By Marcel A. J. van Gerven, Eric Maris (auth.), Timo Honkela, Włodzisław Duch, Mark Girolami, Samuel Kaski (eds.)
This quantity set (LNCS 6791 and LNCS 6792) constitutes the refereed lawsuits of the 21th foreign convention on synthetic Neural Networks, ICANN 2011, held in Espoo, Finland, in June 2011. The 106 revised complete or poster papers offered have been conscientiously reviewed and chosen from a number of submissions. ICANN 2011 had uncomplicated tracks: brain-inspired computing and laptop studying examine, with robust cross-disciplinary interactions and applications.
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Additional resources for Artificial Neural Networks and Machine Learning – ICANN 2011: 21st International Conference on Artificial Neural Networks, Espoo, Finland, June 14-17, 2011, Proceedings, Part II
E): Convergence of the segmentation during learning (inferred mask m superimposed on training image before joint training (left most) and after 10, 20, 100 and 1000 epochs of joint training). At the beginning the segmentation is driven primarily by the background model and thus very noisy. (f ) Two examples of the pairs of images used for the recognition task. 16 5 N. Heess, N. Le Roux, and J. Winn Discussion We have demonstrated how RBMs and layered representations can be combined to obtain a model that is able to represent the joint shape and appearance of foreground objects independently of the background and we have shown how to learn the model of the foreground directly from cluttered training images using only very weak supervision.
Adaptive calibration aims to provide a fast adjustment of the BCI system to mild changes of the signal. Although the INPLS allows treating data arrays of huge dimension, this method cannot be applied for adaptive learning. In this paper a Recursive NPLS (RNPLS) algorithm is proposed. It allows online processing of multi-modal data. Moreover, weighted RNPLS can be applied for adaptive learning to treat time-dependent recordings. This algorithm can be efficiently used for numerous applications beyond BCI.
Acknowledgments. NH is supported by a EPSRC/MRC scholarship from the Neuroinformatics DTC at the University of Edinburgh. NLR is supported by a grant from the European Research Council (SIERRA-ERC-239993). References 1. : Training Products of Experts by Minimizing Contrastive Divergence. Neural Computation 14(8), 1771–1800 (2002) 2. : A fast learning algorithm for deep belief nets. Neural Computation 18, 1527–1554 (2006) 3. : Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations.
Artificial Neural Networks and Machine Learning – ICANN 2011: 21st International Conference on Artificial Neural Networks, Espoo, Finland, June 14-17, 2011, Proceedings, Part II by Marcel A. J. van Gerven, Eric Maris (auth.), Timo Honkela, Włodzisław Duch, Mark Girolami, Samuel Kaski (eds.)