Sponsor
Portland State University. Systems Science Graduate Program
First Advisor
Wayne Wakeland
Date of Publication
Fall 1-23-2014
Document Type
Thesis
Degree Name
Master of Science (M.S.) in Systems Science
Department
Systems Science
Language
English
Subjects
Neural networks (Computer science), Artificial intelligence -- Data processing -- Mathematical models, Machine learning -- Mathematical models, Computer vision -- Mathematical models
DOI
10.15760/etd.1549
Physical Description
1 online resource (iv, 37 pages)
Abstract
One of the most impressive qualities of the brain is its neuro-plasticity. The neocortex has roughly the same structure throughout its whole surface, yet it is involved in a variety of different tasks from vision to motor control, and regions which once performed one task can learn to perform another. Machine learning algorithms which aim to be plausible models of the neocortex should also display this plasticity. One such candidate is the stacked denoising autoencoder (SDA). SDA's have shown promising results in the field of machine perception where they have been used to learn abstract features from unlabeled data. In this thesis I develop a flexible distributed implementation of an SDA and train it on images and audio spectrograms to experimentally determine properties comparable to neuro-plasticity. Specifically, I compare the visual-auditory generalization between a multi-level denoising autoencoder trained with greedy, layer-wise pre-training (GLWPT), to one trained without. I test a hypothesis that multi-modal networks will perform better than uni-modal networks due to the greater generality of features that may be learned. Furthermore, I also test the hypothesis that the magnitude of improvement gained from this multi-modal training is greater when GLWPT is applied than when it is not. My findings indicate that these hypotheses were not confirmed, but that GLWPT still helps multi-modal networks adapt to their second sensory modality.
Rights
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Persistent Identifier
http://archives.pdx.edu/ds/psu/10572
Recommended Citation
Nifong, Nathaniel H., "Learning General Features From Images and Audio With Stacked Denoising Autoencoders" (2014). Dissertations and Theses. Paper 1550.
https://doi.org/10.15760/etd.1549