Thursday Morning Talk: Leon Sixt (Biorobotics Lab, FU Berlin): Opportunities and Challenges in Interpetable ML

Abstract: Deep neural networks underlie many state of the art solutions to hard problems in computer vision, natural language processing or playing Go. Yet, their power comes with a price. Deep networks transform inputs gradually into outputs, using many parameters and intermediary activations. Understanding what a network has learned, how inputs are mapped to outputs, is inherently difficult. In my talk I will focus on attribution methods, algorithms that provide an explanation as to which input variables were relevant for the network’s decision. I will present some of my recent work in this field by first showing how attribution methods may fail and then presenting a new method that is based on information bottlenecks. In the remainder of my talk I will discuss the challenges we face in interpretable ML and how it may provide the opportunity to gain insight into the datasets themselves.

The Zoom Link will be sent the day before the lecture. (Contact communication@scioi.de for specific questions)