8 citations · 18 across the 12 of their papers we have counts for
5 papers · 1 filter
MILES: Modality-Informed Learning Rate Scheduler for Balancing Multimodal Learning
Alejandro Guerra-Manzanares, Farah E. Shamout
The aim of multimodal neural networks is to combine diverse data sources, referred to as modalities, to achieve enhanced performance compared to relying on a single modality. Howev…
BlendFL: Blended Federated Learning for Handling Multimodal Data Heterogeneity
Alejandro Guerra-Manzanares, Omar El-Herraoui, Michail Maniatakos +1
One of the key challenges of collaborative machine learning, without data sharing, is multimodal data heterogeneity in real-world settings. While Federated Learning (FL) enables mo…
Uncertainty Quantification for Machine Learning in Healthcare: A Survey
L. Julián Lechuga López, Shaza Elsharief, Dhiyaa Al Jorf +3
Uncertainty Quantification (UQ) is pivotal in enhancing the robustness, reliability, and interpretability of Machine Learning (ML) systems for healthcare, optimizing resources and…
MIND: Modality-Informed Knowledge Distillation Framework for Multimodal Clinical Prediction Tasks
Alejandro Guerra-Manzanares, Farah E. Shamout
Multimodal fusion leverages information across modalities to learn better feature representations with the goal of improving performance in fusion-based tasks. However, multimodal…
Privacy-preserving machine learning for healthcare: open challenges and future perspectives
Alejandro Guerra-Manzanares, L. Julian Lechuga Lopez, Michail Maniatakos +1
Machine Learning (ML) has recently shown tremendous success in modeling various healthcare prediction tasks, ranging from disease diagnosis and prognosis to patient treatment. Due…