Can stable and accurate neural networks be computed? -- On the barriers of deep learning and Smale's 18th problem

20 January 2021

Papers citing "Can stable and accurate neural networks be computed? -- On the barriers of deep learning and Smale's 18th problem"

16 / 16 papers shown

Title
On the uncertainty principle of neural networks Jun-Jie Zhang Dong-xiao Zhang Jian-Nan Chen L. Pang Deyu Meng 57 2 0 17 Jan 2025
Mathematical Algorithm Design for Deep Learning under Societal and Judicial Constraints: The Algorithmic Transparency Requirement Holger Boche Adalbert Fono Gitta Kutyniok FaML 28 4 0 18 Jan 2024
When can you trust feature selection? -- I: A condition-based analysis of LASSO and generalised hardness of approximation Alexander Bastounis Felipe Cucker Anders C. Hansen 20 2 0 18 Dec 2023
Training Neural Networks Using Reproducing Kernel Space Interpolation and Model Reduction Eric A. Werneburg 16 0 0 31 Aug 2023
Computability of Optimizers Yunseok Lee Holger Boche Gitta Kutyniok 27 16 0 15 Jan 2023
Restarts subject to approximate sharpness: A parameter-free and optimal scheme for first-order methods Ben Adcock Matthew J. Colbrook Maksym Neyra-Nesterenko 27 2 0 05 Jan 2023
To be or not to be stable, that is the question: understanding neural networks for inverse problems David Evangelista J. Nagy E. Morotti E. L. Piccolomini 23 4 0 24 Nov 2022
Limitations of neural network training due to numerical instability of backpropagation Clemens Karner V. Kazeev P. Petersen 32 3 0 03 Oct 2022
Physics-informed compressed sensing for PC-MRI: an inverse Navier-Stokes problem Alexandros Kontogiannis M. Juniper 24 10 0 04 Jul 2022
Localized adversarial artifacts for compressed sensing MRI Rima Alaifari Giovanni S. Alberti Tandri Gauksson AAML 14 4 0 10 Jun 2022
Physical Activation Functions (PAFs): An Approach for More Efficient Induction of Physics into Physics-Informed Neural Networks (PINNs) J. Abbasi Paal Ostebo Andersen PINN AI4CE 25 14 0 29 May 2022
NESTANets: Stable, accurate and efficient neural networks for analysis-sparse inverse problems Maksym Neyra-Nesterenko Ben Adcock 25 9 0 02 Mar 2022
Limitations of Deep Learning for Inverse Problems on Digital Hardware Holger Boche Adalbert Fono Gitta Kutyniok 24 25 0 28 Feb 2022
Solving Inverse Problems With Deep Neural Networks -- Robustness Included? Martin Genzel Jan Macdonald M. März AAML OOD 21 101 0 09 Nov 2020
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition Hamed Karimi J. Nutini Mark W. Schmidt 133 1,198 0 16 Aug 2016
Convolutional Neural Networks Analyzed via Convolutional Sparse Coding V. Papyan Yaniv Romano Michael Elad 56 284 0 27 Jul 2016