Deep learning and structured data
Name
1036987853-MIT.pdf
Description
Full printable version
Size
9.72 MB
Format
Adobe PDF
Checksum (MD5)
6790006651b3b705b2cbeb055ca939ea
Author(s)
Zhang, Chiyuan, Ph. D. Massachusetts Institute of Technology
Advisor(s)
Tomaso Poggio.
Date Issued
2018
Publisher
Massachusetts Institute of Technology
Abstract
In the recent years deep learning has witnessed successful applications in many different domains such as visual object recognition, detection and segmentation, automatic speech recognition, natural language processing, and reinforcement learning. In this thesis, we will investigate deep learning from a spectrum of different perspectives. First of all, we will study the question of generalization, which is one of the most fundamental notion in machine learning theory. We will show how, in the regime of deep learning, the characterization of generalization becomes different from the conventional way, and propose alternative ways to approach it. Moving from theory to more practical perspectives, we will show two different applications of deep learning. One is originated from a real world problem of automatic geophysical feature detection from seismic recordings to help oil & gas exploration; the other is motivated from a computational neuroscientific modeling and studying of human auditory system. More specifically, we will show how deep learning could be adapted to play nicely with the unique structures associated with the problems from different domains. Lastly, we move to the computer system design perspective, and present our efforts in building better deep learning systems to allow efficient and flexible computation in both academic and industrial worlds.
Description
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 135-150).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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