Benchmarking functional connectome-based predictive models for resting-state fMRI
Functional connectomes reveal biomarkers of individual psychological or clinical traits.
However, there is great variability in the analytic pipelines typically used to derive them from …
However, there is great variability in the analytic pipelines typically used to derive them from …
Generalized shape metrics on neural representations
Understanding the operation of biological and artificial networks remains a difficult and
important challenge. To identify general principles, researchers are increasingly interested …
important challenge. To identify general principles, researchers are increasingly interested …
Differences in white matter fiber tract development present from 6 to 24 months in infants with autism
Objective: Evidence from prospective studies of high-risk infants suggests that early
symptoms of autism usually emerge late in the first or early in the second year of life after a …
symptoms of autism usually emerge late in the first or early in the second year of life after a …
First-order methods for geodesically convex optimization
Geodesic convexity generalizes the notion of (vector space) convexity to nonlinear metric
spaces. But unlike convex optimization, geodesically convex (g-convex) optimization is …
spaces. But unlike convex optimization, geodesically convex (g-convex) optimization is …
Pedestrian detection via classification on riemannian manifolds
We present a new algorithm to detect pedestrian in still images utilizing covariance matrices
as object descriptors. Since the descriptors do not form a vector space, well known machine …
as object descriptors. Since the descriptors do not form a vector space, well known machine …
Beyond euclid: An illustrated guide to modern machine learning with geometric, topological, and algebraic structures
The enduring legacy of Euclidean geometry underpins classical machine learning, which,
for decades, has been primarily developed for data lying in Euclidean space. Yet, modern …
for decades, has been primarily developed for data lying in Euclidean space. Yet, modern …
Non-Euclidean statistics for covariance matrices, with applications to diffusion tensor imaging
The statistical analysis of covariance matrix data is considered and, in particular,
methodology is discussed which takes into account the non-Euclidean nature of the space of …
methodology is discussed which takes into account the non-Euclidean nature of the space of …
White matter microstructure and atypical visual orienting in 7-month-olds at risk for autism
Objective The authors sought to determine whether specific patterns of oculomotor
functioning and visual orienting characterize 7-month-old infants who later meet criteria for …
functioning and visual orienting characterize 7-month-old infants who later meet criteria for …
Riemannian geometry of symmetric positive definite matrices via Cholesky decomposition
Z Lin - SIAM Journal on Matrix Analysis and Applications, 2019 - SIAM
We present a new Riemannian metric, termed Log-Cholesky metric, on the manifold of
symmetric positive definite (SPD) matrices via Cholesky decomposition. We first construct a …
symmetric positive definite (SPD) matrices via Cholesky decomposition. We first construct a …
Tensor-cspnet: A novel geometric deep learning framework for motor imagery classification
Deep learning (DL) has been widely investigated in a vast majority of applications in
electroencephalography (EEG)-based brain–computer interfaces (BCIs), especially for …
electroencephalography (EEG)-based brain–computer interfaces (BCIs), especially for …