Revisiting Bi-Linear State Transitions in Recurrent Neural Networks
The role of hidden units in recurrent neural networks is typically seen as
modeling memory, with research focusing on enhancing information retention
through gating mechanisms. A less explored perspective views hidden units as
active participants in the computation performed by the network, rather than
passive memory stores. In this work, we revisit bi-linear operations, which
involve multiplicative interactions between hidden units and input embeddings.
We demonstrate theoretically and empirically that they constitute a natural
inductive bias for representing the evolution of hidden states in state
tracking tasks. These are the simplest type of task that require hidden units
to actively contribute to the behavior of the network. We also show that
bi-linear state updates form a natural hierarchy corresponding to state
tracking tasks of increasing complexity, with popular linear recurrent networks
such as Mamba residing at the lowest-complexity center of that hierarchy.
683db6ff97f03081fefa4e9f
M. Reza Ebrahimi
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2025-06-03T08:49:11.196Z
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M.Reza Ebrahimi
user
mamaj92
683db6ff97f03081fefa4ea0
Roland Memisevic
Revisiting Bi-Linear State Transitions in Recurrent Neural Networks
The role of hidden units in recurrent neural networks is typically seen as
modeling memory, with research focusing on enhancing information retention
through gating mechanisms. A less explored perspective views hidden units as
active participants in the computation performed by the network, rather than
passive memory stores. In this work, we revisit bi-linear operations, which
involve multiplicative interactions between hidden units and input embeddings.
We demonstrate theoretically and empirically that they constitute a natural
inductive bias for representing the evolution of hidden states in state
tracking tasks. These are the simplest type of task that require hidden units
to actively contribute to the behavior of the network. We also show that
bi-linear state updates form a natural hierarchy corresponding to state
tracking tasks of increasing complexity, with popular linear recurrent networks
such as Mamba residing at the lowest-complexity center of that hierarchy.
Bilinear operations in recurrent neural networks are shown to be a natural bias for state tracking tasks, forming a hierarchical structure where linear recurrent networks are the simplest form.
hidden units recurrent neural networks gating mechanisms bi-linear operations input embeddings state tracking tasks bi-linear state updates Mamba