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Error bounds for approximations with deep ReLU networks

3 October 2016

Papers citing "Error bounds for approximations with deep ReLU networks"

27 / 27 papers shown

Title
Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Duc Anh Nguyen Ernesto Araya Adalbert Fono Gitta Kutyniok 157 0 0 23 May 2025
Approximation theory for 1-Lipschitz ResNets Davide Murari Takashi Furuya Carola-Bibiane Schönlieb 76 0 0 17 May 2025
An extension of linear self-attention for in-context learning Katsuyuki Hagiwara 91 0 0 31 Mar 2025
Higher Order Approximation Rates for ReLU CNNs in Korobov Spaces Yuwen Li Guozhi Zhang 184 1 0 20 Jan 2025
Deep Partially Linear Transformation Model for Right-Censored Survival Data Junkai Yin Yue Zhang Zhangsheng Yu 296 0 0 10 Dec 2024
On the expressiveness and spectral bias of KANs Yixuan Wang Jonathan W. Siegel Ziming Liu Thomas Y. Hou 107 13 0 02 Oct 2024
Cauchy activation function and XNet Xin Li Zhihong Xia Hongkun Zhang 182 6 0 28 Sep 2024
Theoretical Insights into CycleGAN: Analyzing Approximation and Estimation Errors in Unpaired Data Generation Luwei Sun Dongrui Shen Han Feng 106 3 0 16 Jul 2024
Equidistribution-based training of Free Knot Splines and ReLU Neural Networks Simone Appella S. Arridge Chris Budd Teo Deveney L. Kreusser 73 0 0 02 Jul 2024
Neural Networks Trained by Weight Permutation are Universal Approximators Yongqiang Cai Gaohang Chen Zhonghua Qiao 191 1 0 01 Jul 2024
Posterior and variational inference for deep neural networks with heavy-tailed weights Ismael Castillo Paul Egels BDL 109 4 0 05 Jun 2024
KAN: Kolmogorov-Arnold Networks Ziming Liu Yixuan Wang Sachin Vaidya Fabian Ruehle James Halverson Marin Soljacic Thomas Y. Hou Max Tegmark 294 577 0 30 Apr 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 493 5 0 05 Jan 2024
Neural Snowflakes: Universal Latent Graph Inference via Trainable Latent Geometries Haitz Sáez de Ocáriz Borde Anastasis Kratsios 178 5 0 23 Oct 2023
Statistical Guarantees of Group-Invariant GANs Ziyu Chen Markos A. Katsoulakis Luc Rey-Bellet Wei-wei Zhu 215 2 0 22 May 2023
When Deep Learning Meets Polyhedral Theory: A Survey Joey Huchette Gonzalo Muñoz Thiago Serra Calvin Tsay AI4CE 154 37 0 29 Apr 2023
Hedonic Prices and Quality Adjusted Price Indices Powered by AI Patrick Bajari Zhihao Cen Victor Chernozhukov Manoj Manukonda Suhas Vijaykumar ... Ramon Huerta Junbo Li Ling Leng George Monokroussos Shan Wan 90 13 0 28 Apr 2023
Performative Prediction with Neural Networks Mehrnaz Mofakhami Ioannis Mitliagkas Gauthier Gidel 95 19 0 14 Apr 2023
Designing Universal Causal Deep Learning Models: The Case of Infinite-Dimensional Dynamical Systems from Stochastic Analysis Luca Galimberti Anastasis Kratsios Giulia Livieri OOD 102 14 0 24 Oct 2022
Why Deep Neural Networks for Function Approximation? Shiyu Liang R. Srikant 140 385 0 13 Oct 2016
Deep vs. shallow networks : An approximation theory perspective H. Mhaskar T. Poggio 165 341 0 10 Aug 2016
On the Expressive Power of Deep Neural Networks M. Raghu Ben Poole Jon M. Kleinberg Surya Ganguli Jascha Narain Sohl-Dickstein 84 791 0 16 Jun 2016
Benefits of depth in neural networks Matus Telgarsky 380 609 0 14 Feb 2016
Representation Benefits of Deep Feedforward Networks Matus Telgarsky 88 242 0 27 Sep 2015
On the Expressive Power of Deep Learning: A Tensor Analysis Nadav Cohen Or Sharir Amnon Shashua 81 472 0 16 Sep 2015
Deep Learning in Neural Networks: An Overview Jürgen Schmidhuber HAI 250 16,405 0 30 Apr 2014
On the Number of Linear Regions of Deep Neural Networks Guido Montúfar Razvan Pascanu Kyunghyun Cho Yoshua Bengio 96 1,256 0 08 Feb 2014