GitHub - sanjay-kv/Semi-supervised-Sequence-Learning: Two approaches that use unlabeled data to improve sequence learning with recurrent networks. The first approach is to predict what comes next in a sequence, which is a conventional language model in natural language processing.

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Two approaches that use unlabeled data to improve sequence learning with recurrent networks. The first approach is to predict what comes next in a sequence, which is a conventional language model in natural language processing.

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% readme.txt for sagej.cls
% Version 1.20 released 14 January 2017
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% This software may only be used to prepare an article for publication in a
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% The release consists of the following files:
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%   readme.txt     		this file
%   sagej.cls      		the LaTeX2e class file
%   Sage_LaTeX_Guidelines.tex   authors' instructions
%   Sage_Latex_Guidelines.pdf   authors' instructions in PDF format
%   SageV.bst      		SAGE Vancouver style bst
%   SageH.bst      		SAGE Harvard style bst
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% Typeset Sage_Latex_Guidelines.tex for instructions and examples, or view the PDF.
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% Simply place sagej.cls and sagedoc.tex in your systems usual 
% directories and typeset using your LaTeX2e/PDFLaTeX command.
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