Fitting logic gates as 4D multilinear polynomials with covariance Jacobian selection matches or beats 16D softmax baselines on seven datasets and remains stable at 12-layer depth where the baseline drops 37 points on CIFAR-10.
Convo- lutional differentiable logic gate networks
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 2years
2026 2roles
background 1polarities
background 1representative citing papers
A modular neural architecture learns complete K3 logic and shows uncertainty-verdict asymmetric propagation plus a reliability spectrum for long discretized composition.
citing papers explorer
-
Fitting Multilinear Polynomials for Logic Gate Networks
Fitting logic gates as 4D multilinear polynomials with covariance Jacobian selection matches or beats 16D softmax baselines on seven datasets and remains stable at 12-layer depth where the baseline drops 37 points on CIFAR-10.
-
THEIA: Learning Complete Kleene Three-Valued Logic in a Pure-Neural Modular Architecture
A modular neural architecture learns complete K3 logic and shows uncertainty-verdict asymmetric propagation plus a reliability spectrum for long discretized composition.