The power of deeper networks for expressing natural functions

16 May 2017

Papers citing "The power of deeper networks for expressing natural functions"

30 / 30 papers shown

Title
System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Rebecca G. Hart Omkar Sudhir Patil Zachary I. Bell Warren E. Dixon 14 0 0 15 May 2025
Fundamental Limits of Deep Learning-Based Binary Classifiers Trained with Hinge Loss T. Getu Georges Kaddoum M. Bennis 40 1 0 13 Sep 2023
A Survey of Geometric Optimization for Deep Learning: From Euclidean Space to Riemannian Manifold Yanhong Fei Xian Wei Yingjie Liu Zhengyu Li Mingsong Chen 30 6 0 16 Feb 2023
MOSAIC, acomparison framework for machine learning models Mattéo Papin Yann Beaujeault-Taudiere F. Magniette VLM 21 0 0 30 Jan 2023
Minimal Width for Universal Property of Deep RNN Changhoon Song Geonho Hwang Jun ho Lee Myung-joo Kang 25 9 0 25 Nov 2022
When Expressivity Meets Trainability: Fewer than $n$ Neurons Can Work Jiawei Zhang Yushun Zhang Mingyi Hong Ruoyu Sun Zhi-Quan Luo 31 10 0 21 Oct 2022
Neural Networks as Paths through the Space of Representations Richard D. Lange Devin Kwok Jordan K Matelsky Xinyue Wang David Rolnick Konrad Paul Kording 37 4 0 22 Jun 2022
Explicitly antisymmetrized neural network layers for variational Monte Carlo simulation Jeffmin Lin Gil Goldshlager Lin Lin 48 22 0 07 Dec 2021
On the approximation of functions by tanh neural networks Tim De Ryck S. Lanthaler Siddhartha Mishra 29 138 0 18 Apr 2021
Augmenting Deep Classifiers with Polynomial Neural Networks Grigorios G. Chrysos Markos Georgopoulos Jiankang Deng Jean Kossaifi Yannis Panagakis Anima Anandkumar 24 18 0 16 Apr 2021
Deep ReLU Networks Preserve Expected Length Boris Hanin Ryan Jeong David Rolnick 29 14 0 21 Feb 2021
Depth separation beyond radial functions Luca Venturi Samy Jelassi Tristan Ozuch Joan Bruna 25 15 0 02 Feb 2021
On Representing (Anti)Symmetric Functions Marcus Hutter 17 22 0 30 Jul 2020
Expressivity of Deep Neural Networks Ingo Gühring Mones Raslan Gitta Kutyniok 16 51 0 09 Jul 2020
Interpreting and Disentangling Feature Components of Various Complexity from DNNs Jie Ren Mingjie Li Zexu Liu Quanshi Zhang CoGe 19 18 0 29 Jun 2020
Deep Residual Mixture Models Perttu Hämäläinen Martin Trapp Tuure Saloheimo Arno Solin 36 8 0 22 Jun 2020
Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond Fanghui Liu Xiaolin Huang Yudong Chen Johan A. K. Suykens BDL 44 172 0 23 Apr 2020
On Interpretability of Artificial Neural Networks: A Survey Fenglei Fan Jinjun Xiong Mengzhou Li Ge Wang AAML AI4CE 38 301 0 08 Jan 2020
Optimal Function Approximation with Relu Neural Networks Bo Liu Yi Liang 25 33 0 09 Sep 2019
A Review on Deep Learning in Medical Image Reconstruction Hai-Miao Zhang Bin Dong MedIm 35 122 0 23 Jun 2019
Deep Network Approximation Characterized by Number of Neurons Zuowei Shen Haizhao Yang Shijun Zhang 26 182 0 13 Jun 2019
A Selective Overview of Deep Learning Jianqing Fan Cong Ma Yiqiao Zhong BDL VLM 38 136 0 10 Apr 2019
Nonlinear Approximation via Compositions Zuowei Shen Haizhao Yang Shijun Zhang 26 92 0 26 Feb 2019
Understanding Geometry of Encoder-Decoder CNNs J. C. Ye Woon Kyoung Sung 3DV AI4CE 17 72 0 22 Jan 2019
Multitask Learning Deep Neural Networks to Combine Revealed and Stated Preference Data Shenhao Wang Qingyi Wang Jinhuan Zhao AI4TS 6 21 0 02 Jan 2019
On a Sparse Shortcut Topology of Artificial Neural Networks Fenglei Fan Dayang Wang Hengtao Guo Qikui Zhu Pingkun Yan Ge Wang Hengyong Yu 38 22 0 22 Nov 2018
Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity Chulhee Yun S. Sra Ali Jadbabaie 28 117 0 17 Oct 2018
ResNet with one-neuron hidden layers is a Universal Approximator Hongzhou Lin Stefanie Jegelka 43 227 0 28 Jun 2018
Approximating Continuous Functions by ReLU Nets of Minimal Width Boris Hanin Mark Sellke 50 229 0 31 Oct 2017
Benefits of depth in neural networks Matus Telgarsky 155 603 0 14 Feb 2016