activity
20102022
most citedSimple, Distributed, and Accelerated Probabilistic Programming

39 citations · 89 across the 16 of their papers we have counts for

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Showing cs.PLShow all

7 papers · 1 filter

cs.PL2022

The Foil: Capture-Avoiding Substitution With No Sharp Edges

Dougal Maclaurin, Alexey Radul, Adam Paszke

Correctly manipulating program terms in a compiler is surprisingly difficult because of the need to avoid name capture. The rapier from "Secrets of the Glasgow Haskell Compiler inl…

cs.PL2022★ 1 cited

You Only Linearize Once: Tangents Transpose to Gradients

Alexey Radul, Adam Paszke, Roy Frostig +2

Automatic differentiation (AD) is conventionally understood as a family of distinct algorithms, rooted in two "modes" -- forward and reverse -- which are typically presented (and i…

cs.PL2021★ 3 cited

Decomposing reverse-mode automatic differentiation

Roy Frostig, Matthew J. Johnson, Dougal Maclaurin +2

We decompose reverse-mode automatic differentiation into (forward-mode) linearization followed by transposition. Doing so isolates the essential difference between forward- and rev…

cs.PL2021★ 1 cited

Getting to the Point. Index Sets and Parallelism-Preserving Autodiff for Pointful Array Programming

Adam Paszke, Daniel Johnson, David Duvenaud +5

We present a novel programming language design that attempts to combine the clarity and safety of high-level functional languages with the efficiency and parallelism of low-level n…

cs.PL2020★ 3 cited

The Base Measure Problem and its Solution

Alexey Radul, Boris Alexeev

Probabilistic programming systems generally compute with probability density functions, leaving the base measure of each such function implicit. This mostly works, but creates prob…

cs.PL2012

AD in Fortran, Part 2: Implementation via Prepreprocessor

Alexey Radul, Barak A. Pearlmutter, Jeffrey Mark Siskind

We describe an implementation of the Farfel Fortran AD extensions. These extensions integrate forward and reverse AD directly into the programming model, with attendant benefits to…

cs.PL2012

AD in Fortran, Part 1: Design

Alexey Radul, Barak A. Pearlmutter, Jeffrey Mark Siskind

We propose extensions to Fortran which integrate forward and reverse Automatic Differentiation (AD) directly into the programming model. Irrespective of implementation technology,…