Sergey M. Plis

发表

Block Coordinate Descent for Sparse NMF pdf

Thomas P. Hayes, Vamsi K. Potluru, Sergey M. Plis, 2013, ICLR.

Large scale collaboration with autonomy: Decentralized data ICA

Anand D. Sarwate, Vince D. Calhoun, Sergey M. Plis, 2015, 2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP).

Blind Source Separation for Unimodal and Multimodal Brain Networks: A Unifying Framework for Subspace Modeling

Vince D. Calhoun, Marios S. Pattichis, Sergey M. Plis, 2016, IEEE Journal of Selected Topics in Signal Processing.

CometCloudCare (C3): Distributed Machine LearningPlatform-as-a-Service with Privacy Preservation

Anand D. Sarwate, Vince D. Calhoun, Vamsi K. Potluru, 2014 .

UNCORRECTED PROOF 1 Functional and effective connectivity of stopping

Vince D. Calhoun, Sergey M. Plis, Christoph Herrmann, 2014 .

Causal Discovery from Subsampled Time Series Data by Constraint Optimization

Sergey M. Plis, Frederick Eberhardt, David Danks, 2016, Probabilistic Graphical Models.

Variational Autoencoders for Feature Detection of Magnetic Resonance Imaging Data pdf

Vince D. Calhoun, Sergey M. Plis, R. Devon Hjelm, 2016, ArXiv.

Decentralized Analysis of Brain Imaging Data: Voxel-Based Morphometry and Dynamic Functional Network Connectivity

Vince D. Calhoun, Sergey M. Plis, Bradley T. Baker, 2018, Front. Neuroinform..

Multiplicative updates For Non-Negative Kernel SVM pdf

Vince D. Calhoun, Vamsi K. Potluru, Sergey M. Plis, 2009, ArXiv.

Adaptive Parallel/Serial Sampling Mechanisms for Particle Filtering in Dynamic Bayesian Networks

Sergey M. Plis, Eva Besada-Portas, Jesús Manuel de la Cruz, 2010, ECML/PKDD.

Functional and effective connectivity of stopping

Vince D. Calhoun, Sergey M. Plis, Christoph S. Herrmann, 2014, NeuroImage.

Improved Differentially Private Decentralized Source Separation for fMRI Data pdf

Anand D. Sarwate, Sergey M. Plis, Hafiz Imtiaz, 2019, ArXiv.

The tenth annual MLSP competition: Schizophrenia classification challenge

Vince D. Calhoun, Vamsi K. Potluru, Sergey M. Plis, 2014, 2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP).

See without looking: joint visualization of sensitive multi-site datasets

Vince D. Calhoun, Sergey M. Plis, Sandeep R. Panta, 2017, IJCAI.

COINSTAC: A Privacy Enabled Model and Prototype for Leveraging and Processing Decentralized Brain Imaging Data

Anand D. Sarwate, Jessica A. Turner, Vince D. Calhoun, 2016, Front. Neurosci..

Deep independence network analysis of structural brain imaging: A simulation study

Jessica A. Turner, Vince D. Calhoun, Sergey M. Plis, 2015, 2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP).

From private sites to big data without compromising privacy: A case of neuroimaging data classification

Anand D. Sarwate, Jessica A. Turner, Vince D. Calhoun, 2014 .

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

Sergey M. Plis, Zening Fu, Vince D. Calhoun, 2020, MICCAI.

Bayesian brain source imaging based on combined MEG/EEG and fMRI using MCMC

Sergey M. Plis, John S. George, E. Juliana Paré-Blagoev, 2008, NeuroImage.

Spatiotemporal Bayesian inference dipole analysis for MEG neuroimaging data

Sergey M. Plis, John S. George, E. Juliana Paré-Blagoev, 2005, NeuroImage.

Discriminating schizophrenia from normal controls using resting state functional network connectivity: A deep neural network and layer-wise relevance propagation method

Vince D. Calhoun, Sergey M. Plis, Tianzi Jiang, 2017, 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP).

Improving the information derived from human brain mapping experiments

Sergey M. Plis, 2007 .

Data-weighted ensemble learning for privacy-preserving distributed learning

Anand D. Sarwate, Sergey M. Plis, Liyang Xie, 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Decentralized temporal independent component analysis: Leveraging fMRI data in collaborative settings

Anand D. Sarwate, Vince D. Calhoun, Sergey M. Plis, 2019, NeuroImage.

On self-supervised multi-modal representation learning: An application to Alzheimer's disease pdf

Sergey M. Plis, Lei Wu, Vince D. Calhoun, 2020, ArXiv.

Group-level component analyses of EEG: validation and evaluation

Vince D. Calhoun, Sergey M. Plis, René J. Huster, 2015, Front. Neurosci..

Generation of synthetic structural magnetic resonance images for deep learning pre-training

Jessica A. Turner, Vince D. Calhoun, Sergey M. Plis, 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

Multidataset independent subspace analysis extends independent vector analysis

Vince D. Calhoun, Sergey M. Plis, Rogers F. Silva, 2014, 2014 IEEE International Conference on Image Processing (ICIP).

Efficient Multiplicative Updates for Support Vector Machines

Vince D. Calhoun, Vamsi K. Potluru, Sergey M. Plis, 2009, SDM.

High-order interactions observed in multi-task intrinsic networks are dominant indicators of aberrant brain function in schizophrenia

Vince D. Calhoun, Vamsi K. Potluru, Sergey M. Plis, 2014, NeuroImage.

Multidataset Independent Subspace Analysis With Application to Multimodal Fusion

Vince D. Calhoun, Marios S. Pattichis, Sergey M. Plis, 2019, IEEE Transactions on Image Processing.

A Tool for Interactive Data Visualization: Application to Over 10,000 Brain Imaging and Phantom MRI Data Sets

Jessica A. Turner, Vince D. Calhoun, Sergey M. Plis, 2016, Front. Neuroinform..

Deep Independence Network Analysis of Structural Brain Imaging: Application to Schizophrenia

Jessica A. Turner, Vince D. Calhoun, Sergey M. Plis, 2016, IEEE Transactions on Medical Imaging.

Rate-Agnostic (Causal) Structure Learning

Vince D. Calhoun, Sergey M. Plis, David Danks, 2015, NIPS.

Reading the (functional) writing on the (structural) wall: Multimodal fusion of brain structure and function via a deep neural network based translation approach reveals novel impairments in schizophrenia

Hyo Jong Lee, Vince D. Calhoun, Sergey M. Plis, 2018, NeuroImage.

A statistically motivated framework for simulation of stochastic data fusion models applied to multimodal neuroimaging

Vince D. Calhoun, Sergey M. Plis, Rogers F. Silva, 2014, NeuroImage.

Classification As a Criterion to Select Model Order For Dynamic Functional Connectivity States in Rest-fMRI Data

Vince D. Calhoun, Sergey M. Plis, Eswar Damaraju, 2019, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).

A classification-based approach to estimate the number of resting fMRI dynamic functional connectivity states

Vince D. Calhoun, Sergey M. Plis, Eswar Damaraju, 2020, bioRxiv.

NEUROINFORMATICS Sharing privacy-sensitive access to neuroimaging and genetics data : a review and preliminary validation

Anand D. Sarwate, Jessica A. Turner, Vince D. Calhoun, 2018 .

Multimodal fusion of brain structural and functional imaging with a deep neural machine translation approach

Vince D. Calhoun, Sergey M. Plis, Kyunghyun Cho, 2016, 2016 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI).

Directional Statistics on Permutations

Vince D. Calhoun, Sergey M. Plis, Terran Lane, 2010, AISTATS.

A constraint optimization approach to causal discovery from subsampled time series data

Sergey M. Plis, Frederick Eberhardt, David Danks, 2017, Int. J. Approx. Reason..

Mesochronal Structure Learning

Sergey M. Plis, Jianyu Yang, David Danks, 2015, UAI.

Vince D. Calhoun, Vamsi K. Potluru, Sergey M. Plis, 2009, 2009 IEEE International Workshop on Machine Learning for Signal Processing.

Deep learning for neuroimaging: a validation study

Vince D. Calhoun, Sergey M. Plis, Ruslan Salakhutdinov, 2013, Front. Neurosci..

Parallel Subspace Sampling for Particle Filtering in Dynamic Bayesian Networks

Sergey M. Plis, Eva Besada-Portas, Jesús Manuel de la Cruz, 2009, ECML/PKDD.

Statelets: high dimensional predominant shapes in dynamic functional network connectivity

Vince D. Calhoun, Sergey M. Plis, Eswar Damaraju, 2020, bioRxiv.

MEG and fMRI for nonlinear estimation of neural activity

Vince D. Calhoun, Sergey M. Plis, Terran Lane, 2009, 2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers.

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).

MEG and fMRI Fusion for Non-Linear Estimation of Neural and BOLD Signal Changes

Vince D. Calhoun, Sergey M. Plis, Tom Eichele, 2010, Front. Neuroinform..

Improving Classification Rate of Schizophrenia Using a Multimodal Multi-Layer Perceptron Model with Structural and Functional MR pdf

Vince D. Calhoun, Sergey M. Plis, Alvaro Ulloa, 2018, 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).

Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments pdf

Sergey M. Plis, Helge J. Ritter, Sergey Levine, 2018, ArXiv.

Cooperative learning: Decentralized data neural network

Vince D. Calhoun, Sergey M. Plis, Noah Lewis, 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

Sparseness and a reduction from Totally Nonnegative Least Squares to SVM

Thomas P. Hayes, Vince D. Calhoun, Vamsi K. Potluru, 2011, The 2011 International Joint Conference on Neural Networks.

Permutations as Angular Data: Efficient Inference in Factorial Spaces

Vince D. Calhoun, Sergey M. Plis, Terran Lane, 2010, 2010 IEEE International Conference on Data Mining.

Privacy-preserving source separation for distributed data using independent component analysis

Anand D. Sarwate, Vince D. Calhoun, Sergey M. Plis, 2016, 2016 Annual Conference on Information Science and Systems (CISS).

Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasks

Vince D. Calhoun, Sergey M. Plis, Jong-Hwan Lee, 2017, NeuroImage.

Impact of autocorrelation on functional connectivity

Vince D. Calhoun, Daniel H. Mathalon, Adrian Preda, 2014, NeuroImage.

COINSTAC: Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation

Anand D. Sarwate, Sergey M. Plis, Hafiz Imtiaz, 2020, J. Open Source Softw..

Almost instant brain atlas segmentation for large-scale studies pdf

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

Spatio-Temporal Dynamics of Intrinsic Networks in Functional Magnetic Imaging Data Using Recurrent Neural Networks

Vince D. Calhoun, Sergey M. Plis, Kyunghyun Cho, 2016, Front. Neurosci..

Run, skeleton, run: skeletal model in a physics-based simulation pdf

Sergey M. Plis, Mikhail Pavlov, Sergey Kolesnikov, 2018, AAAI Spring Symposia.

Restricted Boltzmann machines for neuroimaging: An application in identifying intrinsic networks

Vince D. Calhoun, Sergey M. Plis, Tülay Adali, 2014, NeuroImage.

Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls

Vince D. Calhoun, Sergey M. Plis, Jing Sui, 2017, NeuroImage.

Effective connectivity analysis of fMRI and MEG data collected under identical paradigms

Vince D. Calhoun, Sergey M. Plis, Eswar Damaraju, 2011, Comput. Biol. Medicine.

A deep-learning approach to translate between brain structure and functional connectivity

Vince D. Calhoun, Sergey M. Plis, Eswar Damaraju, 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Decentralized independent vector analysis

Anand D. Sarwate, Vince D. Calhoun, Sergey M. Plis, 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Taxonomy of multimodal self-supervised representation learning pdf

Sergey M. Plis, Lei Wu, Vince D. Calhoun, 2020, ArXiv.

Decentralized distribution-sampled classification models with application to brain imaging

Vince D. Calhoun, Sergey M. Plis, Harshvardhan Gazula, 2020, Journal of Neuroscience Methods.

Synthetic structural magnetic resonance image generator improves deep learning prediction of schizophrenia

Vince D. Calhoun, Sergey M. Plis, Erik B. Erhardt, 2015, 2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP).

Single-mode technological Nd:YAG laser with combined Q-switching

Sergey M. Plis, Svetlana A. Kyalbieva, Alexander V. Fedin, 2000, Advanced High-Power Lasers and Applications.

Recurrent Neural Networks for Spatiotemporal Dynamics of Intrinsic Networks from fMRI Data pdf

Vince D. Calhoun, Sergey M. Plis, R. Devon Hjelm, 2016, ArXiv.

Efficient Distributed Auto-Differentiation pdf

Vince D. Calhoun, Sergey M. Plis, Bradley T. Baker, 2021, ArXiv.

Statelets: A Novel Multi-Dimensional State-Shape Representation Of Brain Functional Connectivity Dynamics

Vince D. Calhoun, Sergey M. Plis, Eswar Damaraju, 2021, 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI).

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

Vince D. Calhoun, Sergey M. Plis, Lei Wu, 2021, ArXiv.

3-way Parallel Fusion of Spatial (sMRI/dMRI) and Spatio-temporal (fMRI) Data with Application to Schizophrenia

Vince D. Calhoun, Sergey M. Plis, Rogers F. Silva, 2021, 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI).

Algorithm-Agnostic Explainability for Unsupervised Clustering pdf

Vince D. Calhoun, Sergey M. Plis, Charles A. Ellis, 2021, ArXiv.

A Deep Learning Model for Data-Driven Discovery of Functional Connectivity pdf

Vince D. Calhoun, Sergey M. Plis, Zening Fu, 2021, Algorithms.

A generalized spatiotemporal covariance model for stationary background in analysis of MEG data

Sergey M. Plis, David M. Schmidt, Sung C. Jun, 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

Learning Dynamic Structure from Undersampled Data

Sergey M. Plis, David Danks, D. Danks, 2017 .

The influence of visuospatial attention on unattended auditory 40 Hz responses

Vince D. Calhoun, Sergey M. Plis, Eswar Damaraju, 2013, Front. Hum. Neurosci..

Efficient sequential inference in DBNs : steps towards joint MEG / fMRI connectivity analysis

Vince D. Calhoun, Sergey M. Plis, Tom Eichele, 2009 .

Spatiotemporal noise covariance model for MEG/EEG data source analysis

Sergey M. Plis, Sung Chan Jun, John S. George, 2005 .