Quality of randomness and node dropout regularization for fitting neural networks
Authors:
- Aki KoivuAKAki Koivu
- Joona-Pekko KakkoJKJoona-Pekko Kakko
- Santeri MäntyniemiSMSanteri Mäntyniemi
- Mikko SairanenMSMikko Sairanen
Open Access
- 26 June 2022
- journal article
- research article
- Published by Elsevier in Expert Systems with Applications
- Vol. 207
Abstract
No abstract availableKeywords
This publication has 23 references indexed in Scilit:
- Real time demonstration of high bitrate quantum random number generation with coherent laser lightApplied Physics Letters, 2011
- TestU01ACM Transactions on Mathematical Software, 2007
- A fast and compact quantum random number generatorReview of Scientific Instruments, 2000
- Mersenne twisterACM Transactions on Modeling and Computer Simulation, 1998
- Neural network studies. 1. Comparison of overfitting and overtrainingJournal of Chemical Information and Computer Sciences, 1995
- Noise injection into inputs in back-propagation learningIEEE Transactions on Systems, Man, and Cybernetics, 1992
- Thermal noise in mechanical experimentsPhysical Review D, 1990
- Neural networks and physical systems with emergent collective computational abilities.Proceedings of the National Academy of Sciences, 1982
- Nondeterministic AlgorithmsJournal of the ACM, 1967
- The Species Problem in IrisAnnals of the Missouri Botanical Garden, 1936