Learning LSTM: Gates, Variants, Training, Uses

Long short-term memory overview

Long short-term memory (LSTM) is a recurrent neural network architecture designed to address the vanishing gradient problem that limits traditional RNNs when learning long-range dependencies in sequential data. Unlike standard RNNs, LSTMs can maintain information for thousands of time steps by using a dedicated memory cell whose state is preserved or updated over time. This makes LSTMs well-suited for sequence learning tasks where relevant information may appear far apart in the input. An LSTM cell typically contains a memory cell and three interacting gates: an input gate, a forget gate, and an output gate. The forget gate decides what portion of the previous cell state to retain, the input gate controls what new information to write into the cell, and the output gate determines what part of the cell state to expose as the hidden state (output). Variants such as peephole LSTM allow gates to also “look” at the previous cell state, and peephole convolutional LSTM extends the idea using convolution operations for spatial/structured inputs. During training, LSTMs are commonly optimized with backpropagation through time; their “error carousel” helps gradients flow through the cell state, improving learning of long-term dependencies. LSTMs are widely used in applications including time series prediction, speech recognition, machine translation, and other sequence labeling and forecasting problems.

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Recurrent neural networks and sequence processing

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