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/Mentor
Michael Gashler
Committee Member
John Gauch
Second Committee Member
Wing Ning Li
Keywords
Applied sciences, Forward bipartite alignment, Machine learning, Neutral network
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.
Citation
Ashmore, S. C. (2015). Evaluating the Intrinsic Similarity between Neural Networks. Graduate Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/1395