A Rate-Distortion Framework for Explaining Deep Neural Network Decisions

Contributed Talk


We propose a rate-distortion framework for explaining neural network decisions. We formulate the task of determining the most relevant signal components for a classifier prediction as an optimisation problem. For the case of binary signals and Boolean classifier functions we show that it is hard to solve and to approximate. Finally, we present a heuristic solution strategy for deep ReLU neural network classifiers. We present numerical experiments and compare our method to other established methods.

Apr 2, 2019 12:00 PM — 12:25 PM
TUM Science and Study Center Raitenhaslach
Jan Macdonald
Jan Macdonald

My research is at the interface of applied and computational mathematics and scientific machine learning. I am interested in inverse problems, signal- and image recovery, and robust and interpretable deep learning.