Please use this identifier to cite or link to this item:
https://doi.org/10.21256/zhaw-30408Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Meyer, Benjamin | - |
| dc.contributor.author | Stadelmann, Thilo | - |
| dc.contributor.author | Lüthi, Marcel | - |
| dc.date.accessioned | 2024-03-27T15:50:42Z | - |
| dc.date.available | 2024-03-27T15:50:42Z | - |
| dc.date.issued | 2024-05-31 | - |
| dc.identifier.uri | https://digitalcollection.zhaw.ch/handle/11475/30408 | - |
| dc.description.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. | de_CH |
| dc.language.iso | en | de_CH |
| dc.publisher | ZHAW Zürcher Hochschule für Angewandte Wissenschaften | de_CH |
| dc.rights | Licence according to publishing contract | de_CH |
| dc.subject | Automatic differentiation | de_CH |
| dc.subject | Scala 3 | de_CH |
| dc.subject | ScalaGrad | de_CH |
| dc.subject.ddc | 005: Computerprogrammierung, Programme und Daten | de_CH |
| dc.title | ScalaGrad : a statically typed automatic differentiation library for safer data science | de_CH |
| dc.type | Konferenz: Paper | de_CH |
| dcterms.type | Text | de_CH |
| zhaw.departement | School of Engineering | de_CH |
| zhaw.organisationalunit | Centre for Artificial Intelligence (CAI) | de_CH |
| dc.identifier.doi | 10.21256/zhaw-30408 | - |
| zhaw.conference.details | 11th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 30-31 May 2024 | de_CH |
| zhaw.funding.eu | No | de_CH |
| zhaw.originated.zhaw | Yes | de_CH |
| zhaw.publication.status | acceptedVersion | de_CH |
| zhaw.publication.review | Peer review (Publikation) | de_CH |
| zhaw.webfeed | Datalab | de_CH |
| zhaw.webfeed | Machine Perception and Cognition | de_CH |
| zhaw.webfeed | ZHAW digital | de_CH |
| zhaw.author.additional | No | de_CH |
| zhaw.display.portrait | Yes | de_CH |
| 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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