Conveners
Compression
- Javier Mauricio Duarte (Univ. of California San Diego (US))
- Frederic Alexandre Dreyer (University of Oxford)
Deep generative models are becoming widely used across science and industry for a variety of purposes. A common challenge is achieving a precise implicit or explicit representation of the data probability density. Recent proposals have suggested using classifier weights to refine the learned density of deep generative models. We extend this idea to all types of generative models and show how...
Data compression plays a major role in the field of Machine Learning and recent works based on generative models such as Generative Adversarial Networks (GANs) have shown that deep-learning-based compression can outperform state-of-the-art classical compression methodologies. Such techniques can be adapted and applied to various areas in high energy physics, in particular to the study of the...
Due to the expected increase in LHC data from the HL upgrade it is important to work on the efficiency of MC Event Generators in order to make theoretical predictions with the necessary precision accessible. One part of the calculation that could benefit from improvements is the generation of unweighted parton-level events. While adaptive multi-channel importance sampling combined with the...
Symmetries are ubiquitous and essential in physics, and the framework to describe symmetries is group theory. The symmetry described by the Lorentz group is essential in the dynamics of all particle physics experiments. A Lorentz-group-equivariant deep neural network framework, called the Lorentz group network (LGN), has been introduced by Bogatskiy et al. and tested for performance in...
