13 citations · 30 across the 11 of their papers we have counts for
12 papers
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…
When are Deep Networks really better than Decision Forests at small sample sizes, and how?
Haoyin Xu, Kaleab A. Kinfu, Will LeVine +9
Deep networks and decision forests (such as random forests and gradient boosted trees) are the leading machine learning methods for structured and tabular data, respectively. Many…
Leveraging semantically similar queries for ranking via combining representations
Hayden S. Helm, Marah Abdin, Benjamin D. Pedigo +8
In modern ranking problems, different and disparate representations of the items to be ranked are often available. It is sensible, then, to try to combine these representations to…
Dynamic Silos: Increased Modularity in Intra-organizational Communication Networks during the Covid-19 Pandemic
Tiona Zuzul, Emily Cox Pahnke, Jonathan Larson +9
Workplace communications around the world were drastically altered by Covid-19, related work-from-home orders, and the rise of remote work. To understand these shifts, we analyzed…
Learning without gradient descent encoded by the dynamics of a neurobiological model
Vivek Kurien George, Vikash Morar, Weiwei Yang +6
The success of state-of-the-art machine learning is essentially all based on different variations of gradient descent algorithms that minimize some version of a cost or loss functi…