03. – 08. November 2019, Dagstuhl-Seminar 19452
Letzte Aktualisierung: 14. Dezember 2019
Machine Learning Meets Visualization to Make Artificial Intelligence Interpretable
Participants
- Rushil Anirudh (LLNL – Livermore, US) [dblp]
- Enrico Bertini (NYU – Brooklyn, US) [dblp]
- Alexander Binder (Singapore University of Technology and Design, SG) [dblp]
- Peer-Timo Bremer (LLNL – Livermore, US) [dblp]
- Mennatallah El-Assady (Universität Konstanz, DE) [dblp]
- Sorelle Friedler (Haverford College, US) [dblp]
- Beatrice Gobbo (Polytechnic University of Milan, IT)
- Nikou Guennemann (Siemens AG – München, DE) [dblp]
- Nathan Hodas (Pacific Northwest National Lab. – Richland, US) [dblp]
- Daniel A. Keim (Universität Konstanz, DE) [dblp]
- Been Kim (Google Brain – Mountain View, US) [dblp]
- Gordon Kindlmann (University of Chicago, US) [dblp]
- Sebastian Lapuschkin (Fraunhofer-Institut – Berlin, DE) [dblp]
- Heike Leitte (TU Kaiserslautern, DE) [dblp]
- Yao Ming (HKUST – Kowloon, HK) [dblp]
- Elisabeth Moore (Los Alamos National Laboratory, US) [dblp]
- Daniela Oelke (Siemens AG – München, DE) [dblp]
- Steve Petruzza (University of Utah – Salt Lake City, US) [dblp]
- Maria Riveiro (Univ. of Skövde, SE & Univ. of Jönköning, SE) [dblp]
- Carlos E. Scheidegger (University of Arizona – Tucson, US) [dblp]
- Sarah Schulz (Ada Health – Berlin, DE) [dblp]
- Hendrik Strobelt (MIT-IBM Watson AI Lab – Cambridge, US) [dblp]
- Simone Stumpf (City, University of London, GB) [dblp]
- Jayaraman Thiagarajan (LLNL – Livermore, US) [dblp]
- Jarke J. van Wijk (TU Eindhoven, NL) [dblp]
Im Falle von Fehlern oder Fragen bezüglich den Links zu dblp, kontaktieren Sie bitte Michael Wagner