2 citations · 4 across the 3 of their papers we have counts for
3 papers
CoreDPPL: Towards a Sound Composition of Differentiation, ODE Solving, and Probabilistic Programming
Oscar Eriksson, Anders Ågren Thuné, Johannes Borgström +1
In recent years, there has been extensive research on how to extend general-purpose programming language semantics with domain-specific modeling constructs. Two areas of particular…
Suspension Analysis and Selective Continuation-Passing Style for Universal Probabilistic Programming Languages
Daniel Lundén, Lars Hummelgren, Jan Kudlicka +2
Universal probabilistic programming languages (PPLs) make it relatively easy to encode and automatically solve statistical inference problems. To solve inference problems, PPL impl…
Expression Acceleration: Seamless Parallelization of Typed High-Level Languages
Lars Hummelgren, John Wikman, Oscar Eriksson +2
Efficient parallelization of algorithms on general-purpose GPUs is essential in many areas today. However, it is a non-trivial task for software engineers to utilize GPUs to improv…