12 citations · 31 across the 12 of their papers we have counts for
14 papers
CheXmask-U: Quantifying uncertainty in landmark-based anatomical segmentation for X-ray images
Matias Cosarinsky, Nicolas Gaggion, Rodrigo Echeveste +1
In this work, we study uncertainty estimation for anatomical landmark-based segmentation on chest X-rays. Inspired by hybrid neural network architectures that combine standard imag…
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…
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…
Open Challenges on Fairness of Artificial Intelligence in Medical Imaging Applications
Enzo Ferrante, Rodrigo Echeveste
Recently, the research community of computerized medical imaging has started to discuss and address potential fairness issues that may emerge when developing and deploying AI syste…
Remedying uncertainty representations in visual inference through Explaining-Away Variational Autoencoders
Josefina Catoni, Domonkos Martos, Ferenc Csikor +5
Optimal computations under uncertainty require an adequate probabilistic representation about beliefs. Deep generative models, and specifically Variational Autoencoders (VAEs), hav…
Unsupervised bias discovery in medical image segmentation
Nicolás Gaggion, Rodrigo Echeveste, Lucas Mansilla +2
It has recently been shown that deep learning models for anatomical segmentation in medical images can exhibit biases against certain sub-populations defined in terms of protected…
Towards unraveling calibration biases in medical image analysis
María Agustina Ricci Lara, Candelaria Mosquera, Enzo Ferrante +1
In recent years the development of artificial intelligence (AI) systems for automated medical image analysis has gained enormous momentum. At the same time, a large body of work ha…
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…
Bridging physiological and perceptual views of autism by means of sampling-based Bayesian inference
Rodrigo Echeveste, Enzo Ferrante, Diego H. Milone +1
Theories for autism spectrum disorder (ASD) have been formulated at different levels: ranging from physiological observations to perceptual and behavioral descriptions. Understandi…
Asymptotic scaling properties of the posterior mean and variance in the Gaussian scale mixture model
Rodrigo Echeveste, Guillaume Hennequin, Máté Lengyel
The Gaussian scale mixture model (GSM) is a simple yet powerful probabilistic generative model of natural image patches. In line with the well-established idea that sensory process…
Drifting states and synchronization induced chaos in autonomous networks of excitable neurons
Rodrigo Echeveste, Claudius Gros
The study of balanced networks of excitatory and inhibitory neurons has led to several open questions. On the one hand it is yet unclear whether the asynchronous state observed in…
An objective function for self-limiting neural plasticity rules
Rodrigo Echeveste, Claudius Gros
Self-organization provides a framework for the study of systems in which complex patterns emerge from simple rules, without the guidance of external agents or fine tuning of parame…
Two-trace model for spike-timing-dependent synaptic plasticity
Rodrigo Echeveste, Claudius Gros
We present an effective model for timing-dependent synaptic plasticity (STDP) in terms of two interacting traces, corresponding to the fraction of activated NMDA receptors and the…
Generating functionals for computational intelligence: the Fisher information as an objective function for self-limiting Hebbian learning rules
Rodrigo Echeveste, Claudius Gros
Generating functionals may guide the evolution of a dynamical system and constitute a possible route for handling the complexity of neural networks as relevant for computational in…