Tilting the playing field: Dynamical loss functions for machine learning

7 February 2021

Papers citing "Tilting the playing field: Dynamical loss functions for machine learning"

6 / 6 papers shown

Title
Understanding Edge-of-Stability Training Dynamics with a Minimalist Example Xingyu Zhu Zixuan Wang Xiang Wang Mo Zhou Rong Ge 64 35 0 07 Oct 2022
An Analytical Theory of Curriculum Learning in Teacher-Student Networks Luca Saglietti Stefano Sarao Mannelli Andrew M. Saxe 22 25 0 15 Jun 2021
The large learning rate phase of deep learning: the catapult mechanism Aitor Lewkowycz Yasaman Bahri Ethan Dyer Jascha Narain Sohl-Dickstein Guy Gur-Ari ODL 159 234 0 04 Mar 2020
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 224 348 0 14 Jun 2018
Neural Architecture Search with Reinforcement Learning Barret Zoph Quoc V. Le 271 5,329 0 05 Nov 2016
The Loss Surfaces of Multilayer Networks A. Choromańska Mikael Henaff Michaël Mathieu Gerard Ben Arous Yann LeCun ODL 179 1,185 0 30 Nov 2014