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JMLR
2010
202views more  JMLR 2010»
15 years 1 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...
TDSC
2010
172views more  TDSC 2010»
15 years 1 months ago
Proactive Detection of Computer Worms Using Model Checking
Although recent estimates are speaking of 200,000 different viruses, worms, and Trojan horses, the majority of them are variants of previously existing malware. As these variants m...
Johannes Kinder, Stefan Katzenbeisser, Christian S...
PPOPP
2012
ACM
14 years 2 months ago
Massively parallel breadth first search using a tree-structured memory model
Analysis of massive graphs has emerged as an important area for massively parallel computation. In this paper, it is shown how the Fresh Breeze trees-of-chunks memory model may be...
Tom St. John, Jack B. Dennis, Guang R. Gao
JMLR
2012
13 years 9 months ago
Learning Low-order Models for Enforcing High-order Statistics
Models such as pairwise conditional random fields (CRFs) are extremely popular in computer vision and various other machine learning disciplines. However, they have limited expre...
Patrick Pletscher, Pushmeet Kohli
ICASSP
2011
IEEE
14 years 11 months ago
Fundamental diversity, multiplexing, and array gain tradeoff under different MIMO channel models
Following the seminal work of Zheng and Tse on the diversity and multiplexing tradeoff (DMT) of MIMO channels, in this paper we introduce the array gain to investigate the fundame...
Luis Garcia Ordóñez, Daniel Pé...