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NLE
2010
112views more  NLE 2010»
15 years 11 days 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 3 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»
15 years 2 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 4 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 8 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