Restricted Boltzman Machines (RBMs) have been successfully used in recommender systems. However, as with most of other collaborative filtering techniques, it cannot solve cold start problems for there is no rating for a new item. In this paper, we first apply conditional RBM (CRBM) which could take extra information into account and show that CRBM could solve cold start problem very well, espec...

To solve the problems of delayed prediction results and large errors in one-dimensional time series prediction, a method based on Error-Continuous Restricted Boltzmann Machines (E-CRBM) is proposed this paper. This constructs deep conversion framework, which composed two E-CRBMs neural network (NN). Firstly, E-CRBM models original input sequence target are trained, respectively, to extract feat...

This paper describes a novel energy-based probabilistic distribution that represents complex-valued data and explains how to apply it to direct feature extraction from complex-valued spectra. The proposed model, the complex-valued restricted Boltzmann machine (CRBM), is designed to deal with complex-valued visible units as an extension of the wellknown restricted Boltzmann machine (RBM). Like t...

This paper presents VLSI circuits with continuous-valued probabilistic behaviour realized by injecting noise into each computing unit(neuron). Interconnecting the noisy neurons forms a Continuous Restricted Boltzmann Machine (CRBM), which has shown promising performance in modelling and classifying noisy biomedical data. The Minimising-Contrastive-Divergence learning algorithm for CRBM is also ...

Many speech enhancement algorithms have been proposed for speech restoration from distorted speech. However, if some components of the signal are completely missed or distorted, there is no way for those algorithms to restore the clean speech. Considering that the restricted Boltzmann machine (RBM) is a stochastic version of the Hopfield network which can be used as an associative memory, we pr...

Although Graphics Processing Units (GPUs) seem to currently be the best platform to train machine learning models, most research laboratories are still only equipped with standard CPU systems. In this paper, we investigate multiple techniques to speedup the training of Restricted Boltzmann Machine (RBM) models and Convolutional RBM (CRBM) models on CPU with the Contrastive Divergence (CD) algor...

Anomaly separation using stream sediment geochemical data has an essential role in regional exploration. Many different techniques have been proposed to distinguish anomalous from study area. In this research, a continuous restricted Boltzmann machine (CRBM), which is a generative stochastic artificial neural network, was used to recognize the mineral potential area in Korit 1:100000 sheet, loc...

Sm and Sm-like proteins represent an evolutionarily conserved family with key roles in RNA metabolism in Pro- and Eukaryotes. In this study, a collection of 53 mRNAs that co-purified with Sulfolobus solfataricus (Sso) SmAP2 were surveyed for a specific RNA binding motif (RBM). SmAP2 was shown to bind with high affinity to the deduced consensus RNA binding motif (SmAP2-cRBM) in vitro. Residues i...

In this paper, we propose a new energy-based probabilistic model where a restricted Boltzmann machine (RBM) is extended to deal with complex-valued visible units. The RBM that automatically learns the relationships between visible units and hidden units (but without connections in the visible or the hidden units) has been widely used as a feature extractor, a generator, a classifier, pre-traini...