2 citations · 2 across the 1 of their papers we have counts for
3 papers
cs.LG2020★ 2 cited
Rationalizing Text Matching: Learning Sparse Alignments via Optimal Transport
Kyle Swanson, Lili Yu, Tao Lei
Selecting input features of top relevance has become a popular method for building self-explaining models. In this work, we extend this selective rationalization approach to text m…
cs.LG2020
Uncertainty Quantification Using Neural Networks for Molecular Property Prediction
Lior Hirschfeld, Kyle Swanson, Kevin Yang +2
Uncertainty quantification (UQ) is an important component of molecular property prediction, particularly for drug discovery applications where model predictions direct experimental…
cond-mat.soft2019
Deep Learning for Automated Classification and Characterization of Amorphous Materials
Kirk Swanson, Shubhendu Trivedi, Joshua Lequieu +2
It is difficult to quantify structure-property relationships and to identify structural features of complex materials. The characterization of amorphous materials is especially cha…