Deep Learning Neural Networks and Bayesian Neural Networks in Data Analysis
| Title: | Deep Learning Neural Networks and Bayesian Neural Networks in Data Analysis |
|---|---|
| Authors: | Chernoded Andrey; Dudko Lev; Myagkov Igor; Volkov Petr |
| Source: | EPJ Web of Conferences, Vol 158, p 06008 (2017) |
| Publisher Information: | EDP Sciences |
| Publication Year: | 2017 |
| Collection: | Directory of Open Access Journals: DOAJ Articles |
| Subject Terms: | Physics; QC1-999 |
| Description: | Most of the modern analyses in high energy physics use signal-versus-background classification techniques of machine learning methods and neural networks in particular. Deep learning neural network is the most promising modern technique to separate signal and background and now days can be widely and successfully implemented as a part of physical analysis. In this article we compare Deep learning and Bayesian neural networks application as a classifiers in an instance of top quark analysis. |
| Document Type: | article in journal/newspaper |
| Language: | English |
| Relation: | https://doi.org/10.1051/epjconf/201715806008; https://doaj.org/toc/2100-014X; https://doaj.org/article/1a757969374b48df8ba08b304312fce2 |
| DOI: | 10.1051/epjconf/201715806008 |
| Availability: | https://doi.org/10.1051/epjconf/201715806008; https://doaj.org/article/1a757969374b48df8ba08b304312fce2 |
| Accession Number: | edsbas.34D77758 |
| Database: | BASE |