The field of Deep Learning (DL) has undergone explosive growth during the last decade, with a substantial impact on Natural Language Processing (NLP) as well. Yet, as with other fields employing DL techniques, there has been a lack of common experimental standards compared to more established disciplines. Starting from fundamental scientific principles, we distill ongoing discussions on experimental standards in DL into a single, widely-applicable methodology. Following these best practices is crucial to strengthening experimental evidence, improve reproducibility and enable scientific progress. These standards are further collected in a public repository to help them transparently adapt to future needs.
|Publikationsdato||29 apr. 2022|
|Status||Udgivet - 29 apr. 2022|
|Begivenhed||ML Evaluation Standards Workshop at ICLR 2022 - |
Varighed: 29 apr. 2022 → …
|Konference||ML Evaluation Standards Workshop at ICLR 2022|
|Periode||29/04/2022 → …|