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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. Recurrent neural network - Wikipedia

    en.wikipedia.org/wiki/Recurrent_neural_network

    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]

  4. 1-2-AX working memory task - Wikipedia

    en.wikipedia.org/wiki/1-2-AX_working_memory_task

    The 1-2-AX working memory task is a cognitive test which requires working memory to be solved. It can be used as a test case for learning algorithms to test their ability to remember some old data. This task can be used to demonstrate the working memory abilities of algorithms like PBWM or Long short-term memory. [1]

  5. 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.

  6. Transformer (deep learning architecture) - Wikipedia

    en.wikipedia.org/wiki/Transformer_(deep_learning...

    A 380M-parameter model for machine translation uses two long short-term memories (LSTM). [23] Its architecture consists of two parts. 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.

  7. Types of artificial neural networks - Wikipedia

    en.wikipedia.org/wiki/Types_of_artificial_neural...

    Memory networks [69] [70] incorporate long-term memory. The long-term memory can be read and written to, with the goal of using it for prediction. These models have been applied in the context of question answering (QA) where the long-term memory effectively acts as a (dynamic) knowledge base and the output is a textual response. [71]

  8. NFL hot seat rankings: Which coaches are in most trouble ...

    www.aol.com/nfl-hot-seat-rankings-coaches...

    Several coaches are squarely on the NFL hot seat entering Week 18, with Mike McCarthy and Brian Daboll among those facing uncertain futures.

  9. Memory and retention in learning - Wikipedia

    en.wikipedia.org/wiki/Memory_and_Retention_in...

    Model of the Memory Process. Human memory is the process in which information and material is encoded, stored and retrieved in the brain. [1] Memory is a property of the central nervous system, with three different classifications: short-term, long-term and sensory memory. [2]