Search Results for author: Sergey Plis

Found 14 papers, 3 papers with code

Multi network InfoMax: A pre-training method involving graph convolutional networks

no code implementations1 Nov 2021 Usman Mahmood, Zening Fu, Vince Calhoun, Sergey Plis

Since almost every DL model is an ensemble of multiple networks, we take our high-level embeddings from two different networks of a model --a convolutional and a graph network--.

Graph Classification

Brain dynamics via Cumulative Auto-Regressive Self-Attention

no code implementations1 Nov 2021 Usman Mahmood, Zening Fu, Vince Calhoun, Sergey Plis

The supervised training of the model as a classifier between patients and controls results in a model that generates directed connectivity graphs and highlights the components of the time-series that are predictive for each subject.

Time Series

Attend to connect: end-to-end brain functional connectivity estimation

no code implementations ICLR Workshop GTRL 2021 Usman Mahmood, Zening Fu, Vince Calhoun, Sergey Plis

Functional connectivity (FC) studies have demonstrated the benefits of investigating the brain and its disorders through the undirected weighted graph of fMRI correlation matrix.

Connectivity Estimation

Grouped sparse projection

no code implementations9 Dec 2019 Nicolas Gillis, Riyasat Ohib, Sergey Plis, Vamsi Potluru

In this paper, we design a new sparse projection method for a set of vectors in order to achieve a desired average level of sparsity which is measured using the ratio of the $\ell_1$ and $\ell_2$ norms.

Transfer Learning of fMRI Dynamics

no code implementations16 Nov 2019 Usman Mahmood, Md Mahfuzur Rahman, Alex Fedorov, Zening Fu, Sergey Plis

In this paper, we demonstrate a self-supervised pre-training method that enables us to pre-train directly on fMRI dynamics of healthy control subjects and transfer the learning to much smaller datasets of schizophrenia.

Small Data Image Classification Transfer Learning

Prediction of Progression to Alzheimer's disease with Deep InfoMax

no code implementations24 Apr 2019 Alex Fedorov, R. Devon Hjelm, Anees Abrol, Zening Fu, Yuhui Du, Sergey Plis, Vince D. Calhoun

Arguably, unsupervised learning plays a crucial role in the majority of algorithms for processing brain imaging.

General Classification

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

no code implementations4 Apr 2018 Alvaro Ulloa, Sergey Plis, Vince Calhoun

We propose the use of a multimodal multi-layer perceptron model to enhance the predictive power of structural and functional magnetic resonance imaging (sMRI and fMRI) combined.

General Classification

Almost instant brain atlas segmentation for large-scale studies

1 code implementation1 Nov 2017 Alex Fedorov, Eswar Damaraju, Vince Calhoun, Sergey Plis

Complexity of the task increases even further when segmenting structural MRI of the brain into an atlas with more than 50 regions.

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

1 code implementation3 Dec 2016 Alex Fedorov, Jeremy Johnson, Eswar Damaraju, Alexei Ozerin, Vince Calhoun, Sergey Plis

Segmenting a structural magnetic resonance imaging (MRI) scan is an important pre-processing step for analytic procedures and subsequent inferences about longitudinal tissue changes.

Causal Discovery from Subsampled Time Series Data by Constraint Optimization

no code implementations25 Feb 2016 Antti Hyttinen, Sergey Plis, Matti Järvisalo, Frederick Eberhardt, David Danks

This paper focuses on causal structure estimation from time series data in which measurements are obtained at a coarser timescale than the causal timescale of the underlying system.

Causal Discovery Time Series

Rate-Agnostic (Causal) Structure Learning

no code implementations NeurIPS 2015 Sergey Plis, David Danks, Cynthia Freeman, Vince Calhoun

That is, these algorithms all learn causal structure without assuming any particular relation between the measurement and system timescales; they are thus rate-agnostic.

Time Series

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