Arthur Choi | DeepAI

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∙ 07/03/2020

On Symbolically Encoding the Behavior of Random Forests

Recent work has shown that the input-output behavior of some machine lea...

0 Arthur Choi, et al. ∙

∙ 06/12/2020

A New Perspective on Learning Context-Specific Independence

Local structure such as context-specific independence (CSI) has received...

0 Yujia Shen, et al. ∙

∙ 04/05/2020

On Tractable Representations of Binary Neural Networks

We consider the compilation of a binary neural network's decision functi...

0 Weijia Shi, et al. ∙

∙ 12/21/2018

On the Relative Expressiveness of Bayesian and Neural Networks

A neural network computes a function. A central property of neural netwo...

0 Arthur Choi, et al. ∙

∙ 05/09/2018

A Symbolic Approach to Explaining Bayesian Network Classifiers

We propose an approach for explaining Bayesian network classifiers, whic...

0 Andy Shih, et al. ∙

∙ 08/22/2017

On Relaxing Determinism in Arithmetic Circuits

The past decade has seen a significant interest in learning tractable pr...

0 Arthur Choi, et al. ∙

∙ 04/05/2015

Dual Decomposition from the Perspective of Relax, Compensate and then Recover

Relax, Compensate and then Recover (RCR) is a paradigm for approximate i...

0 Arthur Choi, et al. ∙

∙ 11/25/2014

Efficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data

We propose an efficient family of algorithms to learn the parameters of ...

0 Guy Van den Broeck, et al. ∙

∙ 10/16/2012

New Advances and Theoretical Insights into EDML

EDML is a recently proposed algorithm for learning MAP parameters in Bay...

0 Khaled S. Refaat, et al. ∙

∙ 10/16/2012

Lifted Relax, Compensate and then Recover: From Approximate to Exact Lifted Probabilistic Inference

We propose an approach to lifted approximate inference for first-order p...

0 Guy Van den Broeck, et al. ∙

∙ 07/04/2012

On Bayesian Network Approximation by Edge Deletion

We consider the problem of deleting edges from a Bayesian network for th...

0 Arthur Choi, et al. ∙

∙ 02/14/2012

EDML: A Method for Learning Parameters in Bayesian Networks

We propose a method called EDML for learning MAP parameters in binary Ba...

0 Arthur Choi, et al. ∙

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