LSTM: Solving the Memory Problem
Long Short-Term Memory networks were specifically designed to learn long-term dependencies that vanilla RNNs cannot capture. Introduced in 1997, LSTMs remained the go-to architecture for sequence modeling until Transformers emerged. Their gated mechanism elegantly solves the vanishing gradient problem.
LSTMs use three gates to control information flow. The forget gate decides what to discard from cell state. The input gate determines what new information to store. The output gate controls what the cell outputs. Each gate learns its own parameters during training.
The key innovation is the cell state – a highway running through the entire sequence with only minor linear interactions. Gradients flow through this highway relatively unchanged, allowing the network to maintain and access information over hundreds of timesteps without degradation.
GRUs (Gated Recurrent Units) simplify LSTMs by combining the forget and input gates into a single update gate, reducing parameters while achieving similar performance. For many applications, the choice between LSTM and GRU comes down to experimentation rather than theoretical superiority.