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ICMLA
2009
15 years 1 months ago
Learning Deep Neural Networks for High Dimensional Output Problems
State-of-the-art pattern recognition methods have difficulty dealing with problems where the dimension of the output space is large. In this article, we propose a new framework ba...
Benjamin Labbé, Romain Hérault, Cl&e...
IPSN
2010
Springer
15 years 1 months ago
i-MAC - a MAC that learns
Traffic patterns in manufacturing machines exhibit strong temporal correlations due to the underlying repetitive nature of their operations. A MAC protocol can potentially learn t...
Krishna Kant Chintalapudi
ACSAC
2003
IEEE
15 years 9 months ago
A Failure to Learn from the Past
On the evening of 2 November 1988, someone “infected” the Internet with a worm program. That program exploited flaws in utility programs in systems based on BSD-derived versi...
Eugene H. Spafford
ICANN
2009
Springer
15 years 1 months ago
MINLIP: Efficient Learning of Transformation Models
Abstract. This paper studies a risk minimization approach to estimate a transformation model from noisy observations. It is argued that transformation models are a natural candidat...
Vanya Van Belle, Kristiaan Pelckmans, Johan A. K. ...
ICML
2007
IEEE
16 years 4 months ago
Multi-task learning for sequential data via iHMMs and the nested Dirichlet process
A new hierarchical nonparametric Bayesian model is proposed for the problem of multitask learning (MTL) with sequential data. Sequential data are typically modeled with a hidden M...
Kai Ni, Lawrence Carin, David B. Dunson