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Tilting the playing field: Dynamical loss functions for machine learning

Tilting the playing field: Dynamical loss functions for machine learning

7 February 2021
M. Ruíz-García
Ge Zhang
S. Schoenholz
Andrea J. Liu
ArXivPDFHTML

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
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
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
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
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
227
348
0
14 Jun 2018
Neural Architecture Search with Reinforcement Learning
Neural Architecture Search with Reinforcement Learning
Barret Zoph
Quoc V. Le
271
5,329
0
05 Nov 2016
The Loss Surfaces of Multilayer Networks
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
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