Benefits of depth in neural networks

14 February 2016

Papers citing "Benefits of depth in neural networks"

50 / 353 papers shown

Title
Provably Good Solutions to the Knapsack Problem via Neural Networks of Bounded Size Christoph Hertrich M. Skutella 50 21 0 28 May 2020
PDE constraints on smooth hierarchical functions computed by neural networks Khashayar Filom Konrad Paul Kording Roozbeh Farhoodi 11 0 0 18 May 2020
The Information Bottleneck Problem and Its Applications in Machine Learning Ziv Goldfeld Yury Polyanskiy 15 129 0 30 Apr 2020
Rational neural networks Nicolas Boullé Y. Nakatsukasa Alex Townsend 12 79 0 04 Apr 2020
Batch Normalization Provably Avoids Rank Collapse for Randomly Initialised Deep Networks Hadi Daneshmand Jonas Köhler Francis R. Bach Thomas Hofmann Aurélien Lucchi OOD ODL 6 4 0 03 Mar 2020
Better Depth-Width Trade-offs for Neural Networks through the lens of Dynamical Systems Vaggos Chatziafratis Sai Ganesh Nagarajan Ioannis Panageas 12 15 0 02 Mar 2020
Uncertainty Quantification for Sparse Deep Learning Yuexi Wang Veronika Rockova BDL UQCV 23 31 0 26 Feb 2020
A closer look at the approximation capabilities of neural networks Kai Fong Ernest Chong 11 16 0 16 Feb 2020
A Deep Conditioning Treatment of Neural Networks Naman Agarwal Pranjal Awasthi Satyen Kale AI4CE 17 14 0 04 Feb 2020
Overfitting Can Be Harmless for Basis Pursuit, But Only to a Degree Peizhong Ju Xiaojun Lin Jia Liu 74 7 0 02 Feb 2020
A Corrective View of Neural Networks: Representation, Memorization and Learning Guy Bresler Dheeraj M. Nagaraj MLT 13 18 0 01 Feb 2020
Approximation smooth and sparse functions by deep neural networks without saturation Xia Liu 11 1 0 13 Jan 2020
Lossless Compression of Deep Neural Networks Thiago Serra Abhinav Kumar Srikumar Ramalingam 24 56 0 01 Jan 2020
Depth-Width Trade-offs for ReLU Networks via Sharkovsky's Theorem Vaggos Chatziafratis Sai Ganesh Nagarajan Ioannis Panageas Xiao Wang 17 21 0 09 Dec 2019
Exploring the Ideal Depth of Neural Network when Predicting Question Deletion on Community Question Answering Souvick Ghosh Satanu Ghosh 18 4 0 08 Dec 2019
Analysis of Deep Neural Networks with Quasi-optimal polynomial approximation rates Joseph Daws Clayton Webster 18 8 0 04 Dec 2019
Stationary Points of Shallow Neural Networks with Quadratic Activation Function D. Gamarnik Eren C. Kizildag Ilias Zadik 11 12 0 03 Dec 2019
Neural Contextual Bandits with UCB-based Exploration Dongruo Zhou Lihong Li Quanquan Gu 22 15 0 11 Nov 2019
Theoretical Guarantees for Model Auditing with Finite Adversaries Mario Díaz Peter Kairouz Jiachun Liao Lalitha Sankar MLAU AAML 26 2 0 08 Nov 2019
Lipschitz Constrained Parameter Initialization for Deep Transformers Hongfei Xu Qiuhui Liu Josef van Genabith Deyi Xiong Jingyi Zhang ODL 12 26 0 08 Nov 2019
ChebNet: Efficient and Stable Constructions of Deep Neural Networks with Rectified Power Units via Chebyshev Approximations Shanshan Tang Bo Li Haijun Yu 11 7 0 07 Nov 2019
Deep Learning for MIMO Channel Estimation: Interpretation, Performance, and Comparison Qiang Hu Feifei Gao Hao Zhang Shi Jin Geoffrey Ye Li 18 10 0 05 Nov 2019
Mean-field inference methods for neural networks Marylou Gabrié AI4CE 16 32 0 03 Nov 2019
Approximation capabilities of neural networks on unbounded domains Ming-xi Wang Yang Qu 17 19 0 21 Oct 2019
The Local Elasticity of Neural Networks Hangfeng He Weijie J. Su 21 44 0 15 Oct 2019
A Function Space View of Bounded Norm Infinite Width ReLU Nets: The Multivariate Case Greg Ongie Rebecca Willett Daniel Soudry Nathan Srebro 13 160 0 03 Oct 2019
Deep Model Reference Adaptive Control Girish Joshi Girish Chowdhary BDL AI4CE 17 57 0 18 Sep 2019
Learning Hierarchically Structured Concepts Nancy A. Lynch Frederik Mallmann-Trenn 19 10 0 10 Sep 2019
Optimal Function Approximation with Relu Neural Networks Bo Liu Yi Liang 25 33 0 09 Sep 2019
Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models Zhaoqiang Liu Jonathan Scarlett 14 38 0 28 Aug 2019
Deep ReLU network approximation of functions on a manifold Johannes Schmidt-Hieber 17 92 0 02 Aug 2019
Two-hidden-layer Feedforward Neural Networks are Universal Approximators: A Constructive Approach Rocio Gonzalez-Diaz Miguel A. Gutiérrez-Naranjo Eduardo Paluzo-Hidalgo 14 12 0 26 Jul 2019
Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology Nima Dehmamy Albert-László Barabási Rose Yu GNN 22 131 0 11 Jul 2019
Error bounds for deep ReLU networks using the Kolmogorov--Arnold superposition theorem Hadrien Montanelli Haizhao Yang 4 88 0 27 Jun 2019
A Review on Deep Learning in Medical Image Reconstruction Hai-Miao Zhang Bin Dong MedIm 32 122 0 23 Jun 2019
The phase diagram of approximation rates for deep neural networks Dmitry Yarotsky Anton Zhevnerchuk 17 121 0 22 Jun 2019
DeepSquare: Boosting the Learning Power of Deep Convolutional Neural Networks with Elementwise Square Operators Sheng-Wei Chen Xu Wang Chao Chen Yifan Lu Xijin Zhang Linfu Wen 24 2 0 12 Jun 2019
The Convergence Rate of Neural Networks for Learned Functions of Different Frequencies Ronen Basri David Jacobs Yoni Kasten S. Kritchman 8 215 0 02 Jun 2019
Function approximation by deep networks H. Mhaskar T. Poggio 19 23 0 30 May 2019
Equivalent and Approximate Transformations of Deep Neural Networks Abhinav Kumar Thiago Serra Srikumar Ramalingam 10 21 0 27 May 2019
Graph Neural Networks Exponentially Lose Expressive Power for Node Classification Kenta Oono Taiji Suzuki GNN 30 27 0 27 May 2019
A Polynomial-Based Approach for Architectural Design and Learning with Deep Neural Networks Joseph Daws Clayton Webster 11 9 0 24 May 2019
Tucker Decomposition Network: Expressive Power and Comparison Ye Liu Junjun Pan Michael K. Ng 6 1 0 23 May 2019
Approximation spaces of deep neural networks Rémi Gribonval Gitta Kutyniok M. Nielsen Felix Voigtländer 10 124 0 03 May 2019
Stability and Generalization of Graph Convolutional Neural Networks Saurabh Verma Zhi-Li Zhang GNN MLT 21 153 0 03 May 2019
Depth Separations in Neural Networks: What is Actually Being Separated? Itay Safran Ronen Eldan Ohad Shamir MDE 11 35 0 15 Apr 2019
The Impact of Neural Network Overparameterization on Gradient Confusion and Stochastic Gradient Descent Karthik A. Sankararaman Soham De Zheng Xu W. R. Huang Tom Goldstein ODL 11 103 0 15 Apr 2019
A Selective Overview of Deep Learning Jianqing Fan Cong Ma Yiqiao Zhong BDL VLM 25 136 0 10 Apr 2019
Deep Fundamental Factor Models M. Dixon Nicholas G. Polson 26 9 0 18 Mar 2019
Is Deeper Better only when Shallow is Good? Eran Malach Shai Shalev-Shwartz 20 45 0 08 Mar 2019