7 citations · 7 across the 2 of their papers we have counts for
6 papers
Self-supervised edge features for improved Graph Neural Network training
Arijit Sehanobish, Neal G. Ravindra, David van Dijk
Graph Neural Networks (GNN) have been extensively used to extract meaningful representations from graph structured data and to perform predictive tasks such as node classification…
Learning aligned embeddings for semi-supervised word translation using Maximum Mean Discrepancy
Antonio H. O. Fonseca, David van Dijk
Word translation is an integral part of language translation. In machine translation, each language is considered a domain with its own word embedding. The alignment between word e…
Disease State Prediction From Single-Cell Data Using Graph Attention Networks
Neal G. Ravindra, Arijit Sehanobish, Jenna L. Pappalardo +2
Single-cell RNA sequencing (scRNA-seq) has revolutionized biological discovery, providing an unbiased picture of cellular heterogeneity in tissues. While scRNA-seq has been used ex…
TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics
Alexander Tong, Jessie Huang, Guy Wolf +2
It is increasingly common to encounter data from dynamic processes captured by static cross-sectional measurements over time, particularly in biomedical settings. Recent attempts t…
Compressed Diffusion
Scott Gigante, Jay S. Stanley, Ngan Vu +4
Diffusion maps are a commonly used kernel-based method for manifold learning, which can reveal intrinsic structures in data and embed them in low dimensions. However, as with most…
Finding Archetypal Spaces Using Neural Networks
David van Dijk, Daniel Burkhardt, Matthew Amodio +3
Archetypal analysis is a data decomposition method that describes each observation in a dataset as a convex combination of "pure types" or archetypes. These archetypes represent ex…