Practical data efficient deep learning trough contrastive self-supervised learning
Auf einen Blick
- Projektleiter/in : Lukas Tuggener
- Projektvolumen : CHF 60'480
- Projektstatus : abgeschlossen
- Drittmittelgeber : Öffentliche Hand (ohne Bund)
- Projektpartner : Universität Zürich / Neural Learning and Intelligent Systems Group
- Kontaktperson : Lukas Tuggener
Beschreibung
Deep Learning is the key building block of most modern AI systems, but its data hunger is a problem - especially from an applied perspective. The goal of this project is to enable data efficient practical deep learning by developing novel contrastive learning methods.
Publikationen
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Tuggener, Lukas; Sager, Pascal; Taoudi-Benchekroun, Yassine; Grewe, Benjamin F.; Stadelmann, Thilo,
2024.
So you want your private LLM at home? : a survey and benchmark of methods for efficient GPTs [Paper].
In:
11th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 30-31 May 2024.
ZHAW Zürcher Hochschule für Angewandte Wissenschaften.
Verfügbar unter: https://doi.org/10.21256/zhaw-30279