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A Supervised Neural Autoregressive Topic Model for Simultaneous Image Classification and Annotation
ASupervised Neural Autoregressive Topic Model Simultaneous Image Classification Annotation
2013/6/17
Topic modeling based on latent Dirichlet allocation (LDA) has been a framework of choice to perform scene recognition and annotation. Recently, a new type of topic model called the Document Neural Aut...
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 ...
Adaptable product configuration system based on neural network
knowledge acquisition neural network
2011/10/15
In the rapidly diversifying and globalising market, product configuration is implemented in a dynamic environment with continuous change of configuration knowledge. The adaptability of the product con...
Nonsmooth Formulation of the Support Vector Machine for a Neural Decoding Problem
Optimization and Control (math.OC) Numerical Analysis (math.NA) Statistics Theory (math.ST)
2010/12/17
This paper formulates a generalized classification algorithm with an application to classifying (or `decoding') neural activity in the brain. Medical doctors and researchers have long been interested ...
Semi-parametric dynamic time series modelling with applications to detecting neural dynamics
Dynamic time series modeling change-point testing Bayesian statistics statistics for neural data
2010/11/8
This paper illustrates novel methods for nonstationary time se-ries modeling along with their applications to selected problems in neuroscience. These methods are semi-parametric in that inferences ar...
Missing Data：A Comparison of Neural Network and Expectation Maximisation Techniques
Missing Data Neural Network Expectation Maximisation Techniques
2010/4/28
Two techniques have emerged from the recent literature as candidate solutions to the problem
of missing data imputation, and these are the Expectation Maximisation (EM) Algorithm and the
auto-associ...
In France, for administrative reasons, unemployed workers may actually be involved in occasional work while remaining identified as unemployed (and receiving the corresponding benefit). This is due to...
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...
Some theoretical results on neural spike train probability models
Boundary crossing probability change of measure conditional intensity countingprocess importance sampling metric entropy
2010/4/27
This article contains two main theoretical results on neural spike train models.
The first assumes that the spike train is modeled as a counting or point process
on the real line where the condition...
Bootstrap techniques (also called resampling computation techniques) have introduced
new advances in modeling and model evaluation [10]. Using resampling
methods to construct a series of new samples...
