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Faustyna Krawiec

dblp:279/2892 · DBLP profile ↗
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2ranked-venue papers
1as first author
2since 2021 · last 2022
0000-0003-2410-7454ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Software engineering, systems software and programming languages · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Software engineering, system software, and programming languages
1 paper
Compilers and program optimization · 56% Programming languages and type systems · 36% Program verification · 8%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Compilers and program optimization
automatic differentiation		 0.612022
Provably correct, asymptotically efficient, higher-order reverse-mode automatic differentiation · Proc. ACM Program. Lang. 2022
Programming languages and type systems › functional programming
higher-order functions		 0.612022
Provably correct, asymptotically efficient, higher-order reverse-mode automatic differentiation · Proc. ACM Program. Lang. 2022
Compilers and program optimization › automatic differentiation
reverse-mode automatic differentiation		 0.612022
Provably correct, asymptotically efficient, higher-order reverse-mode automatic differentiation · Proc. ACM Program. Lang. 2022
Program verification
correctness proof		 0.212022
Provably correct, asymptotically efficient, higher-order reverse-mode automatic differentiation · Proc. ACM Program. Lang. 2022
Programming languages and type systems
logical relations		 0.212022
Provably correct, asymptotically efficient, higher-order reverse-mode automatic differentiation · Proc. ACM Program. Lang. 2022

Methods — techniques the papers use, named apart from their topics

program translation · 0.6logical relations · 0.6
YearPublicationVenuePosition
2022 Provably correct, asymptotically efficient, higher-order reverse-mode automatic differentiation
abstract
In this paper, we give a simple and efficient implementation of reverse-mode automatic differentiation, which both extends easily to higher-order functions, and has run time and memory consumption linear in the run time of the original program. In addition to a formal description of the translation, we also describe an implementation of this algorithm, and prove its correctness by means of a logical relations argument.
Faustyna Krawiec, Simon L. Peyton Jones, Neelakantan R. Krishnaswami, Tom Ellis, Richard A. Eisenberg, Andrew W. Fitzgibbon
 Proc. ACM Program. Lang.1
2021 Detecting anomalies in X-ray diffraction images using convolutional neural networks
Adam Czyzewski, Faustyna Krawiec, Dariusz Brzezinski, Przemyslaw J. Porebski, Wladek Minor
 Expert Syst. Appl.2