490 citations · 1k across the 74 of their papers we have counts for
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ADEV: Sound Automatic Differentiation of Expected Values of Probabilistic Programs
Alexander K. Lew, Mathieu Huot, Sam Staton +1
Optimizing the expected values of probabilistic processes is a central problem in computer science and its applications, arising in fields ranging from artificial intelligence to o…
ProbNeRF: Uncertainty-Aware Inference of 3D Shapes from 2D Images
Matthew D. Hoffman, Tuan Anh Le, Pavel Sountsov +4
The problem of inferring object shape from a single 2D image is underconstrained. Prior knowledge about what objects are plausible can help, but even given such prior knowledge the…
Abstract Interpretation for Generalized Heuristic Search in Model-Based Planning
Tan Zhi-Xuan, Joshua B. Tenenbaum, Vikash K. Mansinghka
Domain-general model-based planners often derive their generality by constructing search heuristics through the relaxation or abstraction of symbolic world models. We illustrate ho…
Solving the Baby Intuitions Benchmark with a Hierarchically Bayesian Theory of Mind
Tan Zhi-Xuan, Nishad Gothoskar, Falk Pollok +3
To facilitate the development of new models to bridge the gap between machine and human social intelligence, the recently proposed Baby Intuitions Benchmark (arXiv:2102.11938) prov…
Recursive Monte Carlo and Variational Inference with Auxiliary Variables
Alexander K. Lew, Marco Cusumano-Towner, Vikash K. Mansinghka
A key design constraint when implementing Monte Carlo and variational inference algorithms is that it must be possible to cheaply and exactly evaluate the marginal densities of pro…
DURableVS: Data-efficient Unsupervised Recalibrating Visual Servoing via online learning in a structured generative model
Nishad Gothoskar, Miguel Lázaro-Gredilla, Yasemin Bekiroglu +4
Visual servoing enables robotic systems to perform accurate closed-loop control, which is required in many applications. However, existing methods either require precise calibratio…