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» Hierarchical Gaussian process latent variable models
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ASPDAC
2008
ACM
200views Hardware» more  ASPDAC 2008»
15 years 1 months ago
Non-Gaussian statistical timing analysis using second-order polynomial fitting
In the nanometer manufacturing region, process variation causes significant uncertainty for circuit performance verification. Statistical static timing analysis (SSTA) is thus dev...
Lerong Cheng, Jinjun Xiong, Lei He
NIPS
1997
15 years 1 months ago
Nonlinear Markov Networks for Continuous Variables
We address the problem of learning structure in nonlinear Markov networks with continuous variables. This can be viewed as non-Gaussian multidimensional density estimation exploit...
Reimar Hofmann, Volker Tresp
NIPS
2004
15 years 1 months ago
Multiple Alignment of Continuous Time Series
Multiple realizations of continuous-valued time series from a stochastic process often contain systematic variations in rate and amplitude. To leverage the information contained i...
Jennifer Listgarten, Radford M. Neal, Sam T. Rowei...
ICCV
2007
IEEE
16 years 1 months ago
Learning Multiscale Representations of Natural Scenes Using Dirichlet Processes
We develop nonparametric Bayesian models for multiscale representations of images depicting natural scene categories. Individual features or wavelet coefficients are marginally de...
Jyri J. Kivinen, Erik B. Sudderth, Michael I. Jord...
ENTCS
2008
102views more  ENTCS 2008»
14 years 11 months ago
Encoding Distributed Process Calculi into LMNtal
Towards a unifying model of concurrency, we have designed and implemented LMNtal (pronounced "elemental"), a model and language based on hierarchical graph rewriting tha...
Kazunori Ueda