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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.
A RNN (often a LSTM) where a series is decomposed into a number of scales where every scale informs the primary length between two consecutive points. A first order scale consists of a normal RNN, a second order consists of all points separated by two indices and so on. The Nth order RNN connects the first and last node.
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.
Logistic activation function. The activation function of a node in an artificial neural network is a function that calculates the output of the node based on its individual inputs and their weights.
RNNs were rarely used in practice before the introduction of the ESN, because of the complexity involved in adjusting their connections (e.g., lack of autodifferentiation, susceptibility to vanishing/exploding gradients, etc.). RNN training algorithms were slow and often vulnerable to issues, such as branching errors. [16] Convergence could ...
Structure of RNN and BRNN [1] The principle of BRNN is to split the neurons of a regular RNN into two directions, one for positive time direction (forward states), and another for negative time direction (backward states). Those two states' output are not connected to inputs of the opposite direction states.
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Seq2seq RNN encoder-decoder with attention mechanism, training Seq2seq RNN encoder-decoder with attention mechanism, training and inferring The attention mechanism is an enhancement introduced by Bahdanau et al. in 2014 to address limitations in the basic Seq2Seq architecture where a longer input sequence results in the hidden state output of ...