Representation Benefits of Deep Feedforward Networks

27 September 2015

Papers citing "Representation Benefits of Deep Feedforward Networks"

13 / 63 papers shown

Title
Analysis and Design of Convolutional Networks via Hierarchical Tensor Decompositions Nadav Cohen Or Sharir Yoav Levine Ronen Tamari David Yakira Amnon Shashua 20 38 0 05 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
Understanding Deep Neural Networks with Rectified Linear Units R. Arora A. Basu Poorya Mianjy Anirbit Mukherjee PINN 41 637 0 04 Nov 2016
Error bounds for approximations with deep ReLU networks Dmitry Yarotsky 42 1,223 0 03 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
Inductive Bias of Deep Convolutional Networks through Pooling Geometry Nadav Cohen Amnon Shashua 22 132 0 22 May 2016
Learning Functions: When Is Deep Better Than Shallow H. Mhaskar Q. Liao T. Poggio 33 144 0 03 Mar 2016
Efficient Representation of Low-Dimensional Manifolds using Deep Networks Ronen Basri David Jacobs 3DPC 22 44 0 15 Feb 2016
Benefits of depth in neural networks Matus Telgarsky 155 603 0 14 Feb 2016
The Power of Depth for Feedforward Neural Networks Ronen Eldan Ohad Shamir 65 731 0 12 Dec 2015
Expressiveness of Rectifier Networks Xingyuan Pan Vivek Srikumar OffRL 19 46 0 18 Nov 2015
On the Expressive Power of Deep Learning: A Tensor Analysis Nadav Cohen Or Sharir Amnon Shashua 39 469 0 16 Sep 2015