Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2018; 27th International C Věra K?rková,Yannis Manolopoulos,Ilias Maglogianni Confe

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Training Neural Networks Using Predictor-Corrector Gradient Descentcent (PCGD). PCGD uses predictor-corrector inspired techniques to enhance gradient descent. This method uses a sparse history of network parameter values to make periodic predictions of future parameter values in an effort to skip unnecessary training iterations. This method can cut the number of tr

sacrum

Investigating the Role of Astrocyte Units in a Feedforward Neural Networkooperating units in this function. Recent evidence sheds new light on astrocytes and presents them as important regulators of neuronal activity and synaptic plasticity. In this paper, we present a multi-layer perceptron (MLP) with artificial astrocyte units which listen to and regulate hidden neuron

GNAT

Laconic

Implementing Neural Turing Machines from memory by introducing an external memory unit. NTMs have demonstrated superior performance over Long Short-Term Memory Cells in several sequence learning tasks. A number of open source implementations of NTMs exist but are unstable during training and/or fail to replicate the reported performa

CURL

Tonometry

Practical Fractional-Order Neuron Dynamics for Reservoir Computingmal leaky integrator. In general, fractional-order derivative needs all memories leading to the current state from the initial state. Although this feature is useful as a viewpoint of memory capacity, to keep all memories is intractable, in particular, for reservoir computing with many neurons. A re

SUE

馬籠頭

Towards End-to-End Raw Audio Music Synthesis timing, pitch accuracy and pattern generalization for automated music generation when processing raw audio data. To this end, we present a proof of concept and build a recurrent neural network architecture capable of generalizing appropriate musical raw audio tracks.

Dissonance

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