A Framework for the Construction of Upper Bounds on the Number of Affine Linear Regions of ReLU Feed-Forward Neural Networks
Author:
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Library and Information Sciences,Computer Science Applications,Information Systems
Link
http://xplorestaging.ieee.org/ielx7/18/8876732/08756157.pdf?arnumber=8756157
Cited by 10 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Deep ReLU networks and high-order finite element methods II: Chebyšev emulation;Computers & Mathematics with Applications;2024-09
2. On the Number of Linear Regions of Convolutional Neural Networks With Piecewise Linear Activations;IEEE Transactions on Pattern Analysis and Machine Intelligence;2024-07
3. On the number of regions of piecewise linear neural networks;Journal of Computational and Applied Mathematics;2024-05
4. Achieving Sharp Upper Bounds on the Expressive Power of Neural Networks via Tropical Polynomials;IEEE Transactions on Neural Networks and Learning Systems;2024
5. Integrating geometries of ReLU feedforward neural networks;Frontiers in Big Data;2023-11-14
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