[record] add HWNODE record - 0.5527

[lucamignatti]

In this submission I improve upon the baseline of PR #800 5654 by introducing Hammersetein-Wiener Neural ODEs (HWNODEs).

This is a novel architecture that uses a linear Neural ODE wrapped between two non-linear projections, mirroring Hammerstein-Wiener models from classic control theory, to dynamically generate functionally deeper layers from a single set of shared weights. We take the Taylor series approximation for the matrix exponential exp(A), which is differentiable, to generate theoretically N continuous layers of diminishing scale. Practically, we truncate the Taylor expansion at order 2 to maximize computational speed, compiling the entire ODE trajectory into a single dense matrix pass. This allows the network to model deep continuous trajectories completely linearly inside the bottleneck, while the Hammerstein/Wiener boundaries handle the non-linear mappings. This effectively outperforms identically parameterized MLPs natively, with significant room for scope expansion.