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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.
Gating mechanisms are the centerpiece of long short-term memory (LSTM). [1] They were proposed to mitigate the vanishing gradient problem often encountered by regular RNNs. An LSTM unit contains three gates: An input gate, which controls the flow of new information into the memory cell
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]
Long short-term memory unit. Long short-term memory (LSTM) is the most widely used RNN architecture. It was designed to solve the vanishing gradient problem. LSTM is normally augmented by recurrent gates called "forget gates". [54] LSTM prevents backpropagated errors from vanishing or exploding. [55]
For recurrent neural networks, the long short-term memory (LSTM) network was designed to solve the problem (Hochreiter & Schmidhuber, 1997). [9]
This led to the long short-term memory (LSTM), a type of recurrent neural network. 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.
This is the brain area that stores long-term memories. Elephant brains are structured similarly to human brains, which means they are capable of a wide variety of intellectual abilities, including ...
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]