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The Long Short-Term Memory (LSTM) cell can process data sequentially and keep its hidden state through time. 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.
That is, LSTM can learn tasks that require memories of events that happened thousands or even millions of discrete time steps earlier. Problem-specific LSTM-like topologies can be evolved. [56] LSTM works even given long delays between significant events and can handle signals that mix low and high-frequency components.
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 of tokens. Similarly, another 130M-parameter model used gated recurrent units (GRU) instead of LSTM. [ 22 ]
A RNN (often a LSTM) where a series is decomposed into a number of scales where every scale informs the primary length between two consecutive points. A first order scale consists of a normal RNN, a second order consists of all points separated by two indices and so on. The Nth order RNN connects the first and last node.
An LSTM unit contains three gates: An input gate, which controls the flow of new information into the memory cell; A forget gate, which controls how much information is retained from the previous time step; An output gate, which controls how much information is passed to the next layer. The equations for LSTM are: [2]
The name LSTM was introduced in a tech report (1995) leading to the most cited LSTM publication (1997), co-authored by Hochreiter and Schmidhuber. [19] It was not yet the standard LSTM architecture which is used in almost all current applications. The standard LSTM architecture was introduced in 2000 by Felix Gers, Schmidhuber, and Fred Cummins ...
Professor Jonathan Ball, from the Liverpool School of Tropical Medicine (LSTM), said: “Even in the rare cases where people do become infected, onward human to human transmission is very unlikely.
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]