2 citations · 3 across the 2 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
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…