2021/01/20 by Jesse Sigal, Sigal, Jesse
Computer Science · #Evolutionary Algorithms and Applications #F.3.2 #F.3.3 #FOS: Computer and information sciences #Formal Methods in Verification #G.1.4 #Logic, Reasoning, and Knowledge #Logic, programming, and type systems #Machine Learning (cs.LG) #Programming Languages (cs.PL)
paper · pdf · doi:10.48550/arxiv.2101.08095
openalex publication_date 2021/01/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Automatic differentiation (AD) is an important family of algorithms which enables derivative based optimization. We show that AD can be simply implemented with effects and handlers by doing so in the Frank language. By considering how our implementation behaves in Frank's operational semantics, we show how our code performs the dynamic creation of programs during evaluation.