Who Cited It

Dropout as a Bayesian Approximation: Representing Model Uncertainty in\n Deep Learning

2015 · arXiv (Cornell University) · 4,187 citations · 5 from inside this corpus

Yarin Gal, Zoubin Ghahramani

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.

Dropout as a Bayesian Approximation: Representing Model Uncertainty in\n Deep Learning (2015)Dropout as a Bayesian Approxi…[No title in the source record — DROPS (Schloss Dagstuhl – Leibniz Center for Informatics… (2015)[No title in the source recor…Dropout: a simple way to prevent neural networks from overfitting (2014)Dropout: a simple way to prev…Auto-Encoding Variational Bayes (2013)Auto-Encoding Variational Bay…Practical Bayesian Optimization of Machine Learning Algorithms (2012)Practical Bayesian Optimizati…Bayesian Learning for Neural Networks (1996)Bayesian Learning for Neural …A Practical Bayesian Framework for Backpropagation Networks (1992)A Practical Bayesian Framewor…Probabilistic machine learning and artificial intelligence (2015)Probabilistic machine learnin…Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) (2005)Gaussian Processes for Machin…Stochastic variational inference (2013)Stochastic variational infere…Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised … (2018)Virtual Adversarial Training:…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…On Calibration of Modern Neural Networks (2017)On Calibration of Modern Neur…Agnostic Learning with Unknown Utilities (2016)Agnostic Learning with Unknow…
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.
this paper works it cites works citing it node size = global citations · hover for the full title

What this paper cites, inside the corpus

PaperYearCited
[No title in the source record — DROPS (Schloss Dagstuhl – Leibniz Center for Informatics…201550,396
Dropout: a simple way to prevent neural networks from overfitting201434,236
Auto-Encoding Variational Bayes201315,583
Practical Bayesian Optimization of Machine Learning Algorithms20125,674
Bayesian Learning for Neural Networks19964,397
A Practical Bayesian Framework for Backpropagation Networks19922,962
Probabilistic machine learning and artificial intelligence20152,031
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)20051,859
Stochastic variational inference20131,480

What cites it, inside the corpus

PaperYearCited
Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised …20182,873
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
On Calibration of Modern Neural Networks20171,741
Agnostic Learning with Unknown Utilities20161,416

Links

DOI · Open access copy · OpenAlex record

Topics

Gaussian Processes and Bayesian InferenceComputer Science
Adversarial Robustness in Machine LearningComputer Science
Model Reduction and Neural NetworksPhysics and Astronomy

Is this record sound?

complete

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

  • supports2 author record(s) attached.
  • supports36 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 2026-09-04T03:58:46+00:00.

sha256 bba2969b3567609a…