Understanding Recurrent Neural Networks: From Forgetting to Remembering

This article explores the limitations of traditional neural network architectures in processing sequential data, and how Recurrent Neural Networks (RNNs) were developed to address this gap. It explains the concept of hidden state and backpropagation through time in training RNNs, as well as the problem of vanishing gradients. The article then introduces Long Short-Term Memory (LSTM) networks as a solution to selectively retain important information in long sequences.

Details

The article starts by highlighting how traditional neural network architectures, such as perceptrons, MLPs, and CNNs, all treat the input as a static snapshot, without considering the order or sequence of the data. This assumption works well for tasks like image recognition, but falls short for sequential data like language, audio, and time series, where the meaning is heavily dependent on the context and order of the inputs. The article then introduces Recurrent Neural Networks (RNNs) as a solution to this problem. RNNs process sequences one step at a time, maintaining a hidden state that summarizes the information seen so far. This hidden state is updated at each time step, blending the new input with the previous hidden state. The same set of weights is reused across all time steps, allowing the network to learn patterns in the sequence. However, the article points out that training RNNs using backpropagation through time suffers from the vanishing gradient problem, similar to what was discussed in a previous post. As the gradients flow backward through the many time steps, they tend to shrink, making it difficult for the network to learn long-term dependencies. To address this issue, the article introduces Long Short-Term Memory (LSTM) networks, which have two states: a hidden state and a cell state. The cell state is the key innovation, as it runs through the sequence with only small, controlled modifications, allowing the network to selectively retain important information and discard irrelevant details. This enables LSTMs to effectively process long sequences without forgetting the crucial context.