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Why and When Can Deep -- but Not Shallow -- Networks Avoid the Curse of Dimensionality: a Review

2 November 2016

Papers citing "Why and When Can Deep -- but Not Shallow -- Networks Avoid the Curse of Dimensionality: a Review"

50 / 238 papers shown

Title
Uncertainty Quantification for Sparse Deep Learning Yuexi Wang Veronika Rockova BDL UQCV 99 31 0 26 Feb 2020
Approximation Bounds for Random Neural Networks and Reservoir Systems Lukas Gonon Lyudmila Grigoryeva Juan-Pablo Ortega 94 67 0 14 Feb 2020
Translating Diffusion, Wavelets, and Regularisation into Residual Networks Tobias Alt Joachim Weickert Pascal Peter DiffM 52 8 0 07 Feb 2020
Deep Network Approximation for Smooth Functions Jianfeng Lu Zuowei Shen Haizhao Yang Shijun Zhang 147 248 0 09 Jan 2020
Analysis of Deep Neural Networks with Quasi-optimal polynomial approximation rates Joseph Daws Clayton Webster 74 8 0 04 Dec 2019
Stationary Points of Shallow Neural Networks with Quadratic Activation Function D. Gamarnik Eren C. Kizildag Ilias Zadik 43 14 0 03 Dec 2019
ChebNet: Efficient and Stable Constructions of Deep Neural Networks with Rectified Power Units via Chebyshev Approximations Shanshan Tang Bo Li Haijun Yu 31 11 0 07 Nov 2019
Stochastic Feedforward Neural Networks: Universal Approximation Thomas Merkh Guido Montúfar 43 8 0 22 Oct 2019
Neural network integral representations with the ReLU activation function Armenak Petrosyan Anton Dereventsov Clayton Webster 54 22 0 07 Oct 2019
Deep Model Reference Adaptive Control Girish Joshi Girish Chowdhary BDL AI4CE 64 59 0 18 Sep 2019
Optimal Function Approximation with Relu Neural Networks Bo Liu Yi Liang 57 33 0 09 Sep 2019
PowerNet: Efficient Representations of Polynomials and Smooth Functions by Deep Neural Networks with Rectified Power Units Bo Li Shanshan Tang Haijun Yu 41 20 0 09 Sep 2019
Dimension independent bounds for general shallow networks H. Mhaskar 106 22 0 26 Aug 2019
Theoretical Issues in Deep Networks: Approximation, Optimization and Generalization T. Poggio Andrzej Banburski Q. Liao ODL 126 165 0 25 Aug 2019
Deep ReLU network approximation of functions on a manifold Johannes Schmidt-Hieber 108 95 0 02 Aug 2019
DeepXDE: A deep learning library for solving differential equations Lu Lu Xuhui Meng Zhiping Mao George Karniadakis PINN AI4CE 101 1,554 0 10 Jul 2019
Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting Shiyang Li Xiaoyong Jin Yao Xuan Xiyou Zhou Wenhu Chen Yu Wang Xifeng Yan AI4TS 134 1,449 0 29 Jun 2019
Error bounds for deep ReLU networks using the Kolmogorov--Arnold superposition theorem Hadrien Montanelli Haizhao Yang 70 92 0 27 Jun 2019
The phase diagram of approximation rates for deep neural networks Dmitry Yarotsky Anton Zhevnerchuk 88 122 0 22 Jun 2019
Meta-learning Pseudo-differential Operators with Deep Neural Networks Jordi Feliu-Fabà Yuwei Fan Lexing Ying 64 40 0 16 Jun 2019
Bayesian Nonparametric Federated Learning of Neural Networks Mikhail Yurochkin Mayank Agarwal S. Ghosh Kristjan Greenewald T. Hoang Y. Khazaeni FedML 145 732 0 28 May 2019
Expression of Fractals Through Neural Network Functions Nadav Dym B. Sober Ingrid Daubechies 76 15 0 27 May 2019
Doctor of Crosswise: Reducing Over-parametrization in Neural Networks J. Curtò I. Zarza Kris Kitani Irwin King Michael R. Lyu MedIm 16 0 0 24 May 2019
Nonlinear Approximation and (Deep) ReLU Networks Ingrid Daubechies Ronald A. DeVore S. Foucart Boris Hanin G. Petrova 110 146 0 05 May 2019
A neural network-based framework for financial model calibration Shuaiqiang Liu Anastasia Borovykh L. Grzelak C. Oosterlee 82 103 0 23 Apr 2019
Depth Separations in Neural Networks: What is Actually Being Separated? Itay Safran Ronen Eldan Ohad Shamir MDE 65 36 0 15 Apr 2019
A Selective Overview of Deep Learning Jianqing Fan Cong Ma Yiqiao Zhong BDL VLM 206 135 0 10 Apr 2019
Information Bottleneck and its Applications in Deep Learning Hassan Hafez-Kolahi S. Kasaei 66 19 0 07 Apr 2019
On functions computed on trees Roozbeh Farhoodi Khashayar Filom I. Jones Konrad Paul Kording PINN 21 5 0 04 Apr 2019
A Theoretical Analysis of Deep Neural Networks and Parametric PDEs Gitta Kutyniok P. Petersen Mones Raslan R. Schneider 102 198 0 31 Mar 2019
Sparse Learning for Variable Selection with Structures and Nonlinearities Magda Gregorova 65 1 0 26 Mar 2019
Data Augmentation for Bayesian Deep Learning YueXing Wang Nicholas G. Polson Vadim Sokolov UQCV BDL 88 5 0 22 Mar 2019
Adaptive Genomic Evolution of Neural Network Topologies (AGENT) for State-to-Action Mapping in Autonomous Agents A. Behjat Sharat Chidambaran Souma Chowdhury 48 14 0 17 Mar 2019
Rectified deep neural networks overcome the curse of dimensionality for nonsmooth value functions in zero-sum games of nonlinear stiff systems C. Reisinger Yufei Zhang 55 70 0 15 Mar 2019
Is Deeper Better only when Shallow is Good? Eran Malach Shai Shalev-Shwartz 79 45 0 08 Mar 2019
Limiting Network Size within Finite Bounds for Optimization Linu Pinto Sasi Gopalan 41 2 0 07 Mar 2019
A lattice-based approach to the expressivity of deep ReLU neural networks V. Corlay J. Boutros P. Ciblat L. Brunel 58 4 0 28 Feb 2019
Complexity of Linear Regions in Deep Networks Boris Hanin David Rolnick 78 234 0 25 Jan 2019
When Can Neural Networks Learn Connected Decision Regions? Trung Le Dinh Q. Phung MLT 57 1 0 25 Jan 2019
Training Neural Networks as Learning Data-adaptive Kernels: Provable Representation and Approximation Benefits Xialiang Dou Tengyuan Liang MLT 83 42 0 21 Jan 2019
Realizing data features by deep nets Zheng-Chu Guo Lei Shi Shao-Bo Lin 44 20 0 01 Jan 2019
Non-attracting Regions of Local Minima in Deep and Wide Neural Networks Henning Petzka C. Sminchisescu 88 10 0 16 Dec 2018
PDE-Net 2.0: Learning PDEs from Data with A Numeric-Symbolic Hybrid Deep Network Zichao Long Yiping Lu Bin Dong AI4CE 89 553 0 30 Nov 2018
Deep learning for pedestrians: backpropagation in CNNs L. Boué 3DV PINN 39 4 0 29 Nov 2018
Data Driven Governing Equations Approximation Using Deep Neural Networks Tong Qin Kailiang Wu D. Xiu PINN 95 275 0 13 Nov 2018
Biologically-plausible learning algorithms can scale to large datasets Y. Chitour Honglin Chen Zhenyu Liao T. Poggio 97 76 0 08 Nov 2018
Depth with Nonlinearity Creates No Bad Local Minima in ResNets Kenji Kawaguchi Yoshua Bengio ODL 104 64 0 21 Oct 2018
Deep Reinforcement Learning Yuxi Li VLM OffRL 194 144 0 15 Oct 2018
Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes Roman Novak Lechao Xiao Jaehoon Lee Yasaman Bahri Greg Yang Jiri Hron Daniel A. Abolafia Jeffrey Pennington Jascha Narain Sohl-Dickstein UQCV BDL 125 310 0 11 Oct 2018
Towards WARSHIP: Combining Components of Brain-Inspired Computing of RSH for Image Super Resolution Wendi Xu Ming Zhang 39 1 0 03 Oct 2018