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"

9 / 9 papers shown

Title
On the uncertainty principle of neural networks Jun-Jie Zhang Dong-xiao Zhang Jian-Nan Chen L. Pang Deyu Meng 47 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 15 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 8 2 0 18 Dec 2023
Training Neural Networks Using Reproducing Kernel Space Interpolation and Model Reduction Eric A. Werneburg 11 0 0 31 Aug 2023
Computability of Optimizers Yunseok Lee Holger Boche Gitta Kutyniok 14 16 0 15 Jan 2023
Physics-informed compressed sensing for PC-MRI: an inverse Navier-Stokes problem Alexandros Kontogiannis M. Juniper 14 10 0 04 Jul 2022
Localized adversarial artifacts for compressed sensing MRI Rima Alaifari Giovanni S. Alberti Tandri Gauksson AAML 6 4 0 10 Jun 2022
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition Hamed Karimi J. Nutini Mark W. Schmidt 114 1,190 0 16 Aug 2016
Convolutional Neural Networks Analyzed via Convolutional Sparse Coding V. Papyan Yaniv Romano Michael Elad 48 283 0 27 Jul 2016