Fetching the paper…
Reading the bibliography…
We present a variational approximation to the information bottleneck of Tishby et al.
Acceleration of stochastic approximation by averaging
Boris T Polyak and Anatoli B Juditsky · 1992
Earlier work this paper cites.
The information bottleneck method
N. Tishby, F.C. Pereira, and W. Biale · 1999
Earlier work this paper cites.
Predictability, complexity, and learning
William Bialek, Ilya Nemenman, and Naftali Tishby · 2001
Earlier work this paper cites.
Information theory, inference and learning algorithms
David JC MacKay · 2003
Earlier work this paper cites.
The IM algorithm: a variational approach to information maximization
David Barber Felix Agakov · 2004
Earlier work this paper cites.
How many clusters? an information-theoretic perspective
Susanne Still and William Bialek · 2004
Earlier work this paper cites.
Information bottleneck for gaussian variables
G. Chechik, A Globersonand N. Tishby, and Y. Weiss · 2005
Earlier work this paper cites.
Information-based clustering
Noam Slonim, Gurinder Singh Atwal, Gašper Tkačik, and William Bialek · 2005
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
Earlier work this paper cites.
Learning and generalization with the information bottleneck
Ohad Shamir, Sivan Sabato, and Naftali Tishby · 2010
Earlier work this paper cites.
Auto-encoding variational Bayes
Diederik P Kingma and Max Welling · 2014
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 · 2014
Earlier work this paper cites.
The virtues of peer pressure: A simple method for discovering high-value mistakes
Shumeet Baluja, Michele Covell, and Rahul Sukthankar · 2015
Earlier work this paper cites.
Towards open world recognition
Abhijit Bendale and Terrance Boult · 2015
Cited alongside, same era.
Weight uncertainty in neural networks
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
Cited alongside, same era.
Multivariate sharp quadratic bounds via Σ \Sigma -strong convexity and the fenchel connection
Ryan P. Browne and Paul D. McNicholas · 2015
Cited alongside, same era.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
Cited alongside, same era.
Learning with a strong adversary
Ruitong Huang, Bing Xu, Dale Schuurmans, and Csaba Szepesvári · 2015
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2015
Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2016
Closest in time.
Relevant sparse codes with variational information bottleneck
Matthew Chalk, Olivier Marre, and Gasper Tkacik · 2016
Closest in time.
Differential privacy as a mutual information constraint
Paul Cuff and Lanqing Yu · 2016
Closest in time.
Robustness of classifiers: from adversarial to random noise
Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, and Pascal Frossard · 2016
Closest in time.
The variational fair autoencoder
Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, and Richard Zemel · 2016
Closest in time.
Universal adversarial perturbations
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, and Pascal Frossard · 2016
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Variational information maximisation for intrinsically motivated reinforcement learning
Shakir Mohamed and Danilo Jimenez Rezende · 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.
Predictive information in a sensory population
Stephanie E Palmer, Olivier Marre, Michael J Berry, and William Bialek · 2015
Cited alongside, same era.
The limitations of deep learning in adversarial settings
Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z Berkay Celik, and Ananthram Swami · 2015
Cited alongside, same era.
Confusing deep convolution networks by relabelling
Leigh Robinson and Benjamin Graham · 2015
Cited alongside, same era.
Deep learning and the information bottleneck principle
Naftali Tishby and Noga Zaslavsky · 2015
Cited alongside, same era.
Closest in time.
Deepfool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016
Closest in time.
Adversarial manipulation of deep representations
Sara Sabour, Yanshuai Cao, Fartash Faghri, and David J Fleet · 2016
Closest in time.
Inception-v4, inception-resnet and the impact of residual connections on learning
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alex Alemi · 2016
Closest in time.
Deep variational canonical correlation analysis
Weiran Wang, Honglak Lee, and Karen Livescu · 2016
Closest in time.
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
Closest in time.
Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2017
Closest in time.
Regularizing neural networks by penalizing confident output predictions
Gabriel Pereyra, George Tuckery, Jan Chorowski, and Lukasz Kaiser · 2017
Closest in time.