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cs.LG2026
Fitting Multilinear Polynomials for Logic Gate Networks
Youngsung Kim
We study learnable logic gate networks that stack layers of 2-input Boolean gates to build combinational circuits. Every 2-input gate has a unique multilinear polynomial with 4 coe…
cs.LG2026
Align Forward, Adapt Backward: Closing the Discretization Gap in Logic Gate Networks
Youngsung Kim
In neural network models, soft mixtures of fixed candidate components (e.g., logic gates and sub-networks) are often used during training for stable optimization, while hard select…