Normalizing flows provide a general mechanism for defining expressive probability
distributions, only requiring the specification of a (usually simple) base distribution and a …

The Schrödinger bridge problem (SBP) finds the most likely stochastic evolution between
two probability distributions given a prior stochastic evolution. As well as applications in the …

Likelihood-free methods perform parameter inference in stochastic simulator models where
evaluating the likelihood is intractable but sampling synthetic data is possible. One class of …

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

We consider the solution of nonlinear inverse problems where the forward problem is a
discretization of a partial differential equation. Such problems are notoriously difficult to …

Flexible models for probability distributions are an essential ingredient in many machine
learning tasks. We develop and investigate a new class of probability distributions, which we …

Abstract Machine-learned likelihoods (MLL) combines machine-learning classification
techniques with likelihood-based inference tests to estimate the experimental sensitivity of …

The behavior of polyatomic molecules around their equilibrium positions can be regarded as
that of quantum-coupled anharmonic oscillators. Solving the corresponding Schrödinger …

Cosmological inferences typically rely on explicit expressions for the likelihood and
covariance of the data vector, which normally consists of a set of summary statistics …

Distribution drift is an important issue for practical applications of machine learning (ML). In
particular, in streaming ML, the data distribution may change over time, yielding the problem …