  We introduce the \textbf{B}i-Directional \textbf{S}parse \textbf{Hop}field
Network (\textbf{BiSHop}), a novel end-to-end framework for deep tabular
learning. BiSHop handles the two major challenges of deep tabular learning:
non-rotationally invariant data structure and feature sparsity in tabular data.
Our key motivation comes from the recent established connection between
associative memory and attention mechanisms. Consequently, BiSHop uses a
dual-component approach, sequentially processing data both column-wise and
row-wise through two interconnected directional learning modules.
Computationally, these modules house layers of generalized sparse modern
Hopfield layers, a sparse extension of the modern Hopfield model with adaptable
sparsity. Methodologically, BiSHop facilitates multi-scale representation
learning, capturing both intra-feature and inter-feature interactions, with
adaptive sparsity at each scale. Empirically, through experiments on diverse
real-world datasets, we demonstrate that BiSHop surpasses current SOTA methods
with significantly less HPO runs, marking it a robust solution for deep tabular
learning.
