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Testing Stationarity of Brain Functional Connectivity Using Change-Point Detection in fMRI Data.

, , and . CVPR Workshops, page 981-989. IEEE Computer Society, (2016)

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Analyzing Dynamical Brain Functional Connectivity as Trajectories on Space of Covariance Matrices., , and . IEEE Trans. Medical Imaging, 39 (3): 611-620 (2020)Analyzing Dynamical Brain Functional Connectivity As Trajectories on Space of Covariance Matrices., , and . CoRR, (2019)Testing Stationarity of Brain Functional Connectivity Using Change-Point Detection in fMRI Data., , and . CVPR Workshops, page 981-989. IEEE Computer Society, (2016)Discovering Change-Point Patterns in Dynamic Functional Brain Connectivity of a Population., , and . IPMI, volume 10265 of Lecture Notes in Computer Science, page 361-372. Springer, (2017)Adversarial Manifold Matching via Deep Metric Learning for Generative Modeling., and . CoRR, (2021)Video-Based Action Recognition Using Dimension Reduction of Deep Covariance Trajectories., and . CVPR Workshops, page 611-620. Computer Vision Foundation / IEEE, (2019)Discovering common change-point patterns in functional connectivity across subjects., , and . Medical Image Anal., (2019)Adaptive Feature Interpolation for Low-Shot Image Generation., , and . ECCV (15), volume 13675 of Lecture Notes in Computer Science, page 254-270. Springer, (2022)Manifold Matching via Deep Metric Learning for Generative Modeling., and . ICCV, page 6567-6577. IEEE, (2021)AWGAN: Empowering High-Dimensional Discriminator Output for Generative Adversarial Networks., , and . CoRR, (2021)