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CATEGORIES:Concerts & Performances
DESCRIPTION:Public Dissertation Defense by Indupama Herath\, March 1\n\nDoc
toral candidate Indupama Herath discusses her dissertation\, "Multivariate
Regression using Neural Networks and Sums of Separable Functions\," on Tues
day March 1\, at 9 a.m. via Teams.\n\nJoin the meeting via Teams. \n\nHerat
h is a graduate student in Mathematics in the College of Arts & Sciences.\n
\n \n\nAbstract: Currently\, artificial neural networks are the most popula
r approach to machine learning problems such as high-dimensional multivaria
te regression. Methods using sums of separable functions are designed to re
present functions in high dimensions and can be applied to high-dimensional
multivariate regression. Here we compare the ability of these two methods
to approximate function spaces in order to assess their relative expressive
power.\n\n \n\nWe show that translation of approximation results from one
method to the other is possible under certain conditions. We identify gener
al approximation schemes in both the single-layer and deep-layer settings t
hat apply to both methods for approximating certain function classes. In pa
rticular\, we show that sums of separable functions give the same error rat
es as neural networks for function classes such as Barron's functions and b
and-limited functions. Inspired by deep neural networks\, we also introduce
deep layer sums of separable functions that shows similar results as deep
neural networks for functions with compositional structure.
DTEND:20220301T150000Z
DTSTAMP:20241123T045539Z
DTSTART:20220301T140000Z
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SUMMARY:Public Dissertation Defense by Indupama Herath\, March 1
UID:tag:localist.com\,2008:EventInstance_39245597788725
URL:https://calendar.ohio.edu/event/public_dissertation_defense_by_indupama
_herath_march_1
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