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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. Sepp Hochreiter - Wikipedia

    en.wikipedia.org/wiki/Sepp_Hochreiter

    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).

  4. Vanishing gradient problem - Wikipedia

    en.wikipedia.org/wiki/Vanishing_gradient_problem

    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 ...

  5. Recurrent neural network - Wikipedia

    en.wikipedia.org/wiki/Recurrent_neural_network

    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]

  6. History of artificial neural networks - Wikipedia

    en.wikipedia.org/wiki/History_of_artificial...

    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.

  7. Jürgen Schmidhuber - Wikipedia

    en.wikipedia.org/wiki/Jürgen_Schmidhuber

    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.

  8. Timeline of machine learning - Wikipedia

    en.wikipedia.org/wiki/Timeline_of_machine_learning

    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

  9. Neural network (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Neural_network_(machine...

    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.