4 citations · 7 across the 4 of their papers we have counts for
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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…
cs.LG2020★ 4 cited
Improving Molecular Design by Stochastic Iterative Target Augmentation
Kevin Yang, Wengong Jin, Kyle Swanson +2
Generative models in molecular design tend to be richly parameterized, data-hungry neural models, as they must create complex structured objects as outputs. Estimating such models…