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CrossCat: A Fully Bayesian Nonparametric Method for Analyzing Heterogeneous, High Dimensional Data
Vikash Mansinghka, Patrick Shafto, Eric Jonas +3
There is a widespread need for statistical methods that can analyze high-dimensional datasets with- out imposing restrictive or opaque modeling assumptions. This paper describes a…
Modeling Human Understanding of Complex Intentional Action with a Bayesian Nonparametric Subgoal Model
Ryo Nakahashi, Chris L. Baker, Joshua B. Tenenbaum
Most human behaviors consist of multiple parts, steps, or subtasks. These structures guide our action planning and execution, but when we observe others, the latent structure of th…
Risk and Regret of Hierarchical Bayesian Learners
Jonathan H. Huggins, Joshua B. Tenenbaum
Common statistical practice has shown that the full power of Bayesian methods is not realized until hierarchical priors are used, as these allow for greater "robustness" and the ab…
Deep Convolutional Inverse Graphics Network
Tejas D. Kulkarni, Will Whitney, Pushmeet Kohli +1
This paper presents the Deep Convolution Inverse Graphics Network (DC-IGN), a model that learns an interpretable representation of images. This representation is disentangled with…