Truth or Backpropaganda? An Empirical Investigation of Deep Learning
Theory

Truth or Backpropaganda? An Empirical Investigation of Deep Learning Theory

1 October 2019

Avi Schwarzschild

Michael Moeller

Papers citing "Truth or Backpropaganda? An Empirical Investigation of Deep Learning Theory"

9 / 9 papers shown

Title
Just How Flexible are Neural Networks in Practice? Ravid Shwartz-Ziv Micah Goldblum Arpit Bansal C. Bayan Bruss Yann LeCun Andrew Gordon Wilson 43 4 0 17 Jun 2024
Koopman-based generalization bound: New aspect for full-rank weights Yuka Hashimoto Sho Sonoda Isao Ishikawa Atsushi Nitanda Taiji Suzuki 11 2 0 12 Feb 2023
Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models Liam H. Fowl Jonas Geiping W. Czaja Micah Goldblum Tom Goldstein FedML 38 145 0 25 Oct 2021
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 linearized framework and a new benchmark for model selection for fine-tuning Aditya Deshpande Alessandro Achille Avinash Ravichandran Hao Li L. Zancato Charless C. Fowlkes Rahul Bhotika Stefano Soatto Pietro Perona ALM 115 46 0 29 Jan 2021
Ramifications of Approximate Posterior Inference for Bayesian Deep Learning in Adversarial and Out-of-Distribution Settings John Mitros A. Pakrashi Brian Mac Namee UQCV 26 2 0 03 Sep 2020
Predicting Training Time Without Training L. Zancato Alessandro Achille Avinash Ravichandran Rahul Bhotika Stefano Soatto 26 24 0 28 Aug 2020
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima N. Keskar Dheevatsa Mudigere J. Nocedal M. Smelyanskiy P. T. P. Tang ODL 308 2,890 0 15 Sep 2016
The Loss Surfaces of Multilayer Networks A. Choromańska Mikael Henaff Michaël Mathieu Gerard Ben Arous Yann LeCun ODL 183 1,185 0 30 Nov 2014