Titlebook: Artificial Neural Networks and Machine Learning- ICANN 2011; 21st International C Timo Honkela,W?odzis?aw Duch,Samuel Kaski Conference proc

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Die Gruppe Von Grasse: 1940–1942ibes the selection of the transformed view of the canonical connection weights associated with the unit. This enables the inferences of the model to transform in response to transformed input data in a . way, and avoids learning multiple features differing only with respect to the set of transformat

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Im ?Atelier 17? Bei Stanley William Hayter use a different parameterization of the energy function, which allows for more intuitive interpretation of the parameters and facilitates learning. Secondly, we propose parallel tempering learning for GBRBM. Lastly, we use an adaptive learning rate which is selected automatically in order to stabil

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Im ?Atelier 17? Bei Stanley William Hayterobject-based attention, combining generative principles with attentional ones. We show: (1) How inference in DBMs can be related qualitatively to theories of attentional recurrent processing in the visual cortex; (2) that deepness and topographic receptive fields are important for realizing the atte

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https://doi.org/10.1007/978-3-322-88744-3so methods. We apply this ?.-penalized linear regression mixed-effects model to a large scale real world problem: by exploiting a large set of brain computer interface data we are able to obtain a subject-independent classifier that compares favorably with prior zero-training algorithms. This unifyi

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