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LSTMs are RNNs built to mitigate vanishing gradients and learn long-term dependencies over many timesteps.
Long short-term memory (LSTM) is a recurrent neural network (RNN) architecture designed to address the vanishing gradient problem that makes traditional RNNs difficult to train on long sequences. By using a memory cell that can preserve information over many timesteps, LSTMs enable learning of long-term dependencies with relatively less sensitivity to the length of gaps in the input sequence. Although LSTMs mitigate vanishing gradients, they can still experience exploding gradients. An LSTM unit typically contains a cell state plus 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 (values near 1 keep information; values near 0 discard it). The input gate controls which new candidate information is written into the cell state, and the output gate determines which parts of the updated cell state are exposed as the hidden/output state. In the “peephole” variant, gates can also directly access the cell state (via peephole connections), improving the gate’s ability to regulate information flow. LSTM variants can also incorporate convolution (peephole convolutional LSTM) for spatial/structured inputs. During training, LSTMs are commonly optimized with backpropagation through time in supervised settings, and their “error carousel” helps keep gradients flowing through the cell state. For sequence labeling tasks where alignment between inputs and outputs is unknown, LSTMs are often trained with connectionist temporal classification (CTC), which jointly learns recognition and alignment. LSTMs have been widely applied to tasks such as speech recognition, machine translation, time series analysis, and other sequence-based prediction and classification problems.
LSTMs are RNNs built to mitigate vanishing gradients and learn long-term dependencies over many timesteps.
An LSTM cell state is controlled by input, forget, and output gates that regulate what to store, discard, and output.
Variants like peephole LSTM let gates access the cell state, and training often uses backpropagation through time or CTC for unaligned sequence labeling.
A recurrent neural network architecture that uses a memory cell and gating mechanisms to preserve information and mitigate vanishing gradients on long sequences.
A training issue where gradients shrink toward zero over long time lags, preventing effective learning in standard RNNs.
The internal memory of an LSTM that can carry information across many timesteps.
A gate that decides how much of the previous cell state to retain by producing values between 0 and 1.
A gate that controls how much new candidate information is written into the current cell state.
A gate that determines which parts of the cell state are exposed as the hidden/output state.
Connections that allow LSTM gates to directly use the cell state when computing gate activations.
A training objective for sequence problems that learns label sequences without requiring pre-aligned input-output timing.
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