ResearchTrend.AI
Effects of Depth, Width, and Initialization: A Convergence Analysis of Layer-wise Training for Deep Linear Neural Networks
v1v2 (latest)

Effects of Depth, Width, and Initialization: A Convergence Analysis of Layer-wise Training for Deep Linear Neural Networks

Yeonjong Shin
ArXiv (abs)PDFHTML

Papers citing "Effects of Depth, Width, and Initialization: A Convergence Analysis of Layer-wise Training for Deep Linear Neural Networks"

3 / 3 papers shown
Title
An Analytic End-to-End Deep Learning Algorithm based on Collaborative Learning
An Analytic End-to-End Deep Learning Algorithm based on Collaborative Learning
Sitan Li
C. Cheah
57
1
0
26 May 2023
Recent Theoretical Advances in Non-Convex Optimization
Recent Theoretical Advances in Non-Convex Optimization
Marina Danilova
Pavel Dvurechensky
Alexander Gasnikov
Eduard A. Gorbunov
Sergey Guminov
Dmitry Kamzolov
Innokentiy Shibaev
129
79
0
11 Dec 2020
Optimization for deep learning: theory and algorithms
Optimization for deep learning: theory and algorithms
Ruoyu Sun
ODL
137
169
0
19 Dec 2019

We use cookies and other tracking technologies to improve your browsing experience on our website, to show you personalized content and targeted ads, to analyze our website traffic, and to understand where our visitors are coming from. See our policy.