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… (More)
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Topic mentions per year

Topic mentions per year

1954-2017
05101519542016

Papers overview

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2015
2015
Markov random fields (MRFs) are difficult to evaluate as generative models because computing the test log-probabilities requires… (More)
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Highly Cited
2014
Highly Cited
2014
Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log… (More)
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Highly Cited
2014
Highly Cited
2014
We propose a new benchmark corpus to be used for measuring progress in statistical language modeling. With almost one billion… (More)
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Highly Cited
2009
Highly Cited
2009
The prevalence in Chinese of grammatical structures that translate into English in different word orders is an important cause of… (More)
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Highly Cited
2009
Highly Cited
2009
We introduce a two-layer undirected graphical model, called a “Replicated Softmax”, that can be used to model and automatically… (More)
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2008
2008
We present a simple new Monte Carlo algorithm for evaluating probabilities of observations in complex latent variable models… (More)
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Highly Cited
2008
Highly Cited
2008
Deep Belief Networks (DBN's) are generative models that contain many layers of hidden variables. Efficient greedy algorithms for… (More)
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Review
2006
Review
2006
When training the parameters for a natural language system, one would prefer to minimize 1-best loss (error) on an evaluation set… (More)
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Highly Cited
2005
Highly Cited
2005
We develop and analyze methods for computing provably optimal maximum a posteriori probability (MAP) configurations for a… (More)
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Highly Cited
1999
Highly Cited
1999
One of the central issues in the use of principal component analysis (PCA) for data modelling is that of choosing the appropriate… (More)
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