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Advanced Monte Carlo Methods to Neural Networks

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Recent Advances in Computational Optimization (WCO 2021)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1044))

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Abstract

The paper presents an approach for multidimensional integrals in neural networks, namely a rank-1 lattice sequence in order to resolve multidimensional integrals up to 100 dimensions. The theoretical perspective is presented in detail and the results of the proposed approach are compared to those of three other stochastic techniques. The theoretical perspective is presented in detail and the proposed method was compared with other state of art approaches to solve the same problem. The proposed method behaved better and have significant advantages over the others.

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Acknowledgements

Venelin Todorov is supported by the Bulgarian National Science Fund under Projects KP-06-N52/5 “Efficient methods for modeling, optimization and decision making” and KP-06-N52/2 “Perspective Methods for Quality Prediction in the Next Generation Smart Informational Service Networks”.

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Authors and Affiliations

  1. Department of Information Modeling, Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Acad. Georgi Bonchev Str., Block 8, 1113, Sofia, Bulgaria

    Venelin Todorov

  2. Department of Parallel Algorithms, Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., Block 25 A, 1113, Sofia, Bulgaria

    Venelin Todorov

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  1. Venelin Todorov
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Correspondence to Venelin Todorov .

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Editors and Affiliations

  1. Department Parallel Algorithms, Institute of Information and Communication Technology, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Stefka Fidanova

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Todorov, V. (2022). Advanced Monte Carlo Methods to Neural Networks. In: Fidanova, S. (eds) Recent Advances in Computational Optimization. WCO 2021. Studies in Computational Intelligence, vol 1044. Springer, Cham. https://doi.org/10.1007/978-3-031-06839-3_17

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