Practical Bayesian Optimization of Machine Learning Algorithms

Jasper Snoek; Hugo Larochelle; Ryan P. Adams

doi:https://doi.org/10.48550/arxiv.1206.2944

Practical Bayesian Optimization of Machine Learning Algorithms

2012 · arXiv (Cornell University) · 5,674 citations · 12 from inside this corpus

Jasper Snoek low, Hugo Larochelle, Ryan P. Adams

The source holds an abstract for this work, but its best open-access copy is under no open licence, which does not permit us to republish the text. Read it at the source below.

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Citation edge evidence

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Dropout: a simple way to prevent neural networks from overfitting→Practical Bayesian Optimization of Machine Learning AlgorithmsLegacy edge — source detail unavailable
A survey on deep learning in medical image analysis→Practical Bayesian Optimization of Machine Learning AlgorithmsLegacy edge — source detail unavailable
Dropout as a Bayesian Approximation: Representing Model Uncertainty in\n Deep Learning→Practical Bayesian Optimization of Machine Learning AlgorithmsLegacy edge — source detail unavailable
On hyperparameter optimization of machine learning algorithms: Theory and practice→Practical Bayesian Optimization of Machine Learning AlgorithmsLegacy edge — source detail unavailable
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning→Practical Bayesian Optimization of Machine Learning AlgorithmsLegacy edge — source detail unavailable
A review of uncertainty quantification in deep learning: Techniques, applications and cha…→Practical Bayesian Optimization of Machine Learning AlgorithmsLegacy edge — source detail unavailable
Deep Learning: Methods and Applications→Practical Bayesian Optimization of Machine Learning AlgorithmsLegacy edge — source detail unavailable
AutoML: A survey of the state-of-the-art→Practical Bayesian Optimization of Machine Learning AlgorithmsLegacy edge — source detail unavailable
Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions f…→Practical Bayesian Optimization of Machine Learning AlgorithmsLegacy edge — source detail unavailable
From Word Embeddings To Document Distances→Practical Bayesian Optimization of Machine Learning AlgorithmsLegacy edge — source detail unavailable
Deep Learning Applications in Medical Image Analysis→Practical Bayesian Optimization of Machine Learning AlgorithmsLegacy edge — source detail unavailable
Automated Machine Learning→Practical Bayesian Optimization of Machine Learning AlgorithmsLegacy edge — source detail unavailable
Practical Bayesian Optimization of Machine Learning Algorithms→Gaussian Processes for Machine LearningLegacy edge — source detail unavailable
Practical Bayesian Optimization of Machine Learning Algorithms→Algorithms for Hyper-Parameter OptimizationLegacy edge — source detail unavailable
Algorithms for Hyper-Parameter Optimization→Gaussian Processes for Machine LearningLegacy edge — source detail unavailable
Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions f…→Gaussian Processes for Machine LearningLegacy edge — source detail unavailable
Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions f…→Algorithms for Hyper-Parameter OptimizationLegacy edge — source detail unavailable
Deep Learning Applications in Medical Image Analysis→A survey on deep learning in medical image analysisLegacy edge — source detail unavailable
Dropout as a Bayesian Approximation: Representing Model Uncertainty in\n Deep Learning→Dropout: a simple way to prevent neural networks from overfittingLegacy edge — source detail unavailable
AutoML: A survey of the state-of-the-art→Algorithms for Hyper-Parameter OptimizationLegacy edge — source detail unavailable
AutoML: A survey of the state-of-the-art→Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions f…Legacy edge — source detail unavailable
On hyperparameter optimization of machine learning algorithms: Theory and practice→Algorithms for Hyper-Parameter OptimizationLegacy edge — source detail unavailable
A review of uncertainty quantification in deep learning: Techniques, applications and cha…→Gaussian Processes for Machine LearningLegacy edge — source detail unavailable
A review of uncertainty quantification in deep learning: Techniques, applications and cha…→Dropout: a simple way to prevent neural networks from overfittingLegacy edge — source detail unavailable
A review of uncertainty quantification in deep learning: Techniques, applications and cha…→A survey on deep learning in medical image analysisLegacy edge — source detail unavailable
A review of uncertainty quantification in deep learning: Techniques, applications and cha…→Dropout as a Bayesian Approximation: Representing Model Uncertainty in\n Deep LearningLegacy edge — source detail unavailable
A review of uncertainty quantification in deep learning: Techniques, applications and cha…→Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep LearningLegacy edge — source detail unavailable
Automated Machine Learning→Dropout: a simple way to prevent neural networks from overfittingLegacy edge — source detail unavailable
Automated Machine Learning→Algorithms for Hyper-Parameter OptimizationLegacy edge — source detail unavailable
Automated Machine Learning→Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions f…Legacy edge — source detail unavailable
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning→Dropout: a simple way to prevent neural networks from overfittingLegacy edge — source detail unavailable
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning→Dropout as a Bayesian Approximation: Representing Model Uncertainty in\n Deep LearningLegacy edge — source detail unavailable

What this paper cites, inside the corpus

Paper	Year	Cited
Gaussian Processes for Machine Learning	2005	10,470
Algorithms for Hyper-Parameter Optimization	2011	3,188

What cites it, inside the corpus

Paper	Year	Cited
Dropout: a simple way to prevent neural networks from overfitting	2014	34,236
A survey on deep learning in medical image analysis	2017	15,110
Dropout as a Bayesian Approximation: Representing Model Uncertainty in\n Deep Learning	2015	4,187
On hyperparameter optimization of machine learning algorithms: Theory and practice	2020	3,249
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning	2015	2,672
A review of uncertainty quantification in deep learning: Techniques, applications and cha…	2021	2,644
Deep Learning: Methods and Applications	2014	1,801
AutoML: A survey of the state-of-the-art	2020	1,705
Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions f…	2013	1,645
From Word Embeddings To Document Distances	2015	1,520
Deep Learning Applications in Medical Image Analysis	2017	1,497
Automated Machine Learning	2019	1,451

Links

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Topics

Gaussian Processes and Bayesian Inference	Computer Science
Machine Learning and Algorithms	Computer Science
Machine Learning and Data Classification	Computer Science

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complete

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supports3 author record(s) attached.
supports23 reference(s) recorded.
neutralThe DOI carries no year to check against.
supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:44+00:00.

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