Search Results for author: Davide Eynard

Found 8 papers, 6 papers with code

E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender Systems

1 code implementation20 Apr 2023 Patrick John Chia, Giuseppe Attanasio, Jacopo Tagliabue, Federico Bianchi, Ciro Greco, Gabriel de Souza P. Moreira, Davide Eynard, Fahd Husain

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 retention and fairness, often taking a back seat.

Fairness Model Selection +1

EvalRS 2023. Well-Rounded Recommender Systems For Real-World Deployments

1 code implementation14 Apr 2023 Federico Bianchi, Patrick John Chia, Ciro Greco, Claudio Pomo, Gabriel Moreira, Davide Eynard, Fahd Husain, Jacopo Tagliabue

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 multitude of deployment scenarios.

Fairness Informativeness +1

Beltrami Flow and Neural Diffusion on Graphs

1 code implementation NeurIPS 2021 Benjamin Paul Chamberlain, James Rowbottom, Davide Eynard, Francesco Di Giovanni, Xiaowen Dong, Michael M Bronstein

We propose a novel class of graph neural networks based on the discretised Beltrami flow, a non-Euclidean diffusion PDE.

Temporal Graph Networks for Deep Learning on Dynamic Graphs

8 code implementations18 Jun 2020 Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, Davide Eynard, Federico Monti, Michael Bronstein

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 problems ranging from biology and particle physics to social networks and recommendation systems.

Recommendation Systems

SIGN: Scalable Inception Graph Neural Networks

4 code implementations23 Apr 2020 Fabrizio Frasca, Emanuele Rossi, Davide Eynard, Ben Chamberlain, Michael Bronstein, Federico Monti

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.

Graph Representation Learning Graph Sampling +2

Fake News Detection on Social Media using Geometric Deep Learning

3 code implementations10 Feb 2019 Federico Monti, Fabrizio Frasca, Davide Eynard, Damon Mannion, Michael M. Bronstein

One of the main reasons is that often the interpretation of the news requires the knowledge of political or social context or 'common sense', which current NLP algorithms are still missing.

Common Sense Reasoning Fact Checking +2

Shape-from-intrinsic operator

no code implementations7 Jun 2014 Davide Boscaini, Davide Eynard, Michael M. Bronstein

Shape-from-X is an important class of problems in the fields of geometry processing, computer graphics, and vision, attempting to recover the structure of a shape from some observations.

Structure-preserving color transformations using Laplacian commutativity

no code implementations1 Nov 2013 Davide Eynard, Artiom Kovnatsky, Michael M. Bronstein

Mappings between color spaces are ubiquitous in image processing problems such as gamut mapping, decolorization, and image optimization for color-blind people.

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