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  2. Long short-term memory - Wikipedia

    en.wikipedia.org/wiki/Long_short-term_memory

    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.

  3. Time aware long short-term memory - Wikipedia

    en.wikipedia.org/wiki/Time_aware_long_short-term...

    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 ]

  4. PDF - Wikipedia

    en.wikipedia.org/wiki/PDF

    Interactive Forms is a mechanism to add forms to the PDF file format. PDF currently supports two different methods for integrating data and PDF forms. Both formats today coexist in the PDF specification: [37] [52] [53] [54] AcroForms (also known as Acrobat forms), introduced in the PDF 1.2 format specification and included in all later PDF ...

  5. File:The LSTM Cell.svg - Wikipedia

    en.wikipedia.org/wiki/File:The_LSTM_Cell.svg

    Original file (SVG file, nominally 673 × 461 pixels, file size: 55 KB) This is a file from the Wikimedia Commons. ... English: The Long Short-Term Memory (LSTM) ...

  6. File:LSTM cell.svg - Wikipedia

    en.wikipedia.org/wiki/File:LSTM_cell.svg

    English: Structure of a LSTM (Long Short-term Memory) cell. Orange boxes are activation functions (like sigmoid and tanh), yellow circles are pointwise operations. A linear transformation is used when two arrows merge. When one arrow splits, this is a copy operation.

  7. Training, validation, and test data sets - Wikipedia

    en.wikipedia.org/wiki/Training,_validation,_and...

    A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]

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