CCEM parameterizes compositional energy factors with input-convex neural networks and optimizes over a convex relaxation to enable deterministic scaling from small to large combinatorial reasoning instances.
Moreover, for gradient descent yt+1 =y t −η∇E(y t), there exists a stepsize η >0 and an open neighborhood U⊂ Y of ¯ysuch that every initialization y0 ∈U converges to ¯y
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Convex Compositional Reasoning Models
CCEM parameterizes compositional energy factors with input-convex neural networks and optimizes over a convex relaxation to enable deterministic scaling from small to large combinatorial reasoning instances.