1.4k citations · 3.1k across the 30 of their papers we have counts for
4 papers · 1 filter
Dropout as a Regularizer of Interaction Effects
Benjamin Lengerich, Eric P. Xing, Rich Caruana
We examine Dropout through the perspective of interactions. This view provides a symmetry to explain Dropout: given variables, there are possible sets of va…
How Interpretable and Trustworthy are GAMs?
Chun-Hao Chang, Sarah Tan, Ben Lengerich +2
Generalized additive models (GAMs) have become a leading modelclass for interpretable machine learning. However, there are many algorithms for training GAMs, and these can learn di…
Neural Additive Models: Interpretable Machine Learning with Neural Nets
Rishabh Agarwal, Levi Melnick, Nicholas Frosst +4
Deep neural networks (DNNs) are powerful black-box predictors that have achieved impressive performance on a wide variety of tasks. However, their accuracy comes at the cost of int…
Improving data-driven global weather prediction using deep convolutional neural networks on a cubed sphere
Jonathan A. Weyn, Dale R. Durran, Rich Caruana
We present a significantly-improved data-driven global weather forecasting framework using a deep convolutional neural network (CNN) to forecast several basic atmospheric variables…