no code implementations • 16 Feb 2022 • Junhao Wen, Erdem Varol, Zhijian Yang, Gyujoon Hwang, Dominique Dwyer, Anahita Fathi Kazerooni, Paris Alexandros Lalousis, Christos Davatzikos
The imaging community has increasingly adopted machine learning (ML) methods to provide individualized imaging signatures related to disease diagnosis, prognosis, and response to treatment.
no code implementations • NeurIPS 2021 • Julien Boussard, Erdem Varol, Hyun Dong Lee, Nishchal Dethe, Liam Paninski
Neuropixels (NP) probes are dense linear multi-electrode arrays that have rapidly become essential tools for studying the electrophysiology of large neural populations.
1 code implementation • 20 Oct 2021 • Junhao Wen, Cynthia H. Y. Fu, Duygu Tosun, Yogasudha Veturi, Zhijian Yang, Ahmed Abdulkadir, Elizabeth Mamourian, Dhivya Srinivasan, Jingxuan Bao, Guray Erus, Haochang Shou, Mohamad Habes, Jimit Doshi, Erdem Varol, Scott R Mackin, Aristeidis Sotiras, Yong Fan, Andrew J. Saykin, Yvette I. Sheline, Li Shen, Marylyn D. Ritchie, David A. Wolk, Marilyn Albert, Susan M. Resnick, Christos Davatzikos
We sought to delineate, cross-sectionally and longitudinally, disease-related heterogeneity in LLD linked to neuroanatomy, cognitive functioning, clinical symptomatology, and genetic profiles.
1 code implementation • 1 Jul 2020 • Junhao Wen, Erdem Varol, Ganesh Chand, Aristeidis Sotiras, Christos Davatzikos
There is a growing amount of clinical, anatomical and functional evidence for the heterogeneous presentation of neuropsychiatric and neurodegenerative diseases such as schizophrenia and Alzheimers Disease (AD).
no code implementations • 30 Jun 2020 • Gonzalo Mena, Amin Nejatbakhsh, Erdem Varol, Jonathan Niles-Weed
We study Sinkhorn EM (sEM), a variant of the expectation maximization (EM) algorithm for mixtures based on entropic optimal transport.
no code implementations • 7 Dec 2019 • Erdem Varol, Amin Nejatbakhsh, Conor McGrory
Motion segmentation for natural images commonly relies on dense optic flow to yield point trajectories which can be grouped into clusters through various means including spectral clustering or minimum cost multicuts.
no code implementations • 23 Oct 2019 • Erdem Varol, Amin Nejatbakhsh
In this paper, we expand upon the theory of trend filtering by introducing the use of the Wasserstein metric as a means to control the amount of spatiotemporal variation in filtered time series data.
no code implementations • pproximateinference AABI Symposium 2019 • Gonzalo Mena, Erdem Varol, Amin Nejatbakhsh, Eviatar Yemini, Liam Paninski
This problem is known to quickly become intractable as the size of the permutation increases, since its involves the computation of the permanent of a matrix, a #P-hard problem.
no code implementations • 1 Jun 2019 • Amin Nejatbakhsh, Erdem Varol
Previous theoretical analysis of the problem has been done in a setting where the responses are a complete permutation of the regressed covariates.