What are the two key improvements developed to enhance the performance of RNNs?

The two key improvements that significantly enhance the performance of Recurrent Neural Networks (RNNs) are Long Short-Term Memory (LSTM) units and Gated Recurrent Units (GRUs).

LSTMs were introduced to address the issues of vanishing and exploding gradients that RNNs often experience during training, especially with long sequences. This architecture allows the network to maintain information over longer time periods, which is crucial for tasks such as language modeling or time series prediction. LSTMs include mechanisms called gates that control the flow of information, enabling the network to learn which information to keep or discard.

Gated Recurrent Units also aim to solve similar problems by introducing gating mechanisms, but they simplify the architecture compared to LSTMs while maintaining performance. GRUs merge the forget and input gates of LSTMs into a single update gate, making them more computationally efficient while still addressing the issues of long-term dependencies.

These advancements enable RNNs to better capture temporal relationships in data, resulting in improved performance across various applications in natural language processing, machine translation, and speech recognition.