239 citations · 434 across the 32 of their papers we have counts for
5 papers · 1 filter
Tricks from Deep Learning
Atılım Güneş Baydin, Barak A. Pearlmutter, Jeffrey Mark Siskind
The deep learning community has devised a diverse set of methods to make gradient optimization, using large datasets, of large and highly complex models with deeply cascaded nonlin…
Evolving the Incremental λ Calculus into a Model of Forward Automatic Differentiation (AD)
Robert Kelly, Barak A. Pearlmutter, Jeffrey Mark Siskind
Formal transformations somehow resembling the usual derivative are surprisingly common in computer science, with two notable examples being derivatives of regular expressions and d…
DiffSharp: An AD Library for .NET Languages
Atılım Güneş Baydin, Barak A. Pearlmutter, Jeffrey Mark Siskind
DiffSharp is an algorithmic differentiation or automatic differentiation (AD) library for the .NET ecosystem, which is targeted by the C# and F# languages, among others. The librar…
Efficient Implementation of a Higher-Order Language with Built-In AD
Jeffrey Mark Siskind, Barak A. Pearlmutter
We show that Automatic Differentiation (AD) operators can be provided in a dynamic language without sacrificing numeric performance. To achieve this, general forward and reverse AD…
Binomial Checkpointing for Arbitrary Programs with No User Annotation
Jeffrey Mark Siskind, Barak A. Pearlmutter
Heretofore, automatic checkpointing at procedure-call boundaries, to reduce the space complexity of reverse mode, has been provided by systems like Tapenade. However, binomial chec…