Optimal approximation of continuous functions by very deep ReLU networks

10 February 2018

Papers citing "Optimal approximation of continuous functions by very deep ReLU networks"

50 / 188 papers shown

Title
Approximation theory for 1-Lipschitz ResNets Davide Murari Takashi Furuya Carola-Bibiane Schönlieb 4 0 0 17 May 2025
Contextures: Representations from Contexts Runtian Zhai Kai Yang Che-Ping Tsai Burak Varici Zico Kolter Pradeep Ravikumar 143 0 0 02 May 2025
Kolmogorov-Arnold Networks: Approximation and Learning Guarantees for Functions and their Derivatives Anastasis Kratsios Takashi Furuya 29 0 0 21 Apr 2025
Transformers Can Overcome the Curse of Dimensionality: A Theoretical Study from an Approximation Perspective Yuling Jiao Yanming Lai Yang Wang Bokai Yan 39 0 0 18 Apr 2025
Inferring Outcome Means of Exponential Family Distributions Estimated by Deep Neural Networks Xuran Meng Yi Li BDL 32 0 0 12 Apr 2025
Statistically guided deep learning Michael Kohler A. Krzyżak ODL BDL 79 0 0 11 Apr 2025
Minimum width for universal approximation using squashable activation functions Jonghyun Shin Namjun Kim Geonho Hwang Sejun Park 38 0 0 10 Apr 2025
Nonlocal techniques for the analysis of deep ReLU neural network approximations Cornelia Schneider Mario Ullrich Jan Vybiral 18 0 0 07 Apr 2025
Approximation properties of neural ODEs Arturo De Marinis Davide Murari E. Celledoni Nicola Guglielmi B. Owren Francesco Tudisco 44 1 0 19 Mar 2025
Bi-Lipschitz Ansatz for Anti-Symmetric Functions Nadav Dym Jianfeng Lu Matan Mizrachi 40 1 0 06 Mar 2025
Fourier Multi-Component and Multi-Layer Neural Networks: Unlocking High-Frequency Potential Shijun Zhang Hongkai Zhao Yimin Zhong Haomin Zhou 57 0 0 26 Feb 2025
Curse of Dimensionality in Neural Network Optimization Sanghoon Na Haizhao Yang 56 0 0 07 Feb 2025
Deep Kalman Filters Can Filter Blanka Hovart Anastasis Kratsios Yannick Limmer Xuwei Yang 53 1 0 31 Dec 2024
Theoretical Analysis of Learned Database Operations under Distribution Shift through Distribution Learnability Sepanta Zeighami Cyrus Shahahbi 42 1 0 09 Nov 2024
Identification of Mean-Field Dynamics using Transformers Shiba Biswal Karthik Elamvazhuthi Rishi Sonthalia AI4CE 27 1 0 06 Oct 2024
On the expressiveness and spectral bias of KANs Yixuan Wang Jonathan W. Siegel Ziming Liu Thomas Y. Hou 37 10 0 02 Oct 2024
On Expressive Power of Looped Transformers: Theoretical Analysis and Enhancement via Timestep Encoding Kevin Xu Issei Sato 39 3 0 02 Oct 2024
Approximation Bounds for Recurrent Neural Networks with Application to Regression Yuling Jiao Yang Wang Bokai Yan 23 1 0 09 Sep 2024
On the optimal approximation of Sobolev and Besov functions using deep ReLU neural networks Yunfei Yang 62 2 0 02 Sep 2024
Deep Limit Model-free Prediction in Regression Kejin Wu D. Politis OOD 21 0 0 18 Aug 2024
Structured and Balanced Multi-component and Multi-layer Neural Networks Shijun Zhang Hongkai Zhao Yimin Zhong Haomin Zhou 37 1 0 30 Jun 2024
1-Lipschitz Neural Distance Fields Guillaume Coiffier Louis Bethune 43 3 0 14 Jun 2024
Consistency of Neural Causal Partial Identification Jiyuan Tan Jose Blanchet Vasilis Syrgkanis CML 32 0 0 24 May 2024
Enhancing Learning with Label Differential Privacy by Vector Approximation Puning Zhao Rongfei Fan Huiwen Wu Qingming Li Xiaogang Xu Zhe Liu 36 1 0 24 May 2024
Unified Universality Theorem for Deep and Shallow Joint-Group-Equivariant Machines Sho Sonoda Yuka Hashimoto Isao Ishikawa Masahiro Ikeda 39 0 0 22 May 2024
Approximation and Gradient Descent Training with Neural Networks G. Welper 38 1 0 19 May 2024
Geometry-Aware Instrumental Variable Regression Heiner Kremer Bernhard Schölkopf 49 0 0 19 May 2024
Scalable Subsampling Inference for Deep Neural Networks Kejin Wu D. Politis 21 1 0 14 May 2024
Approximation Error and Complexity Bounds for ReLU Networks on Low-Regular Function Spaces Owen Davis Gianluca Geraci Mohammad Motamed 46 2 0 10 May 2024
Generative adversarial learning with optimal input dimension and its adaptive generator architecture Zhiyao Tan Ling Zhou Huazhen Lin GAN 42 0 0 06 May 2024
Mixture of Experts Soften the Curse of Dimensionality in Operator Learning Anastasis Kratsios Takashi Furuya Jose Antonio Lara Benitez Matti Lassas Maarten V. de Hoop 50 13 0 13 Apr 2024
An Overview of Diffusion Models: Applications, Guided Generation, Statistical Rates and Optimization Minshuo Chen Song Mei Jianqing Fan Mengdi Wang VLM MedIm DiffM 37 48 0 11 Apr 2024
Learning smooth functions in high dimensions: from sparse polynomials to deep neural networks Ben Adcock Simone Brugiapaglia N. Dexter S. Moraga 42 4 0 04 Apr 2024
On the rates of convergence for learning with convolutional neural networks Yunfei Yang Han Feng Ding-Xuan Zhou 40 3 0 25 Mar 2024
Depth Separation in Norm-Bounded Infinite-Width Neural Networks Suzanna Parkinson Greg Ongie Rebecca Willett Ohad Shamir Nathan Srebro MDE 50 2 0 13 Feb 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
Expressive Power of ReLU and Step Networks under Floating-Point Operations Yeachan Park Geonho Hwang Wonyeol Lee Sejun Park 19 2 0 26 Jan 2024
Do stable neural networks exist for classification problems? -- A new view on stability in AI Z. N. D. Liu A. C. Hansen 30 0 0 15 Jan 2024
Semi-Supervised Deep Sobolev Regression: Estimation and Variable Selection by ReQU Neural Network Zhao Ding Chenguang Duan Yuling Jiao Jerry Zhijian Yang 25 1 0 09 Jan 2024
Nonlinear functional regression by functional deep neural network with kernel embedding Zhongjie Shi Jun Fan Linhao Song Ding-Xuan Zhou Johan A. K. Suykens 65 5 0 05 Jan 2024
Deep Neural Networks and Finite Elements of Any Order on Arbitrary Dimensions Juncai He Jinchao Xu 33 7 0 21 Dec 2023
Neural Network Approximation for Pessimistic Offline Reinforcement Learning Di Wu Yuling Jiao Li Shen Haizhao Yang Xiliang Lu OffRL 29 1 0 19 Dec 2023
Deep State-Space Model for Predicting Cryptocurrency Price Shalini Sharma A. Majumdar Émilie Chouzenoux Victor Elvira 28 0 0 21 Nov 2023
Statistical learning by sparse deep neural networks Felix Abramovich BDL 24 1 0 15 Nov 2023
Approximating Langevin Monte Carlo with ResNet-like Neural Network architectures Charles Miranda Janina Enrica Schutte David Sommer Martin Eigel 32 3 0 06 Nov 2023
Efficient kernel surrogates for neural network-based regression S. Qadeer A. Engel Amanda A. Howard Adam Tsou Max Vargas P. Stinis Tony Chiang 21 5 0 28 Oct 2023
Deep Ridgelet Transform: Voice with Koopman Operator Proves Universality of Formal Deep Networks Sho Sonoda Yuka Hashimoto Isao Ishikawa Masahiro Ikeda 27 3 0 05 Oct 2023
Spectral Neural Networks: Approximation Theory and Optimization Landscape Chenghui Li Rishi Sonthalia Nicolas García Trillos 27 1 0 01 Oct 2023
Minimum width for universal approximation using ReLU networks on compact domain Namjun Kim Chanho Min Sejun Park VLM 29 10 0 19 Sep 2023
Approximation Results for Gradient Descent trained Neural Networks G. Welper 48 0 0 09 Sep 2023