37 citations · 108 across the 19 of their papers we have counts for
4 papers · 1 filter
CHEF: A Cheap and Fast Pipeline for Iteratively Cleaning Label Uncertainties (Technical Report)
Yinjun Wu, James Weimer, Susan B. Davidson
High-quality labels are expensive to obtain for many machine learning tasks, such as medical image classification tasks. Therefore, probabilistic (weak) labels produced by weak sup…
Improving Neural Network Robustness via Persistency of Excitation
Kaustubh Sridhar, Oleg Sokolsky, Insup Lee +1
Improving adversarial robustness of neural networks remains a major challenge. Fundamentally, training a neural network via gradient descent is a parameter estimation problem. In a…
ModelGuard: Runtime Validation of Lipschitz-continuous Models
Taylor J. Carpenter, Radoslav Ivanov, Insup Lee +1
This paper presents ModelGuard, a sampling-based approach to runtime model validation for Lipschitz-continuous models. Although techniques exist for the validation of many classes…
Confidence Calibration with Bounded Error Using Transformations
Sooyong Jang, Radoslav Ivanov, Insup Lee +1
As machine learning techniques become widely adopted in new domains, especially in safety-critical systems such as autonomous vehicles, it is crucial to provide accurate output unc…