We present a three-pronged approach to improving Statistical Machine Translation (SMT), building on recent success in the application of neural networks to SMT. First, we propose new features based on neural networks to model various nonlocal translation phenomena. Second, we augment the architecture of the neural network with tensor layers that capture important higher-order interaction among ...

A recurrent neural network with a linear memory update is proposed to exactly represent any matrix product state (MPS) and further generalized 2D lattices using multilinear update. It supports perfect sampling wave-function evaluation in polynomial time, provides exact representation of an area law entanglement entropy, outperforms MPS by orders magnitude parameter efficiency.

drought is random and nonlinear phenomenon and using linear stochastic models, nonlinear artificial neural network and hybrid models is advantaged for drought forecasting. this paper presents the performances of autoregressive integrated moving average (arima), direct multi-step neural network (dmsnn), recursive multi-step neural network (rmsnn), hybrid stochastic neural network of directive ap...

the safety of buried pipes under repeated load has been a challenging task in geotechnical engineering. in this paper artificial neural network and regression model for predicting the vertical deformation of high-density polyethylene (hdpe), small diameter flexible pipes buried in reinforced trenches, which were subjected to repeated loadings to simulate the heavy vehicle loads, are proposed. t...

with the increase of the volume of information and the progress in technology, the deficiency of traditional algorithms for fast information retrieval becomes more clear. when large volumes of data are to be handled, the use of neural network as an artificial intelligent technique is a suitable method to increase the information retrieval speed. neural networks present a suitable representation...

Quantum Annealing (QA) is one of the most promising frameworks for quantum optimization. Here, we focus on problem minimizing complex classical cost functions associated with prototypical discrete neural networks, specifically paradigmatic Hopfield model and binary perceptron. We show that adiabatic time evolution QA can be efficiently represented as a suitable Tensor Network. This representati...

in this paper, the artificial neural network (ann) approach is applied for forecasting groundwater level fluctuation in aghili plain,southwest iran. an optimal design is completed for the two hidden layers with four different algorithms: gradient descent withmomentum (gdm), levenberg marquardt (lm), resilient back propagation (rp), and scaled conjugate gradient (scg). rain,evaporation, relative...