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ECML
2005
Springer
15 years 8 months ago
Inducing Hidden Markov Models to Model Long-Term Dependencies
We propose in this paper a novel approach to the induction of the structure of Hidden Markov Models. The induced model is seen as a lumped process of a Markov chain. It is construc...
Jérôme Callut, Pierre Dupont
107
Voted
MFCS
2004
Springer
15 years 8 months ago
Approximating Boolean Functions by OBDDs
In learning theory and genetic programming, OBDDs are used to represent approximations of Boolean functions. This motivates the investigation of the OBDD complexity of approximatin...
Andre Gronemeier
116
Voted
NIPS
2007
15 years 4 months ago
Privacy-Preserving Belief Propagation and Sampling
We provide provably privacy-preserving versions of belief propagation, Gibbs sampling, and other local algorithms — distributed multiparty protocols in which each party or verte...
Michael Kearns, Jinsong Tan, Jennifer Wortman
115
Voted
CORR
2008
Springer
93views Education» more  CORR 2008»
15 years 2 months ago
An optimization problem on the sphere
We prove existence and uniqueness of the minimizer for the average geodesic distance to the points of a geodesically convex set on the sphere. This implies a corresponding existen...
Andreas Maurer
NECO
2008
146views more  NECO 2008»
15 years 2 months ago
Deep, Narrow Sigmoid Belief Networks Are Universal Approximators
In this paper we show that exponentially deep belief networks [3, 7, 4] can approximate any distribution over binary vectors to arbitrary accuracy, even when the width of each lay...
Ilya Sutskever, Geoffrey E. Hinton