A. Fedorov

发表

Learning deep representations by mutual information estimation and maximization pdf

Yoshua Bengio, Adam Trischler, R. Devon Hjelm, 2018, ICLR.

Learning deep representations by mutual information estimation and maximization

R. Devon Hjelm, Yoshua Bengio, A. Fedorov, 2018, ICLR.

Self-Supervised Multimodal Domino: in Search of Biomarkers for Alzheimer’s Disease

A. Fedorov, V. Calhoun, Lei Wu, 2020, 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI).

Tasting the cake: evaluating self-supervised generalization on out-of-distribution multimodal MRI data

A. Fedorov, V. Calhoun, S. Plis, 2021, ArXiv.

On Self-Supervised Multimodal Representation Learning: An Application To Alzheimer’s Disease

A. Fedorov, V. Calhoun, S. Plis, 2020, 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI).

Self-supervised multimodal neuroimaging yields predictive representations for a spectrum of Alzheimer's phenotypes

R. Devon Hjelm, A. Fedorov, S. Plis, 2022, ArXiv.

Pipeline-Invariant Representation Learning for Neuroimaging

A. Fedorov, A. Abrol, Vincent Calhoun, 2022, ArXiv.

Pipeline-Invariant Representation Learning for Neuroimaging

A. Fedorov, A. Abrol, Gregory Kiar, 2022, ArXiv.

Prediction of Progression to Alzheimer's disease with Deep InfoMax

Vince D. Calhoun, Sergey M. Plis, Zening Fu, 2019, 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI).

Deep residual learning for neuroimaging: An application to predict progression to Alzheimer’s disease

Vince D. Calhoun, Sergey Plis, Yuhui Du, 2018, Journal of Neuroscience Methods.

Interpreting models interpreting brain dynamics

Zening Fu, Sergey Plis, Alex Fedorov, 2021, Scientific Reports.

Whole MILC: generalizing learned dynamics across tasks, datasets, and populations

A. Fedorov, V. Calhoun, S. Plis, 2020, International Conference on Medical Image Computing and Computer-Assisted Intervention.

Learnt dynamics generalizes across tasks, datasets, and populations pdf

V. D. Calhoun, S. M. Plis, U. Mahmood, 2019, ArXiv.

Training a discrete variational autoencoder for generative chemistry and drug design on a quantum annealer pdf

A. S. Boev, A. I. Gircha, K. Avchaciov, 2021, ArXiv.

On Self-Supervised Multimodal Representation Learning: An Application To Alzheimer’s Disease

A. Fedorov, V. Calhoun, S. Plis, 2021, 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI).

Transfer Learning of fMRI Dynamics pdf

Zening Fu, Sergey Plis, Alex Fedorov, 2019, ArXiv.

Almost instant brain atlas segmentation for large-scale studies pdf

Vince D. Calhoun, Sergey M. Plis, Eswar Damaraju, 2017, ArXiv.

End-to-end learning of brain tissue segmentation from imperfect labeling

Vince D. Calhoun, Sergey M. Plis, Eswar Damaraju, 2016, 2017 International Joint Conference on Neural Networks (IJCNN).

Pipeline-Invariant Representation Learning for Neuroimaging

A. Fedorov, A. Abrol, Gregory Kiar, 2022, ArXiv.

Self-supervised multimodal learning for group inferences from MRI data: Discovering disorder-relevant brain regions and multimodal links

R. Devon Hjelm, A. Fedorov, S. Plis, 2023, NeuroImage.

Taxonomy of multimodal self-supervised representation learning

A. Fedorov, V. Calhoun, S. Plis, 2020, ArXiv.

Voxel-wise Fusion of Resting fMRI Networks and Gray Matter Volume for Alzheimer’s Disease Classification using Deep Multimodal Learning

Theodore J. LaGrow, A. Fedorov, A. Abrol, 2023, Research square.

Chromatic fusion: generative multimodal neuroimaging data fusion provides multi-informed insights into schizophrenia

A. Fedorov, Adrian Preda, L. Wu, 2023, medRxiv.

Chromatic fusion: Generative multimodal neuroimaging data fusion provides multi‐informed insights into schizophrenia

A. Fedorov, Adrian Preda, Lei Wu, 2023, Human brain mapping.