Beyond Affine Transformations: A Soft Dominance Layer for Coordinate-Wise Neural Computation

Abstract:This paper presents a preliminary study of an alternative to the affine transformation underlying conventional neural-network layers. In the proposed Soft Dominance Layer, each output unit compares input coordinates with a learnable reference vector and aggregates smooth inequality responses. A sigmoid relaxation makes the comparisons differentiable, while a sharpness parameter $\alpha$ controls their transition toward hard threshold decisions. The aim is to examine the trainability and direct threshold interpretation of this primitive, not to claim a replacement for affine layers. In single-run MNIST experiments, the highest observed Soft Dominance accuracy is $0.9061$ without annealing and $0.9173$ with annealing, compared with $0.9827$ for the MLP baseline. These descriptive results do not establish reliable configuration rankings or a statistically supported annealing benefit. Learned reference vectors exhibit spatial structure, providing qualitative evidence of structured learning. Repeated-seed experiments and broader datasets are required to assess robustness and practical relevance beyond this proof of concept.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2610.00563 [cs.LG]

(or arXiv:2610.00563v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.00563 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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