Continuing our special DIRO talk week, we have *Pieter-Jan Kindermans* from *Ghent University and Google Brain* giving a talk on *Thursday Feb 22* at *11:00AM* in room *AA1355*. As always, email me if you'd be interested in speaking with him!

This talk should be good because I've heard the speaker is great at explaining ML! Michael

*TITLE *Towards a reliably understanding and visualization of deep neural networks

*KEYWORDS *Explainable ML, Testing Deep Learning, Deep Learning Theory

*ABSTRACT*Deep learning has transformed the field of machine learning. Empirically these methods work brilliantly but it is difficult to understand what exactly they have learned. If this were possible, we would have a perfect understanding of our networks.

The community has spend quite a lot of effort on visualising what a neural network learns. However while many methods were invented to better understand deep neural networks, we can show that these methods do not produce the theoretically correct explanation for a linear model, which is a simple neural network. Despite this they are used on multi-layer networks with millions of parameters.

We present the idea of creating unit tests for explanations. The idea behind the unit test is that while it might be impossible to define what a good explanation is, it is much easier to detect failure cases. Hence, by creating more and more reliable unit tests, the community can refine its methods iteratively and converge to a good solution.

Based on an analysis of linear models we propose a generalization that yields two explanation techniques (PatternNet and PatternAttribution) that are theoretically sound for linear models, pass our unit test and produce improved explanations for deep networks.

*BIO*Pieter-Jan Kindermans obtained his PhD degree from Ghent Unviersity in 2014. From 2014 to 2017 he was a postdoc, as a Marie-Curie fellow, in the lab of Klaus-Robert Müller in Berlin. Currently he is a Brain resident at Google. Initially he worked on unsupervised learning for Brain-Computer Interfaces (BCI) and demonstrated that unsupervised learning can replace traditional supervised classifiers. A further extension of this work, in collaboration with the universities of Freiburg and Ghent, ensured that the unsupervised decoder is guarantueed to converge to the optimal supervised solution. This project was nominated for the BCI award in 2017.In addition to his work in BCI, he also explored different subfields of machine learning, including deep learning, large scale training and its applications such as molecular chemistry. His current focus is on better understanding the operation of a deep neural network. His ultimate goal is to make deep learning a knowledge extraction tool for science.