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Log probability

Known as: Log probabilities, Log-probabilities, Log-probability 
In computer science, the use of log probabilities means representing probabilities in logarithmic space, instead of the standard interval. This has… Expand
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Papers overview

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Highly Cited
2017
Highly Cited
2017
Recurrent neural networks are a powerful tool for modeling sequential data, but the dependence of each timestep's computation on… Expand
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Highly Cited
2013
Highly Cited
2013
The tens of thousands of high-quality open source software projects on the Internet raise the exciting possibility of studying… Expand
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Highly Cited
2011
Highly Cited
2011
Generative models of text typically associate a multinomial with every class label or topic. Even in simple models this requires… Expand
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Highly Cited
2010
Highly Cited
2010
We provide a systematic study of the problem of finding the source of a computer virus in a network. We model virus spreading in… Expand
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Highly Cited
2010
Highly Cited
2010
Deep Belief Networks (DBNs) are hierarchical generative models which have been used successfully to model high dimensional visual… Expand
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Highly Cited
2009
Highly Cited
2009
The nested Chinese restaurant process (nCRP) is a powerful nonparametric Bayesian model for learning tree-based hierarchies from… Expand
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Highly Cited
2006
Highly Cited
2006
When training the parameters for a natural language system, one would prefer to minimize 1-best loss (error) on an evaluation set… Expand
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Highly Cited
2004
Highly Cited
2004
Markov random field models provide a robust and unified framework for early vision problems such as stereo and image restoration… Expand
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Highly Cited
2002
Highly Cited
2002
Gait is a spatio-temporal phenomenon that typifies the motion characteristics of an individual. In this paper, we propose a view… Expand
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Highly Cited
2001
Highly Cited
2001
We treat collaborative filtering as a univariate time series problem: given a user's previous votes, predict the next vote. We… Expand
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