Variational inequalities with linear inequality constraints are widely used in constrained optimization and engineering problems. By extending a new recurrent neural network [14], this paper presents a recurrent neural network for solving variational inequalities with general linear constraints in real time. The proposed neural network has onelayer projection structure and is amenable to parall...

Recurrent Neural Networks are in the scope of the machine learning community for many years. In the current paper we discuss the Historical Consistent Recurrent Neural Network and its extension to the complex valued case. We give some insights into complex valued back propagation and its application to the complex valued recurrent neural network training. Finally we present the results for the ...

In a conversation or a dialogue process, attention and intention play intrinsic roles. This paper proposes a neural network based approach that models the attention and intention processes. It essentially consists of three recurrent networks. The encoder network is a word-level model representing source side sentences. The intention network is a recurrent network that models the dynamics of the...

Temporal modeling is the essential to achieve video super-resolution. Most models use alignment or recurrent methods directly exploit temporal information of consecutive frames. However, feature extracted from input frames coarse, which affects performance and generalization ability model. Thus, in this paper, we propose investigate role updating networks. The update module proposed optimize im...

We perform a stationary state replica analysis for a layered network of Ising spin neurons, with recurrent Hebbian interactions within each layer, in combination with strictly feedforward Hebbian interactions between successive layers. This model interpolates between the fully recurrent and symmetric attractor network studied by Amit el al, and the strictly feed-forward attractor network studie...