In the last few years, electron microscopy has experienced a new methodological paradigm
aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine …

Deep learning is usually described as an experiment-driven field under continuous criticizes
of lacking theoretical foundations. This problem has been partially fixed by a large volume of …

We study the problem of designing models for machine learning tasks defined on sets. In
contrast to the traditional approach of operating on fixed dimensional vectors, we consider …

Likelihood-free inference provides a framework for performing rigorous Bayesian inference
using only forward simulations, properly accounting for all physical and observational effects …

Matter evolved under the influence of gravity from minuscule density fluctuations.
Nonperturbative structure formed hierarchically over all scales and developed non …

Abstract Convolutional Neural Networks (CNNs) are a cornerstone of the Deep Learning
toolbox and have led to many breakthroughs in Artificial Intelligence. So far, these neural …

We introduce a simple permutation equivariant layer for deep learning with set structure.
This type of layer, obtained by parameter-sharing, has a simple implementation and linear …

Deep learning is a promising tool to determine the physical model that describes our
universe. To handle the considerable computational cost of this problem, we present …

Dark matter in the universe evolves through gravity to form a complex network of halos,
filaments, sheets and voids, that is known as the cosmic web. Computational models of the …

We apply deep-neural-network-based techniques to quantum state classification and
reconstruction. Our methods demonstrate high classification accuracies and reconstruction …