387 citations · 449 across the 17 of their papers we have counts for
11 papers · 1 filter
From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps
Ghjulia Sialelli, Robin Young, Yuchang Jiang +9
Recent years have seen a rapid expansion in the production of large-scale geospatial maps derived from Earth observation (EO) data, driven largely by advances in machine learning (…
The Accuracy Cost of Weakness: A Theoretical Analysis of Fixed-Segment Weak Labeling for Events in Time
John Martinsson, Tuomas Virtanen, Maria Sandsten +1
Accurate labels are critical for deriving robust machine learning models. Labels are used to train supervised learning models and to evaluate most machine learning paradigms. In th…
Flexible SE(2) graph neural networks with applications to PDE surrogates
Maria Bånkestad, Olof Mogren, Aleksis Pirinen
This paper presents a novel approach for constructing graph neural networks equivariant to 2D rotations and translations and leveraging them as PDE surrogates on non-gridded domain…
Concept-aware clustering for decentralized deep learning under temporal shift
Marcus Toftås, Emilie Klefbom, Edvin Listo Zec +2
Decentralized deep learning requires dealing with non-iid data across clients, which may also change over time due to temporal shifts. While non-iid data has been extensively studi…
Efficient Node Selection in Private Personalized Decentralized Learning
Edvin Listo Zec, Johan Östman, Olof Mogren +1
Personalized decentralized learning is a promising paradigm for distributed learning, enabling each node to train a local model on its own data and collaborate with other nodes to…
EFFGAN: Ensembles of fine-tuned federated GANs
Ebba Ekblom, Edvin Listo Zec, Olof Mogren
Generative adversarial networks have proven to be a powerful tool for learning complex and high-dimensional data distributions, but issues such as mode collapse have been shown to…