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- University of California, Santa BarbaraUS73 papers
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9 papers · 2 filters
Reduction of Maximum Entropy Models to Hidden Markov Models
Joshua Goodman
We show that maximum entropy (maxent) models can be modeled with certain kinds of HMMs, allowing us to construct maxent models with hidden variables, hidden state sequences, or oth…
Factorization of Discrete Probability Distributions
Dan Geiger, Christopher Meek, Bernd Sturmfels
We formulate necessary and sufficient conditions for an arbitrary discrete probability distribution to factor according to an undirected graphical model, or a log-linear model, or…
Finding Optimal Bayesian Networks
David Maxwell Chickering, Christopher Meek
In this paper, we derive optimality results for greedy Bayesian-network search algorithms that perform single-edge modifications at each step and use asymptotically consistent scor…
Practically Perfect
Christopher Meek, David Maxwell Chickering
The property of perfectness plays an important role in the theory of Bayesian networks. First, the existence of perfect distributions for arbitrary sets of variables and directed a…
Textual Features for Programming by Example
Aditya Krishna Menon, Omer Tamuz, Sumit Gulwani +2
In Programming by Example, a system attempts to infer a program from input and output examples, generally by searching for a composition of certain base functions. Performing a nai…
SiGMa: Simple Greedy Matching for Aligning Large Knowledge Bases
Simon Lacoste-Julien, Konstantina Palla, Alex Davies +3
The Internet has enabled the creation of a growing number of large-scale knowledge bases in a variety of domains containing complementary information. Tools for automatically align…