44 citations · 91 across the 11 of their papers we have counts for
7 papers · 1 filter
Permutation invariant networks to learn Wasserstein metrics
Arijit Sehanobish, Neal Ravindra, David van Dijk
Understanding the space of probability measures on a metric space equipped with a Wasserstein distance is one of the fundamental questions in mathematical analysis. The Wasserstein…
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…
Learning Potentials of Quantum Systems using Deep Neural Networks
Arijit Sehanobish, Hector H. Corzo, Onur Kara +1
Attempts to apply Neural Networks (NN) to a wide range of research problems have been ubiquitous and plentiful in recent literature. Particularly, the use of deep NNs for understan…
Gaining Insight into SARS-CoV-2 Infection and COVID-19 Severity Using Self-supervised Edge Features and Graph Neural Networks
Arijit Sehanobish, Neal G. Ravindra, David van Dijk
A molecular and cellular understanding of how SARS-CoV-2 variably infects and causes severe COVID-19 remains a bottleneck in developing interventions to end the pandemic. We sought…
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…