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.)

The ebook constitutes the complaints of the twenty fourth overseas convention on synthetic 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 subject matters: 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 types and time sequence; neuroscience; and applications.

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

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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 }.

The experiments was done using Adaptive Critic Design (ACD) scheme [7] with on-line trainable ESN critic for real time control of a mobile laboratory robot. Comparison of ESN critic behavior trained with and without IP tuning of reservoir showed that IP algorithm improved critic behavior significantly. It was observed that IP tuning prevents uncontrolled increase of reservoir output weights during on-line training. 2 IP Tuning of ESN Reservoir ESNs are a kind of recurrent neural networks that arise from so called “reservoir computing approaches” [8].

After completing a sequence, the simulation was reset to its initial state. 0009}. For mountain car 1 (MC1), we created a training data set with 10 000 and a validation data set with 5000 examples. , 78}. During training, we concatenated the two training and validation data sets and upsampled the data of MC2 to be equal in size with those of MC1. To test the performance of our models, we further created a generalization data set for MC2 sized 100 000. The FTRNN was configured using nh = nfh = 10, nfa = 2 and T = 10.

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