10 citations · 45 across the 26 of their papers we have counts for
28 papers
LazyHMC: Hamiltonian Monte Carlo Simulation for Lazy, Infinite Dimensional Probabilistic Programs
Maria-Nicoleta Crăciun, C. -H. Luke Ong, Tom Schrijvers +1
Hamiltonian Monte Carlo (HMC) is a successful generic inference method in probabilistic programming, but in its ordinary formulation it needs gradients and finite-dimensional param…
Let a Thousand Flowers Bloom: An Algebraic Representation for Edge Graphs
Jack Liell-Cock, Tom Schrijvers
Context: Edge graphs are graphs whose edges are labelled with identifiers, and nodes can have multiple edges between them. They are used to model a wide range of systems, including…
From High to Low: Simulating Nondeterminism and State with State
Wenhao Tang, Tom Schrijvers
Some effects are considered to be higher-level than others. High-level effects provide expressive and succinct abstraction of programming concepts, while low-level effects allow mo…
Automatic Differentiation in Prolog
Tom Schrijvers, Birthe van den Berg, Fabrizio Riguzzi
Automatic differentiation (AD) is a range of algorithms to compute the numeric value of a function's (partial) derivative, where the function is typically given as a computer progr…
A Calculus for Scoped Effects & Handlers
Roger Bosman, Birthe van den Berg, Wenhao Tang +1
Algebraic effects & handlers have become a standard approach for side-effects in functional programming. Their modular composition with other effects and clean separation of syntax…
A Framework for Higher-Order Effects & Handlers
Birthe van den Berg, Tom Schrijvers
Algebraic effects & handlers are a modular approach for modeling side-effects in functional programming. Their syntax is defined in terms of a signature of effectful operations, en…