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. Pub.: 01 Dec '16, Pinned: 07 Dec '16. Docollab. Search, sort and filter With Docollab you can quickly find the content you are looking for.
You can search by keywords and labels, filter your results by ownership and date, and sort them by name or date. Docollab projects help you organize your work and track overall progress Organize projects. How To Publish Like A Huge Content Creation Team (When It’s Really Just You) Generate dozens of ideas, publish daily and collect 830+ leads in 60 days...even if your site’s brand new.
Apr 19th, 2016 by Niklas Goeke Don’t things like these make you want to punch something? If it were that easy to get 1,000 subscribers, wouldn’t we all be internet millionaires by now? We’ve all seen these before. Outlandish claims like this are based on either one of two assumptions: You’re an email marketing expertYou have a massive, pre-existing audience. Of course you can just install a pop-up when you get 100,000 visitors a month and collect an easy extra 1,000 subscribers. On Data Titles. Untitled. Don’t want to store your data in the cloud?
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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.
Data are normally published under the CC0 licence which facilitates data reuse. 3. 3.1 Spreadsheet data Give each column a descriptive heading. 4. Community Collections - Zenodo. Install on Windows (in Docker) — elabftw 1.1.8-p2 documentation. Untitled. Digital tools for researchers. Search for federal grants, foundation grants, and limited submission opportunities.
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