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  2. Long short-term memory - Wikipedia

    en.wikipedia.org/wiki/Long_short-term_memory

    In theory, classic RNNs can keep track of arbitrary long-term dependencies in the input sequences. The problem with classic RNNs is computational (or practical) in nature: when training a classic RNN using back-propagation, the long-term gradients which are back-propagated can "vanish", meaning they can tend to zero due to very small numbers creeping into the computations, causing the model to ...

  3. Connectionist temporal classification - Wikipedia

    en.wikipedia.org/wiki/Connectionist_temporal...

    Connectionist temporal classification (CTC) is a type of neural network output and associated scoring function, for training recurrent neural networks (RNNs) such as LSTM networks to tackle sequence problems where the timing is variable.

  4. Backpropagation through time - Wikipedia

    en.wikipedia.org/wiki/Backpropagation_through_time

    Back_Propagation_Through_Time(a, y) // a[t] is the input at time t. y[t] is the output Unfold the network to contain k instances of f do until stopping criterion is met: x := the zero-magnitude vector // x is the current context for t from 0 to n − k do // t is time. n is the length of the training sequence Set the network inputs to x, a[t ...

  5. Recurrent neural network - Wikipedia

    en.wikipedia.org/wiki/Recurrent_neural_network

    Recurrent neural networks (RNNs) are a class of artificial neural network commonly used for sequential data processing. Unlike feedforward neural networks, which process data in a single pass, RNNs process data across multiple time steps, making them well-adapted for modelling and processing text, speech, and time series.

  6. Recursive neural network - Wikipedia

    en.wikipedia.org/wiki/Recursive_neural_network

    A recursive neural network is a kind of deep neural network created by applying the same set of weights recursively over a structured input, to produce a structured prediction over variable-size input structures, or a scalar prediction on it, by traversing a given structure in topological order.

  7. Download, install, or uninstall AOL Desktop Gold

    help.aol.com/articles/aol-desktop-downloading...

    Learn how to download and install or uninstall the Desktop Gold software and if your computer meets the system requirements.

  8. Transformer (deep learning architecture) - Wikipedia

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

    For many years, sequence modelling and generation was done by using plain recurrent neural networks (RNNs). A well-cited early example was the Elman network (1990). In theory, the information from one token can propagate arbitrarily far down the sequence, but in practice the vanishing-gradient problem leaves the model's state at the end of a long sentence without precise, extractable ...

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