Présidents de session
Representation Learning workshop: Tues Morning
- David Rousseau (IJCLab, Orsay, France)
Representation Learning workshop: Tues afternoon
- Jean-Roch Vlimant (California Institute of Technology (US))
Representation Learning workshop: Wed morning
- Peter Battaglia (DeepMind)
Representation Learning workshop: Wed afternoon
- Andreas Salzburger (CERN)
Symmetry as an organising principle has played a pivotal role in Klein's Erlangen Programme unifying various types of geometry, in modern physics theory unifying different types of interactions. In machine learning, symmetry underlies Geometric Deep Learning, a group-theoretical framework for a principled design of geometric inductive biases by exploiting symmetries arising from the structure...
The reconstruction of particle signals relies on local reconstrution, which involves clustering of granular hits within detector subsystems, followed by global reconstruction, combining signals across detector subsystems for a high-level particle representation of the event. Calorimeter clustering is a local reconstruction method that aims to segment calorimeter hits according to their...
Neural networks that are able to reliably execute algorithmic computation may hold transformative potential to both machine learning and theoretical computer science. On one hand, they could enable the kind of extrapolative generalisation scarcely seen with deep learning models. On another, they may allow for running classical algorithms on inputs previously considered inaccessible to...
We present an approach for using machine learning to automatically discover the governing equations and hidden properties of real physical systems from observations. We train a "graph neural network" to simulate the dynamics of our solar system's Sun, planets, and large moons from 30 years of trajectory data. We then use symbolic regression to discover an analytical expression for the force...
