Search Results for author: Matteo Barbieri

Found 2 papers, 2 papers with code

Scaling Federated Learning for Fine-tuning of Large Language Models

1 code implementation1 Feb 2021 Agrin Hilmkil, Sebastian Callh, Matteo Barbieri, Leon René Sütfeld, Edvin Listo Zec, Olof Mogren

We perform an extensive sweep over the number of clients, ranging up to 32, to evaluate the impact of distributed compute on task performance in the federated averaging setting.

Federated Learning Sentiment Analysis +2

Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images

1 code implementation20 Apr 2020 Ming Y. Lu, Drew F. K. Williamson, Tiffany Y. Chen, Richard J. Chen, Matteo Barbieri, Faisal Mahmood

CLAM is a general-purpose and adaptable method that can be used for a variety of different computational pathology tasks in both clinical and research settings.

Domain Adaptation Multiple Instance Learning +2

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