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Algorithms for solving optimization problems arising from deep neural
  net models: smooth problems

Algorithms for solving optimization problems arising from deep neural net models: smooth problems

30 June 2018
Vyacheslav Kungurtsev
Tomás Pevný
ArXivPDFHTML

Papers citing "Algorithms for solving optimization problems arising from deep neural net models: smooth problems"

5 / 5 papers shown
Title
GOALS: Gradient-Only Approximations for Line Searches Towards Robust and
  Consistent Training of Deep Neural Networks
GOALS: Gradient-Only Approximations for Line Searches Towards Robust and Consistent Training of Deep Neural Networks
Younghwan Chae
D. Wilke
D. Kafka
ODL
23
2
0
23 May 2021
Gradient-only line searches to automatically determine learning rates
  for a variety of stochastic training algorithms
Gradient-only line searches to automatically determine learning rates for a variety of stochastic training algorithms
D. Kafka
D. Wilke
ODL
27
0
0
29 Jun 2020
Investigating the interaction between gradient-only line searches and
  different activation functions
Investigating the interaction between gradient-only line searches and different activation functions
D. Kafka
D. Wilke
27
0
0
23 Feb 2020
Gradient-only line searches: An Alternative to Probabilistic Line
  Searches
Gradient-only line searches: An Alternative to Probabilistic Line Searches
D. Kafka
D. Wilke
ODL
29
14
0
22 Mar 2019
Traversing the noise of dynamic mini-batch sub-sampled loss functions: A
  visual guide
Traversing the noise of dynamic mini-batch sub-sampled loss functions: A visual guide
D. Kafka
D. Wilke
26
0
0
20 Mar 2019
1