Alex Fedorov
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Learning deep representations by mutual information estimation and maximization
RD Hjelm, A Fedorov, S Lavoie-Marchildon, K Grewal, P Bachman, ...
arXiv preprint arXiv:1808.06670, 2018
Deep attention recurrent Q-network
I Sorokin, A Seleznev, M Pavlov, A Fedorov, A Ignateva
arXiv preprint arXiv:1512.01693, 2015
Group ICA for identifying biomarkers in schizophrenia:‘Adaptive’networks via spatially constrained ICA show more sensitivity to group differences than spatio-temporal regression
MS Salman, Y Du, D Lin, Z Fu, A Fedorov, E Damaraju, J Sui, J Chen, ...
NeuroImage: Clinical 22, 101747, 2019
End-to-end learning of brain tissue segmentation from imperfect labeling
A Fedorov, J Johnson, E Damaraju, A Ozerin, V Calhoun, S Plis
2017 International Joint Conference on Neural Networks (IJCNN), 3785-3792, 2017
Deep residual learning for neuroimaging: An application to predict progression to alzheimer’s disease
A Abrol, M Bhattarai, A Fedorov, Y Du, S Plis, V Calhoun, ...
Journal of Neuroscience Methods, 108701, 2020
Almost instant brain atlas segmentation for large-scale studies
A Fedorov, E Damaraju, V Calhoun, S Plis
arXiv preprint arXiv:1711.00457, 2017
Prediction of Progression to Alzheimer's disease with Deep InfoMax
A Fedorov, RD Hjelm, A Abrol, Z Fu, Y Du, S Plis, VD Calhoun
2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), 2019
Learnt dynamics generalizes across tasks, datasets, and populations
U Mahmood, MM Rahman, A Fedorov, Z Fu, VD Calhoun, SM Plis
arXiv preprint arXiv:1912.03130, 2019
Transfer Learning of fMRI Dynamics
U Mahmood, MM Rahman, A Fedorov, Z Fu, S Plis
arXiv preprint arXiv:1911.06813, 2019
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