Approximation Speed of Quantized Versus Unquantized ReLU Neural Networks and Beyond
Author:
Affiliation:
1. Univ Lyon, EnsL, UCBL, CNRS, Inria, LIP, Lyon, France
Funder
French Agence Nationale de la Recherche through the AllegroAssai Project
NuSCAP Project
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Library and Information Sciences,Computer Science Applications,Information Systems
Link
http://xplorestaging.ieee.org/ielx7/18/10130037/10027461.pdf?arnumber=10027461
Reference19 articles.
1. Understanding deep neural networks with rectified linear units;Arora,2018
2. Analysis of the Generalization Error: Empirical Risk Minimization over Deep Artificial Neural Networks Overcomes the Curse of Dimensionality in the Numerical Approximation of Black--Scholes Partial Differential Equations
3. Optimal Approximation with Sparsely Connected Deep Neural Networks
4. Binaryconnect: Training deep neural networks with binary weights during propagations;Courbariaux
5. Neural network approximation
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