22 citations · 128 across the 29 of their papers we have counts for
9 papers · 1 filter
Discrete Probabilistic Inverse Optimal Transport
Wei-Ting Chiu, Pei Wang, Patrick Shafto
Optimal transport (OT) formalizes the problem of finding an optimal coupling between probability measures given a cost matrix. The inverse problem of inferring the cost given a cou…
Conditional Deep Gaussian Processes: empirical Bayes hyperdata learning
Chi-Ken Lu, Patrick Shafto
It is desirable to combine the expressive power of deep learning with Gaussian Process (GP) in one expressive Bayesian learning model. Deep kernel learning showed success in adopti…
Explainable AI for Natural Adversarial Images
Tomas Folke, ZhaoBin Li, Ravi B. Sojitra +2
Adversarial images highlight how vulnerable modern image classifiers are to perturbations outside of their training set. Human oversight might mitigate this weakness, but depends o…
Explainable AI for medical imaging: Explaining pneumothorax diagnoses with Bayesian Teaching
Tomas Folke, Scott Cheng-Hsin Yang, Sean Anderson +1
Limited expert time is a key bottleneck in medical imaging. Due to advances in image classification, AI can now serve as decision-support for medical experts, with the potential fo…
Abstraction, Validation, and Generalization for Explainable Artificial Intelligence
Scott Cheng-Hsin Yang, Tomas Folke, Patrick Shafto
Neural network architectures are achieving superhuman performance on an expanding range of tasks. To effectively and safely deploy these systems, their decision-making must be unde…
Efficient Discretizations of Optimal Transport
Junqi Wang, Pei Wang, Patrick Shafto
Obtaining solutions to Optimal Transportation (OT) problems is typically intractable when the marginal spaces are continuous. Recent research has focused on approximating continuou…