Towards an integrated understanding of neural networks
Author(s)Rolnick, David (David S.)
Massachusetts Institute of Technology. Department of Mathematics.
Nir Shavit, Edward S. Boyden, and Max Tegmark.
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Neural networks underpin both biological intelligence and modern Al systems, yet there is relatively little theory for how the observed behavior of these networks arises. Even the connectivity of neurons within the brain remains largely unknown, and popular deep learning algorithms lack theoretical justification or reliability guarantees. This thesis aims towards a more rigorous understanding of neural networks. We characterize and, where possible, prove essential properties of neural algorithms: expressivity, learning, and robustness. We show how observed emergent behavior can arise from network dynamics, and we develop algorithms for learning more about the network structure of the brain.
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 2018.Cataloged from PDF version of thesis.Includes bibliographical references (pages 123-136).
DepartmentMassachusetts Institute of Technology. Department of Mathematics.
Massachusetts Institute of Technology