10 citations · 35 across the 9 of their papers we have counts for
10 papers
Parallel Dual-Numbers Reverse AD
Tom Smeding, Matthijs Vákár
Where dual-numbers forward-mode automatic differentiation (AD) pairs each scalar value with its tangent value, dual-numbers reverse-mode AD attempts to achieve reverse AD using a s…
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…
CHAD for Expressive Total Languages
Fernando Lucatelli Nunes, Matthijs Vákár
We show how to apply forward and reverse mode Combinatory Homomorphic Automatic Differentiation (CHAD) to total functional programming languages with expressive type systems featur…
CHAD: Combinatory Homomorphic Automatic Differentiation
Matthijs Vákár, Tom Smeding
We introduce Combinatory Homomorphic Automatic Differentiation (CHAD), a principled, pure, provably correct define-then-run method for performing forward- and reverse-mode automati…
Higher Order Automatic Differentiation of Higher Order Functions
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 character…
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…
Denotational Correctness of Forward-Mode Automatic Differentiation for Iteration and Recursion
Matthijs Vákár
We present semantic correctness proofs of forward-mode Automatic Differentiation (AD) for languages with sources of partiality such as partial operations, lazy conditionals on real…
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…
On S-Finite Measures and Kernels
Matthijs Vákár, Luke Ong
In this note, we develop some of the basic theory of s-finite (measures and) kernels, a little-studied class that Staton has recently argued convincingly to be precisely the semant…