20 research outputs found
Inference in "Likelihood-Free" Bayesian Networks
Non UBCUnreviewedAuthor affiliation: Massachusetts Institute of TechnologyGraduat
Estimators of Entropy and Information via Inference in Probabilistic Models
Estimating information-theoretic quantities such as entropy and mutual
information is central to many problems in statistics and machine learning, but
challenging in high dimensions. This paper presents estimators of entropy via
inference (EEVI), which deliver upper and lower bounds on many information
quantities for arbitrary variables in a probabilistic generative model. These
estimators use importance sampling with proposal distribution families that
include amortized variational inference and sequential Monte Carlo, which can
be tailored to the target model and used to squeeze true information values
with high accuracy. We present several theoretical properties of EEVI and
demonstrate scalability and efficacy on two problems from the medical domain:
(i) in an expert system for diagnosing liver disorders, we rank medical tests
according to how informative they are about latent diseases, given a pattern of
observed symptoms and patient attributes; and (ii) in a differential equation
model of carbohydrate metabolism, we find optimal times to take blood glucose
measurements that maximize information about a diabetic patient's insulin
sensitivity, given their meal and medication schedule.Comment: 18 pages, 8 figures. Appearing in AISTATS 202
Cloth grasp point detection based on multiple-view geometric cues with application to robotic towel folding
Abstract — We present a novel vision-based grasp point detection algorithm that can reliably detect the corners of a piece of cloth, using only geometric cues that are robust to variation in texture. Furthermore, we demonstrate the effectiveness of our algorithm in the context of folding a towel using a generalpurpose two-armed mobile robotic platform without the use of specialized end-effectors or tools. The robot begins by picking up a randomly dropped towel from a table, goes through a sequence of vision-based re-grasps and manipulations— partially in the air, partially on the table—and finally stacks the folded towel in a target location. The reliability and robustness of our algorithm enables for the first time a robot with general purpose manipulators to reliably and fully-autonomously fold previously unseen towels, demonstrating success on all 50 out of 50 single-towel trials as well as on a pile of 5 towels. I