Learning smooth functions in high dimensions: from sparse polynomials to deep neural networks

4 April 2024

Papers citing "Learning smooth functions in high dimensions: from sparse polynomials to deep neural networks"

8 / 8 papers shown

Title
Physics-informed deep learning and compressive collocation for high-dimensional diffusion-reaction equations: practical existence theory and numerics Simone Brugiapaglia N. Dexter Samir Karam Weiqi Wang AI4CE DiffM 24 1 0 03 Jun 2024
A practical existence theorem for reduced order models based on convolutional autoencoders N. R. Franco Simone Brugiapaglia AI4CE 21 4 0 01 Feb 2024
Sampling Complexity of Deep Approximation Spaces Ahmed Abdeljawad Philipp Grohs 19 1 0 20 Dec 2023
Neural and spectral operator surrogates: unified construction and expression rate bounds L. Herrmann Christoph Schwab Jakob Zech 36 9 0 11 Jul 2022
NeuFENet: Neural Finite Element Solutions with Theoretical Bounds for Parametric PDEs Biswajit Khara Aditya Balu Ameya Joshi S. Sarkar C. Hegde A. Krishnamurthy Baskar Ganapathysubramanian 22 19 0 04 Oct 2021
Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks Torsten Hoefler Dan Alistarh Tal Ben-Nun Nikoli Dryden Alexandra Peste MQ 136 679 0 31 Jan 2021
Deep Neural Networks Are Effective At Learning High-Dimensional Hilbert-Valued Functions From Limited Data Ben Adcock Simone Brugiapaglia N. Dexter S. Moraga 21 29 0 11 Dec 2020
Fourier Neural Operator for Parametric Partial Differential Equations Zong-Yi Li Nikola B. Kovachki Kamyar Azizzadenesheli Burigede Liu K. Bhattacharya Andrew M. Stuart Anima Anandkumar AI4CE 203 2,254 0 18 Oct 2020