The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
This paper presents a character-level seque-nce-to-sequence learning method, RNNembed. This method allows the system to read raw characters, instead of words generated by preprocessing steps, into a pure single neural network model under an end-to-end framework. Specifically, we embed a recurrent neural network into an encoder–decoder framework and generate character-level sequence representation...
This paper presents a character-level sequence-to-sequence learning method, RNNembed. Specifically, we embed a Recurrent Neural Network (RNN) into an encoder-decoder framework and generate character-level sequence representation as input. The dimension of input feature space can be significantly reduced as well as avoiding the need to handle unknown or rare words in sequences. In the language model,...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.