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English: A diagram for a one-unit Long Short-Term Memory (LSTM). From bottom to top : input state, hidden state and cell state, output state. Gates are sigmoïds or hyperbolic tangents. Other operators : element-wise plus and multiplication. Weights are not displayed. Inspired from Understanding LSTM, Blog of C. Olah
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.
A 380M-parameter model for machine translation uses two long short-term memories (LSTM). [23] Its architecture consists of two parts. The encoder is an LSTM that takes in a sequence of tokens and turns it into a vector. The decoder is another LSTM that converts the vector into a sequence
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.
Generative pretraining (GP) was a long-established concept in machine learning applications. [ 16 ] [ 17 ] It was originally used as a form of semi-supervised learning , as the model is trained first on an unlabelled dataset ( pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.
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.
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