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 court cases of the twenty fourth overseas 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 rigorously reviewed and chosen from 173 submissions. the point of interest of the papers is on following themes: recurrent networks; aggressive studying and self-organisation; clustering and type; bushes and graphs; human-machine interplay; deep networks; concept; reinforcement studying and motion; imaginative and prescient; supervised studying; dynamical types and time sequence; neuroscience; and applications.
Read Online 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 resources for Artificial Neural Networks and Machine Learning – ICANN 2014: 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings
Spieckermann et al. The Factored Tensor Recurrent Neural Network (FTRNN) extends the Tensor Recurrent Neural Network (TRNN) as denoted in . , e|I| }, is the weight matrix associated with a particular system. This way, the linear transformations of multiple systems are independent within the joint model. In our considered application, it seems conclusive that some transformations share a common overall structure and there are merely certain aspects that make them diﬀerent from one another. On top of that, full tensors introduce many additional parameters per system harming data eﬃciency.
This way, the linear transformations of multiple systems are independent within the joint model. In our considered application, it seems conclusive that some transformations share a common overall structure and there are merely certain aspects that make them diﬀerent from one another. On top of that, full tensors introduce many additional parameters per system harming data eﬃciency. The same conclusions were drawn by Taylor et. al.  and Sutskever et. al.  in the context of modeling motion style from images with Restricted Boltzmann Machines and character-level language modeling with recurrent neural networks.
ESN predictions (J) in comparison with utility function (U) during the all training course in parallel with time varying parameter γ 30 P. Koprinkova-Hristova J U 6 J, U 5 4 3 2 1 0 -1 -2 -3 -4 0 500 1000 time 1500 Fig. 4. 5 -2 0 500 1000 time 1500 2000 2500 Fig. 5. Predictions of ESN critic trained with IP tuning of reservoir Next we investigated the predictions of ESN critic in comparison with utility function in the case when IP tuning is not involved (Fig. 4) and in combined IP-RLS training (Fig.