464 citations · 729 across the 22 of their papers we have counts for
7 papers · 1 filter
Fast, simple and accurate handwritten digit classification by training shallow neural network classifiers with the 'extreme learning machine' algorithm
Mark D. McDonnell, Migel D. Tissera, Tony Vladusich +2
Recent advances in training deep (multi-layer) architectures have inspired a renaissance in neural network use. For example, deep convolutional networks are becoming the default op…
Turn Down that Noise: Synaptic Encoding of Afferent SNR in a Single Spiking Neuron
Saeed Afshar, Libin George, Jonathan Tapson +3
We have added a simplified neuromorphic model of Spike Time Dependent Plasticity (STDP) to the Synapto-dendritic Kernel Adapting Neuron (SKAN). The resulting neuron model is the fi…
Racing to Learn: Statistical Inference and Learning in a Single Spiking Neuron with Adaptive Kernels
Saeed Afshar, Libin George, Jonathan Tapson +2
This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron model that performs statistical inference and unsupervised learning of spatiotempo…
Learning ELM network weights using linear discriminant analysis
Philip de Chazal, Jonathan Tapson, André van Schaik
We present an alternative to the pseudo-inverse method for determining the hidden to output weight values for Extreme Learning Machines performing classification tasks. The method…
Explicit Computation of Input Weights in Extreme Learning Machines
Jonathan Tapson, Philip de Chazal, André van Schaik
We present a closed form expression for initializing the input weights in a multi-layer perceptron, which can be used as the first step in synthesis of an Extreme Learning Ma-chine…
ELM Solutions for Event-Based Systems
Jonathan Tapson, André van Schaik
Whilst most engineered systems use signals that are continuous in time, there is a domain of systems in which signals consist of events. Events, like Dirac delta functions, have no…