How does language inform our downstream thinking? In particular, how do humans make
meaning from language--and how can we leverage a theory of linguistic meaning to build …

We present 3DP3, a framework for inverse graphics that uses inference in a structured
generative model of objects, scenes, and images. 3DP3 uses (i) voxel models to represent …

Open-universe probabilistic models enable Bayesian inference about how many objects
underlie data, and how they are related. Effective inference in OUPMs remains a challenge …

Probabilistic inference provides a powerful theoretical framework for engineering intelligent
systems. However, diverse modeling approaches and inference algorithms are needed to …

Having a precise understanding of the distribution over worlds a robot will face is critical to
most problems in robotics. This distribution informs mechanical and software design …

Recent advances in probabilistic programming have enabled the development of
probabilistic generative models for visual perception using a rich abstract representation of …