Mantas Lukoševičius

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Reservoir Computing (RC) is a paradigm of understanding and training Recurrent Neural Networks (RNNs) based on treating the recurrent part (the reservoir) differently than the readouts from it. It started ten years ago and is currently a prolific research area, giving important insights into RNNs, practical machine learning tools, as well as enabling(More)
A survey of new RNN training methods that follow the Reservoir paradigm Summary Echo State Networks (ESNs) and Liquid State Machines (LSMs) introduced a simple new paradigm in artificial recurrent neural network (RNN) training, where an RNN (the reservoir) is generated randomly and only a readout is trained. The paradigm, becoming known as reservoir(More)
Echo State Networks (ESNs) is a recent simple and powerful approach to training recurrent neural networks (RNNs). In this report we present a modification of ESNs-time warping invariant echo state networks (TWIESNs) that can effectively deal with time warping in dynamic pattern recognition. The standard approach to classify time warped input signals is to(More)
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