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ICCV
1999
IEEE
14 years 9 months ago
A Dynamic Bayesian Network Approach to Figure Tracking using Learned Dynamic Models
The human figure exhibits complex and rich dynamic behavior that is both nonlinear and time-varying. However, most work on tracking and synthesizing figure motion has employed eit...
Vladimir Pavlovic, James M. Rehg, Tat-Jen Cham, Ke...
DSP
2007
13 years 7 months ago
Variational and stochastic inference for Bayesian source separation
We tackle the general linear instantaneous model (possibly underdetermined and noisy) where we model the source prior with a Student t distribution. The conjugate-exponential char...
Ali Taylan Cemgil, Cédric Févotte, S...
ATAL
2008
Springer
13 years 9 months ago
Sigma point policy iteration
In reinforcement learning, least-squares temporal difference methods (e.g., LSTD and LSPI) are effective, data-efficient techniques for policy evaluation and control with linear v...
Michael H. Bowling, Alborz Geramifard, David Winga...
WSC
2000
13 years 8 months ago
Variance reduction techniques for value-at-risk with heavy-tailed risk factors
The calculation of value-at-risk (VAR) for large portfolios of complex instruments is among the most demanding and widespread computational challenges facing the financial industr...
Paul Glasserman, Philip Heidelberger, Perwez Shaha...
NIPS
1998
13 years 8 months ago
Learning Nonlinear Dynamical Systems Using an EM Algorithm
The Expectation Maximization EM algorithm is an iterative procedure for maximum likelihood parameter estimation from data sets with missing or hidden variables 2 . It has been app...
Zoubin Ghahramani, Sam T. Roweis