Download Artificial Neural Networks and Machine Learning – ICANN by Stefan Wermter, Cornelius Weber, Włodzisław Duch, Timo PDF

By Stefan Wermter, Cornelius Weber, Włodzisław Duch, Timo Honkela, Petia Koprinkova-Hristova, Sven Magg, Günther Palm, Alessandro E. P. Villa (eds.)

ISBN-10: 3319111787

ISBN-13: 9783319111780

ISBN-10: 3319111795

ISBN-13: 9783319111797

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 lawsuits have been rigorously 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 class; timber and graphs; human-machine interplay; deep networks; conception; reinforcement studying and motion; imaginative and prescient; supervised studying; dynamical versions and time sequence; neuroscience; and applications.

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Extra info for Artificial Neural Networks and Machine Learning – ICANN 2014: 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings

Example text

The same conclusions were drawn by Taylor et. al. [6] and Sutskever et. al. [5] in the context of modeling motion style from images with Restricted Boltzmann Machines and character-level language modeling with recurrent neural networks. They proposed to use the factored representation of a parameter tensor of the form (1) Wvuz z ≈ Wvf diag(Wf z z)Wf u which, in our case, yields the FTRNN equations h1 = σh (Whs s1 + b1 ) ht+1 = σh ( Whfa diag(Wfa z z)Wfa a at + (2a) (2b) Whfh diag(Wfh z z)Wfh h ht + bh ) sˆt+1 = Wsh ht+1 + bs (2c) with the cross-system parameters θcross = {Whs , Whfa , Wfa a , Whfh , Wfh h , b1 , bh } and the system specific parameters θspecific = {Wfa z , Wfh z }.

Dynamic and interactive generation of object handling behaviors by a small humanoid robot using a dynamic neural network model. Neural Networks 19(3), 323–337 (2006) 9. : Emergence of functional hierarchy in a multiple timescale neural network model: A humanoid robot experiment. PLoS Computational Biology 4(11), e1000220(2008) 10. : Learning Internal Representations by Error Propagation. , McClelland, D. ) Parallel Distributed Processing: Explorations in the Microstructure of Cognition, pp. 318–362.

To obtain a more natural state representation for learning, we use the (co)sine components of the pole angle. The cart is moved by applying an external force a ∈ [−1, 1]. 02 s for which the resulting state transitions were observed. After completing a sequence, the simulations was reset to its initial. We observed two different cart-poles whose configurations varied in terms of the pole length lpole and pole mass mpole . 1lpole . For cart-pole 1 (CP1), we created a training data set with 10 000 and a validation data set with 5000 examples.

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Artificial Neural Networks and Machine Learning – ICANN 2014: 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings by Stefan Wermter, Cornelius Weber, Włodzisław Duch, Timo Honkela, Petia Koprinkova-Hristova, Sven Magg, Günther Palm, Alessandro E. P. Villa (eds.)


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