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ICML
2008
IEEE
16 years 8 months ago
Laplace maximum margin Markov networks
We propose Laplace max-margin Markov networks (LapM3 N), and a general class of Bayesian M3 N (BM3 N) of which the LapM3 N is a special case with sparse structural bias, for robus...
Jun Zhu, Eric P. Xing, Bo Zhang
IJCAI
1997
15 years 8 months ago
On the Role of Hierarchy for Neural Network Interpretation
In this paper, we concentrate on the expressive power of hierarchical structures in neural networks. Recently, the so-called SplitNet model was introduced. It develops a dynamic n...
Jürgen Rahmel, Christian Blum, Peter Hahn
UAI
2003
15 years 8 months ago
Large-Sample Learning of Bayesian Networks is NP-Hard
In this paper, we provide new complexity results for algorithms that learn discrete-variable Bayesian networks from data. Our results apply whenever the learning algorithm uses a ...
David Maxwell Chickering, Christopher Meek, David ...
GRAPHITE
2006
ACM
16 years 1 months ago
Two-stage compression for fast volume rendering of time-varying scalar data
This paper presents a two-stage compression method for accelerating GPU-based volume rendering of time-varying scalar data. Our method aims at reducing transfer time by compressin...
Daisuke Nagayasu, Fumihiko Ino, Kenichi Hagihara
SIGOPS
2010
91views more  SIGOPS 2010»
15 years 5 months ago
FastAD: an authenticated directory for billions of objects
We develop techniques that make authenticated directories efficient and scalable toward the goal of managing tens of billions of objects in a single directory. Internet storage s...
Paul T. Stanton, Benjamin McKeown, Randal C. Burns...