We present a unified exact tensor network approach to compute the ground state energy,
identify the optimal configuration, and count the number of solutions for spin glasses. The …

The identification of nonlinear systems is a challenging problem. Physical knowledge of a
system can be used in the identification process to significantly improve the predictive …

A streaming probabilistic program receives a stream of observations and produces a stream
of distributions that are conditioned on these observations. Efficient inference is often …

Compared to the wide array of advanced Monte Carlo methods supported by modern
probabilistic programming languages (PPLs), PPL support for variational inference (VI) is …

Embedding is a language development technique that implements the object language as a
library in a host language. There are many advantages of the approach, including being …

Over the past three decades, the logic programming paradigm has been successfully
expanded to support probabilistic modeling, inference and learning. The resulting paradigm …

We develop operators for construction of proposals in probabilistic programs, which we refer
to as inference combinators. Inference combinators define a grammar over importance …

Multi-wavelength single-molecule fluorescence colocalization (CoSMoS) methods allow
elucidation of complex biochemical reaction mechanisms. However, analysis of CoSMoS …

Array programming is harder than it should be. Major pain points are managing bulk
operations on high-rank arrays, and the associated shape and indexing errors. We describe …

Advanced probabilistic programming languages (PPLs) use hybrid inference systems to
combine symbolic exact inference and Monte Carlo methods to improve inference …