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Taylorized Training: Towards Better Approximation of Neural Network
  Training at Finite Width

Taylorized Training: Towards Better Approximation of Neural Network Training at Finite Width

10 February 2020
Yu Bai
Ben Krause
Huan Wang
Caiming Xiong
R. Socher
ArXivPDFHTML

Papers citing "Taylorized Training: Towards Better Approximation of Neural Network Training at Finite Width"

6 / 6 papers shown
Title
Catapult Dynamics and Phase Transitions in Quadratic Nets
Catapult Dynamics and Phase Transitions in Quadratic Nets
David Meltzer
Junyu Liu
27
9
0
18 Jan 2023
Neural Networks can Learn Representations with Gradient Descent
Neural Networks can Learn Representations with Gradient Descent
Alexandru Damian
Jason D. Lee
Mahdi Soltanolkotabi
SSL
MLT
22
114
0
30 Jun 2022
Identifying good directions to escape the NTK regime and efficiently
  learn low-degree plus sparse polynomials
Identifying good directions to escape the NTK regime and efficiently learn low-degree plus sparse polynomials
Eshaan Nichani
Yunzhi Bai
Jason D. Lee
29
10
0
08 Jun 2022
Neural Networks as Kernel Learners: The Silent Alignment Effect
Neural Networks as Kernel Learners: The Silent Alignment Effect
Alexander B. Atanasov
Blake Bordelon
Cengiz Pehlevan
MLT
26
75
0
29 Oct 2021
LQF: Linear Quadratic Fine-Tuning
LQF: Linear Quadratic Fine-Tuning
Alessandro Achille
Aditya Golatkar
Avinash Ravichandran
M. Polito
Stefano Soatto
29
27
0
21 Dec 2020
Deep Networks and the Multiple Manifold Problem
Deep Networks and the Multiple Manifold Problem
Sam Buchanan
D. Gilboa
John N. Wright
166
39
0
25 Aug 2020
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