Who Cited It

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.

Practical Bayesian Optimization of Machine Learning Algorithms (2012)Practical Bayesian Optimizati…Gaussian Processes for Machine Learning (2005)Gaussian Processes for Machin…Algorithms for Hyper-Parameter Optimization (2011)Algorithms for Hyper-Paramete…Dropout: a simple way to prevent neural networks from overfitting (2014)Dropout: a simple way to prev…A survey on deep learning in medical image analysis (2017)A survey on deep learning in …Dropout as a Bayesian Approximation: Representing Model Uncertainty in\n Deep Learning (2015)Dropout as a Bayesian Approxi…On hyperparameter optimization of machine learning algorithms: Theory and practice (2020)On hyperparameter optimizatio…Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning (2015)Dropout as a Bayesian Approxi…A review of uncertainty quantification in deep learning: Techniques, applications and cha… (2021)A review of uncertainty quant…Deep Learning: Methods and Applications (2014)Deep Learning: Methods and Ap…AutoML: A survey of the state-of-the-art (2020)AutoML: A survey of the state…Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions f… (2013)Making a Science of Model Sea…From Word Embeddings To Document Distances (2015)From Word Embeddings To Docum…Deep Learning Applications in Medical Image Analysis (2017)Deep Learning Applications in…Automated Machine Learning (2019)Automated Machine Learning
14 of 14 neighbouring works in this corpus. Blue is what this paper cites; orange is what cites it, and a dashed line is one neighbour citing another. Only the largest labels are drawn — every node carries its full title on hover.
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What this paper cites, inside the corpus

PaperYearCited
Gaussian Processes for Machine Learning200510,470
Algorithms for Hyper-Parameter Optimization20113,188

What cites it, inside the corpus

PaperYearCited
Dropout: a simple way to prevent neural networks from overfitting201434,236
A survey on deep learning in medical image analysis201715,110
Dropout as a Bayesian Approximation: Representing Model Uncertainty in\n Deep Learning20154,187
On hyperparameter optimization of machine learning algorithms: Theory and practice20203,249
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning20152,672
A review of uncertainty quantification in deep learning: Techniques, applications and cha…20212,644
Deep Learning: Methods and Applications20141,801
AutoML: A survey of the state-of-the-art20201,705
Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions f…20131,645
From Word Embeddings To Document Distances20151,520
Deep Learning Applications in Medical Image Analysis20171,497
Automated Machine Learning20191,451

Links

DOI · Open access copy · OpenAlex record

Topics

Gaussian Processes and Bayesian InferenceComputer Science
Machine Learning and AlgorithmsComputer Science
Machine Learning and Data ClassificationComputer Science

Is this record sound?

complete

Nothing in this record contradicts itself and no field we check is missing.

  • 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.

sha256 648f8491aaceaa34…