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» Scalable, updatable predictive models for sequence data
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ICML
2009
IEEE
14 years 8 months ago
A stochastic memoizer for sequence data
We propose an unbounded-depth, hierarchical, Bayesian nonparametric model for discrete sequence data. This model can be estimated from a single training sequence, yet shares stati...
Frank Wood, Cédric Archambeau, Jan Gasthaus...
PKDD
2009
Springer
155views Data Mining» more  PKDD 2009»
14 years 2 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
ICASSP
2011
IEEE
12 years 11 months ago
Evaluating music sequence models through missing data
Building models of the structure in musical signals raises the question of how to evaluate and compare different modeling approaches. One possibility is to use the model to impute...
Thierry Bertin-Mahieux, Graham Grindlay, Ron J. We...
VLDB
2008
ACM
127views Database» more  VLDB 2008»
14 years 8 months ago
Delay aware querying with Seaweed
Large highly distributed data sets are poorly supported by current query technologies. Applications such as endsystembased network management are characterized by data stored on l...
Dushyanth Narayanan, Austin Donnelly, Richard Mort...
ISAMI
2010
13 years 5 months ago
Employing Compact Intra-genomic Language Models to Predict Genomic Sequences and Characterize Their Entropy
Probabilistic models of languages are fundamental to understand and learn the profile of the subjacent code in order to estimate its entropy, enabling the verification and predicti...
Sérgio A. D. Deusdado, Paulo Carvalho