Search Results for author:
Found 5 papers, 0 papers with code
Best Practices for Data-Efficient Modeling in NLG:How to Train Production-Ready Neural Models with Less Data
In this paper, we present approaches that have helped us deploy data-efficient neural solutions for NLG in conversational systems to production.
Learning Graphical Models With Hubs
We consider the problem of learning a high-dimensional graphical model in which certain hub nodes are highly-connected to many other nodes.
ADMM Algorithm for Graphical Lasso with an $\ell_{\infty}$ Element-wise Norm Constraint
We consider the problem of Graphical lasso with an additional $\ell_{\infty}$ element-wise norm constraint on the precision matrix.
Node-Based Learning of Multiple Gaussian Graphical Models
We consider estimation under two distinct assumptions: (1) differences between the K networks are due to individual nodes that are perturbed across conditions, or (2) similarities among the K networks are due to the presence of common hub nodes that are shared across all K networks.
Structured Learning of Gaussian Graphical Models
We consider estimation of multiple high-dimensional Gaussian graphical models corresponding to a single set of nodes under several distinct conditions.
