Fetching the paper…
Reading the bibliography…
In this work, we propose learnable Bernoulli dropout (LBD), a new model-agnostic dropout scheme that considers the dropout rates as parameters jointly optimized with other model parameters.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
Earlier work this paper cites.
Bayesian learning for neural networks
Radford M Neal · 1995
Earlier work this paper cites.
Evaluating collaborative filtering recommender systems
Jonathan L Herlocker, Joseph A Konstan, Loren G Terveen, and John T Riedl · 2004
Earlier work this paper cites.
Gradient estimation
Michael C Fu · 2006
Earlier work this paper cites.
The netflix prize
James Bennett, Stan Lanning, et al · 2007
Earlier work this paper cites.
Collaborative filtering for implicit feedback datasets
Yifan Hu, Yehuda Koren, and Chris Volinsky · 2008
Earlier work this paper cites.
Semantic object classes in video: A high-definition ground truth database
Gabriel J Brostow, Julien Fauqueur, and Roberto Cipolla · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
Earlier work this paper cites.
The million song dataset
Thierry Bertin-Mahieux, Daniel PW Ellis, Brian Whitman, and Paul Lamere · 2011
Earlier work this paper cites.
Practical variational inference for neural networks
Alex Graves · 2011
Earlier work this paper cites.
Slim: Sparse linear methods for top-n recommender systems
Xia Ning and George Karypis · 2011
Earlier work this paper cites.
Bayesian learning via stochastic gradient langevin dynamics
Max Welling and Yee W Teh · 2011
Earlier work this paper cites.
Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan R Salakhutdinov · 2012
Earlier work this paper cites.
Bayesian learning for neural networks , volume 118
Radford M Neal · 2012
Earlier work this paper cites.
Adaptive dropout for training deep neural networks
Jimmy Ba and Brendan Frey · 2013
Earlier work this paper cites.
Stochastic variational inference
Matthew D Hoffman, David M Blei, Chong Wang, and John Paisley · 2013
Earlier work this paper cites.
Auto-encoding variational Bayes
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Cited alongside, same era.
Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
Cited alongside, same era.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Cited alongside, same era.
Weight uncertainty in neural networks
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
Concrete dropout
Yarin Gal, Jiri Hron, and Alex Kendall · 2017
Later among the works it cites.
Backpropagation through the void: Optimizing control variates for black-box gradient estimation
Will Grathwohl, Dami Choi, Yuhuai Wu, Geoffrey Roeder, and David Duvenaud · 2017
Later among the works it cites.
beta-VAE: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
Later among the works it cites.
Variational gaussian dropout is not bayesian
Jiri Hron, Alexander G de G Matthews, and Zoubin Ghahramani · 2017
Later among the works it cites.
The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation
Simon Jégou, Michal Drozdzal, David Vazquez, Adriana Romero, and Yoshua Bengio · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Variational dropout and the local reparameterization trick
Durk P Kingma, Tim Salimans, and Max Welling · 2015
Cited alongside, same era.
A complete recipe for stochastic gradient MCMC
Y. Ma, T. Chen, and E. Fox · 2015
Cited alongside, same era.
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Anh Nguyen, Jason Yosinski, and Jeff Clune · 2015
Cited alongside, same era.
Tensorflow: A system for large-scale machine learning
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek G. Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2016
Cited alongside, same era.
Uncertainty in deep learning
Yarin Gal · 2016
Cited alongside, same era.
The movielens datasets: History and context
F Maxwell Harper and Joseph A Konstan · 2016
Cited alongside, same era.
Categorical reparameterization with Gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2016
Cited alongside, same era.
Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
Later among the works it cites.
Rebar: Low-variance, unbiased gradient estimates for discrete latent variable models
George Tucker, Andriy Mnih, Chris J Maddison, John Lawson, and Jascha Sohl-Dickstein · 2017
Later among the works it cites.
Accurate uncertainties for deep learning using calibrated regression
Volodymyr Kuleshov, Nathan Fenner, and Stefano Ermon · 2018
Later among the works it cites.
Variational autoencoders for collaborative filtering
Dawen Liang, Rahul G Krishnan, Matthew D Hoffman, and Tony Jebara · 2018
Later among the works it cites.
Predictive uncertainty estimation via prior networks
Andrey Malinin and Mark Gales · 2018
Later among the works it cites.
Evaluating bayesian deep learning methods for semantic segmentation
Jishnu Mukhoti and Yarin Gal · 2018
Later among the works it cites.
VAE with a VampPrior
Jakub Tomczak and Max Welling · 2018
Later among the works it cites.
Semi-implicit variational inference
Mingzhang Yin and Mingyuan Zhou · 2018
Later among the works it cites.
The information autoencoding family: A Lagrangian perspective on latent variable generative models
Shengjia Zhao, Jiaming Song, and Stefano Ermon · 2018
Later among the works it cites.
Adaptive activity monitoring with uncertainty quantification in switching gaussian process models
Randy Ardywibowo, Guang Zhao, Zhangyang Wang, Bobak Mortazavi, Shuai Huang, and Xiaoning Qian · 2019
Later among the works it cites.
The fishyscapes benchmark: Measuring blind spots in semantic segmentation
Hermann Blum, Paul-Edouard Sarlin, Juan Nieto, Roland Siegwart, and Cesar Cadena · 2019
Later among the works it cites.
Safety for mobile robotic systems: A systematic mapping study from a software engineering perspective
Darko Bozhinoski, Davide Di Ruscio, Ivano Malavolta, Patrizio Pelliccione, and Ivica Crnkovic · 2019
Later among the works it cites.
ARM: Augment-REINFORCE-merge gradient for stochastic binary networks
Mingzhang Yin and Mingyuan Zhou · 2019
Later among the works it cites.