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» Variational Inference for Diffusion Processes
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SDM
2008
SIAM
256views Data Mining» more  SDM 2008»
15 years 3 months ago
Graph Mining with Variational Dirichlet Process Mixture Models
Graph data such as chemical compounds and XML documents are getting more common in many application domains. A main difficulty of graph data processing lies in the intrinsic high ...
Koji Tsuda, Kenichi Kurihara
ECCV
2010
Springer
15 years 7 months ago
Inferring 3D Shapes and Deformations from Single Views
Abstract. In this paper we propose a probabilistic framework that models shape variations and infers dense and detailed 3D shapes from a single silhouette. We model two types of sh...
99
Voted
ICASSP
2011
IEEE
14 years 5 months ago
Modeling microstructure noise using Hawkes processes
Hawkes processes are used for modeling tick-by-tick variations of a single or of a pair of asset prices. For each asset, two counting processes (with stochastic intensities) are a...
Emmanuel Bacry, Sylvain Delattre, Marc Hoffmann, J...
ICASSP
2010
IEEE
15 years 2 months ago
Variational nonparametric Bayesian Hidden Markov Model
The Hidden Markov Model (HMM) has been widely used in many applications such as speech recognition. A common challenge for applying the classical HMM is to determine the structure...
Nan Ding, Zhijian Ou
93
Voted
ICIP
2002
IEEE
16 years 3 months ago
Error concealment using a diffusion based method
In this paper, we present a novel PDE based error concealment algorithm. We formulate the error concealment problem as a sequential optimization problem with both smoothing and or...
Hao Jiang, Cecilia Moloney