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LSTMs mitigate the vanishing gradient problem in RNNs by using a memory cell whose state can persist across many time steps.
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.
LSTMs mitigate the vanishing gradient problem in RNNs by using a memory cell whose state can persist across many time steps.
An LSTM cell uses gates—forget, input/update, and output—to control retention, writing, and exposure of information from the cell state.
Common variants include peephole LSTM (gates access the cell state) and peephole convolutional LSTM (convolution-based gates for structured data).
Training often uses backpropagation through time; LSTMs help gradients remain usable via the cell-state “error carousel.”
LSTMs are used broadly for sequence tasks such as speech recognition, machine translation, and time series analysis.
A recurrent neural network architecture that uses a memory cell and gating mechanisms to learn long-range dependencies in sequential data.
A training issue where gradients shrink toward zero during backpropagation through time, preventing effective learning of long-term dependencies.
The internal memory of an LSTM that can carry information across many time steps.
A gate that outputs values between 0 and 1 to decide how much of the previous cell state to keep.
A gate that controls how much new candidate information is written into the cell state.
A gate that determines how much of the current cell state is transformed and exposed as the hidden/output state.
Connections that allow LSTM gates to use the previous cell state when computing gate activations.
A training method for sequence labeling that enables learning from unsegmented data by maximizing label sequence probability given input sequences.
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