March 28-29th, 2023 | Alte Aula, Tübingen, Germany
Deep learning has enabled major advances in machine learning. However, the deployment of deep learning frameworks in settings that are safety-critical or that impact society requires their decision-making to be explainable. This is fundamental for building trustworthy and user-oriented machine learning models. The aim of this workshop is to generate awareness around explainability in machine learning which is a topic of growing interest. Furthermore, we aim to encourage interdisciplinary interaction and collaboration between researchers from the University of Tübingen and other international institutions that work on different aspects of explainability, in particular in the context of computer vision.
Our workshop will consider research on approaches to explainability and on applications of explainability. Topics of interest will include (but will not be limited to):
The workshop will contain both keynote talks from known researchers in the field as well as invited talks and spotlight presentations of recent advancements in the field of explainability.
The final program will be announced soon.
The workshop will take place at the Alte Aula in Tübingen, 28-29th March 2023. Alte Aula is the old auditorium, a historic building whose construction dates back to the 16th century.
Participants will also have the chance to explore the old university town of Tübingen. The city combines the ancient medieval atmosphere with the vibrant life of a cosmopolitan student town. You can get lost in the narrow alleys and timber-framed houses that lead to the 500-years old castle, take a boat trip in the famous "Stocherkahn" or enjoy the tasteful Swabian cuisine. For more information on the city, visit www.tuebingen.de.
There is no registration fee, however the venue has limited capacity. If you would like to attend, please register in advance using this form: we will then contact you to confirm your attendance.
UC Berkeley
DeepMind
Univ. of Bern
Univ. of Tübingen
Princeton University
Fraunhofer HHI & TU Berlin
Univ. of Tübingen
Univ. of Tübingen
Univ. of Tübingen
Univ. of Tübingen
Univ. of Tübingen
Univ. of Tübingen
Workshop funded by the "Cluster of Excellence - Machine Learning for Science" | ![]() | ![]() |
---|