Deep ReLU network approximation of functions on a manifold

2 August 2019

Papers citing "Deep ReLU network approximation of functions on a manifold"

27 / 27 papers shown

Title
Transformers for Learning on Noisy and Task-Level Manifolds: Approximation and Generalization Insights Zhaiming Shen Alex Havrilla Rongjie Lai A. Cloninger Wenjing Liao 39 0 0 06 May 2025
Deep Transfer Learning: Model Framework and Error Analysis Yuling Jiao Huazhen Lin Yuchen Luo Jerry Zhijian Yang 49 1 0 12 Oct 2024
How DNNs break the Curse of Dimensionality: Compositionality and Symmetry Learning Arthur Jacot Seok Hoan Choi Yuxiao Wen AI4CE 94 2 0 08 Jul 2024
Posterior and variational inference for deep neural networks with heavy-tailed weights Ismael Castillo Paul Egels BDL 60 4 0 05 Jun 2024
Approximation by non-symmetric networks for cross-domain learning H. Mhaskar 43 1 0 06 May 2023
Deep Nonparametric Estimation of Intrinsic Data Structures by Chart Autoencoders: Generalization Error and Robustness Hao Liu Alex Havrilla Rongjie Lai Wenjing Liao 39 6 0 17 Mar 2023
Sparse-penalized deep neural networks estimator under weak dependence William Kengne Modou Wade 29 6 0 02 Mar 2023
Deep learning for $ψ$ -weakly dependent processes William Kengne Wade Modou AI4CE 22 1 0 01 Feb 2023
Local transfer learning from one data space to another H. Mhaskar Ryan O'Dowd 27 0 0 01 Feb 2023
Deep Operator Learning Lessens the Curse of Dimensionality for PDEs Ke Chen Chunmei Wang Haizhao Yang AI4CE 24 13 0 28 Jan 2023
Exploring the Approximation Capabilities of Multiplicative Neural Networks for Smooth Functions Ido Ben-Shaul Tomer Galanti S. Dekel 33 3 0 11 Jan 2023
On the Geometry of Reinforcement Learning in Continuous State and Action Spaces Saket Tiwari Omer Gottesman George Konidaris 29 0 0 29 Dec 2022
Effects of Data Geometry in Early Deep Learning Saket Tiwari George Konidaris 82 7 0 29 Dec 2022
A Review of Off-Policy Evaluation in Reinforcement Learning Masatoshi Uehara C. Shi Nathan Kallus OffRL 38 69 0 13 Dec 2022
Fitting an immersed submanifold to data via Sussmann's orbit theorem Joshua Hanson Maxim Raginsky 20 4 0 03 Apr 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
Side Effects of Learning from Low-dimensional Data Embedded in a Euclidean Space Juncai He R. Tsai Rachel A. Ward 46 8 0 01 Mar 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
Drift estimation for a multi-dimensional diffusion process using deep neural networks Akihiro Oga Yuta Koike DiffM 21 5 0 26 Dec 2021
A manifold learning approach for gesture recognition from micro-Doppler radar measurements Eric Mason H. Mhaskar A. Guo 28 2 0 04 Oct 2021
Deep Networks Provably Classify Data on Curves Tingran Wang Sam Buchanan D. Gilboa John N. Wright 28 9 0 29 Jul 2021
Robust Nonparametric Regression with Deep Neural Networks Guohao Shen Yuling Jiao Yuanyuan Lin Jian Huang OOD 33 13 0 21 Jul 2021
Estimation of a regression function on a manifold by fully connected deep neural networks Michael Kohler S. Langer U. Reif 22 4 0 20 Jul 2021
Deep Nonparametric Regression on Approximate Manifolds: Non-Asymptotic Error Bounds with Polynomial Prefactors Yuling Jiao Guohao Shen Yuanyuan Lin Jian Huang 36 50 0 14 Apr 2021
The Kolmogorov-Arnold representation theorem revisited Johannes Schmidt-Hieber 30 126 0 31 Jul 2020
Growing axons: greedy learning of neural networks with application to function approximation Daria Fokina Ivan Oseledets 24 18 0 28 Oct 2019
Benefits of depth in neural networks Matus Telgarsky 179 604 0 14 Feb 2016