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NLE
2010
112views more  NLE 2010»
14 years 10 months ago
A non-negative tensor factorization model for selectional preference induction
Distributional similarity methods have proven to be a valuable tool for the induction of semantic similarity. Up till now, most algorithms use two-way cooccurrence data to compute...
Tim Van de Cruys
UAI
2004
15 years 1 months ago
Convolutional Factor Graphs as Probabilistic Models
Based on a recent development in the area of error control coding, we introduce the notion of convolutional factor graphs (CFGs) as a new class of probabilistic graphical models. ...
Yongyi Mao, Frank R. Kschischang, Brendan J. Frey
AAECC
2007
Springer
87views Algorithms» more  AAECC 2007»
14 years 12 months ago
Towards an accurate performance modeling of parallel sparse factorization
We present a simulation-based performance model to analyze a parallel sparse LU factorization algorithm on modern cached-based, high-end parallel architectures. We consider supern...
Laura Grigori, Xiaoye S. Li
JMLR
2012
13 years 2 months ago
Factorized Asymptotic Bayesian Inference for Mixture Modeling
This paper proposes a novel Bayesian approximation inference method for mixture modeling. Our key idea is to factorize marginal log-likelihood using a variational distribution ove...
Ryohei Fujimaki, Satoshi Morinaga
PKDD
2009
Springer
155views Data Mining» more  PKDD 2009»
15 years 6 months ago
Dynamic Factor Graphs for Time Series Modeling
Abstract. This article presents a method for training Dynamic Factor Graphs (DFG) with continuous latent state variables. A DFG includes factors modeling joint probabilities betwee...
Piotr W. Mirowski, Yann LeCun