Gitta Kutyniok, Klaus-Robert Müller, Wojciech Samek
Alexander Stollenwerk (TU)
01.01.2019 – 30.09.2020
This project will develop a profound theoretical understanding of explainability of deep neural networks in the sense of identifying those features of the input data, which contribute most to the decision. The theory will pay particular attention to quantifying uncertainties in the process.
Please insert any kind of pictures (photos, diagramms, simulations, graphics) related to the project in the above right field (Image with Text), by choosing the green plus image on top of the text editor. (You will be directed to the media library where you can add new files.)
(We need pictures for a lot of purposes in different contexts, like posters, scientific reports, flyers, website,…
Please upload pictures that might be just nice to look at, illustrate, explain or summarize your work.)
As Title in the above form please add a copyright.
And please give a short description of the picture and the context in the above textbox.
Don’t forget to press the “Save changes” button at the bottom of the box.
If you want to add more pictures, please use the “clone”-button at the right top of the above grey box.