Bidirectional recurrent neural networks

<p>Bidirectional <a href="/facts/Recurrent_neural_networks/bx7hBVB1">recurrent neural networks</a> (BRNN) connect two hidden layers of opposite directions to the same output. With this form of <a href="/facts/Generative_model/JUgExNIP">generative deep learning</a>, the output layer can get information from past (backwards) and future (forward) states simultaneously. Invented in 1997 by Schuster and Paliwal, BRNNs were introduced to increase the amount of input information available to the network. For example, <a href="/facts/Multilayer_perceptron/jkq4t7Gy">multilayer perceptron</a> (MLPs) and <a href="/facts/Time_delay_neural_network/evR9vGpn">time delay neural network</a> (TDNNs) have limitations on the input data flexibility, as they require their input data to be fixed. Standard <a href="/facts/Recurrent_neural_network/bx7hBVB1">recurrent neural network</a> (RNNs) also have restrictions as the future input information cannot be reached from the current state. On the contrary, BRNNs do not require their input data to be fixed. Moreover, their future input information is reachable from the current state.
</p><p>BRNN are especially useful when the context of the input is needed. For example, in <a href="/facts/Handwriting_recognition/RgdyWtDQ">handwriting recognition</a>, the performance can be enhanced by knowledge of the letters located before and after the current letter.
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Bidirectional recurrent neural networks open-in-new

Bidirectional recurrent neural networks