Modeling Alzheimer’s disease cognitive scores using multi-task sparse group lasso

Published in Computerized Medical Imaging and Graphics (Journal), 2018

Authors: X Liu, AR Goncalves, P Cao, D Zhao, A Banerjee

We consider the problem of simultaneously predicting several different cognitive scores associated with categorizing subjects as normal, mild cognitive impairment (MCI), or Alzheimer's disease (AD) in a multi-task learning framework using features extracted from brain images obtained from ADNI (Alzheimer's Disease Neuroimaging Initiative).

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On the generalization of fused systems in voice presentation attack detection

Published in 16th International Conference of the Biometrics Special Interest Group, 2017

Authors: AR Goncalves, P Korshunov, RPV Violato, FO Simoes, S Marcel

This paper describes presentation attack detection systems developed for the Automatic Speaker Verification Spoofing and Countermeasures Challenge (ASVspoof 2017). The submitted systems, using calibration and score fusion techniques, combine different sub-systems (up to 18), which are based on eight state of the art features and rely on Gaussian mixture models and feed-forward neural network classifiers.

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Multi-Label Structure Learning with Ising Model Selection.

Published in IJCAI'15, 2015

Authors: AR Goncalves, FJ Von Zuben, A Banerjee

Built on recent advances in structure learning in Ising Markov Random Fields (I-MRF), we propose a multi-label classification algorithm that explicitly estimate and incorporate label dependence into the classifiers learning process by means of a sparse convex multitask learning formulation.

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Multi-task Sparse Structure Learning

Published in CIKM'14, 2014

Authors: AR Goncalves, P Das, S Chatterjee, V Sivakumar, FJ Von Zuben, A Banerjee

In this paper, we present a novel family of models for MTL, applicable to regression and classification problems, capable of learning the structure of task relationships. In particular, we consider a joint estimation problem of the task relationship structure and the individual task parameters, which is solved using alternating minimization. The task relationship structure learning component builds on recent advances in structure learning of Gaussian graphical models based on sparse estimators of the precision (inverse covariance) matrix.

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