Modern neural variants parameterize the state update and output projection so long sequences can be processed efficiently. Some formulations support parallel training while retaining recurrent-style incremental inference.
A state space model represents a sequence through a hidden state that evolves according to learned dynamics.
Modern neural variants parameterize the state update and output projection so long sequences can be processed efficiently. Some formulations support parallel training while retaining recurrent-style incremental inference.