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NIPS
1998
15 years 29 days ago
Approximate Learning of Dynamic Models
Inference is a key component in learning probabilistic models from partially observable data. When learning temporal models, each of the many inference phases requires a complete ...
Xavier Boyen, Daphne Koller
PAMI
2012
13 years 2 months ago
Spacetime Texture Representation and Recognition Based on a Spatiotemporal Orientation Analysis
—This paper is concerned with the representation and recognition of the observed dynamics (i.e., excluding purely spatial appearance cues) of spacetime texture based on a spatiot...
Konstantinos G. Derpanis, Richard P. Wildes
JCB
2008
159views more  JCB 2008»
14 years 11 months ago
BayesMD: Flexible Biological Modeling for Motif Discovery
We present BayesMD, a Bayesian Motif Discovery model with several new features. Three different types of biological a priori knowledge are built into the framework in a modular fa...
Man-Hung Eric Tang, Anders Krogh, Ole Winther
COMPSEC
2004
131views more  COMPSEC 2004»
14 years 11 months ago
Biometric random number generators
Abstract Up to now biometric methods have been used in cryptography for authentication purposes. In this paper we propose to use biological data for generating sequences of random ...
Janusz Szczepanski, Elek Wajnryb, José M. A...
90
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INFOCOM
2003
IEEE
15 years 4 months ago
Chaotic Maps as Parsimonious Bit Error Models of Wireless Channels
Abstract—The error patterns of a wireless digital communication channel can be described by looking at consecutively correct or erroneous bits (runs and bursts) and at the distri...
Andreas Köpke, Andreas Willig, Holger Karl