Deep clustering is a fundamental yet challenging task for data analysis. Recently we witness strong tendency of combining autoencoder and graph neural networks to exploit structure information performance enhancement. However, observe that existing literature 1) lacks dynamic fusion mechanism selectively integrate refine the node attributes consensus representation learning; 2) fails extract fr...

We present a training framework for neural abstractive summarization based on actor-critic approaches from reinforcement learning. In the traditional neural network based methods, the objective is only to maximize the likelihood of the predicted summaries, no other assessment constraints are considered, which may generate low-quality summaries or even incorrect sentences. To alleviate this prob...

Since the advent of deep learning, it has been used to solve various problems using many different architectures. The application of such deep architectures to auditory data is also not uncommon. However, these architectures do not always adequately consider the temporal dependencies in data. We thus propose a new generic architecture called the Deep Belief Network Bidirectional Long ShortTerm ...

Koot - Hamoodi are two units from seventh units out of 1&2 irrigation and drainage network in Dasht-Azadeghan project where was past about 7 years from their operation. Results of the studies were showed before construction of network; ground water table in considered of this area was high and more than 90% of these lands were high and too high in saline and alkaline. After construction and ope...

This paper deals with the effect of fiber aspect ratio of steel fibers on shear strength of steel fiber reinforced concrete deep beams loaded with shear span to depth ratio less than two using the artificial neural network technique. The network model predicts reasonably good results when compared with the equation proposed by previous researchers. The parametric study invol...