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Convergence Analysis of a Momentum Algorithm with Adaptive Step Size for
  Non Convex Optimization

Convergence Analysis of a Momentum Algorithm with Adaptive Step Size for Non Convex Optimization

18 November 2019
Anas Barakat
Pascal Bianchi
ArXivPDFHTML

Papers citing "Convergence Analysis of a Momentum Algorithm with Adaptive Step Size for Non Convex Optimization"

6 / 6 papers shown
Title
Variational Stochastic Gradient Descent for Deep Neural Networks
Variational Stochastic Gradient Descent for Deep Neural Networks
Haotian Chen
Anna Kuzina
Babak Esmaeili
Jakub M. Tomczak
55
0
0
09 Apr 2024
Learning explanations that are hard to vary
Learning explanations that are hard to vary
Giambattista Parascandolo
Alexander Neitz
Antonio Orvieto
Luigi Gresele
Bernhard Schölkopf
FAtt
27
179
0
01 Sep 2020
Optimization for deep learning: theory and algorithms
Optimization for deep learning: theory and algorithms
Ruoyu Sun
ODL
27
168
0
19 Dec 2019
Quasi-hyperbolic momentum and Adam for deep learning
Quasi-hyperbolic momentum and Adam for deep learning
Jerry Ma
Denis Yarats
ODL
84
129
0
16 Oct 2018
Linear Convergence of Gradient and Proximal-Gradient Methods Under the
  Polyak-Łojasiewicz Condition
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition
Hamed Karimi
J. Nutini
Mark Schmidt
139
1,205
0
16 Aug 2016
Calculus of the exponent of Kurdyka-Łojasiewicz inequality and its
  applications to linear convergence of first-order methods
Calculus of the exponent of Kurdyka-Łojasiewicz inequality and its applications to linear convergence of first-order methods
Guoyin Li
Ting Kei Pong
102
292
0
09 Feb 2016
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