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Expectation Propagation for Neural Networks with Sparsity-promoting Priors
expectation propagation neural network multilayer perceptron linear model sparse prior automatic relevance determination
2013/4/28
We propose a novel approach for nonlinear regression using a two-layer neural network (NN) model structure with sparsity-favoring hierarchical priors on the network weights. We present an expectation ...
Denoising Deep Neural Networks Based Voice Activity Detection
Deep learning denoising deep neural net-works voice activity detection
2013/4/28
Recently, the deep-belief-networks (DBN) based voice activity detection (VAD) has been proposed. It is powerful in fusing the advantages of multiple features, and achieves the state-of-the-art perform...
Maximal Information Divergence from Statistical Models defined by Neural Networks
neural network exponential family Kullback-Leibler diver-gence multi-information
2013/4/27
We review recent results about the maximal values of the Kullback-Leibler information divergence from statistical models defined by neural networks, including naive Bayes models, restricted Boltzmann ...
Efficient estimators：the use of neural networks to construct pseudo panels
pseudo-panels Kohonen map measurement error AIDS model
2010/4/26
Pseudo panels constituted with repeated cross-sections are good substitutes to true panel data. But individuals grouped in a cohort are not the same for successive periods, and it results in a measure...
Comment on “Fastest learning in small-world neural networks”
Feed-forward neural network small-world network random network
2010/3/11
This comment reexamines Simard et al.’s work in [D. Simard, L. Nadeau, H. Kröger, Phys.
Lett. A 336 (2005) 8-15]. We found that Simard et al. calculated mistakenly the local connectivity
length...