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Thursday, May 17
Computational Statistics
Advanced Mathematics for Data Analysis
Thu, May 17, 1:30 PM - 3:00 PM
Grand Ballroom E

A Geometric Formulation of Neural Network Training (304559)


*David A. Johannsen, Naval Surface Warfare Center - Dahlgren 

We are concerned with describing neural network model selection and training in the language of differential geometry and topology. Such a formulation is conceptually useful and provides a means to describe training a neural network as generating a sequence in the parameter space which converges to a point in the intersection of embedded submanifolds. Though we have only recently begun this work, we hope that such a formulation may ultimately yield a framework for describing a neural network's predictive ability for observations which differ substantially from the data with which the model was trained.