13 citations · 22 across the 6 of their papers we have counts for
6 papers
Reactive Graphs for Efficient Markov Chain Monte Carlo Inference in Probabilistic Programming Languages
Viktor Palmkvist, Fredrik Ronquist, David Broman
An important aspect of making inference based on a probabilistic program practical is efficiency; faster evaluation enables more work per unit of time, which can be translated into…
Repr Types: One Abstraction to Rule Them All
Viktor Palmkvist, Anders Ågren Thuné, Elias Castegren +1
The choice of how to represent an abstract type can have a major impact on the performance of a program, yet mainstream compilers cannot perform optimizations at such a high level.…
Automatic Alignment in Higher-Order Probabilistic Programming Languages
Daniel Lundén, Gizem Çaylak, Fredrik Ronquist +1
Probabilistic Programming Languages (PPLs) allow users to encode statistical inference problems and automatically apply an inference algorithm to solve them. Popular inference algo…
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…
Compiling Universal Probabilistic Programming Languages with Efficient Parallel Sequential Monte Carlo Inference
Daniel Lundén, Joey Öhman, Jan Kudlicka +3
Probabilistic programming languages (PPLs) allow users to encode arbitrary inference problems, and PPL implementations provide general-purpose automatic inference for these problem…
Resolvable Ambiguity
Viktor Palmkvist, Elias Castegren, Philipp Haller +1
A common standpoint when designing the syntax of programming languages is that the grammar definition has to be unambiguous. However, requiring up front unambiguous grammars can fo…