Fetching the paper…
Reading the bibliography…
This article concerns the expressive power of depth in deep feed-forward neural nets with ReLU activations.
Approximation by superpositions of a sigmoidal function
George Cybenko · 1989
Earlier work this paper cites.
Multilayer feedforward networks are universal approximators
Kurt Hornik, Maxwell Stinchcombe, and Halbert White · 1989
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Introduction to piecewise differentiable equations
Stefan Scholtes · 2012
Earlier work this paper cites.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le · 2014
Earlier work this paper cites.
Rupesh Kumar Srivastava, Klaus Greff, and Jürgen Schmidhuber · 2015
Earlier work this paper cites.
Representation benefits of deep feedforward networks
Matus Telgarsky · 2015
Earlier work this paper cites.
Deep learning and the information bottleneck principle
Naftali Tishby and Noga Zaslavsky · 2015
Earlier work this paper cites.
Understanding deep neural networks with rectified linear units
Raman Arora, Amitabh Basu, Poorya Mianjy, and Anirbit Mukherjee · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Learning real and boolean functions: When is deep better than shallow
Hrushikesh Mhaskar, Qianli Liao, and Tomaso A. Poggio · 2016
Cited alongside, same era.
Deep vs. shallow networks: An approximation theory perspective
Hrushikesh N Mhaskar and Tomaso Poggio · 2016
Cited alongside, same era.
Exponential expressivity in deep neural networks through transient chaos
Ben Poole, Subhaneil Lahiri, Maithreyi Raghu, Jascha Sohl-Dickstein, and Surya Ganguli · 2016
Cited alongside, same era.
Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
Cited alongside, same era.
Error bounds for approximations with deep relu networks
Dmitry Yarotsky · 2016
Later among the works it cites.
Universal function approximation by deep neural nets with bounded width and relu activations
Boris Hanin · 2017
Closest in time.
Why does deep and cheap learning work so well?
Henry W Lin, Max Tegmark, and David Rolnick · 2017
Closest in time.
Mixing complexity and its applications to neural networks
Michal Moshkovitz and Naftali Tishby · 2017
Closest in time.
On the expressive power of deep neural networks
Maithra Raghu, Ben Poole, Jon Kleinberg, Surya Ganguli, and Jascha Sohl-Dickstein · 2017
Closest in time.
The power of deeper networks for expressing natural functions
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
benefits of depth in neural networks
Matus Telgarsky · 2016
Cited alongside, same era.
Residual networks behave like ensembles of relatively shallow networks
Andreas Veit, Michael J Wilber, and Serge Belongie · 2016
Cited alongside, same era.
Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al · 2016
Cited alongside, same era.
David Rolnick and Max Tegmark · 2017
Closest in time.
Opening the black box of deep neural networks via information
Ravid Shwartz-Ziv and Naftali Tishby · 2017
Closest in time.
Neural networks and rational functions
Matus Telgarsky · 2017
Closest in time.