1 citations · 2 across the 5 of their papers we have counts for
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Exploiting Multiple Timescales in Hierarchical Echo State Networks
Luca Manneschi, Matthew O. A. Ellis, Guido Gigante +3
Echo state networks (ESNs) are a powerful form of reservoir computing that only require training of linear output weights whilst the internal reservoir is formed of fixed randomly…
A semi-supervised sparse K-Means algorithm
Avgoustinos Vouros, Eleni Vasilaki
We consider the problem of data clustering with unidentified feature quality and when a small amount of labelled data is provided. An unsupervised sparse clustering method can be e…
An empirical comparison between stochastic and deterministic centroid initialisation for K-Means variations
Avgoustinos Vouros, Stephen Langdell, Mike Croucher +1
K-Means is one of the most used algorithms for data clustering and the usual clustering method for benchmarking. Despite its wide application it is well-known that it suffers from…
Learning sparsity in reservoir computing through a novel bio-inspired algorithm
Luca Manneschi, Andrew C. Lin, Eleni Vasilaki
The mushroom body is the key network for the representation of learned olfactory stimuli in Drosophila and insects. The sparse activity of Kenyon cells, the principal neurons in th…
Is Epicurus the father of Reinforcement Learning?
Eleni Vasilaki
The Epicurean Philosophy is commonly thought as simplistic and hedonistic. Here I discuss how this is a misconception and explore its link to Reinforcement Learning. Based on the l…