NeSyCat Torch gives a monad-parametric, tensor-implemented semantics for neurosymbolic learning that supports neural predicates and shows competitive MNIST-addition performance across HaskTorch, JAX and PyTorch backends.
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Generalizes categorical theories to coherent theories and proves a duality identifying the 2-category of categorical pretopoi with profinite monoids, further realizing the latter as a full sub-2-category of topoi via classifying topos.
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NeSyCat Torch: A Differentiable Tensor Implementation of Categorical Semantics for Neurosymbolic Learning
NeSyCat Torch gives a monad-parametric, tensor-implemented semantics for neurosymbolic learning that supports neural predicates and shows competitive MNIST-addition performance across HaskTorch, JAX and PyTorch backends.
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Duality theory for categorical theories
Generalizes categorical theories to coherent theories and proves a duality identifying the 2-category of categorical pretopoi with profinite monoids, further realizing the latter as a full sub-2-category of topoi via classifying topos.