Extreme learning machine (ELM) has gained increasing interest from various research fields
recently. In this review, we aim to report the current state of the theoretical research and …

In this paper, we present a survey of recent works in develo** neuromorphic or neuro-
inspired hardware systems. In particular, we focus on those systems which can either learn …

Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices,
and models that contrast the pervasive von Neumann computer architecture. This …

Abstract Neuro-biology inspired Spiking Neural Network (SNN) enables efficient learning
and recognition tasks. To achieve a large scale network akin to biology, a power and area …

Semi-supervised anomaly detection is an approach to identify anomalies by learning the
distribution of normal data. Backpropagation neural networks (ie, BP-NNs) based …

We present a method for synthesising deep neural networks using Extreme Learning
Machines (ELMs) as a stack of supervised autoencoders. We test the method using standard …

Increasingly large deep learning architectures, such as Deep Belief Networks (DBNs) are
the focus of current machine learning research and achieve state-of-the-art results in …

Content caching at base stations is a promising solution to address the large demands for
mobile data services over cellular networks. Content caching is a challenging problem as it …

Currently, state-of-the-art motor intention decoding algorithms in brain-machine interfaces
are mostly implemented on a PC and consume significant amount of power. A machine …

Despite significant advances in computational algorithms and development of tactile
sensors, artificial tactile sensing is strikingly less efficient and capable than the human tactile …