How Deep Networks Learn Sparse and Hierarchical Data: the Sparse Random Hierarchy Model

16 April 2024

Papers citing "How Deep Networks Learn Sparse and Hierarchical Data: the Sparse Random Hierarchy Model"

7 / 7 papers shown

Title
U-Nets as Belief Propagation: Efficient Classification, Denoising, and Diffusion in Generative Hierarchical Models Song Mei 3DV AI4CE DiffM 36 11 0 29 Apr 2024
How deep convolutional neural networks lose spatial information with training Umberto M. Tomasini Leonardo Petrini Francesco Cagnetta M. Wyart 35 9 0 04 Oct 2022
A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups Marc Finzi Max Welling A. Wilson 71 185 0 19 Apr 2021
Learning with invariances in random features and kernel models Song Mei Theodor Misiakiewicz Andrea Montanari OOD 44 89 0 25 Feb 2021
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials Simon L. Batzner Albert Musaelian Lixin Sun Mario Geiger J. Mailoa M. Kornbluth N. Molinari Tess E. Smidt Boris Kozinsky 198 1,232 0 08 Jan 2021
On Translation Invariance in CNNs: Convolutional Layers can Exploit Absolute Spatial Location O. Kayhan J. C. V. Gemert 209 232 0 16 Mar 2020
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 226 4,460 0 23 Jan 2020