32 citations · 39 across the 13 of their papers we have counts for
9 papers · 1 filter
Closing the Alignment-Maturity Gap in Federated Prototype Learning
Mario Casado-Diez, Alejandro Dopico-Castro, Verónica Bolón-Canedo +1
Learning discriminative visual representations from distributed, heterogeneous data is a fundamental challenge in Federated Learning (FL). Prototype-based methods address statistic…
Robust gene prioritization for Dietary Restriction via Fast-mRMR Feature Selection techniques
Rubén Fernández-Farelo, Jorge Paz-Ruza, Bertha Guijarro-Berdiñas +2
Gene prioritization (identifying genes potentially associated with a biological process) is increasingly tackled with Artificial Intelligence. However, existing methods struggle wi…
A robust methodology for long-term sustainability evaluation of Machine Learning models
Jorge Paz-Ruza, João Gama, Amparo Alonso-Betanzos +1
Sustainability and efficiency have become essential considerations in the development and deployment of Artificial Intelligence systems, but existing regulatory practices for Green…
CIFNet: An Analytic Neural Learning Framework for Efficient and Calibrated Class-Incremental Learning
Alejandro Dopico-Castro, Oscar Fontenla-Romero, Bertha Guijarro-Berdiñas +1
Class-Incremental Learning (CIL) in deep neural networks is conventionally framed as an iterative gradient-based optimization problem, incurring high computational cost, hyperparam…
Sustainable techniques to improve Data Quality for training image-based explanatory models for Recommender Systems
Jorge Paz-Ruza, David Esteban-Martínez, Amparo Alonso-Betanzos +1
Visual explanations based on user-uploaded images are an effective and self-contained approach to provide transparency to Recommender Systems (RS), but intrinsic limitations of dat…
Positive-Unlabelled Learning for identifying new candidate Dietary Restriction-related genes among Ageing-related genes
Jorge Paz-Ruza, Alex A. Freitas, Amparo Alonso-Betanzos +1
Dietary Restriction (DR) is one of the most popular anti-ageing interventions; recently, Machine Learning (ML) has been explored to identify potential DR-related genes among ageing…
Beyond RMSE and MAE: Introducing EAUC to unmask hidden bias and unfairness in dyadic regression models
Jorge Paz-Ruza, Amparo Alonso-Betanzos, Bertha Guijarro-Berdiñas +2
Dyadic regression models, which output real-valued predictions for pairs of entities, are fundamental in many domains (e.g. obtaining user-product ratings in Recommender Systems) a…
Fast Deep Autoencoder for Federated learning
David Novoa-Paradela, Oscar Romero-Fontenla, Bertha Guijarro-Berdiñas
This paper presents a novel, fast and privacy preserving implementation of deep autoencoders. DAEF (Deep Autoencoder for Federated learning), unlike traditional neural networks, tr…
Explain and Conquer: Personalised Text-based Reviews to Achieve Transparency
Iñigo López-Riobóo Botana, Verónica Bolón-Canedo, Bertha Guijarro-Berdiñas +1
There are many contexts in which dyadic data are present. Social networks are a well-known example. In these contexts, pairs of elements are linked building a network that reflects…