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SIGPRO
2011
275views Hardware» more  SIGPRO 2011»
9 years 2 months ago
Synthesis of multivariate stationary series with prescribed marginal distributions and covariance using circulant matrix embeddi
The problem of synthesizing multivariate stationary series Y [n] = (Y1[n], . . . , YP [n])T , n ∈ Z, with prescribed non-Gaussian marginal distributions, and a targeted covarian...
Hannes Helgason, Vladas Pipiras, Patrice Abry
ACL
2011
9 years 3 months ago
Domain Adaptation by Constraining Inter-Domain Variability of Latent Feature Representation
We consider a semi-supervised setting for domain adaptation where only unlabeled data is available for the target domain. One way to tackle this problem is to train a generative m...
Ivan Titov
ICML
2010
IEEE
10 years 22 days ago
Modeling Interaction via the Principle of Maximum Causal Entropy
The principle of maximum entropy provides a powerful framework for statistical models of joint, conditional, and marginal distributions. However, there are many important distribu...
Brian Ziebart, J. Andrew Bagnell, Anind K. Dey
WSC
1997
10 years 1 months ago
Modeling Dependencies in Stochastic Simulation Inputs
We discuss some basic techniques for modeling dependence between the random variables that are inputs to a simulation model, with the main emphasis being continuous bivariate dist...
James R. Wilson
SIGCOMM
1996
ACM
10 years 3 months ago
On the Relevance of Long-Range Dependence in Network Traffic
There is much experimental evidence that network traffic processes exhibit ubiquitous properties of self-similarity and long-range dependence, i.e., of correlations over a wide ran...
Matthias Grossglauser, Jean-Chrysostome Bolot
CIDM
2007
IEEE
10 years 6 months ago
One-shot Collaborative Filtering
— We propose a new one-shot collaborative filtering method. In contrast to the conventional methods, which predict unobserved ratings individually and independently, our method ...
Shuhei Kuwata, Naonori Ueda
WWW
2009
ACM
11 years 10 days ago
Latent space domain transfer between high dimensional overlapping distributions
Transferring knowledge from one domain to another is challenging due to a number of reasons. Since both conditional and marginal distribution of the training data and test data ar...
Sihong Xie, Wei Fan, Jing Peng, Olivier Verscheure...
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