abstract prediction of input flow into water resources is regarded as one of the most important issues in optimum planning and management in producing electro-water energy and optimum allocation of water into different consumption sources. different parameters affect on input discharge into dams. climate variables including temperature and rainfall have the most effect on input runoff rate to w...

in this paper, linear matrix inequality (lmi) approach for synchronization of chaotic fuzzy cellular neural networks (fcnns) with discrete and unbounded distributed delays based on sampled-data controlis investigated. lyapunov-krasovskii functional combining with the input delay approach as well as the free-weighting matrix approach are employed to derive several sufficient criteria in terms of...

We review the problem of automating hardware-aware architectural design process Deep Neural Networks (DNNs). The field Convolutional Network (CNN) algorithm has led to advancements in many fields, such as computer vision, virtual reality, and autonomous driving. end-to-end a CNN is challenging time-consuming task, it requires expertise multiple areas signal image processing, neural networks, op...

We introduce Dynamic Deep Neural Networks (DNN), a new type of feed-forward deep neural network that allows selective execution. Given an input, only a subset of DNN neurons are executed, and the particular subset is determined by the DNN itself. By pruning unnecessary computation depending on input, DNNs provide a way to improve computational efficiency. To achieve dynamic selective execution,...

Deep neural networks (DNNs) have been successfully applied to a wide variety of acoustic modeling tasks in recent years. These include the applications of DNNs either in a discriminative feature extraction or in a hybrid acoustic modeling scenario. Despite the rapid progress in this area, a number of challenges remain in training DNNs. This paper presents an effective way of training DNNs using...

Collaborative Filtering (CF) is widely used in recommender systems to model user-item interactions. With the great success of Deep Neural Networks (DNNs) various fields, advanced works recently have proposed several DNN-based models for CF, which been proven effective. However, neural networks are all designed manually. As a consequence, it requires designers develop expertise both CF and DNNs,...

Over the past decade, Deep Artificial Neural Networks (DNNs) have become the state-of-the-art algorithms in Machine Learning (ML), speech recognition, computer vision, natural language processing and many other tasks. This was made possible by the advancement in Big Data, Deep Learning (DL) and drastically increased chip processing abilities, especially general-purpose graphical processing unit...

In this paper, we test the applicability of state-of-the-art automatic speech recognition (ASR) to predict phoneme confusions in human listeners. Phoneme-specific response rates are obtained from ASR based on deep neural networks (DNNs) and from listening tests with six normal-hearing subjects. The measure for model quality is the correlation of phoneme recognition accuracies obtained in ASR an...