1k citations · 1.6k across the 56 of their papers we have counts for
Showing 2019 · stat.MLShow all
2 papers · 2 filters
stat.ML2019★ 7 cited
Data Augmentation for Bayesian Deep Learning
Yuexi Wang, Nicholas G. Polson, Vadim O. Sokolov
Deep Learning (DL) methods have emerged as one of the most powerful tools for functional approximation and prediction. While the representation properties of DL have been well stud…
stat.ML2019★ 4 cited
Deep Fundamental Factor Models
Matthew F. Dixon, Nicholas G. Polson
Deep fundamental factor models are developed to automatically capture non-linearity and interaction effects in factor modeling. Uncertainty quantification provides interpretability…