339 search results - page 55 / 68 » Approximate Inference in Probabilistic Graphical Models with... |
106
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ICML
2010
IEEE
15 years 26 days ago
2010
IEEE
This paper proposes a multiple instance learning (MIL) algorithm for Gaussian processes (GP). The GP-MIL model inherits two crucial benefits from GP: (i) a principle manner of lea...
ICTAI
2009
IEEE
15 years 6 months ago
2009
IEEE
Energy-based learning (EBL) is a general framework to describe supervised and unsupervised training methods for probabilistic and non-probabilistic factor graphs. An energy-based ...
106
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NECO
2008
14 years 11 months ago
2008
Deep Belief Networks (DBN) are generative neural network models with many layers of hidden explanatory factors, recently introduced by Hinton et al., along with a greedy layer-wis...
108
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WWW
2004
ACM
16 years 14 days ago
2004
ACM
The ability to determine what day-to-day activity (such as cooking pasta, taking a pill, or watching a video) a person is performing is of interest in many application domains. A ...
CORR
2010
Springer
14 years 12 months ago
2010
Springer
In our previous work (`An Algebraic Watchdog for Wireless Network Coding'), we proposed a new scheme in which nodes can detect malicious behaviors probabilistically, police th...