CVPR 2018 Tutorial
Monday, 18 June 2018
Time: 8.30 am - 12.00 pm.
Location information: Room 355 EF.
|09:15-10:00||Techniques for Interpretability|
|10:30-11:15||Applications of Interpretability|
|11:15-12:00||Further Applications and Wrap-Up|
Machine learning techniques such as deep neural networks (DNN) are able convert large amounts of data into highly predictive models. In complement to their unmatched predictive capability, it is becoming increasingly important to understand qualitatively and quantitatively how these models decide.
Our tutorial will provide a broad overview of techniques for interpreting deep models, and how some of these techniques can be made useful on practical problems. In the first part we will lay a taxonomy of these methods, and explain how the various interpretation techniques can be characterized conceptually and mathematically. The second part of the tutorial will explain when and why we need interpretability.
For background material on the topic, see our reading list.
|part 1||part 2||part 3||part 4|