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Title:A spatial deep network architecture for brain decoding
Author(s):Habeeb, Haroun
Advisor(s):Koyejo, Oluwasanmi
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:M.S.
Genre:Thesis
Subject(s):Machine Learning
Brain Decoding
Deep Learning
Abstract:We propose the Fixed Grouping Layer (FGL); a novel feedforward layer designed to incorporate structured smoothness in a deep learning model. FGL achieves this goal by connecting nodes across layers based on spatial similarity. The inductive bias of structured smoothness implemented by FGL is motivated by applications such as brain image decoding, i.e., predicting behavior based on brain images, where scientific prior knowledge suggests that brain responses conditioned on behaviour are smoothed. Experimental results on simulated and real data is provided. Our proposed model architecture performs better than conventional neural network architectures.
Issue Date:2019-04-22
Type:Text
URI:http://hdl.handle.net/2142/104898
Rights Information:Copyright 2019 Haroun Habeeb
Date Available in IDEALS:2019-08-23
Date Deposited:2019-05


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