Neural Network with Unbounded Activation Functions is Universal Approximator

14 May 2015

Papers citing "Neural Network with Unbounded Activation Functions is Universal Approximator"

9 / 109 papers shown

Title
An Information-Theoretic View for Deep Learning Jingwei Zhang Tongliang Liu Dacheng Tao MLT FAtt 13 25 0 24 Apr 2018
Learning the Localization Function: Machine Learning Approach to Fingerprinting Localization Linchen Xiao Arash Behboodi R. Mathar 17 12 0 21 Mar 2018
On the Universal Approximability and Complexity Bounds of Quantized ReLU Neural Networks Yukun Ding Jinglan Liu Jinjun Xiong Yiyu Shi MQ 37 21 0 10 Feb 2018
Generalization of an Upper Bound on the Number of Nodes Needed to Achieve Linear Separability Marjolein Troost K. Seeliger Marcel van Gerven 15 1 0 10 Feb 2018
Meta-Learning and Universality: Deep Representations and Gradient Descent can Approximate any Learning Algorithm Chelsea Finn Sergey Levine SSL 30 222 0 31 Oct 2017
Learning of Colors from Color Names: Distribution and Point Estimation Lyndon White R. Togneri Wei Liu Bennamoun OOD 31 2 0 27 Sep 2017
Fast learning rate of deep learning via a kernel perspective Taiji Suzuki 26 6 0 29 May 2017
The Upper Bound on Knots in Neural Networks Kevin K. Chen 32 14 0 29 Nov 2016
Tensor Switching Networks Chuan-Yung Tsai Andrew M. Saxe David D. Cox 11 10 0 31 Oct 2016