The phase diagram of approximation rates for deep neural networks

22 June 2019

Papers citing "The phase diagram of approximation rates for deep neural networks"

35 / 35 papers shown

Title
Statistically guided deep learning Michael Kohler A. Krzyżak ODL BDL 79 0 0 11 Apr 2025
Approximation properties of neural ODEs Arturo De Marinis Davide Murari E. Celledoni Nicola Guglielmi B. Owren Francesco Tudisco 52 1 0 19 Mar 2025
Deep Kalman Filters Can Filter Blanka Hovart Anastasis Kratsios Yannick Limmer Xuwei Yang 53 1 0 31 Dec 2024
On the expressiveness and spectral bias of KANs Yixuan Wang Jonathan W. Siegel Ziming Liu Thomas Y. Hou 40 10 0 02 Oct 2024
On the optimal approximation of Sobolev and Besov functions using deep ReLU neural networks Yunfei Yang 62 2 0 02 Sep 2024
Approximation Rates and VC-Dimension Bounds for (P)ReLU MLP Mixture of Experts Anastasis Kratsios Haitz Sáez de Ocáriz Borde Takashi Furuya Marc T. Law MoE 41 1 0 05 Feb 2024
Universal Consistency of Wide and Deep ReLU Neural Networks and Minimax Optimal Convergence Rates for Kolmogorov-Donoho Optimal Function Classes Hyunouk Ko Xiaoming Huo 42 1 0 08 Jan 2024
Deep Learning and Computational Physics (Lecture Notes) Deep Ray Orazio Pinti Assad A. Oberai PINN AI4CE 27 7 0 03 Jan 2023
Instance-Dependent Generalization Bounds via Optimal Transport Songyan Hou Parnian Kassraie Anastasis Kratsios Andreas Krause Jonas Rothfuss 22 6 0 02 Nov 2022
Analysis of the rate of convergence of an over-parametrized deep neural network estimate learned by gradient descent Michael Kohler A. Krzyżak 32 10 0 04 Oct 2022
Approximation results for Gradient Descent trained Shallow Neural Networks in $1d$ R. Gentile G. Welper ODL 56 6 0 17 Sep 2022
On the universal consistency of an over-parametrized deep neural network estimate learned by gradient descent Selina Drews Michael Kohler 30 13 0 30 Aug 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 0 0 25 Jul 2022
A general approximation lower bound in $L^p$ norm, with applications to feed-forward neural networks El Mehdi Achour Armand Foucault Sébastien Gerchinovitz Franccois Malgouyres 32 7 0 09 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
A Note on Machine Learning Approach for Computational Imaging Bin Dong 26 0 0 24 Feb 2022
Designing Universal Causal Deep Learning Models: The Geometric (Hyper)Transformer Beatrice Acciaio Anastasis Kratsios G. Pammer OOD 52 20 0 31 Jan 2022
Deep Nonparametric Estimation of Operators between Infinite Dimensional Spaces Hao Liu Haizhao Yang Minshuo Chen T. Zhao Wenjing Liao 32 36 0 01 Jan 2022
Deep Network Approximation in Terms of Intrinsic Parameters Zuowei Shen Haizhao Yang Shijun Zhang 21 9 0 15 Nov 2021
Deep Network Approximation: Achieving Arbitrary Accuracy with Fixed Number of Neurons Zuowei Shen Haizhao Yang Shijun Zhang 56 36 0 06 Jul 2021
Optimal Approximation Rate of ReLU Networks in terms of Width and Depth Zuowei Shen Haizhao Yang Shijun Zhang 103 115 0 28 Feb 2021
Quantitative approximation results for complex-valued neural networks A. Caragea D. Lee J. Maly G. Pfander F. Voigtlaender 13 5 0 25 Feb 2021
Size and Depth Separation in Approximating Benign Functions with Neural Networks Gal Vardi Daniel Reichman T. Pitassi Ohad Shamir 28 7 0 30 Jan 2021
Reproducing Activation Function for Deep Learning Senwei Liang Liyao Lyu Chunmei Wang Haizhao Yang 36 21 0 13 Jan 2021
The universal approximation theorem for complex-valued neural networks F. Voigtlaender 27 62 0 06 Dec 2020
Neural Network Approximation: Three Hidden Layers Are Enough Zuowei Shen Haizhao Yang Shijun Zhang 30 115 0 25 Oct 2020
Analysis of the rate of convergence of fully connected deep neural network regression estimates with smooth activation function S. Langer 19 22 0 12 Oct 2020
Phase Transitions in Rate Distortion Theory and Deep Learning Philipp Grohs Andreas Klotz F. Voigtlaender 14 7 0 03 Aug 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
Two-Layer Neural Networks for Partial Differential Equations: Optimization and Generalization Theory Tao Luo Haizhao Yang 32 74 0 28 Jun 2020
Approximation in shift-invariant spaces with deep ReLU neural networks Yunfei Yang Zhen Li Yang Wang 34 14 0 25 May 2020
On Deep Instrumental Variables Estimate Ruiqi Liu Zuofeng Shang Guang Cheng 26 26 0 30 Apr 2020
Deep Network Approximation for Smooth Functions Jianfeng Lu Zuowei Shen Haizhao Yang Shijun Zhang 67 247 0 09 Jan 2020
Benefits of depth in neural networks Matus Telgarsky 153 603 0 14 Feb 2016