189 citations · 509 across the 10 of their papers we have counts for
10 papers
Flower Hub: A Reproducible Benchmarking Platform for Federated Learning in Simulation and Deployment
Yan Gao, Mohammad Naseri, Javier Fernandez-Marques +19
Federated learning (FL) has emerged as a key approach for training models across decentralized data, yet benchmarking in FL remains difficult to reproduce, compare, and extend. Exi…
E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender Systems
Patrick John Chia, Giuseppe Attanasio, Jacopo Tagliabue +5
Recommender Systems today are still mostly evaluated in terms of accuracy, with other aspects beyond the immediate relevance of recommendations, such as diversity, long-term user r…
EvalRS 2023. Well-Rounded Recommender Systems For Real-World Deployments
Federico Bianchi, Patrick John Chia, Ciro Greco +5
EvalRS aims to bring together practitioners from industry and academia to foster a debate on rounded evaluation of recommender systems, with a focus on real-world impact across a m…
Beltrami Flow and Neural Diffusion on Graphs
Benjamin Paul Chamberlain, James Rowbottom, Davide Eynard +3
We propose a novel class of graph neural networks based on the discretised Beltrami flow, a non-Euclidean diffusion PDE. In our model, node features are supplemented with positiona…
Temporal Graph Networks for Deep Learning on Dynamic Graphs
Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca +3
Graph Neural Networks (GNNs) have recently become increasingly popular due to their ability to learn complex systems of relations or interactions arising in a broad spectrum of pro…
SIGN: Scalable Inception Graph Neural Networks
Fabrizio Frasca, Emanuele Rossi, Davide Eynard +3
Graph representation learning has recently been applied to a broad spectrum of problems ranging from computer graphics and chemistry to high energy physics and social media. The po…