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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.
Hochreiter developed the long short-term memory (LSTM) neural network architecture in his diploma thesis in 1991 leading to the main publication in 1997. [3] [4] LSTM overcomes the problem of numerical instability in training recurrent neural networks (RNNs) that prevents them from learning from long sequences (vanishing or exploding gradient).
For recurrent neural networks, the long short-term memory (LSTM) network was designed to solve the problem (Hochreiter & Schmidhuber, 1997). [ 9 ] For the exploding gradient problem, (Pascanu et al, 2012) [ 6 ] recommended gradient clipping, meaning dividing the gradient vector g {\displaystyle g} by ‖ g ‖ / g m a x {\displaystyle \|g\|/g ...
Long short-term memory (LSTM) networks were invented by Hochreiter and Schmidhuber in 1995 and set accuracy records in multiple applications domains. [35] [36] It became the default choice for RNN architecture. Bidirectional recurrent neural networks (BRNN) uses two RNN that processes the same input in opposite directions. [37]
Long short-term memory (LSTM) networks were invented by Hochreiter and Schmidhuber in 1995 and set accuracy records in multiple applications domains. [46] [49] It became the default choice for RNN architecture. Around 2006, LSTM started to revolutionize speech recognition, outperforming traditional models in certain speech applications.
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.
Sepp Hochreiter and Jürgen Schmidhuber invent long short-term memory (LSTM) recurrent neural networks, [36] greatly improving the efficiency and practicality of recurrent neural networks. 1998: MNIST database
In 1991, Sepp Hochreiter's diploma thesis [73] identified and analyzed the vanishing gradient problem [73] [74] and proposed recurrent residual connections to solve it. He and Schmidhuber introduced long short-term memory (LSTM), which set accuracy records in multiple applications domains.