Given M with (M) < 1andanunsatur (M)

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Structured state space models (SSMs), the core engine behind prominent neural networks such as S4 and Mamba, are linear dynamical systems adhering to a specified structure, most notably diagonal. In contrast to typical neural network modules, whose parameterizations are real, SSMs often use complex parameter-izations. Theoretically explaining the benefits of complex parameterizations for SSMs is an open problem. The current paper takes a step towards its resolution, by establishing formal gaps between real and complex diagonal SSMs.

We introduce a natural and mild restriction on the meta-level combination functions of the local trials.

Third, we are the first to consider regularized monotone SCP (RMSCP).

Q1: F or what purpose was the dataset created? As stated in Section 1, most previous datasets on DU focus on single-page DU. Our benchmark is constructed to bridge such a gap. Q2: Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., Q3: What support was needed to make this dataset? Q1: What do the instances that comprise the dataset represent (e.g., documents, photos, people, countries)?