Exponential expressivity in deep neural networks through transient chaos

Exponential expressivity in deep neural networks through transient chaos

16 June 2016

Subhaneil Lahiri

Jascha Narain Sohl-Dickstein

Papers citing "Exponential expressivity in deep neural networks through transient chaos"

12 / 162 papers shown

Title
Deep Convolutional Framelets: A General Deep Learning Framework for Inverse Problems J. C. Ye Yoseob Han Eunju Cha 36 16 0 03 Jul 2017
Recovery Guarantees for One-hidden-layer Neural Networks Kai Zhong Zhao Song Prateek Jain Peter L. Bartlett Inderjit S. Dhillon MLT 34 336 0 10 Jun 2017
Robustness of classifiers to universal perturbations: a geometric perspective Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi Omar Fawzi P. Frossard Stefano Soatto AAML 32 118 0 26 May 2017
Classification regions of deep neural networks Alhussein Fawzi Seyed-Mohsen Moosavi-Dezfooli P. Frossard Stefano Soatto 31 51 0 26 May 2017
The power of deeper networks for expressing natural functions David Rolnick Max Tegmark 39 174 0 16 May 2017
Survey of Expressivity in Deep Neural Networks M. Raghu Ben Poole Jon M. Kleinberg Surya Ganguli Jascha Narain Sohl-Dickstein 30 15 0 24 Nov 2016
Deep Residual Learning for Compressed Sensing CT Reconstruction via Persistent Homology Analysis Yoseob Han J. Yoo J. C. Ye 32 203 0 19 Nov 2016
Beyond Deep Residual Learning for Image Restoration: Persistent Homology-Guided Manifold Simplification Woong Bae J. Yoo J. C. Ye SupR 24 177 0 19 Nov 2016
Depth-Width Tradeoffs in Approximating Natural Functions with Neural Networks Itay Safran Ohad Shamir 39 174 0 31 Oct 2016
Why does deep and cheap learning work so well? Henry W. Lin Max Tegmark David Rolnick 40 603 0 29 Aug 2016
On the Expressive Power of Deep Neural Networks M. Raghu Ben Poole Jon M. Kleinberg Surya Ganguli Jascha Narain Sohl-Dickstein 29 778 0 16 Jun 2016
Quantifying the probable approximation error of probabilistic inference programs Marco F. Cusumano-Towner Vikash K. Mansinghka 33 7 0 31 May 2016