35 citations · 56 across the 6 of their papers we have counts for
6 papers
Gaussian mixture models as a proxy for interacting language models
Edward L. Wang, Mohammad Sharifi Kiasari, Tianyu Wang +4
Large language models (LLMs) are powerful tools that, in a number of settings, overlap with the results of human pattern recognition and reasoning. Retrieval-augmented generation (…
Semisupervised regression in latent structure networks on unknown manifolds
Aranyak Acharyya, Joshua Agterberg, Michael W. Trosset +2
Random graphs are increasingly becoming objects of interest for modeling networks in a wide range of applications. Latent position random graph models posit that each node is assoc…
Discovering a change point and piecewise linear structure in a time series of organoid networks via the iso-mirror
Tianyi Chen, Youngser Park, Ali Saad-Eldin +10
Recent advancements have been made in the development of cell-based in-vitro neuronal networks, or organoids. In order to better understand the network structure of these organoids…
Towards a theory of out-of-distribution learning
Jayanta Dey, Ali Geisa, Ronak Mehta +7
Learning is a process wherein a learning agent enhances its performance through exposure of experience or data. Throughout this journey, the agent may encounter diverse learning en…
A central limit theorem for an omnibus embedding of multiple random graphs and implications for multiscale network inference
Keith Levin, Avanti Athreya, Minh Tang +3
Performing statistical analyses on collections of graphs is of import to many disciplines, but principled, scalable methods for multi-sample graph inference are few. Here we descri…
Fast Approximate Quadratic Programming for Large (Brain) Graph Matching
Joshua T. Vogelstein, John M. Conroy, Vince Lyzinski +6
Quadratic assignment problems (QAPs) arise in a wide variety of domains, ranging from operations research to graph theory to computer vision to neuroscience. In the age of big data…