1 citations · 1 across the 2 of their papers we have counts for
4 papers
Dual-Numbers Reverse AD, Efficiently
Tom Smeding, Matthijs Vákár
Where dual-numbers forward-mode automatic differentiation (AD) pairs each scalar value with its tangent derivative, dual-numbers /reverse-mode/ AD attempts to achieve reverse AD us…
Transforming Probabilistic Programs for Model Checking
Ryan Bernstein, Matthijs Vákár, Jeannette Wing
Probabilistic programming is perfectly suited to reliable and transparent data science, as it allows the user to specify their models in a high-level language without worrying abou…
Reverse AD at Higher Types: Pure, Principled and Denotationally Correct
Matthijs Vákár
We show how to define forward- and reverse-mode automatic differentiation source-code transformations or on a standard higher-order functional language. The transformations generat…
Correctness of Automatic Differentiation via Diffeologies and Categorical Gluing
Mathieu Huot, Sam Staton, Matthijs Vákár
We present semantic correctness proofs of Automatic Differentiation (AD). We consider a forward-mode AD method on a higher order language with algebraic data types, and we characte…