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The encoder RNN processes an input sequence into a sequence of hidden vectors, and the decoder RNN processes the sequence of hidden vectors to an output sequence, with an optional attention mechanism. This was used to construct state of the art neural machine translators during the 2014–2017 period.
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.
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.
RNN or rnn may refer to: Random neural network , a mathematical representation of an interconnected network of neurons or cells which exchange spiking signals Recurrent neural network , a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence
The general structure of RNN and BRNN can be depicted in the right diagram. By using two time directions, input information from the past and future of the current time frame can be used unlike standard RNN which requires the delays for including future information. [1]
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 ...
Animation of seq2seq with RNN and attention mechanism. Seq2seq is a family of machine learning approaches used for natural language processing. [1] Applications include language translation, image captioning, conversational models, and text summarization. [2] Seq2seq uses sequence transformation: it turns one sequence into another sequence.
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 ...