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Temporal (causal) convolution computes each output at time t using only the current and past inputs, ensuring no future information leakage.
A Recurrent Neural Network (RNN) processes sequences by carrying a hidden state that is updated at every time step using h_t = f(W_h h_{t-1} + W_x x_t + b).