Colored Markov polycategories provide typed compositional semantics for stochastic systems and support diagrammatic differentiation of expected scalar objectives via local gradients at parameterized vertices.
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ULLER's three independent semantics are unified as instances of monads, enabling modular addition of new semantics and translations between them.
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Colored Markov polycategories and diagrammatic differentiation
Colored Markov polycategories provide typed compositional semantics for stochastic systems and support diagrammatic differentiation of expected scalar objectives via local gradients at parameterized vertices.
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NeSyCat: A Monad-Based Categorical Semantics of the Neurosymbolic ULLER Framework
ULLER's three independent semantics are unified as instances of monads, enabling modular addition of new semantics and translations between them.