dc.contributor.advisor | Tomaso Poggio. | en_US |
dc.contributor.author | Liang, Robert Xinyu. | en_US |
dc.contributor.other | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. | en_US |
dc.date.accessioned | 2019-12-05T18:06:51Z | |
dc.date.available | 2019-12-05T18:06:51Z | |
dc.date.copyright | 2019 | en_US |
dc.date.issued | 2019 | en_US |
dc.identifier.uri | https://hdl.handle.net/1721.1/123163 | |
dc.description | This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. | en_US |
dc.description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019 | en_US |
dc.description | Cataloged from student-submitted PDF version of thesis. | en_US |
dc.description | Includes bibliographical references (page 47). | en_US |
dc.description.abstract | In the recent few years, deep learning has had great successes in many applications such as image recognition. However, theory seems to lag behind application in this field, and one goal has been to provide principles and solve puzzles. Some goals during this thesis work were to develop new software tools for deep learning researchers, run experiments related to the research of CBMM (Center for Brains, Minds, and Machines), and create graphs for papers published by CBMM. | en_US |
dc.description.statementofresponsibility | by Robert Xinyu Liang. | en_US |
dc.format.extent | 47 pages | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Massachusetts Institute of Technology | en_US |
dc.rights | MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. | en_US |
dc.rights.uri | http://dspace.mit.edu/handle/1721.1/7582 | en_US |
dc.subject | Electrical Engineering and Computer Science. | en_US |
dc.title | Experiments on the generalization and learning dynamics of deep neural networks | en_US |
dc.type | Thesis | en_US |
dc.description.degree | M. Eng. | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | en_US |
dc.identifier.oclc | 1129252272 | en_US |
dc.description.collection | M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science | en_US |
dspace.imported | 2019-12-05T18:06:50Z | en_US |
mit.thesis.degree | Master | en_US |
mit.thesis.department | EECS | en_US |