Presented posters:
- Hessian-based toolbox for reliable and interpretable machine learning in physics – Anna Dawid, University of Warsaw & ICFO
- Dynamics and Landscape: Disordered Systems Approach to Deep Learning – Diego Tapias, University of Göttingen
- A deep learning and hybrid quantum-classical approach to matrix quantum mechanics – Enrico Rinaldi, UMich + RIKEN
- Deep Set Generation of Collider Events – Erik Buhmann, Hamburg University
- Control of Stochastic Quantum Dynamics by Differentiable Programming – Frank Schäfer, University of Basel
- 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
- Machine Learning and Optical Quantum Information – Karol Bartkiewicz, Adam Mickiewicz University
- Particle-based Fast Simulation of Jets at the LHC with Variational Autoencoders – Mary Touranakou, CERN
- Accelerating HEP Simulations with ZüNIS – Nicolas Deutschmann, IBM Research Europe
- Identifying optimal cycles in quantum thermal machines with reinforcement-learning – Paolo Andrea Erdman, Scuola Normale Superiore di Pisa
- Optimized Observable Readout from Single-Shot Images of Ultracold Atoms via Machine Learning – Paolo Molignini, University of Cambridge
- 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