A representer theorem for deep neural networks

26 February 2018

Papers citing "A representer theorem for deep neural networks"

22 / 22 papers shown

Title
Kolmogorov-Arnold Networks in Low-Data Regimes: A Comparative Study with Multilayer Perceptrons Farhad Pourkamali-Anaraki 35 5 0 16 Sep 2024
Controlled Learning of Pointwise Nonlinearities in Neural-Network-Like Architectures Michael Unser Alexis Goujon Stanislas Ducotterd 24 2 0 23 Aug 2024
Parseval Convolution Operators and Neural Networks Michael Unser Stanislas Ducotterd 23 3 0 19 Aug 2024
On the Geometry of Deep Learning Randall Balestriero Ahmed Imtiaz Humayun Richard G. Baraniuk AI4CE 39 1 0 09 Aug 2024
Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks Fanghui Liu L. Dadi V. Cevher 72 2 0 29 Apr 2024
When Deep Learning Meets Polyhedral Theory: A Survey Joey Huchette Gonzalo Muñoz Thiago Serra Calvin Tsay AI4CE 91 32 0 29 Apr 2023
A Neural-Network-Based Convex Regularizer for Inverse Problems Alexis Goujon Sebastian Neumayer Pakshal Bohra Stanislas Ducotterd M. Unser 11 26 0 22 Nov 2022
Duality for Neural Networks through Reproducing Kernel Banach Spaces L. Spek T. J. Heeringa Felix L. Schwenninger C. Brune 11 13 0 09 Nov 2022
Real Image Super-Resolution using GAN through modeling of LR and HR process Rao Muhammad Umer C. Micheloni 32 1 0 19 Oct 2022
Sparse Deep Neural Network for Nonlinear Partial Differential Equations Yuesheng Xu T. Zeng 30 5 0 27 Jul 2022
Approximation of Lipschitz Functions using Deep Spline Neural Networks Sebastian Neumayer Alexis Goujon Pakshal Bohra M. Unser 21 15 0 13 Apr 2022
Fully-Connected Network on Noncompact Symmetric Space and Ridgelet Transform based on Helgason-Fourier Analysis Sho Sonoda Isao Ishikawa Masahiro Ikeda 19 15 0 03 Mar 2022
A Data-Augmentation Is Worth A Thousand Samples: Exact Quantification From Analytical Augmented Sample Moments Randall Balestriero Ishan Misra Yann LeCun 27 20 0 16 Feb 2022
Measuring Complexity of Learning Schemes Using Hessian-Schatten Total Variation Shayan Aziznejad Joaquim Campos M. Unser 11 9 0 12 Dec 2021
Training Neural Networks for Solving 1-D Optimal Piecewise Linear Approximation Hangcheng Dong Jing-Xiao Liao Yan Wang Yixin Chen Bingguo Liu Dong Ye Guodong Liu 54 0 0 14 Oct 2021
What Kinds of Functions do Deep Neural Networks Learn? Insights from Variational Spline Theory Rahul Parhi Robert D. Nowak MLT 27 70 0 07 May 2021
Fast Jacobian-Vector Product for Deep Networks Randall Balestriero Richard Baraniuk 23 4 0 01 Apr 2021
Deep Neural Networks Are Effective At Learning High-Dimensional Hilbert-Valued Functions From Limited Data Ben Adcock Simone Brugiapaglia N. Dexter S. Moraga 34 29 0 11 Dec 2020
Native Banach spaces for splines and variational inverse problems M. Unser Julien Fageot 14 17 0 24 Apr 2019
A unifying representer theorem for inverse problems and machine learning M. Unser 13 69 0 02 Mar 2019
On the Spectral Bias of Neural Networks Nasim Rahaman A. Baratin Devansh Arpit Felix Dräxler Min-Bin Lin Fred Hamprecht Yoshua Bengio Aaron Courville 28 1,386 0 22 Jun 2018
Mad Max: Affine Spline Insights into Deep Learning Randall Balestriero Richard Baraniuk AI4CE 28 78 0 17 May 2018