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While Deep Learning generative models can have a relatively slow learning/training time for producing binary codes for document retrieval, the resulting knowledge yields fast inferences which is one major goal of Big Data Analytics.
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Furthermore, the PFNN is applied to be a nonlinear time-varying channel equalizer with simple structure and fast inference.
We use the correlation information to generate a larger set of scenarios and implement a fast inference analysis capability to assess the accuracy of the system cost.
A parametric distribution depending on a single parameter allows the design of a fast inference algorithm on graph cuts, whereas for arbitrary distributions, we propose inference with loopy belief propagation.
DBN has recently become a popular approach in machine learning for its promised advantages such as fast inference and the ability to encode richer and higher order network structures.
We also introduce an R package, AnimalINLA, for easy and fast inference for Bayesian Animal models using INLA.
This information decreases the number of possible network structures and therefore, allows fast inference of reliable, biologically meaningful networks.
We then showed how such a model can be compiled into a simple HMM, which enables fast inference without any loss of accuracy.
Moreover, the use of degenerated codons allow fast inference of trees, in particular with Maximum Likelihood (ML) methods which are faster with nucleotide sequences than with amino acid ones.
To demonstrate the fast inference of INLA, we created a large pedigree from the existing house sparrow pedigree by merging 28 identical pedigrees and simulated data based on this pedigree with n p = 100,072 individuals.
In future studies, it will be valuable to incorporate gradient-based optimization techniques for the fast inference of demographic models containing generalized growth epochs, e.g., by extending the work of Bhaskar et al. (2015).
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