91 citations · 93 across the 5 of their papers we have counts for
7 papers
BM-CL: Bias Mitigation through the lens of Continual Learning
Lucas Mansilla, Rodrigo Echeveste, Camila Gonzalez +2
Biases in machine learning pose significant challenges, particularly when models amplify disparities that affect disadvantaged groups. Traditional bias mitigation techniques often…
Towards Reliable WMH Segmentation under Domain Shift: An Application Study using Maximum Entropy Regularization to Improve Uncertainty Estimation
Franco Matzkin, Agostina Larrazabal, Diego H Milone +2
Accurate segmentation of white matter hyperintensities (WMH) is crucial for clinical decision-making, particularly in the context of multiple sclerosis. However, domain shifts, suc…
Fairness of Deep Ensembles: On the interplay between per-group task difficulty and under-representation
Estanislao Claucich, Sara Hooker, Diego H. Milone +2
Ensembling is commonly regarded as an effective way to improve the general performance of models in machine learning, while also increasing the robustness of predictions. When it c…
Comprehensive benchmarking of large language models for RNA secondary structure prediction
L. I. Zablocki, L. A. Bugnon, M. Gerard +3
Inspired by the success of large language models (LLM) for DNA and proteins, several LLM for RNA have been developed recently. RNA-LLM uses large datasets of RNA sequences to learn…
Domain Generalization via Gradient Surgery
Lucas Mansilla, Rodrigo Echeveste, Diego H. Milone +1
In real-life applications, machine learning models often face scenarios where there is a change in data distribution between training and test domains. When the aim is to make pred…
Hybrid graph convolutional neural networks for landmark-based anatomical segmentation
Nicolás Gaggion, Lucas Mansilla, Diego Milone +1
In this work we address the problem of landmark-based segmentation for anatomical structures. We propose HybridGNet, an encoder-decoder neural architecture which combines standard…