6 papers · 1 filter
Linearized Optimal Transport for Analysis of High-Dimensional Point-Cloud and Single-Cell Data
Tianxiang Wang, Yingtong Ke, Dhananjay Bhaskar +2
Single-cell technologies generate high-dimensional point clouds of cells, enabling detailed characterization of complex patient states and treatment responses. Yet each patient is…
STAGED: A Multi-Agent Neural Network for Learning Cellular Interaction Dynamics
Joao F. Rocha, Ke Xu, Xingzhi Sun +6
The advent of single-cell technology has significantly improved our understanding of cellular states and subpopulations in various tissues under normal and diseased conditions by e…
Principal Curvatures Estimation with Applications to Single Cell Data
Yanlei Zhang, Lydia Mezrag, Xingzhi Sun +6
The rapidly growing field of single-cell transcriptomic sequencing (scRNAseq) presents challenges for data analysis due to its massive datasets. A common method in manifold learnin…
HiPoNet: A Multi-View Simplicial Complex Network for High Dimensional Point-Cloud and Single-Cell Data
Siddharth Viswanath, Hiren Madhu, Dhananjay Bhaskar +7
In this paper, we propose HiPoNet, an end-to-end differentiable neural network for regression, classification, and representation learning on high-dimensional point clouds. Our wor…
Exploring the Manifold of Neural Networks Using Diffusion Geometry
Elliott Abel, Andrew J. Steindl, Selma Mazioud +12
Drawing motivation from the manifold hypothesis, which posits that most high-dimensional data lies on or near low-dimensional manifolds, we apply manifold learning to the space of…
Latent Representation Learning for Multimodal Brain Activity Translation
Arman Afrasiyabi, Dhananjay Bhaskar, Erica L. Busch +5
Neuroscience employs diverse neuroimaging techniques, each offering distinct insights into brain activity, from electrophysiological recordings such as EEG, which have high tempora…