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  2. 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).

  3. 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.

  4. 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.

  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. 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 ...

  7. Exercise can boost your memory — and a new study says the ...

    www.aol.com/lifestyle/exercise-boost-memory...

    Working out can help improve your memory for up to 24 hours, a new study says. (Getty Images) (d3sign via Getty Images)

  8. Gating mechanism - Wikipedia

    en.wikipedia.org/wiki/Gating_mechanism

    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

  9. Wandering can be deadly for the growing number of US ... - AOL

    www.aol.com/wandering-deadly-growing-number-us...

    One consequence of the degenerative brain disorder is wandering, which can be dangerous—and sometimes deadly—for those struggling with short- and long-term memory loss.