Width is Less Important than Depth in ReLU Neural Networks

Width is Less Important than Depth in ReLU Neural Networks

8 February 2022

Gal Vardi

Papers citing "Width is Less Important than Depth in ReLU Neural Networks"

6 / 6 papers shown

Title
The Role of Depth, Width, and Tree Size in Expressiveness of Deep Forest Shen-Huan Lyu Jin-Hui Wu Qin-Cheng Zheng Baoliu Ye 39 0 0 06 Jul 2024
Data Topology-Dependent Upper Bounds of Neural Network Widths Sangmin Lee Jong Chul Ye 26 0 0 25 May 2023
Multi-Path Transformer is Better: A Case Study on Neural Machine Translation Ye Lin Shuhan Zhou Yanyang Li Anxiang Ma Tong Xiao Jingbo Zhu 38 0 0 10 May 2023
Exponential Separations in Symmetric Neural Networks Aaron Zweig Joan Bruna 32 7 0 02 Jun 2022
Size and Depth Separation in Approximating Benign Functions with Neural Networks Gal Vardi Daniel Reichman T. Pitassi Ohad Shamir 26 7 0 30 Jan 2021
Benefits of depth in neural networks Matus Telgarsky 148 602 0 14 Feb 2016