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https://doi.org/10.21256/zhaw-30408| Publication type: | Conference paper |
| Type of review: | Peer review (publication) |
| Title: | ScalaGrad : a statically typed automatic differentiation library for safer data science |
| Authors: | Meyer, Benjamin Stadelmann, Thilo Lüthi, Marcel |
| et. al: | No |
| DOI: | 10.21256/zhaw-30408 |
| Conference details: | 11th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 30-31 May 2024 |
| Issue Date: | 31-May-2024 |
| Publisher / Ed. Institution: | ZHAW Zürcher Hochschule für Angewandte Wissenschaften |
| Language: | English |
| Subjects: | Automatic differentiation; Scala 3; ScalaGrad |
| Subject (DDC): | 005: Computer programming, programs and data |
| Abstract: | While the data science ecosystem is dominated by programming languages that do not feature a strong type system, it is widely agreed that using strongly typed programming languages leads to more maintainable and less error-prone code and ultimately more trustworthy results. We believe Scala 3 would be an excellent contender for data science in a strongly typed language, but it lacks a general automatic differentiation library, e.g., for gradient-based learning.We present ScalaGrad, a general and type-safe automatic differentiation library designed for Scala. It builds on and improves a novel approach from the functional programming community using immutable duals, which is conceptually simple, asymptotically optimal and allows differentiation of higher-order code. We demonstrate the ease of use, robust performance, and versatility of ScalaGrad through its applications to deep learning, higher-order optimization, and gradient-based sampling. Specifically, we show an execution speed comparable to PyTorch for a simple deep learning use case, capabilities for higher-order differentiation, and opportunities to design more specialized libraries decoupled from ScalaGrad. As data science challenges evolve in complexity, ScalaGrad provides a pathway to harness the inherent advantages of strongly typed languages, ensuring both robustness and maintainability. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/30408 |
| Fulltext version: | Accepted version |
| License (according to publishing contract): | Licence according to publishing contract |
| Departement: | School of Engineering |
| Organisational Unit: | Centre for Artificial Intelligence (CAI) |
| Appears in collections: | Publikationen School of Engineering |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2024_Meyer-etal_ScalaGrad_SDS.pdf | Accepted Version | 102.74 kB | Adobe PDF |  View/Open |
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Meyer, B., Stadelmann, T., & Lüthi, M. (2024, May 31). ScalaGrad : a statically typed automatic differentiation library for safer data science. 11th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 30-31 May 2024. https://doi.org/10.21256/zhaw-30408
Meyer, B., Stadelmann, T. and Lüthi, M. (2024) ‘ScalaGrad : a statically typed automatic differentiation library for safer data science’, in 11th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 30-31 May 2024. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. Available at: https://doi.org/10.21256/zhaw-30408.
B. Meyer, T. Stadelmann, and M. Lüthi, “ScalaGrad : a statically typed automatic differentiation library for safer data science,” in 11th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 30-31 May 2024, May 2024. doi: 10.21256/zhaw-30408.
MEYER, Benjamin, Thilo STADELMANN und Marcel LÜTHI, 2024. ScalaGrad : a statically typed automatic differentiation library for safer data science. In: 11th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 30-31 May 2024. Conference paper. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. 31 Mai 2024
Meyer, Benjamin, Thilo Stadelmann, and Marcel Lüthi. 2024. “ScalaGrad : A Statically Typed Automatic Differentiation Library for Safer Data Science.” Conference paper. In 11th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 30-31 May 2024. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. https://doi.org/10.21256/zhaw-30408.
Meyer, Benjamin, et al. “ScalaGrad : A Statically Typed Automatic Differentiation Library for Safer Data Science.” 11th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 30-31 May 2024, ZHAW Zürcher Hochschule für Angewandte Wissenschaften, 2024, https://doi.org/10.21256/zhaw-30408.
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