On practical robustness of machine learning systems
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | eng |
| Published: |
Massachusetts Institute of Technology
2019
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/1721.1/122911 |
| _version_ | 1826207456415449088 |
|---|---|
| author | Ilyas, Andrew. |
| author2 | Constantinos Daskalakis. |
| author_facet | Constantinos Daskalakis. Ilyas, Andrew. |
| author_sort | Ilyas, Andrew. |
| collection | MIT |
| description | This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. |
| first_indexed | 2024-09-23T13:49:56Z |
| format | Thesis |
| id | mit-1721.1/122911 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T13:49:56Z |
| publishDate | 2019 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1229112019-11-16T03:03:28Z On practical robustness of machine learning systems Ilyas, Andrew. Constantinos Daskalakis. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Electrical Engineering and Computer Science. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018 Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 71-79). We consider the importance of robustness in evaluating machine learning systems, an in particular systems involving deep learning. We consider these systems' vulnerability to adversarial examples--subtle, crafted perturbations to inputs which induce large change in output. We show that these adversarial examples are not only theoretical concern, by desigining the first 3D adversarial objects, and by demonstrating that these examples can be constructed even when malicious actors have little power. We suggest a potential avenue for building robust deep learning models by leveraging generative models. by Andrew Ilyas. M. Eng. M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 2019-11-12T18:13:06Z 2019-11-12T18:13:06Z 2018 2018 Thesis https://hdl.handle.net/1721.1/122911 1126543485 eng 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. http://dspace.mit.edu/handle/1721.1/7582 99 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Ilyas, Andrew. On practical robustness of machine learning systems |
| title | On practical robustness of machine learning systems |
| title_full | On practical robustness of machine learning systems |
| title_fullStr | On practical robustness of machine learning systems |
| title_full_unstemmed | On practical robustness of machine learning systems |
| title_short | On practical robustness of machine learning systems |
| title_sort | on practical robustness of machine learning systems |
| topic | Electrical Engineering and Computer Science. |
| url | https://hdl.handle.net/1721.1/122911 |
| work_keys_str_mv | AT ilyasandrew onpracticalrobustnessofmachinelearningsystems |