Many questions of fundamental interest in today's science can be formulated as inference
problems: some partial, or noisy, observations are performed over a set of variables and the …

Inverse problems in statistical physics are motivated by the challenges of 'big data'in
different fields, in particular high-throughput experiments in biology. In inverse problems, the …

Spatially proximate amino acids in a protein tend to coevolve. A protein's three-dimensional
(3D) structure hence leaves an echo of correlations in the evolutionary record. Reverse …

Hybrid quantum-classical algorithms provide ways to use noisy intermediate-scale quantum
computers for practical applications. Expanding the portfolio of such techniques, we propose …

The problem of reconstructing nonlinear and complex dynamical systems from measured
data or time series is central to many scientific disciplines including physical, biological …

An increase in the efficiency of sampling from Boltzmann distributions would have a
significant impact on deep learning and other machine-learning applications. Recently …

Understanding the mechanisms of neural computation and learning will require knowledge
of the underlying circuitry. Because it is difficult to directly measure the wiring diagrams of …

We propose a general framework for solving statistical mechanics of systems with finite size.
The approach extends the celebrated variational mean-field approaches using …

graph underlying an Ising model from iid samples. Over the last fifteen years this problem
has been of significant interest in the statistics, machine learning, and statistical physics …

Mainstream machine-learning techniques such as deep learning and probabilistic
programming rely heavily on sampling from generally intractable probability distributions …