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8 papers · 2 filters
Bethe Bounds and Approximating the Global Optimum
Adrian Weller, Tony Jebara
Inference in general Markov random fields (MRFs) is NP-hard, though identifying the maximum a posteriori (MAP) configuration of pairwise MRFs with submodular cost functions is effi…
Dynamical Systems Trees
Andrew Howard, Tony S. Jebara
We propose dynamical systems trees (DSTs) as a flexible class of models for describing multiple processes that interact via a hierarchy of aggregating parent chains. DSTs extend Ka…
Conditional Chow-Liu Tree Structures for Modeling Discrete-Valued Vector Time Series
Sergey Kirshner, Padhraic Smyth, Andrew Robertson
We consider the problem of modeling discrete-valued vector time series data using extensions of Chow-Liu tree models to capture both dependencies across time and dependencies acros…
Sparse Stochastic Inference for Latent Dirichlet allocation
David Mimno, Matt Hoffman, David Blei
We present a hybrid algorithm for Bayesian topic models that combines the efficiency of sparse Gibbs sampling with the scalability of online stochastic inference. We used our algor…
On the Difficulty of Nearest Neighbor Search
Junfeng He, Sanjiv Kumar, Shih-Fu Chang
Fast approximate nearest neighbor (NN) search in large databases is becoming popular. Several powerful learning-based formulations have been proposed recently. However, not much at…
Exact Recovery of Sparsely-Used Dictionaries
Daniel A. Spielman, Huan Wang, John Wright
We consider the problem of learning sparsely used dictionaries with an arbitrary square dictionary and a random, sparse coefficient matrix. We prove that samples are…