Probabilistic algorithms o er a means of computing that works with the grain of analogue hardware, rather than against it. This paper proposes the use of such an algorithm in applications where the advantages of analogue hardware are most likely to be realised.

OBJECTIVE Recent advances in the field of biomedicine, specifically in the field of genomics, have led to an increase in the information available for conducting expression analysis. Expression analysis is a technique used in transcriptomics, a branch of genomics that deals with the study of messenger ribonucleic acid (mRNA) and the extraction of information contained in the genes. This increas...

Abstract While machine learning has emerged in recent years as a useful tool for the rapid prediction of materials properties, generating sufficient data to reliably train models without overfitting is often impractical. Towards overcoming this limitation, we present general framework leveraging complementary information across different and datasets accurate data-scarce properties. Our approac...

The key purpose of this paper is to present an alternative viewpoint for combining expert opinions based on finite mixture models. Moreover, we consider that the components are not necessarily assumed be from same parametric family. This approach can enable agent make informed decisions about uncertain quantity interest in a flexible manner accounts multiple sources heterogeneity involved expre...

Finite mixture models can be used in estimating complex, unknown probability distributions and also in clustering data. The parameters of the models form a complex representation and are not suitable for interpretation purposes as such. In this paper, we present a methodology to describe the finite mixture of multivariate Bernoulli distributions with a compact and understandable description. Fi...

Visual saliency models have recently begun to incorporate deep learning to achieve predictive capacity much greater than previous unsupervised methods. However, most existing models predict saliency using local mechanisms limited to the receptive field of the network. We propose a model that incorporates global scene semantic information in addition to local information gathered by a convolutio...

This paper introduces a neural network capable of dynamically adapting its architecture to realize time variant nonlinear input-output maps. This network has its roots in the mixture of experts framework but uses a localized model for the gating network. Modules or experts are grown or pruned depending on the complexity of the modeling problem. The structural adaptation procedure addresses the ...

Different models and different selections of architectural parameters for each model give different prediction performance. A combination of them may provide a better performance than that provided by each individual. In this paper, we study this issue based on two models. One is a slightly modified version of Back-Propagation model, and the other one is a new model we propose called RPCL-CLP. ...