Optimized Method based on Lattice Sequences for Multidimensional Integrals in Neural Networks
- Resource Type
- Conference
- Authors
- Todorov, Venelin; Dimov, Ivan; Fidanova, Stefka
- Source
- 2021 16th Conference on Computer Science and Intelligence Systems (FedCSIS) Computer Science and Intelligence Systems (FedCSIS), 2021 16th Conference on. :243-246 Sep, 2021
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Robotics and Control Systems
Signal Processing and Analysis
Computer science
Portable computers
Monte Carlo methods
Computer network reliability
Lattices
Machine learning
Hypercubes
- Language
In this work we investigate advanced stochastic methods for solving a specific multidimensional problem related to neural networks. Monte Carlo and quasi-Monte Carlo techniques have been developed over many years in a range of different fields, but have only recently been applied to the problems in neural networks. As well as providing a consistent framework for statistical pattern recognition, the stochastic approach offers a number of practical advantages including a solution to the problem for higher dimensions. For the first time multidimensional integrals up to 100 dimensions related to this area will be discussed in our numerical study.