Dominique Mercier

"Understanding DNNs: Towards interpretable neural network for time-series analysis"

During the last years deep neural networks have been used in different domains for different tasks. It has been shown that these networks can achieve very good results, but the use of these networks is limited by the lack of interpretability. Therefore, many resources have been invested to develop methods to interpret these networks. The main focus of the research, however, relates to applications in the field of image processing and especially in other areas such as time series analysis, there are significantly fewer methods that contribute to the interpretability of the networks.During my PhD work I try to develop methods which are suitable for the interpretability of networks for time series analysis. I will look at different perspectives, including intrinsic as well as post-hoc methods. Also methods used in the image domain will be investigated and their applicability in time series analysis, after necessary modification and extension, will be considered. The aim of this work is to provide a set of methods that can be used to better understand the networks in the field of time series analysis. This not only serves to facilitate debugging but also targets the end user who needs to understand the system in order to use it. One of the biggest challenges is to design the methods according to the user's needs. In this presentation, I will address the challenges and difficulties of the topic and present first results.