Distribution of p-values when comparing two means. P Summers. Latest Applications of Deep Learning. Learning Informative Features from Restricted Boltzmann Machines Abstract: Abstract In recent years deep learning paradigm achieved important empirical success in a number of practical applications such as object recognition, speech recognition and natural language processing.
A lot of effort has been put on understanding theoretical aspects of this success, however, still there is no common view on how deep architectures should be trained and thus many open questions remain. One hypothesis focuses on formulating good criterion (prior) that may help to learn a set of features capable of disentangling hidden factors. Following this line of thinking, in this paper, we propose to add a penalty (regularization) term to the log-likelihood function that enforces hidden units to maximize entropy and to be pairwise uncorrelated, for given observables.
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Overview This page provides information about data you need to include when publishing an article in F1000Research, where your data can be stored, and how your data should be presented. Please note that adherence to our data policies is only compulsory for articles, not posters or slides. A large number of journals and publishers have confirmed that they welcome research articles reporting analysis and conclusions that are based on previously published datasets: They do not consider the publication of a dataset with a DOI and associated protocol information as a ‘prior publication’ that would preclude subsequent publication of new results obtained from such a dataset. 2. All primary research articles should include the submission of the data underlying the results, together with details of any software used to process results.
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