Interpretable neural networks via alignment and dpstribution Propagation

In this thesis, we aim to develop methodologies to better understand and improve the performance of Deep Neural Networks in various settings where data is limited or missing. Unlike data-rich tasks where neural networks have achieved human-level performance, other problems are naturally data limited where these models have fallen short of human level performance and where there is abundant room for improvement. We focus on three types of problems where data is limited - one-shot learning and open-set recognition in the one-shot setting, unsupervised learning, and classification with missing data. The first setting of limited data that we tackle is when there are only few examples per object type. During object classification, an attention mechanism can be used to highlight the area of the image that the model focuses on thus offering a narrow view into the mechanism of classification.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Malalur, Paresh(Paresh G.)

Advisor dc:contributor.advisor

• Tommi Jaakkola.

• Electrical Engineering and Computer Science.

Statement 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/122686

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/122686