By Stefan Wermter, Cornelius Weber, Włodzisław Duch, Timo Honkela, Petia Koprinkova-Hristova, Sven Magg, Günther Palm, Alessandro E. P. Villa (eds.)
The ebook constitutes the complaints of the twenty fourth foreign convention on man made Neural Networks, ICANN 2014, held in Hamburg, Germany, in September 2014.
The 107 papers integrated within the court cases have been conscientiously reviewed and chosen from 173 submissions. the focal point of the papers is on following issues: recurrent networks; aggressive studying and self-organisation; clustering and type; timber and graphs; human-machine interplay; deep networks; concept; reinforcement studying and motion; imaginative and prescient; supervised studying; dynamical versions and time sequence; neuroscience; and applications.
Read or Download Artificial Neural Networks and Machine Learning – ICANN 2014: 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings PDF
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Additional info for Artificial Neural Networks and Machine Learning – ICANN 2014: 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings
7 are shown changes in gain and bias terms. From these results we conclude that IP tuning prevents uncontrolled increase of all adjustable parameters during on-line training of ESN reservoir. 32 5 P. Koprinkova-Hristova Conclusions The present investigation demonstrated by real time application that IP tuning of reservoir in real time not only improves the ESN stability but also prevents uncontrolled increase of all adjustable parameters of the network. Our further step will be theoretical explanation of the observed results.
The predicted variance works as an inverse weighting factor for the prediction error because the error is divided by the variance in the likelihood function used for prediction learning. Furthermore, Murata et al.  demonstrated that S-CTRNN can learn to correctly predict time-varying mean and variance values through maximum likelihood estimation (MLE) using the gradient descent method. They also demonstrated an S-CTRNN that was able to learn to reproduce 12 ﬂuctuating Lissajous curves with multiple constant values for the noise variance.
2 ct,i = tanh(ut,i ) yt,i = tanh(ut,i ) (i ∈ IC ), (i ∈ IO ), (2) (3) vt,i = exp(ut,i ) (i ∈ IV ). (4) Training Method S-CTRNN is trained through MLE using the gradient descent method. Let XI = (xt )Tt=1 be a ﬂuctuating input sequence and YˆO = (yˆt )Tt=1 be a ﬂuctuating ideal output (training) sequence, where T is the length of the sequence. Here, if the dimensions of the input and output layers are the same, the ideal value yˆt is equal to the next input value xt+1 . The network training is deﬁned as the problem to optimize, given a data set D = (XI , YˆO ), the network parameters θ consisting of weights w, biases b, and an initial internal state of the context neurons u0 .