21 citations · 40 across the 5 of their papers we have counts for
6 papers
SceneCAD: Predicting Object Alignments and Layouts in RGB-D Scans
Armen Avetisyan, Tatiana Khanova, Christopher Choy +3
We present a novel approach to reconstructing lightweight, CAD-based representations of scanned 3D environments from commodity RGB-D sensors. Our key idea is to jointly optimize fo…
A Hybrid Anytime Algorithm for the Constructiion of Causal Models From Sparse Data
Denver Dash, Marek J. Druzdzel
We present a hybrid constraint-based/Bayesian algorithm for learning causal networks in the presence of sparse data. The algorithm searches the space of equivalence classes of mode…
A Robust Independence Test for Constraint-Based Learning of Causal Structure
Denver Dash, Marek J. Druzdzel
Constraint-based (CB) learning is a formalism for learning a causal network with a database D by performing a series of conditional-independence tests to infer structural informati…
Bayesian Biosurveillance of Disease Outbreaks
Gregory F. Cooper, Denver Dash, John Levander +3
Early, reliable detection of disease outbreaks is a critical problem today. This paper reports an investigation of the use of causal Bayesian networks to model spatio-temporal patt…
Efficient inference in persistent Dynamic Bayesian Networks
Tomas Singliar, Denver Dash
Numerous temporal inference tasks such as fault monitoring and anomaly detection exhibit a persistence property: for example, if something breaks, it stays broken until an interven…
Learning Why Things Change: The Difference-Based Causality Learner
Mark Voortman, Denver Dash, Marek J. Druzdzel
In this paper, we present the Difference- Based Causality Learner (DBCL), an algorithm for learning a class of discrete-time dynamic models that represents all causation across tim…