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European Summer School on AI : Trusted AI and Sustainable AI

29 août 2022 - 2 septembre 2022

The Second Inria-DFKI European Summer School on AI (IDESSAI 2022) entitled Trusted AI and Sustainable AI will take place at the University of Saarland, from August 29 to September 2, 2022.

SPEAKERS

Cross-track

  • Titouan Vayer (ENS Lyon) – Less is more? How compressive learning and sketching for large-scale machine works
  • Michael Luck (King’s College London) – Artificial Intelligence: Towards safety and trust

Trusted AI Track:

  • Martin Georg Fränzle (University of Oldenburg) – AI components for high integrity, safety-critical cyber-physical systems: chances and risks
  • André Meyer-Vitali (DFKI) – Trustworthy hybrid team decision support
  • Caterina Urban (Inria) – Formal methods for machine learning
  • Freddy Lecue (Thales & Inria) – Explainable AI: a focus on machine learning and knowledge graph-based approaches
  • Oana Goga (CNRS – LIG) – Security and privacy issues with social computing and online advertising

Sustainable AI Track:

  • Silviu-Ioan Filip (Inria) – Tools for DNN quantization
  • Olivier Sentieys (Inria) – Deploying DNNs on FPGAs
  • Christoph Lüth (DFKI Bremen) – An introduction to the RISC-V ISA
  • Richard Membarth (DFKI Saarbrücken & TH Ilmenau) – Code optimization via specialization
  • Anne-Laure Ligozat (ENSIIE) – Energy consumption and CO2 emissions – Part 1
  • Danilo Carastan dos Santos (Inria) – Energy consumption and CO2 emissions – Part 2
  • Daniel Beutel (Adap) – An Introduction to federated learning with Flower

To ensure a good balance in the number of participants and instructors and maximize the chances of interaction, the number of attendees is limited to 50 per track. Applicants will be selected on the grounds of diversity and benefit gained from attending the selected track.

ORGANIZERS

Co-organized by: Inria, DFKI

Détails

Début :
29 août 2022
Fin :
2 septembre 2022
Catégorie d’évènement:
Étiquettes évènement :
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Site :
https://idessai.eu

Lieu

University of Saarland