Algorithms for Solving Optimization Problems Arsing from Deep Neural Net Models: Smooth Problems
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F16%3A00309135" target="_blank" >RIV/68407700:21230/16:00309135 - isvavai.cz</a>
Result on the web
—
DOI - Digital Object Identifier
—
Alternative languages
Result language
angličtina
Original language name
Algorithms for Solving Optimization Problems Arsing from Deep Neural Net Models: Smooth Problems
Original language description
Machine Learning models incorporating multiple layered learning networks have been seen to provide e_ective models for various classi_cation problems. The resulting optimization problem to solve for the optimal vector minimizing the empirical risk is, however, highly nonlinear. This presents a challenge to application and development of appropriate optimization algorithms for solving the problem. In this paper, we summarize the primary challenges involved and present the case for a Newton-based method incorporating directions of negative curvature, including promising numerical results on data arising from security anomally deetection.
Czech name
—
Czech description
—
Classification
Type
V<sub>souhrn</sub> - Summary research report
CEP classification
JC - Computer hardware and software
OECD FORD branch
—
Result continuities
Project
—
Continuities
N - Vyzkumna aktivita podporovana z neverejnych zdroju
Others
Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Number of pages
5
Place of publication
Praha
Publisher/client name
CISCO SYSTEMS (Czech Republic) s.r.o.
Version
—