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

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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 e-book 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 incorporated within the complaints have been conscientiously reviewed and chosen from 173 submissions. the point of interest of the papers is on following issues: recurrent networks; aggressive studying and self-organisation; clustering and class; bushes and graphs; human-machine interplay; deep networks; idea; 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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An interesting observation was also, that these two additional connection schemes provide a synergy effect. Besides, we showed that DCMs just as LSTMs are able to generalize correctly over millions of time steps, which is, as far as we know, for the first time reported in the literature. Next, we plan to investigate the DCM in more detail and study its performance on some real-world problems. References 1. : Evolving memory cell structures for sequence learning. , Ellinas, G. ) ICANN 2009, Part II.

Thus, not only the system specific parameters of the system of interest but also the crosssystem parameters may adjust unfavorably. By constraining the parameters of the system of interest to remain similar to those of the reference system, the cross-system parameters are less likely to be affected by incomplete information about the system of interest. 5 Experiments We empirically assessed the effectiveness of our regularization technique on the cart-pole [7] and mountain car [8] benchmarks. To be consistent with our previous work [1], we used the same settings for the cart-pole for training and evaluation and show the performance graph of the FTRNN that we obtained in the paper.

6 Conclusion We presented a regularization technique for the Factored Tensor Recurrent Neural Network (FTRNN) to learn the dynamics of an insufficiently observed system by exploiting the similarity to a well observed system in a dual-task learning approach. The FTRNN disentangles cross-system properties from peculiarities enabling to share knowledge efficiently among the systems. In previous work, we discovered that the parameters of the system of interest can converge to unfavorable values when information is insufficient.

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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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