Deep Gaussian Processes (DGPs) are probabilistic deep models obtained by stacking multiple layers implemented through Gaussian Processes (GPs). Although attractive from a theoretical point of view, learning DGPs poses some significant computational challenges that arguably hinder their application to a wider variety of problems for which Deep Neural Networks (DNNs) are the preferred choice. We ...

Following their triumphs in visual recognition tasks, convolutional neural networks (CNNs) have recently been used to learn the emission probabilities of hidden Markov models in speech recognition. The key distinction of CNNs over deep neural networks (DNNs) is that they leverage translational invariance in the frequency domain, such that weights are shared and there are significantly fewer par...

Deep neural networks (DNNs) are powerful types of artificial neural networks (ANNs) that use several hidden layers. They have recently gained considerable attention in the speech transcription and image recognition community (Krizhevsky et al., 2012) for their superior predictive properties including robustness to overfitting. However their application to algorithmic trading has not been previo...

Distant speech recognition is a challenge, particularly due to the corruption of speech signals by reverberation caused by large distances between the speaker and microphone. In order to cope with a wide range of reverberations in real-world situations, we present novel approaches for acoustic modeling including an ensemble of deep neural networks (DNNs) and an ensemble of jointly trained DNNs....

This paper presents our investigations of recurrent neural networks (RNNs) for the phrase break prediction task. With the advent of deep learning, there have been attempts to apply deep neural networks (DNNs) to phrase break prediction. While deep neural networks are able to effectively capture dependencies across features, they lack the ability to capture long-term relations that are spread ov...

This paper presents the application of radial basis neural networks to the development of a novel method for the condition monitoring and fault diagnosis of synchronous generators. In the proposed scheme, flux linkage analysis is used to reach a decision. Probabilistic neural network (PNN) and discrete wavelet transform (DWT) are used in design of fault diagnosis system. PNN as main part of thi...

Recent successes in training large, deep neural networks (DNNs) have prompted active investigation into the underlying representations learned on their intermediate layers. Such research is difficult because it requires making sense of non-linear computations performed by millions of learned parameters. However, despite the difficulty, such research is valuable because it increases our ability ...

To ensure flight safety of aircraft structures, it is necessary to have regular maintenance using visual and nondestructive inspection (NDI) methods. In this paper, we propose an automatic image-based aircraft defect detection using Deep Neural Networks (DNNs). To the best of our knowledge, this is the first work for aircraft defect detection using DNNs. We perform a comprehensive evaluation of...