Many NLP tasks including machine comprehension, answer selection and text entailment require the comparison between sequences. Matching the important units between sequences is a key to solve these problems. In this paper, we present a general “compare-aggregate” framework that performs word-level matching followed by aggregation using Convolutional Neural Networks. We particularly focus on the...

the method of artificial neural network is used as a suitable tool for intelligent interpretation of gravity data in this paper. we have designed a hopfield neural network to estimate the gravity source depth. the designed network was tested by both synthetic and real data. as real data, this artificial neural network was used to estimate the depth of a qanat (an underground channel) located at...

the aim followed in this study was to compare the performance of multiple regression vs neural network models to predict the activity of antioxidant enzymes super oxide dismutase (sod), cat alase (cat), ascorbate pero xidase (apx) and peroxidase (pox) in the shoots of wheat (triticum aestivum), alvand cultivar in a soil polluted with cadmium. the treatments consisted of four levels of cadmium (...

Part 2 of this monograph builds on the introduction to tensor networks and their operations presented in Part 1. It focuses on tensor network models for super-compressed higher-order representation of data/parameters and related cost functions, while providing an outline of their applications in machine learning and data analytics. A particular emphasis is on the tensor train (TT) and Hierarchi...

We present a novel neural network algorithm, the Tensor Switching (TS) network, which generalizes the Rectified Linear Unit (ReLU) nonlinearity to tensor-valued hidden units. The TS network copies its entire input vector to different locations in an expanded representation, with the location determined by its hidden unit activity. In this way, even a simple linear readout from the TS representa...

estimation (forecasting) of industrial production costs is one of the most important factor affecting decisions in the highly competitive markets. thus, accuracy of the estimation is highly desirable. hibrid regression neural network is an approach proposed in this paper to obtain better fitness in comparison with regression analysis and the neural network methods. comparing the estimated resul...

An artificial neural-network-based subgrid-scale (SGS) model, which is capable of predicting turbulent flows at untrained Reynolds numbers and on grid resolution developed. Providing the grid-scale strain-rate tensor alone as an input leads model to predict a SGS stress that aligns with tensor, performs similarly dynamic Smagorinsky model. On other hand, providing resolved in addition found sig...

Recently, we proposed and developed the context-dependent deep neural network hidden Markov models (CD-DNN-HMMs) for large vocabulary speech recognition and achieved highly promising recognition results including over one third fewer word errors than the discriminatively trained, conventional HMM-based systems on the 300hr Switchboard benchmark task. In this paper, we extend DNNs to deep tensor...