Machine comprehension of text is an important problem in natural language processing. A recently released dataset, the Stanford Question Answering Dataset (SQuAD), offers a large number of real questions and their answers created by humans through crowdsourcing. SQuAD provides a challenging testbed for evaluating machine comprehension algorithms, partly because compared with previous datasets, ...

This paper provides a discussion of challenges of neural net adaptive flight control and an examination of stability and convergence issues of adaptive control algorithms. Understanding stability and convergence issues with adaptive control is important in order to advance adaptive control to a higher technology readiness level. The stability and convergence of neural net learning law are inves...

The two or three layered networks 2LNN, 3LNN which originate from stereovision neural network are applied to speech recognition. To accommodate sequential data flow, we consider a window to which new acoustic data enter and from which final neural activities are output. Inside the window recurrent neural network develops neural activity toward a stable point. The process is called Winner-Take-A...

Jan Ben We developed a neural net architecture for segmenting complex images, i.e., to localize two-dimensional geometrical shapes in a scene, without prior knowledge of the objects' positions and sizes. A scale variation is built into the network to deal with varying sizes. This algorithm has been applied to video images of railroad cars, to find their identification numbers. Over 95% of the c...

In this paper we introduce a novel hybrid model architecture for speech recognition and investigate its noise robustness on the Aurora 2 database. Our model is composed of a bidirectional Long Short-Term Memory (BLSTM) recurrent neural net exploiting long-range context information for phoneme prediction and a Dynamic Bayesian Network (DBN) for decoding. The DBN is able to learn pronunciation va...

this paper intends to offer a new iterative method based on arti cial neural networks for finding solution of a fuzzy equations system. our proposed fuzzi ed neural network is a ve-layer feedback neural network that corresponding connection weights to output layer are fuzzy numbers. this architecture of arti cial neural networks, can get a real input vector and calculates its corresponding fu...

Abstract Detection methods for interplanetary coronal mass ejections (ICMEs) from in situ spacecraft measurements are mostly manual, which labor-intensive and time-consuming, being prone to the inconsistencies of identification criteria incompleteness existing catalogs. Therefore, automatic detection ICMEs has aroused interest astrophysical community. Of these methods, convolutional neural netw...