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Office Open XML (OOXML) format was introduced with Microsoft Office 2007 and became the default format of Microsoft Word ever since. Pertaining file extensions include:.docx – Word document.docm – Word macro-enabled document; same as docx, but may contain macros and scripts.dotx – Word template.dotm – Word macro-enabled template; same ...
For example, the word processing application Microsoft Word uses different file extensions for documents and templates: In Word 2003 the file extension .dot is used to indicate a template, in contrast to .doc for a standard document. In Word 2007 and later versions, it's .dotx, instead of .docx for documents. The OpenDocument Format also has ...
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Long short-term memory (LSTM) [1] is a type of recurrent neural network (RNN) aimed at mitigating the vanishing gradient problem [2] commonly encountered by traditional RNNs. Its relative insensitivity to gap length is its advantage over other RNNs, hidden Markov models , and other sequence learning methods.
Microsoft Office Word 2003 XML Format — WordProcessingML or WordML (.XML) Microsoft Office Excel 2002 and Excel 2003 XML Format — SpreadsheetML (.XML) Microsoft Office Visio 2003 XML Format — DataDiagramingML (.VDX, .VSX, .VTX) Microsoft Office InfoPath 2003 XML Format — XML FormTemplate (.XSN) (Compressed XML templates in a Cabinet file)
Time Aware LSTM (T-LSTM) is a long short-term memory (LSTM) unit capable of handling irregular time intervals in longitudinal patient records. T-LSTM was developed by researchers from Michigan State University, IBM Research, and Cornell University and was first presented in the Knowledge Discovery and Data Mining (KDD) conference. [1]
The term template, when used in the context of word processing software, refers to a sample document that has already some details in place; those can (that is added/completed, removed or changed, differently from a fill-in-the-blank of the approach as in a form) either by hand or through an automated iterative process, such as with a software assistant.
Gated recurrent units (GRUs) are a gating mechanism in recurrent neural networks, introduced in 2014 by Kyunghyun Cho et al. [1] The GRU is like a long short-term memory (LSTM) with a gating mechanism to input or forget certain features, [2] but lacks a context vector or output gate, resulting in fewer parameters than LSTM. [3]
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