Representation learning over dynamic graphs has attracted much attention because of its wide applications. Recently, sequential probabilistic generative models have achieved impressive results they can model data distributions. However, modeling the distribution is still extremely challenging. Existing methods usually ignore mutual interference stochastic states and deterministic states. Beside...

In the present study Iran’s rice imports trend is forecasted, using artificial neural networks and econometric methods, during 2009 to 2013, and their results are compared. The results showed that feet forward neural network leading with less forecast error and had better performance in comparison to econometric techniques and also, other methods of neural networks, such as Recurrent networks a...

In this paper we present an alternative to hidden Markov models for the recognition of image sequences. The approach is based on a stochastic version of recurrent neural networks, which we call diffusion networks. Contrary to hidden Markov models, diffusion networks operate with continuous state dynamics, and generate continuous paths. This aspect that may be beneficial in computer vision tasks...

This report focuses on a hybrid approach, including stochastic and connectionist methods , for continuous speech recognition. Hidden Markov Models (HMMs) are a popular stochastic approach used for continuous speech, well suited to cope with the high variability found in natural utterances. On the other hand, artiicial neural networks (NNs) have shown high classiication power for short speech ut...

The proposed IAFC neural networks have both stability and plasticity because theyuse a control structure similar to that of the ART-1(Adaptive Resonance Theory) neural network.The unsupervised IAFC neural network is the unsupervised neural network which uses the fuzzyleaky learning rule. This fuzzy leaky learning rule controls the updating amounts by fuzzymembership values. The supervised IAFC ...

This paper proposes a new type fuzzy neural systems, denoted IT2RFNS-A interval type-2 recurrent fuzzy neural system with asymmetric membership function , for nonlinear systems identification and control. To enhance the performance and approximation ability, the triangular asymmetric fuzzy membership function AFMF and TSK-type consequent part are adopted for IT2RFNS-A. The gradient information ...

Recurrent Neural Networks are very powerful computational tools that are capable of learning many tasks across different domains. However, it is prone to overfitting and can be very difficult to regularize. Inspired by Recurrent Dropout [1] and Skip-connections [2], we describe a new and simple regularization scheme: Stochastic Dropout. It resembles the structure of recurrent dropout, but offer...