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Trust-Region Algorithms for Training Responses: Machine Learning Methods
  Using Indefinite Hessian Approximations

Trust-Region Algorithms for Training Responses: Machine Learning Methods Using Indefinite Hessian Approximations

1 July 2018
Jennifer B. Erway
J. Griffin
Roummel F. Marcia
Riadh Omheni
ArXivPDFHTML

Papers citing "Trust-Region Algorithms for Training Responses: Machine Learning Methods Using Indefinite Hessian Approximations"

5 / 5 papers shown
Title
Symmetric Rank-One Quasi-Newton Methods for Deep Learning Using Cubic Regularization
Symmetric Rank-One Quasi-Newton Methods for Deep Learning Using Cubic Regularization
Aditya Ranganath
Mukesh Singhal
Roummel Marcia
ODL
70
0
0
17 Feb 2025
Gradient Descent and the Power Method: Exploiting their connection to
  find the leftmost eigen-pair and escape saddle points
Gradient Descent and the Power Method: Exploiting their connection to find the leftmost eigen-pair and escape saddle points
R. Tappenden
Martin Takáč
18
0
0
02 Nov 2022
On the efficiency of Stochastic Quasi-Newton Methods for Deep Learning
On the efficiency of Stochastic Quasi-Newton Methods for Deep Learning
M. Yousefi
Angeles Martinez
ODL
16
1
0
18 May 2022
SONIA: A Symmetric Blockwise Truncated Optimization Algorithm
SONIA: A Symmetric Blockwise Truncated Optimization Algorithm
Majid Jahani
M. Nazari
R. Tappenden
A. Berahas
Martin Takávc
ODL
19
10
0
06 Jun 2020
Deep Reinforcement Learning via L-BFGS Optimization
Deep Reinforcement Learning via L-BFGS Optimization
Chris Paxton
Roummel F. Marcia
OffRL
18
0
0
06 Nov 2018
1