1 code implementation • 23 Dec 2023 • Anna Vettoruzzo, Mohamed-Rafik Bouguelia, Thorsteinn Rögnvaldsson
These findings highlight the potential of incorporating contextual information and meta-learning techniques into federated learning, paving the way for advancements in distributed machine learning paradigms.
no code implementations • 1 Dec 2023 • Hamid Sarmadi, Thorsteinn Rögnvaldsson, Nils Roger Carlsson, Mattias Ohlsson, Ibrahim Wahab, Ola Hall
Deep convolutional neural networks (CNNs) have been shown to predict poverty and development indicators from satellite images with surprising accuracy.
no code implementations • 10 Jul 2023 • Anna Vettoruzzo, Mohamed-Rafik Bouguelia, Joaquin Vanschoren, Thorsteinn Rögnvaldsson, KC Santosh
This review provides a comprehensive technical overview of meta-learning, emphasizing its importance in real-world applications where data may be scarce or expensive to obtain.
1 code implementation • 28 Feb 2022 • Abdallah Alabdallah, Mattias Ohlsson, Sepideh Pashami, Thorsteinn Rögnvaldsson
In contrast to such deep learning methods, classical machine learning models deteriorate when the censoring level decreases due to their inability to improve on ranking the events versus other events.
no code implementations • 16 Sep 2019 • Yuantao Fan, Sławomir Nowaczyk, Thorsteinn Rögnvaldsson
In this work, we present a TL method for predicting Remaining Useful Life (RUL) of equipment, under the assumption that labels are available only for the source domain and not the target domain.