Deep Semi-Random Features for Nonlinear Function Approximation

28 February 2017

Papers citing "Deep Semi-Random Features for Nonlinear Function Approximation"

7 / 7 papers shown

Title
Orthogonal Over-Parameterized Training Weiyang Liu Rongmei Lin Zhen Liu James M. Rehg Liam Paull Li Xiong Le Song Adrian Weller 32 41 0 09 Apr 2020
Deep Randomized Neural Networks Claudio Gallicchio Simone Scardapane OOD 45 61 0 27 Feb 2020
Every Local Minimum Value is the Global Minimum Value of Induced Model in Non-convex Machine Learning Kenji Kawaguchi Jiaoyang Huang L. Kaelbling AAML 24 18 0 07 Apr 2019
Deep Neural Networks with Multi-Branch Architectures Are Less Non-Convex Hongyang R. Zhang Junru Shao Ruslan Salakhutdinov 39 14 0 06 Jun 2018
Benefits of depth in neural networks Matus Telgarsky 153 603 0 14 Feb 2016
Norm-Based Capacity Control in Neural Networks Behnam Neyshabur Ryota Tomioka Nathan Srebro 127 577 0 27 Feb 2015
The Loss Surfaces of Multilayer Networks A. Choromańska Mikael Henaff Michaël Mathieu Gerard Ben Arous Yann LeCun ODL 183 1,185 0 30 Nov 2014