Abstract Over the next years, measurements at LHC and HL-LHC will provide us with a wealth of new data. The best hope to answer fundamental questions, like nature dark matter, is adopt big data techniques in simulations analyses extract all relevant information. On theory side, physics crucially relies on our ability simulate events efficiently from first principles. These face unprecedented pr...

We present a machine-learning approach, based on normalizing flows, for modelling atomic solids. Our model transforms an analytically tractable base distribution into the target solid without requiring ground-truth samples training. report Helmholtz free energy estimates cubic and hexagonal ice modelled as monatomic water well truncated shifted Lennard-Jones system, find them to be in excellent...

Auditing is an increasingly important operation for computer programming, for example in security (e.g. to enable history-based access control) and to enable reproducibility and accountability (e.g. provenance in scientific programming). Most proposed auditing techniques are ad hoc or treat auditing as a second-class, extralinguistic operation; logical or semantic foundations for auditing are n...

Deep Learning has revolutionized vision via convolutional neural networks (CNNs) and natural language processing via recurrent neural networks (RNNs). However, success stories of Deep Learning with standard feed-forward neural networks (FNNs) are rare. FNNs that perform well are typically shallow and, therefore cannot exploit many levels of abstract representations. We introduce self-normalizin...