Search Results for author: Thorsteinn Rögnvaldsson

Found 5 papers, 2 papers with code

Personalized Federated Learning with Contextual Modulation and Meta-Learning

1 code implementation23 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.

Meta-Learning Personalized Federated Learning

Towards Explaining Satellite Based Poverty Predictions with Convolutional Neural Networks

no code implementations1 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.

Advances and Challenges in Meta-Learning: A Technical Review

no code implementations10 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.

Continual Learning Domain Adaptation +4

The Concordance Index decomposition: A measure for a deeper understanding of survival prediction models

1 code implementation28 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.

Survival Analysis Survival Prediction

Transfer learning for Remaining Useful Life Prediction Based on Consensus Self-Organizing Models

no code implementations16 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.

Transfer Learning

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