Future generation cellular networks are expected to provide ubiquitous broadband access to
a continuously growing number of mobile users. In this context, LTE systems represent an …

This tutorial provides an overview of the basic theory of hidden Markov models (HMMs) as
originated by LE Baum and T. Petrie (1966) and gives practical details on methods of …

Rectified activation units (rectifiers) are essential for state-of-the-art neural networks. In this
work, we study rectifier neural networks for image classification from two aspects. First, we …

The increasing integration of the Internet of Everything into the industrial value chain has
built the foundation for the next industrial revolution called Industrie 4.0. Although Industrie …

Recently, the hybrid deep neural network (DNN)-hidden Markov model (HMM) has been
shown to significantly improve speech recognition performance over the conventional …

We propose an efficient method for approximating natural gradient descent in neural
networks which we call Kronecker-factored Approximate Curvature (K-FAC). K-FAC is based …

In this paper, we describe the design of Neurogrid, a neuromorphic system for simulating
large-scale neural models in real time. Neuromorphic systems realize the function of …

Cognitive radio has emerged as one of the most promising candidate solutions to improve
spectrum utilization in next generation cellular networks. A crucial requirement for future …

Large-scale computing system characteristics vary by application class, but power and
energy use has become a major problem for all classes. Superconducting computing may …

Deeper neural networks are more difficult to train. We present a residual learning framework
to ease the training of networks that are substantially deeper than those used previously. We …