An introduction to different methods for Interpretability can be found here. During the ICIAM Theoretical advances of deep learning mini-symposia, there were some talks on interpretability, perhaps the most interesting ones were by Wojciech Samek, Fraunhofer Heinrich Hertz Institute, and by Stephan Waeldchen, Technische Universität Berlin. The first talk debated how LRP can be understood as a deep Taylor decomposition of the prediction. Some more information and tutorials on these can be found on their webpage. One of the methods to study the interpretability of a net is sensitivity analysis. For this, the changes of the gradient are used to decompose the neural net, however, the gradient is unreliable.

An introduction to different methods for Interpretability can be found here. During the ICIAM Theoretical advances of deep learning mini-symposia, there were some talks on interpretability, perhaps the most interesting ones were by Wojciech Samek, Fraunhofer Heinrich Hertz Institute, and by Stephan Waeldchen, Technische Universität Berlin. The first talk debated how LRP can be understood as a deep Taylor decomposition of the prediction. Some more information and tutorials on these can be found on their webpage. One of the methods to study the interpretability of a net is sensitivity analysis. For this, the changes of the gradient are used to decompose the neural net, however, the gradient is unreliable.

The excitement and activity surrounding the field of machine learning was clearly evident at the 9th International Congress on Industrial and Applied Mathematics (ICIAM 2019), which took place this summer in Valencia, Spain. Over 25 minisymposia--as well as several prize lectures and invited talks--touched on the theme of "learning," while other invited presentations addressed important mathematical research challenges necessary to advance the field. Panelists Hans De Sterck (University of Waterloo), Gitta Kutyniok (Technische Universität Berlin), James Nagy (Emory University), and Eitan Tadmor (University of Maryland, College Park) represented various core areas of computational and applied mathematics that develop and utilize machine learning techniques, including computational science and engineering, imaging science, linear algebra, and partial differential equations. Discussion broached a variety of issues surrounding machine learning, such as the obvious fact that machine learning will remain, as mathematician Ali Rahimi stated, "an area comparable to alchemy" without new mathematical understanding and developments. Deep learning is among the most transformative technologies of our time, and its many potential applications--from driverless cars to drug discovery--can have tremendous societal impact.