339 search results - page 55 / 68 » Approximate Inference in Probabilistic Graphical Models with... |
97
Voted
ICML
2010
IEEE
14 years 10 months 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...
66
Voted
ICTAI
2009
IEEE
15 years 4 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 ...
90
Voted
NECO
2008
14 years 9 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...
WWW
2004
ACM
15 years 10 months 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 ...
71
Voted
CORR
2010
Springer
14 years 9 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...