Deep vs. shallow networks : An approximation theory perspective

10 August 2016

Papers citing "Deep vs. shallow networks : An approximation theory perspective"

50 / 61 papers shown

Title
An Analysis Framework for Understanding Deep Neural Networks Based on Network Dynamics Yuchen Lin Yong Zhang Sihan Feng Hong Zhao 41 0 0 05 Jan 2025
Deep Kalman Filters Can Filter Blanka Hovart Anastasis Kratsios Yannick Limmer Xuwei Yang 55 1 0 31 Dec 2024
Golden Ratio-Based Sufficient Dimension Reduction Wenjing Yang Yuhong Yang 33 0 0 25 Oct 2024
Just How Flexible are Neural Networks in Practice? Ravid Shwartz-Ziv Micah Goldblum Arpit Bansal C. Bayan Bruss Yann LeCun Andrew Gordon Wilson 47 4 0 17 Jun 2024
Federated Learning with Flexible Architectures Jong-Ik Park Carlee Joe-Wong FedML 45 3 0 14 Jun 2024
Understanding Encoder-Decoder Structures in Machine Learning Using Information Measures Jorge F. Silva Victor Faraggi Camilo Ramírez Álvaro F. Egaña Eduardo Pavez 25 1 0 30 May 2024
Spectral complexity of deep neural networks Simmaco Di Lillo Domenico Marinucci Michele Salvi Stefano Vigogna BDL 82 1 0 15 May 2024
Fundamental Limits of Deep Learning-Based Binary Classifiers Trained with Hinge Loss T. Getu Georges Kaddoum M. Bennis 40 1 0 13 Sep 2023
Rates of Approximation by ReLU Shallow Neural Networks Tong Mao Ding-Xuan Zhou 29 19 0 24 Jul 2023
Approximation by non-symmetric networks for cross-domain learning H. Mhaskar 36 1 0 06 May 2023
Deep neural network approximation of composite functions without the curse of dimensionality Adrian Riekert 26 0 0 12 Apr 2023
Error convergence and engineering-guided hyperparameter search of PINNs: towards optimized I-FENN performance Panos Pantidis Habiba Eldababy Christopher Miguel Tagle M. Mobasher 35 20 0 03 Mar 2023
Effects of Data Geometry in Early Deep Learning Saket Tiwari George Konidaris 82 7 0 29 Dec 2022
The BUTTER Zone: An Empirical Study of Training Dynamics in Fully Connected Neural Networks Charles Edison Tripp J. Perr-Sauer L. Hayne M. Lunacek Jamil Gafur AI4CE 28 1 0 25 Jul 2022
On the Number of Regions of Piecewise Linear Neural Networks Alexis Goujon Arian Etemadi M. Unser 44 14 0 17 Jun 2022
Qualitative neural network approximation over R and C: Elementary proofs for analytic and polynomial activation Josiah Park Stephan Wojtowytsch 26 1 0 25 Mar 2022
De Rham compatible Deep Neural Network FEM M. Longo J. Opschoor Nico Disch Christoph Schwab Jakob Zech 33 8 0 14 Jan 2022
Structural Sieves Konrad Menzel 11 1 0 01 Dec 2021
Learning with convolution and pooling operations in kernel methods Theodor Misiakiewicz Song Mei MLT 15 29 0 16 Nov 2021
Theory of Deep Convolutional Neural Networks III: Approximating Radial Functions Tong Mao Zhongjie Shi Ding-Xuan Zhou 16 33 0 02 Jul 2021
Predicting Generalization in Deep Learning via Local Measures of Distortion Abhejit Rajagopal Vamshi C. Madala S. Chandrasekaran P. Larson 13 1 0 13 Dec 2020
LOss-Based SensiTivity rEgulaRization: towards deep sparse neural networks Enzo Tartaglione Andrea Bragagnolo Attilio Fiandrotti Marco Grangetto ODL UQCV 20 34 0 16 Nov 2020
deep21: a Deep Learning Method for 21cm Foreground Removal T. Lucas Makinen Lachlan Lancaster F. Villaescusa-Navarro Peter Melchior S. Ho Laurence Perreault Levasseur D. Spergel 3DPC 30 28 0 29 Oct 2020
Analysis of three dimensional potential problems in non-homogeneous media with physics-informed deep collocation method using material transfer learning and sensitivity analysis Hongwei Guo X. Zhuang Pengwan Chen N. Alajlan Timon Rabczuk 27 58 0 03 Oct 2020
Deep Equals Shallow for ReLU Networks in Kernel Regimes A. Bietti Francis R. Bach 30 86 0 30 Sep 2020
The Kolmogorov-Arnold representation theorem revisited Johannes Schmidt-Hieber 30 126 0 31 Jul 2020
Expressivity of Deep Neural Networks Ingo Gühring Mones Raslan Gitta Kutyniok 16 51 0 09 Jul 2020
Hierarchically Compositional Tasks and Deep Convolutional Networks Arturo Deza Q. Liao Andrzej Banburski T. Poggio BDL OOD 33 2 0 24 Jun 2020
The Curious Case of Convex Neural Networks S. Sivaprasad Ankur Singh Naresh Manwani Vineet Gandhi 46 27 0 09 Jun 2020
How hard is to distinguish graphs with graph neural networks? Andreas Loukas GNN 25 6 0 13 May 2020
Overall error analysis for the training of deep neural networks via stochastic gradient descent with random initialisation Arnulf Jentzen Timo Welti 22 15 0 03 Mar 2020
On Interpretability of Artificial Neural Networks: A Survey Fenglei Fan Jinjun Xiong Mengzhou Li Ge Wang AAML AI4CE 38 300 0 08 Jan 2020
Variational Physics-Informed Neural Networks For Solving Partial Differential Equations E. Kharazmi Z. Zhang George Karniadakis 24 237 0 27 Nov 2019
Stochastic Feedforward Neural Networks: Universal Approximation Thomas Merkh Guido Montúfar 17 8 0 22 Oct 2019
The Local Elasticity of Neural Networks Hangfeng He Weijie J. Su 40 44 0 15 Oct 2019
Optimal Function Approximation with Relu Neural Networks Bo Liu Yi Liang 25 33 0 09 Sep 2019
Theoretical Issues in Deep Networks: Approximation, Optimization and Generalization T. Poggio Andrzej Banburski Q. Liao ODL 34 161 0 25 Aug 2019
Super-resolution meets machine learning: approximation of measures H. Mhaskar SupR 11 4 0 10 Jul 2019
Nonlinear Approximation and (Deep) ReLU Networks Ingrid Daubechies Ronald A. DeVore S. Foucart Boris Hanin G. Petrova 22 138 0 05 May 2019
Stability and Generalization of Graph Convolutional Neural Networks Saurabh Verma Zhi-Li Zhang GNN MLT 30 153 0 03 May 2019
Deep Neural Networks for Rotation-Invariance Approximation and Learning C. Chui Shao-Bo Lin Ding-Xuan Zhou 32 34 0 03 Apr 2019
Is Deeper Better only when Shallow is Good? Eran Malach Shai Shalev-Shwartz 28 45 0 08 Mar 2019
Shallow Neural Networks for Fluid Flow Reconstruction with Limited Sensors N. Benjamin Erichson L. Mathelin Z. Yao Steven L. Brunton Michael W. Mahoney J. Nathan Kutz AI4CE 27 34 0 20 Feb 2019
Deep Neural Network Approximation Theory Dennis Elbrächter Dmytro Perekrestenko Philipp Grohs Helmut Bölcskei 16 207 0 08 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
Implicit Regularization of Stochastic Gradient Descent in Natural Language Processing: Observations and Implications Deren Lei Zichen Sun Yijun Xiao William Yang Wang 35 14 0 01 Nov 2018
Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning Charles H. Martin Michael W. Mahoney AI4CE 47 192 0 02 Oct 2018
Deep Neural Networks for Estimation and Inference M. Farrell Tengyuan Liang S. Misra BDL 27 254 0 26 Sep 2018
A proof that deep artificial neural networks overcome the curse of dimensionality in the numerical approximation of Kolmogorov partial differential equations with constant diffusion and nonlinear drift coefficients Arnulf Jentzen Diyora Salimova Timo Welti AI4CE 24 116 0 19 Sep 2018
A proof that artificial neural networks overcome the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equations Philipp Grohs F. Hornung Arnulf Jentzen Philippe von Wurstemberger 16 167 0 07 Sep 2018