Perspectives on the State and Future of Deep Learning - 2023

Perspectives on the State and Future of Deep Learning - 2023

7 December 2023

Richard Baraniuk

Zachary Chase Lipton

Melanie Mitchell

Preetum Nakkiran

Andrew Gordon Wilson

Papers citing "Perspectives on the State and Future of Deep Learning - 2023"

5 / 5 papers shown

Title
Spin glass model of in-context learning Yuhao Li Ruoran Bai Haiping Huang LRM 42 0 0 05 Aug 2024
Calibrating Large Language Models Using Their Generations Only Dennis Ulmer Martin Gubri Hwaran Lee Sangdoo Yun Seong Joon Oh UQLM 424 18 1 09 Mar 2024
Better, Not Just More: Data-Centric Machine Learning for Earth Observation R. Roscher M. Rußwurm Caroline Gevaert Michael C. Kampffmeyer J. A. dos Santos ... Ronny Hansch Stine Hansen Keiller Nogueira Jonathan Prexl D. Tuia 32 10 0 08 Dec 2023
Stochastic Training is Not Necessary for Generalization Jonas Geiping Micah Goldblum Phillip E. Pope Michael Moeller Tom Goldstein 89 72 0 29 Sep 2021
A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups Marc Finzi Max Welling A. Wilson 76 185 0 19 Apr 2021