Automatic, dynamic, and nearly optimal learning rate specification via local quadratic approximation
- 1 September 2021
- journal article
- research article
- Published by Elsevier in Neural Networks
- Vol. 141, 11-29
Abstract
No abstract availableKeywords
All Related Versions
Funding Information
- National Natural Science Foundation of China (11701560, 12071477, 11525101, 11831008, 2020-XY-30, 71532001, 71873137)
- Key Laboratory of Engineering Plastics, Chinese Academy of Sciences (11701560, 12071477, 11525101, 11831008, 2020-XY-30, 71532001, 71873137)
This publication has 15 references indexed in Scilit:
- Human-level control through deep reinforcement learningNature, 2015
- An optimal method for stochastic composite optimizationMathematical Programming, 2011
- An Incremental Gradient(-Projection) Method with Momentum Term and Adaptive Stepsize RuleSIAM Journal on Optimization, 1998
- Gradient-based learning applied to document recognitionProceedings of the IEEE, 1998
- Algorithm 778: L-BFGS-BACM Transactions on Mathematical Software, 1997
- Historical Development of the Newton–Raphson MethodSIAM Review, 1995
- Approximation capabilities of multilayer feedforward networksNeural Networks, 1991
- Backpropagation Applied to Handwritten Zip Code RecognitionNeural Computation, 1989
- Learning representations by back-propagating errorsNature, 1986
- A Stochastic Approximation MethodThe Annals of Mathematical Statistics, 1951