What is Temporal Convolutional Network (TCN) and how does it differ from RNNs?

TCNs use causal convolutions and dilations to model sequences, offering parallelism and stable gradients compared to RNNs.

Temporal Convolutional Networks use 1D convolutions with causal padding to ensure no future information leakage. Dilated convolutions expand receptive fields efficiently. Unlike RNNs, TCNs process sequences in parallel, making them faster and more stable during training. They also avoid vanishing gradient issues common in RNNs.