Approximation and Learning with Deep Convolutional Models: a Kernel Perspective

19 February 2021

Papers citing "Approximation and Learning with Deep Convolutional Models: a Kernel Perspective"

12 / 12 papers shown

Title
U-Nets as Belief Propagation: Efficient Classification, Denoising, and Diffusion in Generative Hierarchical Models Song Mei 3DV AI4CE DiffM 41 11 0 29 Apr 2024
Theoretical Analysis of Inductive Biases in Deep Convolutional Networks Zihao Wang Lei Wu 23 19 0 15 May 2023
The SSL Interplay: Augmentations, Inductive Bias, and Generalization Vivien A. Cabannes B. Kiani Randall Balestriero Yann LeCun A. Bietti SSL 11 31 0 06 Feb 2023
Strong inductive biases provably prevent harmless interpolation Michael Aerni Marco Milanta Konstantin Donhauser Fanny Yang 30 9 0 18 Jan 2023
On the Shift Invariance of Max Pooling Feature Maps in Convolutional Neural Networks Hubert Leterme K. Polisano V. Perrier Alahari Karteek FAtt 38 2 0 19 Sep 2022
On the Spectral Bias of Convolutional Neural Tangent and Gaussian Process Kernels Amnon Geifman Meirav Galun David Jacobs Ronen Basri 19 13 0 17 Mar 2022
Tight Convergence Rate Bounds for Optimization Under Power Law Spectral Conditions Maksim Velikanov Dmitry Yarotsky 4 6 0 02 Feb 2022
Learning with convolution and pooling operations in kernel methods Theodor Misiakiewicz Song Mei MLT 15 29 0 16 Nov 2021
A Johnson--Lindenstrauss Framework for Randomly Initialized CNNs Ido Nachum Jan Hkazla Michael C. Gastpar Anatoly Khina 28 0 0 03 Nov 2021
Dataset Distillation with Infinitely Wide Convolutional Networks Timothy Nguyen Roman Novak Lechao Xiao Jaehoon Lee DD 27 229 0 27 Jul 2021
Learning with invariances in random features and kernel models Song Mei Theodor Misiakiewicz Andrea Montanari OOD 46 89 0 25 Feb 2021
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks Lechao Xiao Yasaman Bahri Jascha Narain Sohl-Dickstein S. Schoenholz Jeffrey Pennington 220 348 0 14 Jun 2018