03. – 08. November 2019, Dagstuhl-Seminar 19452
Letzte Aktualisierung: 31. März 2020

Machine Learning Meets Visualization to Make Artificial Intelligence Interpretable

Teilnehmer

  • 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