Date of Graduation

12-2015

Document Type

Thesis

Degree Name

Master of Science in Computer Science (MS)

Degree Level

Graduate

Department

Computer Science & Computer Engineering

Advisor

Michael Gashler

Committee Member

John Gauch

Second Committee Member

Wing Ning Li

Abstract

We present Forward Bipartite Alignment (FBA), a method that aligns the topological structures of two neural networks. Neural networks are considered to be a black box, because neural networks contain complex model surface determined by their weights that combine attributes non-linearly. Two networks that make similar predictions on training data may still generalize differently. FBA enables a diversity of applications, including visualization and canonicalization of neural networks, ensembles, and cross-over between unrelated neural networks in evolutionary optimization. We describe the FBA algorithm, and describe implementations for three applications: genetic algorithms, visualization, and ensembles. We demonstrate FBA's usefulness by comparing a bag of neural networks to a bag of FBA-aligned neural networks. We also show that aligning, and then combining two neural networks has no appreciable loss in accuracy which means that Forward Bipartite Alignment aligns neural networks in a meaningful way.

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