We present a variational renormalization group (RG) approach based on a reversible
generative model with hierarchical architecture. The model performs hierarchical change-of …

People routinely infer the goals of others by observing their actions over time. Remarkably,
we can do so even when those actions lead to failure, enabling us to assist others when we …

Clues from human movement disorders have long suggested that the neurotransmitter
dopamine plays a role in motor control, but how the endogenous dopaminergic system …

We describe a simple, low-level approach for embedding probabilistic programming in a
deep learning ecosystem. In particular, we distill probabilistic programming down to a single …

We introduce inference metaprogramming for probabilistic programming languages,
including new language constructs, a formalism, and the rst demonstration of e ectiveness in …

We present a new approach to the design and implementation of probabilistic programming
languages (PPLs), based on the idea of stochastically estimating the probability density …

The Planning Domain Definition Language (PDDL) is a formal specification language for
symbolic planning problems and domains that is widely used by the AI planning community …

What makes us move? Human movement disorders like Parkinson's disease have long
suggested a vital role for dopamine in movement initiation, but there exists surprisingly little …

We formalize the notion of nesting probabilistic programming queries and investigate the
resulting statistical implications. We demonstrate that while query nesting allows the …

In this paper we present a method for automatically deriving a Reversible Jump Markov
chain Monte Carlo sampler from probabilistic programs that specify the target and proposal …