "transformer" ]
direct_ml_text
gh-ml-relevance-v1
2026-10-05T20:10:04Z
false
13
111,234,239
http://www.xavierdupre.fr/app/mlinsights/helpsphinx/index.html
Python
MIT
[ "language-model", "transformer" ]
sdpython/mlinsights
[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]
3
2026-10-23T15:47:21Z
[]
2026-01-18T12:52:10Z
[ "llm.transformers" ]
8ea1788bee1a0abd598f1f2c4b7300a04a64d0ff
2026-10-05T21:36:53Z
gh-ml-readme-evidence-v2
"8ea1788bee1a0abd598f1f2c4b7300a04a64d0ff"
[]
[ "method-contribution", "ml-method-context" ]
ok
2026-10-05T21:36:53Z
sdpython/mlinsights
specific-method-with-novelty-claim
[ "method-contribution", "method-tied-novelty-claim", "ml-method-context", "ml-method-cue" ]
include
ml-contribution-v5
73
[ "algorithms", "machine-learning", "python3", "scikit-learn" ]
2026-06-14T06:45:33Z
https://github.com/sdpython/mlinsights
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-v4
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
952
112,133,243
null
Python
MIT
[ "generative-modeling", "image-to-image-translation" ]
yunjey/stargan
[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]
7
2026-10-22T15:57:46Z
[]
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
5,296
[ "cvpr2018", "generative-models", "image-to-image-translation", "pytorch", "stargan" ]
2026-10-13T22:14:22Z
https://github.com/yunjey/stargan
null
[ "reinforcement-learning" ]
[ "deep-reinforcement-learning", "policy-learning", "reinforcement-learning" ]
[ "description", "github-topics", "license-metadata", "paper-reference", "query-match", "repository-metadata" ]
[ "rl.deep" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2018-01-03T12:53:15Z
Source code for paper Classification with Costly Features using Deep Reinforcement Learning.
[ "reinforcement-learning" ]
[ "reinforcement-learning", "classifier" ]
direct_ml_text
gh-ml-relevance-v1
2026-09-29T17:30:08Z
false
24
116,136,597
https://arxiv.org/abs/1711.07364
Python
MIT
[ "deep-reinforcement-learning", "policy-learning", "reinforcement-learning" ]
jaromiru/cwcf
[ "description", "github-topics", "license-metadata", "paper-reference", "query-match", "repository-metadata" ]
2
2026-10-18T14:31:49Z
[]
2021-10-05T03:02:39Z
[ "rl.deep" ]
dd46e60b15eda09803a6fd956e249f092f9db0b6
2026-10-01T19:11:32Z
gh-ml-readme-evidence-v2
"dd46e60b15eda09803a6fd956e249f092f9db0b6"
[]
[ "ml-method-context", "paper-code-relationship", "paper-reference" ]
ok
2026-10-01T19:11:32Z
jaromiru/cwcf
official-paper-method-implementation
[ "ml-method-context", "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue", "paper-code-relationship", "paper-reference" ]
include
ml-contribution-v5
58
[ "classification", "costly-features", "deep-reinforcement-learning" ]
2026-07-28T10:46:51Z
https://github.com/jaromiru/cwcf
null
[ "deep-learning", "representation-learning" ]
[ "representation-learning", "self-supervised-learning" ]
[ "description", "license-metadata", "query-match", "repository-metadata" ]
[ "general.self-supervised-learning" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2018-01-07T13:28:45Z
An pytorch implementation of time-contrastive networks as presented in the paper "Time-Contrastive Networks: Self-Supervised Learning from Multi-View Observation".
[ "deep-learning", "representation-learning" ]
[ "self-supervised-learning" ]
direct_ml_text
gh-ml-relevance-v1
2026-09-27T16:09:59Z
false
14
116,567,651
null
Python
MIT
[ "representation-learning", "self-supervised-learning" ]
kekeblom/tcn
[ "description", "license-metadata", "query-match", "repository-metadata" ]
2
2026-10-20T15:46:47Z
[]
2018-01-07T13:46:12Z
[ "general.self-supervised-learning" ]
72dedfdb92a91de766e31abecbec2f9a54f3a9cf
2026-10-01T19:11:32Z
gh-ml-readme-evidence-v2
"72dedfdb92a91de766e31abecbec2f9a54f3a9cf"
[ "other" ]
[ "method-contribution", "ml-method-context", "paper-code-relationship", "paper-reference" ]
ok
2026-10-01T19:11:32Z
kekeblom/tcn
readme-supported-paper-method-implementation
[ "contribution-language", "method-contribution", "ml-method-context", "ml-method-cue", "paper-and-code-cue", "paper-code-relationship", "paper-reference" ]
include
ml-contribution-v5
58
[]
2025-11-13T01:14:49Z
https://github.com/kekeblom/tcn
null
[ "computer-vision", "health-and-biomedicine", "medical-imaging" ]
[ "image-analysis", "segmentation" ]
[ "description", "license-metadata", "query-match", "repository-metadata" ]
[ "medical.medical-image-segmentation", "medical.medical-imaging", "vision.segmentation" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2018-01-09T08:12:19Z
Code for the paper 'Generative Adversarial Network based Synthesis for Supervised Medical Image Segmentation'
[ "computer-vision" ]
[]
no_text_signal
gh-ml-relevance-v1
2026-09-26T14:50:46Z
false
13
116,786,201
null
Python
MIT
[ "segmentation" ]
thomasneff/Generative-Adversarial-Network-based-Synthesis-for-Supervised-Medical-Image-Segmentation
[ "description", "license-metadata", "query-match", "repository-metadata" ]
6
2026-10-20T15:46:47Z
[]
2018-01-09T08:54:49Z
[ "vision.segmentation" ]
eb5b8f414a005f5e3eb37778036c6201e48b1606
2026-10-01T19:11:32Z
gh-ml-readme-evidence-v2
"eb5b8f414a005f5e3eb37778036c6201e48b1606"
[ "other" ]
[ "ml-method-context", "paper-code-relationship", "paper-reference" ]
ok
2026-10-01T19:11:32Z
thomasneff/Generative-Adversarial-Network-based-Synthesis-for-Supervised-Medical-Image-Segmentation
readme-supported-paper-method-implementation
[ "ml-method-context", "paper-and-code-cue", "paper-code-relationship", "paper-reference" ]
include
ml-contribution-v5
17
[]
2025-04-13T14:07:39Z
https://github.com/thomasneff/Generative-Adversarial-Network-based-Synthesis-for-Supervised-Medical-Image-Segmentation
null
[ "general-ml", "representation-learning", "transfer-learning" ]
[ "deep-learning", "domain-adaptation", "paper-implementation", "transfer-learning" ]
[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]
[ "general.arxiv", "general.domain-adaptation", "general.topic-deep-learning" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2018-01-28T21:33:54Z
Code for the paper "Transductive Adversarial Networks (TAN)" by Sean Rowan (2018).
[ "general-ml" ]
[ "deep-learning", "machine-learning" ]
direct_ml_text
gh-ml-relevance-v1
2026-09-28T18:50:00Z
false
0
119,299,839
null
TeX
MIT
[ "deep-learning" ]
seanrowan/tan
[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]
4
2026-10-25T15:11:36Z
[]
2018-09-13T09:13:19Z
[ "general.topic-deep-learning" ]
74adc3df78b0da075e9562eaed967815d3b047b2
2026-10-02T18:21:54Z
gh-ml-readme-evidence-v2
"74adc3df78b0da075e9562eaed967815d3b047b2"
[ "other" ]
[ "method-contribution", "ml-method-context", "paper-reference" ]
ok
2026-10-02T18:21:54Z
seanrowan/tan
readme-supported-paper-method-implementation
[ "method-contribution", "ml-context-only", "ml-method-context", "paper-and-code-cue", "paper-reference" ]
include
ml-contribution-v5
4
[ "deep-learning", "generative-adversarial-network", "machine-learning-algorithms", "transfer-learning" ]
2024-01-04T16:20:20Z
https://github.com/seanrowan/tan
null
[ "classical-ml" ]
[ "kernel-methods", "support-vector-machine" ]
[ "description", "query-match", "repository-metadata" ]
[ "general.support-vector-machine" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2018-02-07T16:03:03Z
In all the previous Support Vector Machine implementations, points which lie very close to the dividing hyperplane are not taken care of and are usually ignored. If we run the prediction function, there are high chances that these points can randomly lie either side of the hyperplane and affect the accuracy of the algo...
[ "classical-ml" ]
[ "classifier" ]
ml_related_text
gh-ml-relevance-v1
2026-10-03T15:45:52Z
false
0
120,637,455
null
Jupyter Notebook
null
[ "kernel-methods", "support-vector-machine" ]
rajat708/BreastCancerPredictionthroughSVM
[ "description", "query-match", "repository-metadata" ]
1
2026-10-03T15:45:52Z
[]
2018-02-07T16:04:54Z
[ "general.support-vector-machine" ]
a1887435c4c0d1687ee9771573141f0dfe636d79
2026-10-03T17:01:43Z
gh-ml-readme-evidence-v2
"a1887435c4c0d1687ee9771573141f0dfe636d79"
[ "other" ]
[]
ok
2026-10-03T17:01:43Z
rajat708/BreastCancerPredictionthroughSVM
specific-method-with-novelty-claim
[ "method-tied-novelty-claim", "ml-method-cue" ]
include
ml-contribution-v5
0
[]
2018-02-07T16:04:55Z
https://github.com/rajat708/BreastCancerPredictionthroughSVM
null
[ "efficient-ml", "machine-learning-systems", "model-compression" ]
[ "distillation", "model-compression", "quantization" ]
[ "description", "license-metadata", "query-match", "repository-metadata" ]
[ "efficiency.model-compression", "efficiency.quantization" ]
false
candidate
ml-candidate-v4
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" ]
8
2026-10-20T15:46:47Z
[]
2024-07-25T10:12:38Z
[ "efficiency.model-compression", "efficiency.quantization" ]
24ff75f43cc3711a1f513ea4c0a9d18deb2c6dd9
2026-10-03T17:01:43Z
gh-ml-readme-evidence-v2
"24ff75f43cc3711a1f513ea4c0a9d18deb2c6dd9"
[ "method", "other", "usage" ]
[ "paper-reference" ]
ok
2026-10-03T17:01:43Z
antspy/quantized_distillation
official-paper-method-implementation
[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue", "paper-reference" ]
include
ml-contribution-v5
335
[]
2026-09-07T07:57:04Z
https://github.com/antspy/quantized_distillation
null
[ "deep-learning", "representation-learning" ]
[ "representation-learning", "self-supervised-learning" ]
[ "description", "query-match", "repository-metadata" ]
[ "general.self-supervised-learning" ]
false
candidate
ml-candidate-v4
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" ]
7
2026-10-20T15:46:47Z
[]
2018-02-15T20:15:37Z
[ "general.self-supervised-learning" ]
08fb111d7f9393543f085a413cf08101907349f3
2026-10-03T17:01:43Z
gh-ml-readme-evidence-v2
"08fb111d7f9393543f085a413cf08101907349f3"
[ "citation", "installation", "method", "other" ]
[ "ml-method-context", "paper-code-relationship", "paper-reference" ]
ok
2026-10-03T17:01:43Z
htung0101/3d_smpl
official-paper-method-implementation
[ "ml-method-context", "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue", "paper-code-relationship", "paper-reference" ]
include
ml-contribution-v5
90
[]
2025-04-05T22:43:41Z
https://github.com/htung0101/3d_smpl
null
[ "general-ml", "reinforcement-learning" ]
[ "meta-learning", "paper-implementation", "policy-gradient", "policy-learning", "policy-optimization", "reinforcement-learning" ]
[ "description", "github-topics", "license-metadata", "paper-reference", "query-match", "repository-metadata" ]
[ "general.arxiv", "rl.core", "rl.policy-gradient" ]
true
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2018-02-23T00:54:49Z
Code for the paper "Evolved Policy Gradients"
[ "reinforcement-learning" ]
[ "machine-learning", "reinforcement-learning" ]
direct_ml_text
gh-ml-relevance-v1
2026-09-24T10:49:00Z
false
55
122,554,700
https://arxiv.org/abs/1802.04821
Python
MIT
[ "meta-learning", "policy-gradient", "policy-optimization", "reinforcement-learning" ]
openai/EPG
[ "description", "github-topics", "license-metadata", "paper-reference", "query-match", "repository-metadata" ]
10
2026-10-19T17:33:59Z
[]
2018-11-22T06:05:34Z
[ "rl.policy-gradient" ]
9528d2962b78debb49c1343f7c8e19e33f45681c
2026-10-03T17:01:43Z
gh-ml-readme-evidence-v2
"9528d2962b78debb49c1343f7c8e19e33f45681c"
[ "citation", "installation", "other", "results" ]
[ "ml-method-context", "paper-reference" ]
ok
2026-10-03T17:01:43Z
openai/EPG
readme-supported-paper-method-implementation
[ "ml-method-context", "ml-method-cue", "paper-and-code-cue", "paper-reference" ]
include
ml-contribution-v5
253
[ "continuous-control", "evolutionary-strategy", "machine-learning", "meta-learning", "paper", "reinforcement-learning" ]
2026-09-18T11:30:01Z
https://github.com/openai/EPG
null
[ "applied-mathematics", "applied-physics", "computational-science", "generative-modeling" ]
[ "data-assimilation", "inverse-problems", "reconstruction" ]
[ "description", "license-metadata", "query-match", "repository-metadata" ]
[ "science.inverse-problems" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2018-02-26T11:21:34Z
Code for the paper: Solving Linear Inverse Problems using GAN priors
[ "applied-mathematics", "applied-physics", "computational-science", "generative-modeling" ]
[]
no_text_signal
gh-ml-relevance-v1
2026-09-24T10:49:00Z
false
8
122,959,548
null
Python
MIT
[ "data-assimilation", "inverse-problems", "reconstruction" ]
shahviraj/pgdgan
[ "description", "license-metadata", "query-match", "repository-metadata" ]
5
2026-10-19T17:33:59Z
[]
2021-09-28T23:45:13Z
[ "science.inverse-problems" ]
e113bfb250ba1abad50404224f145e7e37c3c8e2
2026-10-03T17:01:43Z
gh-ml-readme-evidence-v2
"e113bfb250ba1abad50404224f145e7e37c3c8e2"
[ "installation", "other" ]
[ "ml-method-context", "paper-code-relationship", "paper-reference" ]
ok
2026-10-03T17:01:43Z
shahviraj/pgdgan
readme-supported-paper-method-implementation
[ "ml-method-context", "paper-and-code-cue", "paper-code-relationship", "paper-reference" ]
include
ml-contribution-v5
10
[]
2024-05-15T14:59:37Z
https://github.com/shahviraj/pgdgan
null
[ "general-ml" ]
[ "neural-network", "paper-implementation", "self-attention" ]
[ "description", "github-topics", "license-metadata", "paper-reference", "query-match", "repository-metadata" ]
[ "general.arxiv" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2018-03-02T10:20:26Z
Pytorch implementation of the Graph Attention Network model by Veličković et. al (2017, https://arxiv.org/abs/1710.10903)
[ "general-ml" ]
[ "neural-network" ]
direct_ml_text
gh-ml-relevance-v1
2026-09-24T10:49:00Z
false
701
123,565,023
null
Python
MIT
[ "neural network", "paper-implementation", "self attention" ]
diegoantognini/pyGAT
[ "description", "github-topics", "license-metadata", "paper-reference", "query-match", "repository-metadata" ]
2
2026-09-24T15:29:21Z
[]
2023-07-06T21:23:03Z
[ "general.arxiv" ]
602dc9063b875c9a329d89f6395f927045b3f10e
2026-10-03T17:01:43Z
gh-ml-readme-evidence-v2
"602dc9063b875c9a329d89f6395f927045b3f10e"
[ "installation", "other" ]
[ "method-contribution", "ml-method-context", "paper-code-relationship", "paper-reference" ]
ok
2026-10-03T17:01:43Z
diegoantognini/pyGAT
readme-supported-paper-method-implementation
[ "contribution-language", "method-contribution", "ml-method-context", "ml-method-cue", "paper-and-code-cue", "paper-code-relationship", "paper-reference" ]
include
ml-contribution-v5
3,122
[ "attention-mechanism", "graph-attention-networks", "neural-networks", "python", "pytorch", "self-attention" ]
2026-09-03T09:14:15Z
https://github.com/diegoantognini/pyGAT
null
[ "natural-language-processing" ]
[ "named-entity-recognition" ]
[ "description", "github-topics", "license-metadata", "paper-reference", "query-match", "repository-metadata" ]
[ "nlp.named-entity-recognition" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2018-03-02T19:45:57Z
TensorFlow Implementation For [Neural Architecture for Named Entity Recognition](https://arxiv.org/abs/1603.01360)
[ "natural-language-processing" ]
[]
no_text_signal
gh-ml-relevance-v1
2026-10-03T15:45:52Z
false
0
123,623,364
null
Jupyter Notebook
Apache-2.0
[ "named-entity-recognition" ]
shengc/tf-lstm-crf-tagger
[ "description", "github-topics", "license-metadata", "paper-reference", "query-match", "repository-metadata" ]
1
2026-10-03T15:45:52Z
[]
2018-03-04T05:03:50Z
[ "nlp.named-entity-recognition" ]
8461d99d9e45e818ebc4ebc986b95ff2d659ef5e
2026-10-03T17:01:43Z
gh-ml-readme-evidence-v2
"8461d99d9e45e818ebc4ebc986b95ff2d659ef5e"
[]
[ "method-contribution", "ml-method-context", "paper-code-relationship", "paper-reference" ]
ok
2026-10-03T17:01:43Z
shengc/tf-lstm-crf-tagger
readme-supported-paper-method-implementation
[ "contribution-language", "method-contribution", "ml-context-only", "ml-method-context", "paper-and-code-cue", "paper-code-relationship", "paper-reference" ]
include
ml-contribution-v5
12
[ "crf-rnn-tensorflow", "named-entity-recognition", "nlp", "sequence-labeling", "tensorflow" ]
2024-04-22T02:43:35Z
https://github.com/shengc/tf-lstm-crf-tagger
null
[ "general-ml", "natural-language-processing" ]
[ "deep-learning", "language-model" ]
[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]
[ "general.topic-deep-learning", "recall.name.language-model" ]
true
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2018-03-15T15:59:56Z
Generating Trump tweets with deep learning. Spoiler: the results suck. The new GPT models do *much* better.
[ "general-ml", "natural-language-processing" ]
[ "deep-learning" ]
direct_ml_text
gh-ml-relevance-v1
2026-10-23T15:59:01Z
false
0
125,392,184
null
TeX
MIT
[ "deep-learning" ]
Notgnoshi/trumperator
[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]
2
2026-10-30T17:16:29Z
[]
2018-04-24T00:33:16Z
[ "general.topic-deep-learning" ]
null
null
null
null
null
null
null
null
null
specific-method-with-novelty-claim
[ "method-tied-novelty-claim", "ml-method-cue" ]
include
ml-contribution-v5
2
[ "deep-learning", "keras", "lstm", "natural-language-processing", "text-generation", "tweets", "twitter" ]
2023-01-29T16:13:48Z
https://github.com/Notgnoshi/trumperator
null
[ "audio", "speech", "speech-and-audio" ]
[ "architecture", "convolutional-neural-network", "evaluation", "inference", "neural-network", "training" ]
[ "description", "license-metadata", "query-match", "repository-metadata" ]
[ "audio-asr" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2018-03-22T20:51:31Z
This is the code for the paper 'Quaternion Convolutional Neural Networks for End-to-End Automatic Speech Recognition'. It provides all the Keras classes to run a Quaternion Convolutional NN.
[ "audio", "speech", "speech-and-audio" ]
[ "neural-network" ]
direct_ml_text
gh-ml-relevance-v1
2026-09-24T10:27:52Z
false
17
126,393,620
null
Python
GPL-3.0
[ "architecture", "convolutional-neural-network", "evaluation", "inference", "neural-network", "training" ]
Orkis-Research/Quaternion-Convolutional-Neural-Networks-for-End-to-End-Automatic-Speech-Recognition
[ "description", "license-metadata", "query-match", "repository-metadata" ]
7
2026-10-17T13:06:16Z
[]
2019-01-24T10:43:03Z
[ "audio-asr" ]
1a7c832a5f8748c837718aa0e08803b4f306622a
2026-10-04T17:18:56Z
gh-ml-readme-evidence-v2
"1a7c832a5f8748c837718aa0e08803b4f306622a"
[]
[ "method-contribution", "ml-method-context", "paper-code-relationship", "paper-reference" ]
ok
2026-10-04T17:18:56Z
Orkis-Research/Quaternion-Convolutional-Neural-Networks-for-End-to-End-Automatic-Speech-Recognition
readme-supported-paper-method-implementation
[ "method-contribution", "ml-context-only", "ml-method-context", "paper-and-code-cue", "paper-code-relationship", "paper-reference" ]
include
ml-contribution-v5
67
[]
2026-03-03T14:16:34Z
https://github.com/Orkis-Research/Quaternion-Convolutional-Neural-Networks-for-End-to-End-Automatic-Speech-Recognition
null
[ "general-ml", "program-analysis", "software-engineering" ]
[ "machine-learning", "paper-implementation", "program-repair" ]
[ "description", "paper-reference", "query-match", "repository-metadata" ]
[ "general.arxiv", "growth26.software-automated-program-repair" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2018-03-23T15:42:25Z
58069 Java source code diffs. http://arxiv.org/pdf/1807.03200
[ "general-ml" ]
[]
no_text_signal
gh-ml-relevance-v1
2026-09-26T22:51:24Z
false
15
126,506,099
http://arxiv.org/pdf/1807.03200
null
null
[ "paper-implementation" ]
ASSERT-KTH/CodRep
[ "description", "paper-reference", "query-match", "repository-metadata" ]
2
2026-10-05T20:10:04Z
[]
2019-07-21T17:57:27Z
[ "general.arxiv" ]
4f2c79e6c22be90299caa74689bbaa8b06e41548
2026-10-04T17:18:56Z
gh-ml-readme-evidence-v2
"4f2c79e6c22be90299caa74689bbaa8b06e41548"
[ "other" ]
[ "method-contribution", "ml-method-context", "paper-code-relationship", "paper-reference" ]
ok
2026-10-04T17:18:56Z
ASSERT-KTH/CodRep
readme-supported-paper-method-implementation
[ "method-contribution", "ml-method-context", "paper-and-code-cue", "paper-code-relationship", "paper-reference" ]
include
ml-contribution-v5
94
[]
2025-11-06T02:01:55Z
https://github.com/ASSERT-KTH/CodRep
null
[ "general-ml", "representation-learning" ]
[ "representation-learning", "semi-supervised-learning" ]
[ "description", "query-match", "repository-metadata" ]
[ "general.semi-supervised-learning" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2018-03-24T05:40:13Z
PyTorch implementation of the Mask-X-RCNN network proposed in the 'Learning to Segment Everything' paper by Facebook AI Research
[ "general-ml", "representation-learning" ]
[]
no_text_signal
gh-ml-relevance-v1
2026-09-29T17:30:08Z
false
34
126,569,627
null
Python
null
[ "representation-learning", "semi-supervised-learning" ]
skrish13/PyTorch-mask-x-rcnn
[ "description", "query-match", "repository-metadata" ]
2
2026-10-20T15:46:47Z
[]
2018-06-08T10:50:50Z
[ "general.semi-supervised-learning" ]
30036d56cbe42a843e4b6b3c72643ce28b7bae30
2026-10-04T17:18:56Z
gh-ml-readme-evidence-v2
"30036d56cbe42a843e4b6b3c72643ce28b7bae30"
[ "method", "other", "overview", "references" ]
[ "method-contribution", "ml-method-context", "paper-code-relationship", "paper-reference" ]
ok
2026-10-04T17:18:56Z
skrish13/PyTorch-mask-x-rcnn
readme-supported-paper-method-implementation
[ "contribution-language", "method-contribution", "ml-context-only", "ml-method-context", "paper-and-code-cue", "paper-code-relationship", "paper-reference" ]
include
ml-contribution-v5
156
[]
2025-03-31T09:19:18Z
https://github.com/skrish13/PyTorch-mask-x-rcnn
null
[]
[ "meta-learning" ]
[ "description", "github-topics", "license-metadata", "repository-metadata" ]
[]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2018-03-28T12:14:14Z
PyTorch code for CVPR 2018 paper: Learning to Compare: Relation Network for Few-Shot Learning (Few-Shot Learning part)
[]
[]
no_text_signal
gh-ml-relevance-v1
2026-09-26T20:57:28.489717Z
false
263
127,135,121
null
Python
MIT
[ "meta-learning" ]
floodsung/LearningToCompare_FSL
[ "description", "github-topics", "license-metadata", "repository-metadata" ]
1
2026-09-26T20:57:28.489717Z
[]
2019-10-22T03:19:44Z
[]
ba561e0371776e641be1557a586964e24b1a8025
2026-10-04T17:18:56Z
gh-ml-readme-evidence-v2
"ba561e0371776e641be1557a586964e24b1a8025"
[ "installation", "other" ]
[ "ml-method-context", "paper-code-relationship", "paper-reference" ]
ok
2026-10-04T17:18:56Z
floodsung/LearningToCompare_FSL
official-paper-method-implementation
[ "ml-method-context", "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue", "paper-code-relationship", "paper-reference" ]
include
ml-contribution-v5
1,076
[ "few-shot-learning", "meta-learning" ]
2026-09-05T07:28:58Z
https://github.com/floodsung/LearningToCompare_FSL
{"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["meta-learning"]}
[ "computer-vision", "generative-ai" ]
[ "generative-modeling", "image-to-image-translation" ]
[ "description", "license-metadata", "query-match", "repository-metadata" ]
[ "vision.image-to-image-translation" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2018-04-12T19:14:01Z
TensorFlow implementation of the CVPR 2018 spotlight paper, Deep Photo Enhancer: Unpaired Learning for Image Enhancement from Photographs with GANs
[ "computer-vision", "generative-ai" ]
[]
no_text_signal
gh-ml-relevance-v1
2026-09-28T18:50:00Z
false
110
129,299,963
null
null
MIT
[ "generative-modeling", "image-to-image-translation" ]
nothinglo/Deep-Photo-Enhancer
[ "description", "license-metadata", "query-match", "repository-metadata" ]
2
2026-10-19T17:33:59Z
[]
2019-09-11T14:31:59Z
[ "vision.image-to-image-translation" ]
8a46405bde82b60e038fb1822023ab691eea3c82
2026-10-05T21:36:53Z
gh-ml-readme-evidence-v2
"8a46405bde82b60e038fb1822023ab691eea3c82"
[ "citation", "method", "other", "usage" ]
[ "method-contribution", "ml-method-context", "paper-code-relationship", "paper-reference" ]
ok
2026-10-05T21:36:53Z
nothinglo/Deep-Photo-Enhancer
readme-supported-paper-method-implementation
[ "contribution-language", "method-contribution", "ml-method-context", "paper-and-code-cue", "paper-code-relationship", "paper-reference" ]
include
ml-contribution-v5
808
[]
2026-09-30T05:14:28Z
https://github.com/nothinglo/Deep-Photo-Enhancer
null
[ "natural-language-processing", "reinforcement-learning" ]
[ "machine-translation", "neural-machine-translation", "reinforcement-learning" ]
[ "description", "query-match", "repository-metadata" ]
[ "nlp.machine-translation" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2018-05-03T08:24:03Z
Code for our paper "A Reinforcement Learning Approach to Interactive-Predictive Neural Machine Translation"
[ "natural-language-processing", "reinforcement-learning" ]
[ "reinforcement-learning" ]
direct_ml_text
gh-ml-relevance-v1
2026-09-30T16:47:43Z
false
4
131,964,452
null
Python
null
[ "machine-translation", "neural-machine-translation", "reinforcement-learning" ]
heidelkin/BIPNMT
[ "description", "query-match", "repository-metadata" ]
2
2026-10-19T17:33:59Z
[]
2018-07-15T18:36:07Z
[ "nlp.machine-translation" ]
4c089de1347e685b136f55a76a6a3943e97efc96
2026-10-05T21:36:53Z
gh-ml-readme-evidence-v2
"4c089de1347e685b136f55a76a6a3943e97efc96"
[ "other" ]
[ "ml-method-context", "paper-code-relationship", "paper-reference" ]
ok
2026-10-05T21:36:53Z
heidelkin/BIPNMT
official-paper-method-implementation
[ "ml-method-context", "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue", "paper-code-relationship", "paper-reference" ]
include
ml-contribution-v5
7
[]
2024-01-04T16:22:46Z
https://github.com/heidelkin/BIPNMT
null
[ "linguistics", "natural-language-processing", "reinforcement-learning" ]
[ "language-model", "language-modeling", "policy-learning", "reinforcement-learning" ]
[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]
[ "recall.computational-linguistics", "recall.name.language-model", "rl.core" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2018-05-08T16:04:53Z
Code for paper "End-to-End Reinforcement Learning for Automatic Taxonomy Induction", ACL 2018
[ "natural-language-processing", "reinforcement-learning" ]
[ "reinforcement-learning" ]
direct_ml_text
gh-ml-relevance-v1
2026-10-01T17:50:46Z
false
12
132,633,365
null
Python
MIT
[ "language-model", "reinforcement-learning" ]
morningmoni/TaxoRL
[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]
4
2026-10-24T14:31:13Z
[]
2018-10-10T15:36:06Z
[ "recall.name.language-model" ]
c4a340d3a18dfe8b58e0826f81ef1e6f01dfa86e
2026-10-06T18:32:27Z
gh-ml-readme-evidence-v2
"c4a340d3a18dfe8b58e0826f81ef1e6f01dfa86e"
[ "citation", "installation", "other" ]
[ "ml-method-context", "paper-code-relationship", "paper-reference" ]
ok
2026-10-06T18:32:27Z
morningmoni/TaxoRL
official-paper-method-implementation
[ "ml-method-context", "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue", "paper-code-relationship", "paper-reference" ]
include
ml-contribution-v5
65
[ "dynet", "reinforcement-learning", "taxonomy", "taxonomy-construction", "taxonomy-induction", "wordnet" ]
2025-08-07T12:22:48Z
https://github.com/morningmoni/TaxoRL
null
[ "language", "linguistics", "natural-language-processing" ]
[ "language-model", "language-modeling" ]
[ "description", "license-metadata", "query-match", "repository-metadata" ]
[ "llm.language-model", "nlp.language-modeling", "recall.computational-linguistics", "recall.name.language-model" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2018-05-11T17:26:31Z
Code for the ACL 2018 paper "Sharp Nearby, Fuzzy Far Away: How Neural Language Models Use Context"
[ "natural-language-processing" ]
[]
no_text_signal
gh-ml-relevance-v1
2026-09-30T16:47:43Z
false
8
133,069,004
null
Python
Apache-2.0
[ "language-model" ]
urvashik/lm-context-analysis
[ "description", "license-metadata", "query-match", "repository-metadata" ]
7
2026-10-24T14:31:13Z
[]
2018-05-15T00:45:26Z
[ "recall.name.language-model" ]
a8b5cd76935ce66f225b106285edefddad11c339
2026-10-06T18:32:27Z
gh-ml-readme-evidence-v2
"a8b5cd76935ce66f225b106285edefddad11c339"
[ "installation", "method", "other", "results" ]
[ "method-contribution", "ml-method-context", "paper-code-relationship", "paper-reference" ]
ok
2026-10-06T18:32:27Z
urvashik/lm-context-analysis
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
54
[]
2025-01-20T10:49:39Z
https://github.com/urvashik/lm-context-analysis
null
[ "general-ml" ]
[ "paper-implementation" ]
[ "description", "paper-reference", "query-match", "repository-metadata" ]
[ "general.arxiv" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2018-05-12T20:45:49Z
This is the PyTorch implementation of work presented in 'Modelling Context with User Embeddings for Sarcasm Detection in Social Media' (https://arxiv.org/pdf/1607.00976.pdf). We further extend the approach by proposing a hybrid NN architecture and perform experiments on a newly collected data.
[ "general-ml" ]
[ "embedding" ]
ml_related_text
gh-ml-relevance-v1
2026-10-05T20:10:04Z
false
17
133,182,212
null
Python
null
[ "paper-implementation" ]
rishabhmisra/Sarcasm-Detection-using-NN
[ "description", "paper-reference", "query-match", "repository-metadata" ]
1
2026-10-05T20:10:04Z
[]
2024-07-09T14:12:53Z
[ "general.arxiv" ]
02eac2bc33be679341c905b2fab073c3db48e258
2026-10-06T18:32:27Z
gh-ml-readme-evidence-v2
"02eac2bc33be679341c905b2fab073c3db48e258"
[ "citation", "method", "other", "results" ]
[ "method-contribution", "ml-method-context", "paper-code-relationship", "paper-reference" ]
ok
2026-10-06T18:32:27Z
rishabhmisra/Sarcasm-Detection-using-NN
readme-supported-paper-method-implementation
[ "contribution-language", "method-contribution", "ml-method-context", "paper-and-code-cue", "paper-code-relationship", "paper-reference" ]
include
ml-contribution-v5
35
[]
2026-09-02T08:27:13Z
https://github.com/rishabhmisra/Sarcasm-Detection-using-NN
null
[ "general-ml", "natural-language-processing" ]
[ "deep-learning", "language-model", "neural-network", "paper-implementation", "recurrent-neural-network" ]
[ "description", "github-topics", "paper-reference", "query-match", "repository-metadata" ]
[ "general.arxiv", "general.topic-deep-learning", "recall.name.language-model" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2018-05-21T07:27:48Z
📃 𝖀𝖓𝖔𝖋𝖋𝖎𝖈𝖎𝖆𝖑 PyTorch Implementation of DA-RNN (arXiv:1704.02971)
[ "general-ml" ]
[ "deep-learning", "neural-network" ]
direct_ml_text
gh-ml-relevance-v1
2026-09-24T10:49:00Z
false
138
134,233,989
null
Jupyter Notebook
null
[ "deep-learning", "neural-network", "recurrent-neural-network" ]
Zhenye-Na/DA-RNN
[ "description", "github-topics", "paper-reference", "query-match", "repository-metadata" ]
5
2026-11-01T17:09:41Z
[]
2020-11-22T11:33:08Z
[ "general.topic-deep-learning" ]
32e76017a9bd13c16dcb4de08ea8abb4d2539573
2026-10-06T18:32:27Z
gh-ml-readme-evidence-v2
"32e76017a9bd13c16dcb4de08ea8abb4d2539573"
[ "dataset", "method", "other", "overview", "references", "results", "usage" ]
[ "method-contribution", "ml-method-context", "paper-code-relationship", "paper-reference" ]
ok
2026-10-06T18:32:27Z
Zhenye-Na/DA-RNN
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
422
[ "attention-mechanism", "deep-learning", "lstm-neural-networks", "paper-implementations", "recurrent-neural-networks", "rnn-pytorch" ]
2026-09-21T12:59:40Z
https://github.com/Zhenye-Na/DA-RNN
null
[ "computer-vision" ]
[ "action-recognition", "video-understanding" ]
[ "description", "license-metadata", "query-match", "repository-metadata" ]
[ "vision.action-recognition" ]
true
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2018-05-24T11:29:37Z
PyTorch implementation of the R2Plus1D convolution based ResNet architecture described in the paper "A Closer Look at Spatiotemporal Convolutions for Action Recognition"
[ "computer-vision" ]
[]
no_text_signal
gh-ml-relevance-v1
2026-09-26T14:50:46Z
false
62
134,705,771
null
Python
MIT
[ "action-recognition", "video-understanding" ]
irhum/R2Plus1D-PyTorch
[ "description", "license-metadata", "query-match", "repository-metadata" ]
3
2026-10-18T14:31:49Z
[]
2020-05-21T21:25:17Z
[ "vision.action-recognition" ]
0500b02f706e3c955848745cda85069f34c67686
2026-10-06T18:32:27Z
gh-ml-readme-evidence-v2
"0500b02f706e3c955848745cda85069f34c67686"
[ "installation", "other", "overview" ]
[ "method-contribution", "ml-method-context", "model-training-artifact", "paper-code-relationship", "paper-reference" ]
ok
2026-10-06T18:32:27Z
irhum/R2Plus1D-PyTorch
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
364
[]
2026-10-01T13:38:25Z
https://github.com/irhum/R2Plus1D-PyTorch
null
[ "general-ml" ]
[ "paper-implementation" ]
[ "description", "license-metadata", "paper-reference", "query-match", "repository-metadata" ]
[ "general.arxiv" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2018-05-28T14:30:23Z
This package provide matlab code to realized 3 popular machine learning method for Dynamic Treatment Regime. A intermediate version of the paper is https://arxiv.org/abs/1611.02314. The paper started in 2012, and is currently in press at Stat in Medicine. We also provide R code for functions in the package in 'DTRlearn...
[ "general-ml" ]
[ "machine-learning" ]
direct_ml_text
gh-ml-relevance-v1
2026-10-07T18:07:52Z
false
0
135,172,188
https://cran.r-project.org/web/packages/DTRlearn/index.html
Matlab
GPL-3.0
[ "paper-implementation" ]
summeryingliu/AOL
[ "description", "license-metadata", "paper-reference", "query-match", "repository-metadata" ]
1
2026-10-07T18:07:52Z
[]
2018-05-28T15:09:59Z
[ "general.arxiv" ]
241a7c4d385b14a2bad9114cd318dcb0d8890f26
2026-10-07T19:34:31Z
gh-ml-readme-evidence-v2
"241a7c4d385b14a2bad9114cd318dcb0d8890f26"
[ "other" ]
[ "method-contribution", "ml-method-context", "paper-code-relationship", "paper-reference" ]
ok
2026-10-07T19:34:31Z
summeryingliu/AOL
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
2
[]
2026-07-08T16:56:25Z
https://github.com/summeryingliu/AOL
null
[ "computer-vision", "image-processing", "low-level-vision" ]
[ "image-denoising", "image-restoration", "neural-network", "self-supervised-learning" ]
[ "description", "query-match", "repository-metadata" ]
[ "recall26.image-denoising" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2018-06-09T10:00:17Z
Official implementation of "CocoNet: A deep neural network for mapping pixel coordinates to color values" paper
[ "computer-vision", "image-processing", "low-level-vision" ]
[ "neural-network" ]
direct_ml_text
gh-ml-relevance-v1
2026-09-30T16:47:43Z
false
4
136,711,627
null
Python
null
[ "image-denoising", "image-restoration", "neural-network", "self-supervised-learning" ]
paulbricman/python-fuse-coconet
[ "description", "query-match", "repository-metadata" ]
1
2026-09-30T16:47:43Z
[]
2018-08-30T16:57:55Z
[ "recall26.image-denoising" ]
a02544a14813ea9799b12b835b0b68d3f635d51b
2026-10-07T19:34:31Z
gh-ml-readme-evidence-v2
"a02544a14813ea9799b12b835b0b68d3f635d51b"
[ "citation", "installation", "other" ]
[ "method-contribution", "ml-method-context", "paper-reference" ]
ok
2026-10-07T19:34:31Z
paulbricman/python-fuse-coconet
readme-supported-paper-method-implementation
[ "contribution-language", "method-contribution", "ml-context-only", "ml-method-context", "paper-and-code-cue", "paper-reference" ]
include
ml-contribution-v5
11
[]
2024-02-23T20:41:22Z
https://github.com/paulbricman/python-fuse-coconet
null
[ "reinforcement-learning", "robotics", "robotics-and-control" ]
[ "policy-learning", "reinforcement-learning", "robot-learning" ]
[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]
[ "rl.core", "robotics.robotics-topic" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2018-06-11T15:59:09Z
Implementation of the paper "Overcoming Exploration in Reinforcement Learning with Demonstrations" Nair et al. over the HER baselines from OpenAI
[ "reinforcement-learning", "robotics", "robotics-and-control" ]
[ "reinforcement-learning" ]
direct_ml_text
gh-ml-relevance-v1
2026-10-04T15:58:58Z
false
32
136,949,251
null
Python
MIT
[ "reinforcement-learning", "robot-learning" ]
jangirrishabh/Overcoming-exploration-from-demos
[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]
3
2026-10-19T17:33:59Z
[]
2021-10-25T01:04:27Z
[ "robotics.robotics-topic" ]
5e113eedbd3230488d7518aaad6c9f422836126b
2026-10-07T19:34:31Z
gh-ml-readme-evidence-v2
"5e113eedbd3230488d7518aaad6c9f422836126b"
[ "installation", "other", "usage" ]
[ "method-contribution", "ml-method-context", "paper-code-relationship", "paper-reference" ]
ok
2026-10-07T19:34:31Z
jangirrishabh/Overcoming-exploration-from-demos
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
154
[ "actor-critic", "ddpg-algorithm", "gazebo", "hindsight-experience-replay", "learning-from-demonstration", "openai-gym", "reinforcement-learning", "reinforcement-learning-agent", "robotics", "ros" ]
2026-09-06T15:59:39Z
https://github.com/jangirrishabh/Overcoming-exploration-from-demos
null
[ "data-centric-ai", "machine-learning" ]
[ "active-learning", "convolutional-neural-network", "neural-network", "sample-selection" ]
[ "description", "license-metadata", "query-match", "repository-metadata" ]
[ "general.active-learning" ]
false
candidate
ml-candidate-v4
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" ]
6
2026-10-18T14:31:49Z
[]
2018-10-23T13:57:25Z
[ "general.active-learning" ]
f11add02e1521786214a4ae1ca40a1d605d99fa2
2026-10-07T19:34:31Z
gh-ml-readme-evidence-v2
"f11add02e1521786214a4ae1ca40a1d605d99fa2"
[ "other" ]
[ "ml-method-context", "model-training-artifact", "paper-code-relationship", "paper-reference" ]
ok
2026-10-07T19:34:31Z
ozansener/active_learning_coreset
official-paper-method-implementation
[ "ml-context-only", "ml-method-context", "model-training-artifact", "official-paper-implementation-cue", "paper-and-code-cue", "paper-code-relationship", "paper-reference" ]
include
ml-contribution-v5
282
[]
2026-08-24T20:31:26Z
https://github.com/ozansener/active_learning_coreset
null
[ "deep-learning", "graph-learning", "information-retrieval", "linguistics", "natural-language-processing" ]
[ "graph-neural-network", "language-modeling", "message-passing", "neural-network", "question-answering", "reading-comprehension" ]
[ "description", "license-metadata", "query-match", "repository-metadata" ]
[ "graph.gnn-description", "nlp.question-answering", "recall.computational-linguistics" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2018-06-12T14:15:16Z
Accompanying code for our COLING 2018 paper "Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering"
[ "graph-learning", "information-retrieval", "natural-language-processing" ]
[ "neural-network" ]
direct_ml_text
gh-ml-relevance-v1
2026-09-27T16:09:59Z
false
26
137,081,892
null
Python
Apache-2.0
[ "graph-neural-network", "neural-network", "question-answering", "reading-comprehension" ]
UKPLab/coling2018-graph-neural-networks-question-answering
[ "description", "license-metadata", "query-match", "repository-metadata" ]
5
2026-10-19T17:33:59Z
[]
2020-02-12T13:06:17Z
[ "nlp.question-answering" ]
f5b742c550ca94454cf1cbc14c6bd3d1120c328d
2026-10-07T19:34:31Z
gh-ml-readme-evidence-v2
"f5b742c550ca94454cf1cbc14c6bd3d1120c328d"
[ "abstract", "installation", "method", "other", "results" ]
[ "method-contribution", "ml-method-context", "paper-reference" ]
ok
2026-10-07T19:34:31Z
UKPLab/coling2018-graph-neural-networks-question-answering
official-paper-method-implementation
[ "method-contribution", "ml-method-context", "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue", "paper-reference" ]
include
ml-contribution-v5
175
[]
2026-04-27T14:30:42Z
https://github.com/UKPLab/coling2018-graph-neural-networks-question-answering
null
[ "reinforcement-learning" ]
[ "deep-reinforcement-learning", "policy-learning", "reinforcement-learning" ]
[ "description", "license-metadata", "query-match", "repository-metadata" ]
[ "rl.deep" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2018-06-26T11:53:36Z
Official Tensorflow implementation of drl-RPN: Deep Reinforcement Learning of Region Proposal Networks (CVPR 2018 paper)
[ "reinforcement-learning" ]
[ "reinforcement-learning" ]
direct_ml_text
gh-ml-relevance-v1
2026-09-29T17:30:08Z
false
21
138,731,040
null
Jupyter Notebook
MIT
[ "deep-reinforcement-learning", "policy-learning", "reinforcement-learning" ]
aleksispi/drl-rpn-tf
[ "description", "license-metadata", "query-match", "repository-metadata" ]
2
2026-10-18T14:31:49Z
[]
2024-02-22T13:54:19Z
[ "rl.deep" ]
null
null
null
null
null
null
null
null
null
official-paper-method-implementation
[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]
include
ml-contribution-v5
78
[]
2025-10-08T12:41:18Z
https://github.com/aleksispi/drl-rpn-tf
null
[ "efficient-ml", "machine-learning-systems", "model-compression" ]
[ "neural-network-pruning", "pruning", "structured-pruning" ]
[ "description", "query-match", "repository-metadata" ]
[ "efficiency.pruning" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2018-07-21T14:10:04Z
Recreate some new algorithms of pruning network via TensorFlow.
[ "efficient-ml", "machine-learning-systems", "model-compression" ]
[]
no_text_signal
gh-ml-relevance-v1
2026-10-05T20:10:04Z
false
0
141,818,831
null
null
null
[ "neural-network-pruning", "pruning", "structured-pruning" ]
jianyuheng/pruning-network-tf
[ "description", "query-match", "repository-metadata" ]
1
2026-10-05T20:10:04Z
[]
2018-07-30T13:28:04Z
[ "efficiency.pruning" ]
c7e913a1c5bab32d609e0d4a4ac2465236b390fc
2026-10-05T21:36:53Z
gh-ml-readme-evidence-v2
"c7e913a1c5bab32d609e0d4a4ac2465236b390fc"
[ "other" ]
[]
ok
2026-10-05T21:36:53Z
jianyuheng/pruning-network-tf
specific-method-with-novelty-claim
[ "method-tied-novelty-claim", "ml-method-cue" ]
include
ml-contribution-v5
0
[]
2018-07-30T13:28:05Z
https://github.com/jianyuheng/pruning-network-tf
null
[ "linguistics", "natural-language-processing", "reinforcement-learning" ]
[ "language-modeling", "natural-language-inference", "reinforcement-learning", "textual-entailment" ]
[ "description", "query-match", "repository-metadata" ]
[ "nlp-natural-language-inference", "recall.computational-linguistics" ]
false
candidate
ml-candidate-v4
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" ]
4
2026-10-18T14:31:49Z
[]
2018-08-05T09:21:45Z
[ "nlp-natural-language-inference" ]
null
null
null
null
null
null
null
null
null
official-paper-method-implementation
[ "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]
include
ml-contribution-v5
17
[]
2022-02-24T07:13:19Z
https://github.com/ZJULearning/DMP
null
[ "automl", "general-ml" ]
[ "few-shot-learning", "meta-learning" ]
[ "description", "license-metadata", "query-match", "repository-metadata" ]
[ "general.meta-learning" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2018-08-18T04:36:49Z
code for our IJCAI 2018 paper : "Lifelong Domain Word Embedding via Meta-Learning"
[ "automl", "general-ml" ]
[ "embedding" ]
ml_related_text
gh-ml-relevance-v1
2026-09-28T18:50:00Z
false
4
145,190,284
null
C
MIT
[ "few-shot-learning", "meta-learning" ]
howardhsu/L-DEM
[ "description", "license-metadata", "query-match", "repository-metadata" ]
2
2026-10-20T15:46:47Z
[]
2019-04-02T21:40:48Z
[ "general.meta-learning" ]
null
null
null
null
null
null
null
null
null
official-paper-method-implementation
[ "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]
include
ml-contribution-v5
11
[]
2020-02-18T03:55:57Z
https://github.com/howardhsu/L-DEM
null
[ "efficient-ml", "machine-learning-systems", "model-compression" ]
[ "neural-network", "neural-network-pruning", "pruning", "structured-pruning" ]
[ "description", "license-metadata", "query-match", "repository-metadata" ]
[ "efficiency.pruning" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2018-09-22T19:20:00Z
This repository provides the implementation of the method proposed in our paper "Pruning Deep Neural Networks using Partial Least Squares"
[ "efficient-ml", "machine-learning-systems", "model-compression" ]
[ "neural-network" ]
direct_ml_text
gh-ml-relevance-v1
2026-09-30T16:47:43Z
false
7
149,909,429
null
Python
MIT
[ "neural-network", "neural-network-pruning", "pruning", "structured-pruning" ]
arturjordao/PruningNeuralNetworks
[ "description", "license-metadata", "query-match", "repository-metadata" ]
3
2026-10-20T15:46:47Z
[]
2020-08-21T19:11:16Z
[ "efficiency.pruning" ]
null
null
null
null
null
null
null
null
null
official-paper-method-implementation
[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]
include
ml-contribution-v5
26
[]
2025-10-08T12:36:38Z
https://github.com/arturjordao/PruningNeuralNetworks
null
[ "deep-learning", "representation-learning" ]
[ "continual-learning", "lifelong-learning", "neural-network" ]
[ "description", "github-topics", "license-metadata", "paper-reference", "query-match", "repository-metadata" ]
[ "general.continual-learning" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2018-10-12T15:47:58Z
This code refers to all experiments in our paper "Autonomous Deep Learning: Continual Learning Approach for Dynamic Environments"
[ "deep-learning", "representation-learning" ]
[ "deep-learning", "neural-network" ]
direct_ml_text
gh-ml-relevance-v1
2026-09-27T16:09:59Z
false
1
152,773,989
https://epubs.siam.org/doi/abs/10.1137/1.9781611975673.75
MATLAB
NOASSERTION
[ "continual-learning", "lifelong-learning", "neural-network" ]
andriash001/ADL
[ "description", "github-topics", "license-metadata", "paper-reference", "query-match", "repository-metadata" ]
2
2026-10-19T17:33:59Z
[]
2019-11-23T14:54:51Z
[ "general.continual-learning" ]
null
null
null
null
null
null
null
null
null
official-paper-method-implementation
[ "ml-context-only", "official-paper-implementation-cue", "paper-and-code-cue" ]
include
ml-contribution-v5
2
[ "autonomous-deep-learning", "continual-learning", "data-streams", "deep-learning", "deep-neural-networks" ]
2022-01-25T09:12:32Z
https://github.com/andriash001/ADL
null
[ "general-ml", "reinforcement-learning" ]
[ "artificial-intelligence", "deep-reinforcement-learning", "neural-network", "policy-learning", "reinforcement-learning" ]
[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]
[ "general.artificial-intelligence-description", "general.topic-artificial-intelligence", "rl.deep" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2018-10-22T14:36:21Z
Universal AI: Artificial Intelligence with Python through Deep Reinforcement Learning and Other Novel Learning Algorithms. And, Universal AI: State-of-the-art AI Developing AI, Advancing Tomorrow's State-of-the-art AI, that Builds/Develops Revolutionary Advanced AI, "Empowering Humanity Universally!"
[ "general-ml", "reinforcement-learning" ]
[ "deep-learning", "machine-learning", "neural-network", "reinforcement-learning" ]
direct_ml_text
gh-ml-relevance-v1
2026-10-05T20:10:04Z
false
0
154,159,732
https://arunkumarramanan.github.io/Artificial-Intelligence-with-Python-through-Deep-Reinforcement-Learning-Novel-Learning-Algorithms/
null
Apache-2.0
[ "artificial-intelligence", "neural-network", "reinforcement-learning" ]
ArunkumarRamanan/Enterprise-Universal-AI
[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]
3
2026-10-31T16:49:26Z
[]
2018-11-19T06:59:01Z
[ "general.artificial-intelligence-description" ]
f3ff8f718d87bf81f72b49016ccd260bf8e39e35
2026-10-05T21:36:53Z
gh-ml-readme-evidence-v2
"f3ff8f718d87bf81f72b49016ccd260bf8e39e35"
[ "other" ]
[ "ml-method-context" ]
ok
2026-10-05T21:36:53Z
ArunkumarRamanan/Enterprise-Universal-AI
specific-method-with-novelty-claim
[ "method-tied-novelty-claim", "ml-method-context", "ml-method-cue" ]
include
ml-contribution-v5
1
[ "artificial-intelligence", "deep-learning", "deep-neural-networks", "deep-reinforcement-learning", "machine-learning", "machine-learning-algorithms", "python" ]
2023-04-11T02:00:39Z
https://github.com/ArunkumarRamanan/Enterprise-Universal-AI
null
[ "general-ml", "reinforcement-learning", "robotics", "robotics-and-control" ]
[ "artificial-intelligence", "deep-reinforcement-learning", "neural-network", "policy-learning", "reinforcement-learning", "robot-learning" ]
[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]
[ "general.topic-artificial-intelligence", "rl.deep", "robotics.robotics-topic" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2018-10-22T15:07:29Z
Universal Bot: Robotics with Python through Deep Reinforcement Learning and Other Novel Learning Algorithms
[ "general-ml", "reinforcement-learning", "robotics-and-control" ]
[ "deep-learning", "machine-learning", "neural-network", "reinforcement-learning" ]
direct_ml_text
gh-ml-relevance-v1
2026-10-05T20:10:04Z
false
0
154,164,639
https://arunkumarramanan.github.io/Robotics-with-Python-through-Deep-Reinforcement-Learning-as-Universal-Bot/
null
Apache-2.0
[ "artificial-intelligence", "neural-network", "reinforcement-learning" ]
ArunkumarRamanan/Universal-Bot
[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]
3
2026-10-18T14:44:29Z
[]
2018-11-19T06:37:14Z
[ "general.topic-artificial-intelligence" ]
40308e652c2d901a9bd9853b642dc5cc1002d663
2026-10-05T21:36:53Z
gh-ml-readme-evidence-v2
"40308e652c2d901a9bd9853b642dc5cc1002d663"
[ "other" ]
[ "ml-method-context" ]
ok
2026-10-05T21:36:53Z
ArunkumarRamanan/Universal-Bot
specific-method-with-novelty-claim
[ "method-tied-novelty-claim", "ml-method-context", "ml-method-cue" ]
include
ml-contribution-v5
1
[ "artificial-intelligence", "deep-learning", "deep-neural-networks", "deep-reinforcement-learning", "machine-learning", "python", "robotics" ]
2023-04-11T01:44:29Z
https://github.com/ArunkumarRamanan/Universal-Bot
null
[ "classical-ml", "tabular-and-structured-data", "tabular-ml" ]
[ "ensemble-learning", "gradient-boosting" ]
[ "description", "github-topics", "query-match", "repository-metadata" ]
[ "general.gradient-boosting", "probable.classical-gradient-boosting-readme" ]
false
candidate
ml-candidate-v4
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" ]
10
2026-10-24T14:31:13Z
[]
2018-11-19T07:28:10Z
[ "probable.classical-gradient-boosting-readme" ]
null
null
null
null
null
null
null
null
null
official-paper-method-implementation
[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]
include
ml-contribution-v5
103
[ "gbdt", "gradient-boosting-decision-trees", "mgbdt", "representation-learning", "target-propagation" ]
2026-10-06T08:55:12Z
https://github.com/kingfengji/mGBDT
null
[ "deep-learning", "graph-learning" ]
[ "graph-neural-network", "message-passing", "neural-network" ]
[ "description", "github-topics", "query-match", "repository-metadata" ]
[ "graph.gnn-description" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2018-11-05T15:16:38Z
Source code for our AAAI paper "Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks".
[ "deep-learning", "graph-learning" ]
[ "deep-learning", "neural-network" ]
direct_ml_text
gh-ml-relevance-v1
2026-09-26T20:57:28.489717Z
false
44
156,237,794
null
C++
null
[ "graph-neural-network", "message-passing", "neural-network" ]
chrsmrrs/k-gnn
[ "description", "github-topics", "query-match", "repository-metadata" ]
3
2026-10-18T14:31:49Z
[]
2022-03-22T12:39:40Z
[ "graph.gnn-description" ]
null
null
null
null
null
null
null
null
null
official-paper-method-implementation
[ "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]
include
ml-contribution-v5
191
[ "deep-learning", "end-to-end", "graph-algorithms", "graph-neural-networks", "graphs", "higher-order", "pytorch", "weisfeier-leman", "weisfeiler-lehman" ]
2026-04-08T10:37:08Z
https://github.com/chrsmrrs/k-gnn
null
[ "classical-ml", "recommender-systems", "unsupervised-learning" ]
[ "dimensionality-reduction", "k-means", "representation-learning" ]
[ "description", "query-match", "repository-metadata" ]
[ "applied.recommender-systems", "general.dimensionality-reduction" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2018-11-07T04:52:27Z
Using Unsupervised Machine Learning techniques to confirm expected outcomes / trends as well as discover new insights - techniques include K-Means Clustering + Latent Dirichlet Allocation topic modeling
[ "recommender-systems" ]
[ "machine-learning" ]
direct_ml_text
gh-ml-relevance-v1
2026-10-23T15:59:01Z
false
0
156,492,052
null
Jupyter Notebook
null
[ "k-means" ]
stevay/Machine-Learning-with-Twitter-Data
[ "description", "query-match", "repository-metadata" ]
2
2026-10-25T15:11:36Z
[]
2019-01-10T17:57:09Z
[ "applied.recommender-systems" ]
null
null
null
null
null
null
null
null
null
specific-method-with-novelty-claim
[ "method-tied-novelty-claim", "ml-method-cue" ]
include
ml-contribution-v5
0
[]
2019-01-10T17:57:11Z
https://github.com/stevay/Machine-Learning-with-Twitter-Data
null
[ "general-ml", "generative-ai", "language" ]
[ "language-model", "transformer" ]
[ "description", "license-metadata", "query-match", "repository-metadata" ]
[ "llm.transformers", "recall.name.transformer-model" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2018-11-13T22:59:03Z
Alpha Zero equipped with Transformer with various novel techniques for speedup in tree search
[ "general-ml" ]
[ "transformer" ]
ml_related_text
gh-ml-relevance-v1
2026-10-08T18:15:14Z
false
1
157,458,435
null
Python
Apache-2.0
[ "transformer" ]
AranKomat/Alpha-Transformer
[ "description", "license-metadata", "query-match", "repository-metadata" ]
2
2026-10-19T17:46:36Z
[]
2018-11-15T07:43:19Z
[ "recall.name.transformer-model" ]
null
null
null
null
null
null
null
null
null
specific-method-with-novelty-claim
[ "method-tied-novelty-claim", "ml-method-cue" ]
include
ml-contribution-v5
28
[]
2026-08-23T12:45:45Z
https://github.com/AranKomat/Alpha-Transformer
null
[ "classical-ml", "computer-vision", "efficient-ml", "model-compression", "representation-learning" ]
[ "computer-vision", "distillation", "knowledge-distillation", "metric-learning", "neural-network", "representation-learning" ]
[ "description", "github-topics", "query-match", "repository-metadata" ]
[ "efficiency.knowledge-distillation", "general.metric-learning", "vision.topic-computer-vision" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2018-11-24T13:25:12Z
Official pytorch Implementation of Relational Knowledge Distillation, CVPR 2019
[ "computer-vision" ]
[ "deep-learning", "neural-network" ]
direct_ml_text
gh-ml-relevance-v1
2026-09-24T10:49:00Z
false
51
158,938,672
null
Python
null
[ "computer-vision", "distillation", "knowledge-distillation", "neural-network" ]
lenscloth/RKD
[ "description", "github-topics", "query-match", "repository-metadata" ]
13
2026-10-21T15:48:06Z
[]
2021-05-17T04:00:24Z
[ "vision.topic-computer-vision" ]
null
null
null
null
null
null
null
null
null
official-paper-method-implementation
[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue" ]
include
ml-contribution-v5
421
[ "computer-vision", "deep-learning", "deep-neural-networks", "knowledge-distillation", "metric-learning" ]
2026-10-15T12:55:46Z
https://github.com/lenscloth/RKD
null
[ "reinforcement-learning" ]
[ "policy-learning", "reinforcement-learning" ]
[ "description", "github-topics", "query-match", "repository-metadata" ]
[ "rl.core" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2018-12-04T06:06:31Z
Pytorch implementation of the Persistent Advantage reinforcement learning operator proposed in paper 'Increasing the Action Gap: New Operators for Reinforcement Learning'. This repository includes source code for training, demo, action gap visualization and trained models for 5 Atari games.
[ "reinforcement-learning" ]
[ "reinforcement-learning" ]
direct_ml_text
gh-ml-relevance-v1
2026-10-10T16:53:26Z
false
0
160,307,390
null
Python
null
[ "policy-learning", "reinforcement-learning" ]
chaitanya100100/Increasing-the-Action-Gap-RL-Pytorch
[ "description", "github-topics", "query-match", "repository-metadata" ]
1
2026-10-10T16:53:26Z
[]
2018-12-04T10:45:40Z
[ "rl.core" ]
null
null
null
null
null
null
null
null
null
specific-method-with-novelty-claim
[ "contribution-language", "method-tied-novelty-claim", "ml-method-cue", "paper-and-code-cue" ]
include
ml-contribution-v5
1
[ "advantage-learning", "atari2600", "deep-q-network", "deep-reinforcement-learning", "dqn", "persistent-advantage-learning", "reinforcement-learning" ]
2020-07-27T05:49:36Z
https://github.com/chaitanya100100/Increasing-the-Action-Gap-RL-Pytorch
null
[ "reinforcement-learning" ]
[ "policy-learning", "reinforcement-learning" ]
[ "description", "github-topics", "query-match", "repository-metadata" ]
[ "rl.core" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2018-12-08T09:51:33Z
Pytorch implementation of the Persistent Advantage reinforcement learning operator proposed in paper 'Increasing the Action Gap: New Operators for Reinforcement Learning'
[ "reinforcement-learning" ]
[ "reinforcement-learning" ]
direct_ml_text
gh-ml-relevance-v1
2026-10-10T16:53:26Z
false
0
160,925,595
null
Python
null
[ "policy-learning", "reinforcement-learning" ]
kulinshah98/Increasing-the-Action-Gap-RL-pytorch
[ "description", "github-topics", "query-match", "repository-metadata" ]
1
2026-10-10T16:53:26Z
[]
2018-12-08T09:59:09Z
[ "rl.core" ]
null
null
null
null
null
null
null
null
null
specific-method-with-novelty-claim
[ "contribution-language", "method-tied-novelty-claim", "ml-method-cue", "paper-and-code-cue" ]
include
ml-contribution-v5
1
[ "advantage-learning", "al-algorithm", "atari2600", "deep-reinforcement-learning", "dqn", "pal-algorithm", "persistent-advantage-learning", "reinforcement-learning" ]
2018-12-23T05:23:38Z
https://github.com/kulinshah98/Increasing-the-Action-Gap-RL-pytorch
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-v4
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" ]
6
2026-10-20T15:46:47Z
[]
2024-07-25T10:14:50Z
[ "trust.federated-learning" ]
null
null
null
null
null
null
null
null
null
official-paper-method-implementation
[ "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]
include
ml-contribution-v5
315
[]
2026-10-10T11:52:21Z
https://github.com/ebagdasa/backdoor_federated_learning
null
[ "reinforcement-learning" ]
[ "distributional-reinforcement-learning", "policy-learning", "reinforcement-learning", "value-based-reinforcement-learning" ]
[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]
[ "rl.core", "rl.distributional" ]
false
candidate
ml-candidate-v4
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" ]
3
2026-10-15T15:06:57Z
[]
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
[ "general-ml", "generative-ai", "interpretability-and-safety", "language", "linguistics", "natural-language-processing" ]
[ "language-model", "language-modeling", "transformer" ]
[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]
[ "llm.language-model", "llm.transformers", "nlp.language-modeling", "recall.computational-linguistics", "recall.name.language-model", "recall.name.transformer-model" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2019-01-22T15:41:03Z
Official Pytorch implementation of (Roles and Utilization of Attention Heads in Transformer-based Neural Language Models), ACL 2020
[ "interpretability-and-safety", "natural-language-processing" ]
[ "deep-learning", "transformer" ]
direct_ml_text
gh-ml-relevance-v1
2026-10-04T15:58:58Z
false
4
167,019,640
null
Python
MIT
[ "language-model", "transformer" ]
heartcored98/transformer_anatomy
[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]
5
2026-11-01T17:09:41Z
[]
2025-03-21T21:57:18Z
[ "recall.name.language-model" ]
null
null
null
null
null
null
null
null
null
official-paper-method-implementation
[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue" ]
include
ml-contribution-v5
16
[ "attention-head", "interpretability", "interpretable-deep-learning", "transformer-encoder" ]
2024-11-06T14:39:53Z
https://github.com/heartcored98/transformer_anatomy
null
[ "efficient-ml", "machine-learning-systems", "model-compression" ]
[ "neural-network-pruning", "pruning", "structured-pruning" ]
[ "description", "license-metadata", "query-match", "repository-metadata" ]
[ "efficiency.pruning" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2019-01-23T13:45:52Z
Code for paper ''UHRP: Uncertainty-Based Pruning Method for Anonymized Data Linear Regression''
[ "efficient-ml", "machine-learning-systems", "model-compression" ]
[]
no_text_signal
gh-ml-relevance-v1
2026-10-05T20:10:04Z
false
0
167,190,478
null
Jupyter Notebook
MIT
[ "neural-network-pruning", "pruning", "structured-pruning" ]
build2last/UHRP
[ "description", "license-metadata", "query-match", "repository-metadata" ]
1
2026-10-05T20:10:04Z
[]
2019-04-27T07:45:25Z
[ "efficiency.pruning" ]
null
null
null
null
null
null
null
null
null
official-paper-method-implementation
[ "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]
include
ml-contribution-v5
0
[]
2019-04-27T07:45:27Z
https://github.com/build2last/UHRP
null
[ "automl", "general-ml" ]
[ "few-shot-learning", "meta-learning" ]
[ "description", "license-metadata", "query-match", "repository-metadata" ]
[ "general.meta-learning" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2019-01-28T15:00:04Z
Official code for the paper titled "Meta Learning Deep Visual Words for Fast Video Object Segmentation"
[ "automl", "general-ml" ]
[]
no_text_signal
gh-ml-relevance-v1
2026-09-28T18:50:00Z
false
3
167,980,836
null
Python
MIT
[ "few-shot-learning", "meta-learning" ]
harkiratbehl/MetaVOS
[ "description", "license-metadata", "query-match", "repository-metadata" ]
1
2026-09-28T18:50:00Z
[]
2020-04-13T10:58:10Z
[ "general.meta-learning" ]
null
null
null
null
null
null
null
null
null
official-paper-method-implementation
[ "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]
include
ml-contribution-v5
23
[]
2024-04-09T09:03:15Z
https://github.com/harkiratbehl/MetaVOS
null
[ "reinforcement-learning" ]
[ "batch-reinforcement-learning", "offline-reinforcement-learning", "reinforcement-learning" ]
[ "description", "github-topics", "query-match", "repository-metadata" ]
[ "rl.offline" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2019-02-02T09:18:31Z
[AAAI 2022] The official implementation of "DeepThermal: Combustion Optimization for Thermal Power Generating Units Using Offline Reinforcement Learning"
[ "reinforcement-learning" ]
[ "reinforcement-learning" ]
direct_ml_text
gh-ml-relevance-v1
2026-09-24T10:49:00Z
false
2
168,814,886
null
Python
null
[ "batch-reinforcement-learning", "offline-reinforcement-learning", "reinforcement-learning" ]
ryanxhr/DeepThermal
[ "description", "github-topics", "query-match", "repository-metadata" ]
8
2026-10-16T15:23:09Z
[]
2022-07-21T07:40:15Z
[ "rl.offline" ]
null
null
null
null
null
null
null
null
null
official-paper-method-implementation
[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue" ]
include
ml-contribution-v5
21
[ "model-based-reinforcement-learning", "offline-reinforcement-learning", "tensorflow" ]
2026-07-20T07:41:57Z
https://github.com/ryanxhr/DeepThermal
null
[ "3d", "computer-vision", "robotics" ]
[ "3d-perception", "neural-network", "point-cloud-learning" ]
[ "description", "query-match", "repository-metadata" ]
[ "multimodal.point-cloud" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2019-02-20T10:50:15Z
Attempting to design a new point cloud neural network based on attention mechanism.
[ "3d", "computer-vision", "robotics" ]
[ "neural-network" ]
direct_ml_text
gh-ml-relevance-v1
2026-10-08T18:15:14Z
false
0
171,655,212
null
Python
null
[ "3d-perception", "neural-network", "point-cloud-learning" ]
why-scholar/PointCloudAttentionNeuralNetwork
[ "description", "query-match", "repository-metadata" ]
1
2026-10-08T18:15:14Z
[]
2019-03-15T12:50:05Z
[ "multimodal.point-cloud" ]
null
null
null
null
null
null
null
null
null
specific-method-with-novelty-claim
[ "method-tied-novelty-claim", "ml-method-cue" ]
include
ml-contribution-v5
1
[]
2021-01-02T14:42:31Z
https://github.com/why-scholar/PointCloudAttentionNeuralNetwork
null
[ "general-ml", "language", "natural-language-processing" ]
[ "language-model", "language-modeling", "transformer" ]
[ "description", "license-metadata", "query-match", "repository-metadata" ]
[ "llm.language-model", "nlp.language-modeling", "recall.name.transformer-model" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2019-02-21T07:01:20Z
R Package for exploring Samples of generated texts from Open AI's new GPT-2 language model
[ "general-ml", "natural-language-processing" ]
[]
no_text_signal
gh-ml-relevance-v1
2026-10-04T15:58:58Z
false
3
171,818,191
null
R
NOASSERTION
[ "transformer" ]
kanishkamisra/gpt2samples
[ "description", "license-metadata", "query-match", "repository-metadata" ]
2
2026-10-22T16:09:07Z
[]
2019-02-21T19:54:49Z
[ "recall.name.transformer-model" ]
cd5f93b18b4317a4c3493dad9ef1a06b67de26da
2026-10-04T17:18:56Z
gh-ml-readme-evidence-v2
"cd5f93b18b4317a4c3493dad9ef1a06b67de26da"
[ "installation", "other", "usage" ]
[ "ml-method-context" ]
ok
2026-10-04T17:18:56Z
kanishkamisra/gpt2samples
specific-method-with-novelty-claim
[ "method-tied-novelty-claim", "ml-method-context", "ml-method-cue" ]
include
ml-contribution-v5
8
[]
2019-11-10T23:50:33Z
https://github.com/kanishkamisra/gpt2samples
null
[ "general-ml", "linguistics", "natural-language-processing" ]
[ "language-modeling", "transformer" ]
[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]
[ "recall.computational-linguistics", "recall.name.transformer-model" ]
false
candidate
ml-candidate-v4
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"
[ "general-ml" ]
[]
no_text_signal
gh-ml-relevance-v1
2026-09-25T16:01:25Z
false
110
172,388,988
null
Python
Apache-2.0
[ "transformer" ]
howardhsu/BERT-for-RRC-ABSA
[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]
7
2026-10-21T15:48:06Z
[]
2021-02-05T05:58:43Z
[ "recall.name.transformer-model" ]
null
null
null
null
null
null
null
null
null
official-paper-method-implementation
[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue", "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" ]
[ "computer-vision", "explainable-ai", "interpretability" ]
[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]
[ "trust.explainable-ai", "vision.topic-computer-vision" ]
false
candidate
ml-candidate-v4
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" ]
[ "deep-learning", "machine-learning" ]
direct_ml_text
gh-ml-relevance-v1
2026-09-24T10:49:00Z
false
19
174,995,387
null
Python
MIT
[ "computer-vision" ]
Nicholasli1995/VisualizingNDF
[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]
6
2026-10-22T16:09:07Z
[]
2022-03-12T06:32:59Z
[ "vision.topic-computer-vision" ]
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
[ "general-ml", "generative-ai", "language" ]
[ "fine-tuning", "parameter-efficient-fine-tuning", "transformer" ]
[ "description", "license-metadata", "query-match", "repository-metadata" ]
[ "llm.finetuning", "recall.name.transformer-model" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2019-03-25T02:05:03Z
Code for paper Fine-tune BERT for Extractive Summarization
[ "general-ml" ]
[]
no_text_signal
gh-ml-relevance-v1
2026-09-24T10:49:00Z
false
410
177,497,186
null
Python
Apache-2.0
[ "transformer" ]
nlpyang/BertSum
[ "description", "license-metadata", "query-match", "repository-metadata" ]
10
2026-10-21T15:48:06Z
[]
2022-01-11T07:58:23Z
[ "recall.name.transformer-model" ]
null
null
null
null
null
null
null
null
null
official-paper-method-implementation
[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]
include
ml-contribution-v5
1,504
[]
2026-09-24T17:44:57Z
https://github.com/nlpyang/BertSum
null
[ "general-ml", "generative-ai", "language" ]
[ "language-model", "transformer" ]
[ "description", "query-match", "repository-metadata" ]
[ "llm.transformers", "recall.name.transformer-model" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2019-03-26T16:15:23Z
New model for generation of text based on the "Transformer" that learns sentence based edition.
[ "general-ml" ]
[ "transformer" ]
ml_related_text
gh-ml-relevance-v1
2026-10-10T16:53:26Z
false
0
177,825,566
null
Python
null
[ "transformer" ]
grll/edit-transformer
[ "description", "query-match", "repository-metadata" ]
2
2026-10-22T16:09:07Z
[]
2019-04-01T12:33:22Z
[ "recall.name.transformer-model" ]
null
null
null
null
null
null
null
null
null
specific-method-with-novelty-claim
[ "method-tied-novelty-claim", "ml-method-cue" ]
include
ml-contribution-v5
5
[]
2026-05-13T04:43:03Z
https://github.com/grll/edit-transformer
null
[ "natural-language-processing" ]
[ "text-generation" ]
[ "description", "query-match", "repository-metadata" ]
[ "nlp.text-generation" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2019-03-29T19:07:12Z
Code in support of our NAACL 2019 short paper "Neural Text Generation from Rich Semantic Representations"
[ "natural-language-processing" ]
[]
no_text_signal
gh-ml-relevance-v1
2026-10-11T16:48:52Z
false
4
178,462,157
null
Shell
null
[ "text-generation" ]
shlurbee/dmrs-text-generation-naacl2019
[ "description", "query-match", "repository-metadata" ]
1
2026-10-11T16:48:52Z
[]
2022-12-08T02:30:54Z
[ "nlp.text-generation" ]
null
null
null
null
null
null
null
null
null
official-paper-method-implementation
[ "ml-context-only", "official-paper-implementation-cue", "paper-and-code-cue" ]
include
ml-contribution-v5
1
[]
2019-10-14T16:31:32Z
https://github.com/shlurbee/dmrs-text-generation-naacl2019
null
[ "automl", "general-ml" ]
[ "few-shot-learning", "meta-learning" ]
[ "description", "license-metadata", "query-match", "repository-metadata" ]
[ "general.meta-learning" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2019-04-15T06:50:49Z
Code for paper "Learning to Guide: Guidance Law Based on Deep Meta-learning and Model Predictive Path Integral Control"
[ "automl", "general-ml" ]
[]
no_text_signal
gh-ml-relevance-v1
2026-09-28T18:50:00Z
false
2
181,428,954
null
Python
MIT
[ "few-shot-learning", "meta-learning" ]
tccliangchen/deep_meta-learning_guidance_law
[ "description", "license-metadata", "query-match", "repository-metadata" ]
1
2026-09-28T18:50:00Z
[]
2019-05-26T10:52:29Z
[ "general.meta-learning" ]
null
null
null
null
null
null
null
null
null
official-paper-method-implementation
[ "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]
include
ml-contribution-v5
12
[]
2026-09-26T16:45:43Z
https://github.com/tccliangchen/deep_meta-learning_guidance_law
null
[ "computer-vision", "efficient-ml", "machine-learning-systems", "model-compression", "reinforcement-learning" ]
[ "computer-vision", "neural-network-pruning", "pruning", "reinforcement-learning", "structured-pruning" ]
[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]
[ "efficiency.pruning", "vision.topic-computer-vision" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2019-04-23T18:38:11Z
The original experiments code for AAAI 2020 paper, "AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates"
[ "computer-vision", "machine-learning-systems", "reinforcement-learning" ]
[ "deep-learning", "machine-learning", "reinforcement-learning" ]
direct_ml_text
gh-ml-relevance-v1
2026-10-06T17:10:23Z
false
0
183,075,297
https://ojs.aaai.org/index.php/AAAI/article/view/5924
Jupyter Notebook
Apache-2.0
[ "computer-vision", "pruning", "reinforcement-learning" ]
elphinkuo/distiller
[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]
2
2026-10-23T15:59:01Z
[]
2023-09-08T18:32:39Z
[ "vision.topic-computer-vision" ]
null
null
null
null
null
null
null
null
null
official-paper-method-implementation
[ "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]
include
ml-contribution-v5
1
[ "auto-ml", "computer-vision", "deep-learning", "deep-reinforcement-learning", "model-compression", "model-pruning" ]
2024-03-25T05:23:50Z
https://github.com/elphinkuo/distiller
null
[ "general-ml", "generative-ai", "language", "natural-language-processing" ]
[ "language-model", "transformer" ]
[ "description", "query-match", "repository-metadata" ]
[ "llm.transformers", "recall.name.transformer-model" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2019-05-08T13:22:37Z
This repository contain my Bachelor Project which is a Neural Translation Model for Translating English to Persian based on google new Transformer Model.
[ "general-ml", "natural-language-processing" ]
[ "transformer" ]
ml_related_text
gh-ml-relevance-v1
2026-10-12T20:31:19Z
false
0
185,610,445
null
Python
null
[ "transformer" ]
AsmaFaraji/Neural-Machine-Translation
[ "description", "query-match", "repository-metadata" ]
2
2026-10-23T15:59:01Z
[]
2019-05-12T07:07:16Z
[ "recall.name.transformer-model" ]
null
null
null
null
null
null
null
null
null
specific-method-with-novelty-claim
[ "method-tied-novelty-claim", "ml-method-cue" ]
include
ml-contribution-v5
2
[]
2021-07-17T17:53:07Z
https://github.com/AsmaFaraji/Neural-Machine-Translation
null
[ "distributed-ml", "privacy-and-federated-learning" ]
[ "collaborative-learning", "federated-learning" ]
[ "description", "license-metadata", "query-match", "repository-metadata" ]
[ "trust.federated-learning" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2019-05-09T02:34:51Z
Code for paper "Interpret Federated Learning with Shapley Values"
[ "distributed-ml", "privacy-and-federated-learning" ]
[]
no_text_signal
gh-ml-relevance-v1
2026-09-27T16:09:59Z
false
5
185,712,679
null
Jupyter Notebook
Apache-2.0
[ "collaborative-learning", "federated-learning" ]
crownpku/federated_shap
[ "description", "license-metadata", "query-match", "repository-metadata" ]
2
2026-10-17T13:06:16Z
[]
2019-05-18T01:58:52Z
[ "trust.federated-learning" ]
null
null
null
null
null
null
null
null
null
official-paper-method-implementation
[ "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]
include
ml-contribution-v5
39
[]
2026-08-10T20:20:16Z
https://github.com/crownpku/federated_shap
null
[ "reinforcement-learning" ]
[ "meta-learning", "policy-learning", "reinforcement-learning" ]
[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]
[ "rl.core" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
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", "policy-learning", "reinforcement-learning" ]
llan-ml/tesp
[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]
2
2026-10-12T20:31:19Z
[]
2019-05-17T11:21:17Z
[ "rl.core" ]
null
null
null
null
null
null
null
null
null
official-paper-method-implementation
[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]
include
ml-contribution-v5
35
[ "meta-learning", "meta-reinforcement", "meta-rl", "reinforcement-learning", "tesp" ]
2025-11-16T07:43:02Z
https://github.com/llan-ml/tesp
null
[]
[ "meta-learning" ]
[ "description", "github-topics", "repository-metadata" ]
[]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2019-05-17T04:22:53Z
The code for paper "CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning"
[]
[]
no_text_signal
gh-ml-relevance-v1
2026-09-26T20:57:28.489717Z
false
33
187,146,048
null
Python
null
[ "meta-learning" ]
icoz69/CaNet
[ "description", "github-topics", "repository-metadata" ]
4
2026-10-02T18:08:49.004055Z
[]
2020-06-06T10:55:56Z
[]
null
null
null
null
null
null
null
null
null
official-paper-method-implementation
[ "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]
include
ml-contribution-v5
197
[ "meta-learning" ]
2026-09-29T07:02:29Z
https://github.com/icoz69/CaNet
{"candidate_rule":"liberal-ml-ai-v1","discovery_source":"topic","queryless":true,"topic_names":["meta-learning"]}
[ "linguistics", "natural-language-processing" ]
[ "language-modeling", "text-clustering", "topic-modeling" ]
[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]
[ "nlp.topic-modeling", "recall.computational-linguistics" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2019-05-18T12:28:50Z
The official implementation of ACL 2019 paper "Topic-Aware Neural Keyphrase Generation for Social Media Language"
[ "natural-language-processing" ]
[]
no_text_signal
gh-ml-relevance-v1
2026-10-05T20:10:04Z
false
31
187,355,267
https://www.aclweb.org/anthology/P19-1240
Python
MIT
[ "text-clustering", "topic-modeling" ]
yuewang-cuhk/TAKG
[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]
6
2026-10-22T16:09:07Z
[]
2019-08-05T08:37:16Z
[ "nlp.topic-modeling" ]
null
null
null
null
null
null
null
null
null
official-paper-method-implementation
[ "contribution-language", "ml-context-only", "official-paper-implementation-cue", "paper-and-code-cue" ]
include
ml-contribution-v5
153
[ "keyphrase-generation", "nlp", "social-media", "topic-modeling" ]
2026-10-20T03:00:03Z
https://github.com/yuewang-cuhk/TAKG
null
[ "classical-ml", "tabular-and-structured-data", "tabular-ml" ]
[ "ensemble-learning", "gradient-boosting" ]
[ "description", "query-match", "repository-metadata" ]
[ "probable.classical-gradient-boosting-readme" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2019-05-22T15:57:26Z
Python code for the new XGBoost algorithm. Spyder IDE used.
[ "classical-ml", "tabular-and-structured-data", "tabular-ml" ]
[]
no_text_signal
gh-ml-relevance-v1
2026-11-01T17:09:41Z
false
0
188,070,911
null
Python
null
[ "ensemble-learning", "gradient-boosting" ]
GauravRoy48/XGBoost
[ "description", "query-match", "repository-metadata" ]
1
2026-11-01T17:09:41Z
[]
2019-05-22T16:15:01Z
[ "probable.classical-gradient-boosting-readme" ]
null
null
null
null
null
null
null
null
null
specific-method-with-novelty-claim
[ "method-tied-novelty-claim", "ml-method-cue" ]
include
ml-contribution-v5
0
[]
2019-05-22T16:15:02Z
https://github.com/GauravRoy48/XGBoost
null
[ "linguistics", "natural-language-processing" ]
[ "language-modeling", "transformer" ]
[ "description", "github-topics", "query-match", "repository-metadata" ]
[ "recall.computational-linguistics" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2019-05-26T11:55:39Z
Official code for semantic parsing model "HSP" in paper "Complex Question Decomposition for Semantic Parsing"(ACL'19).
[ "linguistics", "natural-language-processing" ]
[ "transformer" ]
ml_related_text
gh-ml-relevance-v1
2026-10-02T18:08:49.004055Z
false
7
188,676,934
null
Python
null
[ "language-modeling", "transformer" ]
cairoHy/HSP
[ "description", "github-topics", "query-match", "repository-metadata" ]
2
2026-10-08T18:15:14Z
[]
2023-07-06T21:22:42Z
[ "recall.computational-linguistics" ]
null
null
null
null
null
null
null
null
null
official-paper-method-implementation
[ "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]
include
ml-contribution-v5
31
[ "paper-implementations", "semantic-parsing", "sequence-to-sequence", "tensorflow", "transformer" ]
2026-05-17T04:12:07Z
https://github.com/cairoHy/HSP
null
[ "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-v4
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.
[ "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
[ "distillation", "knowledge-distillation" ]
irfanICMLL/structure_knowledge_distillation
[ "description", "license-metadata", "query-match", "repository-metadata" ]
10
2026-10-16T15:23:09Z
[]
2020-04-20T06:49:03Z
[ "efficiency.knowledge-distillation" ]
null
null
null
null
null
null
null
null
null
official-paper-method-implementation
[ "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]
include
ml-contribution-v5
739
[]
2026-08-20T21:02:24Z
https://github.com/irfanICMLL/structure_knowledge_distillation
null
[ "linguistics", "natural-language-processing" ]
[ "language-modeling", "neural-network" ]
[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]
[ "recall.computational-linguistics" ]
false
candidate
ml-candidate-v4
true
selected-by-current-rule
[]
2019-05-28T02:30:54Z
Implementation of the NLI model in our ACL 2019 paper: Augmenting Neural Networks with First-order Logic.
[ "linguistics", "natural-language-processing" ]
[ "neural-network" ]
direct_ml_text
gh-ml-relevance-v1
2026-10-08T18:15:14Z
false
9
188,939,802
null
Python
Apache-2.0
[ "language-modeling", "neural-network" ]
utahnlp/layer_augmentation
[ "description", "github-topics", "license-metadata", "query-match", "repository-metadata" ]
1
2026-10-08T18:15:14Z
[]
2020-11-03T20:00:42Z
[ "recall.computational-linguistics" ]
null
null
null
null
null
null
null
null
null
official-paper-method-implementation
[ "contribution-language", "ml-method-cue", "official-paper-implementation-cue", "paper-and-code-cue" ]
include
ml-contribution-v5
47
[ "acl2019", "attention", "attention-mechanism", "decomposable-attention", "first-order-logic", "logic", "nli", "pytorch", "snli" ]
2026-09-10T16:13:03Z
https://github.com/utahnlp/layer_augmentation
null