activity
20172020
most citedDynamic Prediction of ICU Mortality Risk Using Domain Adaptation

47 citations · 72 across the 6 of their papers we have counts for

collaborators

8 papers

cs.LG2020

Modeling Pharmacological Effects with Multi-Relation Unsupervised Graph Embedding

Dehua Chen, Amir Jalilifard, Adriano Veloso +1

A pharmacological effect of a drug on cells, organs and systems refers to the specific biochemical interaction produced by a drug substance, which is called its mechanism of action…

cs.LG2020

Explainable Deep CNNs for MRI-Based Diagnosis of Alzheimer's Disease

Eduardo Nigri, Nivio Ziviani, Fabio Cappabianco +2

Deep Convolutional Neural Networks (CNNs) are becoming prominent models for semi-automated diagnosis of Alzheimer's Disease (AD) using brain Magnetic Resonance Imaging (MRI). Altho…

cs.LG20201 cited

Assessing the Reliability of Visual Explanations of Deep Models with Adversarial Perturbations

Dan Valle, Tiago Pimentel, Adriano Veloso

The interest in complex deep neural networks for computer vision applications is increasing. This leads to the need for improving the interpretable capabilities of these models. Re…

cs.LG2020

Automatic Tag Recommendation for Painting Artworks Using Diachronic Descriptions

Gianlucca Zuin, Adriano Veloso, João Cândido Portinari +1

In this paper, we deal with the problem of automatic tag recommendation for painting artworks. Diachronic descriptions containing deviations on the vocabulary used to describe each…

eess.AS201923 cited

Learning Transferable Features for Speech Emotion Recognition

Alison Marczewski, Adriano Veloso, Nívio Ziviani

Emotion recognition from speech is one of the key steps towards emotional intelligence in advanced human-machine interaction. Identifying emotions in human speech requires learning…

cs.LG201947 cited

Dynamic Prediction of ICU Mortality Risk Using Domain Adaptation

Tiago Alves, Alberto Laender, Adriano Veloso +1

Early recognition of risky trajectories during an Intensive Care Unit (ICU) stay is one of the key steps towards improving patient survival. Learning trajectories from physiologica…