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» Tractable Inference for Complex Stochastic Processes
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NIPS
2007
15 years 1 months ago
Robust Regression with Twinned Gaussian Processes
We propose a Gaussian process (GP) framework for robust inference in which a GP prior on the mixing weights of a two-component noise model augments the standard process over laten...
Andrew Naish-Guzman, Sean B. Holden
ICML
2010
IEEE
15 years 20 days ago
Continuous-Time Belief Propagation
Many temporal processes can be naturally modeled as a stochastic system that evolves continuously over time. The representation language of continuous-time Bayesian networks allow...
Tal El-Hay, Ido Cohn, Nir Friedman, Raz Kupferman
CJ
2004
141views more  CJ 2004»
14 years 11 months ago
Modeling and Analysis of a Scheduled Maintenance System: a DSPN Approach
This paper describes a way to manage the modeling and analysis of Scheduled Maintenance Systems (SMS) within an analytically tractable context. We chose a significant case study h...
Andrea Bondavalli, Roberto Filippini
ACL
2006
15 years 1 months ago
Exact Decoding for Jointly Labeling and Chunking Sequences
There are two decoding algorithms essential to the area of natural language processing. One is the Viterbi algorithm for linear-chain models, such as HMMs or CRFs. The other is th...
Nobuyuki Shimizu, Andrew R. Haas
JMLR
2010
202views more  JMLR 2010»
14 years 6 months ago
Learning the Structure of Deep Sparse Graphical Models
Deep belief networks are a powerful way to model complex probability distributions. However, it is difficult to learn the structure of a belief network, particularly one with hidd...
Ryan Prescott Adams, Hanna M. Wallach, Zoubin Ghah...