4 citations · 9 across the 6 of their papers we have counts for
6 papers
Beyond Backbone Backpropagation: A Decoupled Strategy for Efficient Transfer Learning
Daniel Vila-Cruz, Laura Morán-Fernández, Verónica Bolón-Canedo
Deep learning models achieve state-of-the-art image classification but face deployment challenges due to computational costs and energy demands. We propose a lightweight training s…
HydraCIL: Decoupled Class-Incremental Learning through Prototype-Guided Multi-Head Classifiers
Daniel Vila-Cruz, Laura Morán-Fernández, Verónica Bolón-Canedo
We present HydraCIL, a decoupled continual learning model based on prototype-guided multi-head classifiers, targeting sustainable deployment in embedded and resource-constrained en…
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…
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…
E2E-FS: An End-to-End Feature Selection Method for Neural Networks
Brais Cancela, Verónica Bolón-Canedo, Amparo Alonso-Betanzos
Classic embedded feature selection algorithms are often divided in two large groups: tree-based algorithms and lasso variants. Both approaches are focused in different aspects: whi…
On the effectiveness of convolutional autoencoders on image-based personalized recommender systems
E. Blanco-Mallo, B. Remeseiro, V. Bolón-Canedo +1
Recommender systems (RS) are increasingly present in our daily lives, especially since the advent of Big Data, which allows for storing all kinds of information about users' prefer…