Block layer decomposition schemes for training deep neural networks
- 15 November 2019
- journal article
- research article
- Published by Springer Nature in Journal of Global Optimization
- Vol. 77 (1) , 97-124
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This publication has 17 references indexed in Scilit:
- Efficient block-coordinate descent algorithms for the Group LassoMathematical Programming Computation, 2013
- On the Convergence of Block Coordinate Descent Type MethodsSIAM Journal on Optimization, 2013
- Efficiency of Coordinate Descent Methods on Huge-Scale Optimization ProblemsSIAM Journal on Optimization, 2012
- Extreme learning machines: a surveyInternational Journal of Machine Learning and Cybernetics, 2011
- Convergent Decomposition Techniques for Training RBF Neural NetworksNeural Computation, 2001
- Gradient Convergence in Gradient methods with ErrorsSIAM Journal on Optimization, 2000
- Globally convergent block-coordinate techniques for unconstrained optimizationOptimization Methods and Software, 1999
- Nonlinear ProgrammingJournal of the Operational Research Society, 1997
- Incremental Least Squares Methods and the Extended Kalman FilterSIAM Journal on Optimization, 1996
- A Stochastic Approximation MethodThe Annals of Mathematical Statistics, 1951