By Stefan Wermter, Cornelius Weber, Włodzisław Duch, Timo Honkela, Petia Koprinkova-Hristova, Sven Magg, Günther Palm, Alessandro E. P. Villa (eds.)
The e-book constitutes the lawsuits of the twenty fourth foreign convention on synthetic 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 point of interest of the papers is on following subject matters: recurrent networks; aggressive studying and self-organisation; clustering and class; timber and graphs; human-machine interplay; deep networks; thought; 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
Here we propose a general approach to understand how language can be acquired based on a simple and generic neural architecture, namely recurrent neural networks. , 2014). The proposed model is not embodied, but it has many requirements to be included in a more global embodied architecture as it uses a generic architecture that is not hand-crafted for a particular task, but can be used for a broad range of applications (see Lukoševičius et al. 2009 for a review). Mapping the surface form onto the meaning (or deep structure) of a sentence is not an easy task since simply associating words to specific actions or objects is not sufficient to take into account the expressive content of sentences in language.
Oral Presentation (2010) 10. : Learning recurrent neural networks with Hessian-Free Optimization. In: Proceedings of the 28th International Conference on Machine Learning, pp. 1033–1040 (2011) 11. : Training deep and recurrent networks with Hessian-Free Optimization. -R. ) NN: Tricks of the Trade, 2nd edn. LNCS, vol. 7700, pp. 479–535. Springer, Heidelberg (2012) 12. : The markov decision process extraction network. In: European Symposium on Artiﬁcial Neural Networks, ESANN (2010) On-line Training of ESN and IP Tuning Effect Petia Koprinkova-Hristova IICT, Bulgarian Academy of Sciences, Acad.
On average, the LSTM achieved correct predictions for n = 213, 004, whereas the DCM could generalize up to n = 314, 580. 8 4 S. Otte, M. Liwicki, and A. Zell Conclusion In this work the dynamic cortex memory (DCM), which is an extension of the long short term memory (LSTM), was initially presented. Due to several novel trainable connections, namely connections from each gate to each other gate (in both directions) and a self-recurrent connection for each gate, the gates are enabled to share information directly and, thus, learn collectively.