Talk / Overview

Presented posters:

  1. Hessian-based toolbox for reliable and interpretable machine learning in physics – Anna Dawid, University of Warsaw & ICFO
  2. Dynamics and Landscape: Disordered Systems Approach to Deep Learning – Diego Tapias, University of Göttingen
  3. A deep learning and hybrid quantum-classical approach to matrix quantum mechanics – Enrico Rinaldi, UMich + RIKEN
  4. Deep Set Generation of Collider Events – Erik Buhmann, Hamburg University
  5. Control of Stochastic Quantum Dynamics by Differentiable Programming – Frank Schäfer, University of Basel
  6. Using Deep LSD to build operators in GANs latent space with meaning in real space – J. Quetzalcoatl Toledo-Marin, University of British Columbia & BC Children's Hospital
  7. Machine Learning and Optical Quantum Information – Karol Bartkiewicz, Adam Mickiewicz University
  8. Particle-based Fast Simulation of Jets at the LHC with Variational Autoencoders – Mary Touranakou, CERN
  9. Accelerating HEP Simulations with ZüNIS – Nicolas Deutschmann, IBM Research Europe
  10. Identifying optimal cycles in quantum thermal machines with reinforcement-learning – Paolo Andrea Erdman, Scuola Normale Superiore di Pisa
  11. Optimized Observable Readout from Single-Shot Images of Ultracold Atoms via Machine Learning – Paolo Molignini, University of Cambridge
  12. Soft Mode in the Dynamics of Over-realizable On-line Learning for Soft Committee Machines – Roman Worschech, Max Planck Institute for Mathematics in the Sciences

AMLD / Global partners