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NIPS
2008
14 years 11 months ago
Stochastic Relational Models for Large-scale Dyadic Data using MCMC
Stochastic relational models (SRMs) [15] provide a rich family of choices for learning and predicting dyadic data between two sets of entities. The models generalize matrix factor...
Shenghuo Zhu, Kai Yu, Yihong Gong
KDD
2009
ACM
191views Data Mining» more  KDD 2009»
15 years 10 months ago
Scalable pseudo-likelihood estimation in hybrid random fields
Learning probabilistic graphical models from high-dimensional datasets is a computationally challenging task. In many interesting applications, the domain dimensionality is such a...
Antonino Freno, Edmondo Trentin, Marco Gori
NIPS
2007
14 years 11 months ago
Learning Bounds for Domain Adaptation
Empirical risk minimization offers well-known learning guarantees when training and test data come from the same domain. In the real world, though, we often wish to adapt a classi...
John Blitzer, Koby Crammer, Alex Kulesza, Fernando...
JUCS
2007
108views more  JUCS 2007»
14 years 9 months ago
On Pipelining Sequences of Data-Dependent Loops
: Sequences of data-dependent tasks, each one traversing large data sets, exist in many applications (such as video, image and signal processing applications). Those tasks usually ...
Rui Rodrigues, João M. P. Cardoso
ASPDAC
2006
ACM
116views Hardware» more  ASPDAC 2006»
15 years 3 months ago
A robust detailed placement for mixed-size IC designs
— The rapid increase in IC design complexity and wide-spread use of intellectual-property (IP) blocks have made the so-called mixed-size placement a very important topic in recen...
Jason Cong, Min Xie