computer-vision

We propose three novel evaluation schemes to better understand the faithfulness and differences between attribution methods, and use them to study strengths and shortcomings of some widely used attribution methods.

We show that location-optimization significantly strengthens adversarial patch attacks, and then show that adversarial training on these stronger attacks significantly improves robustness without reducing accuracy

Adversarial Training against Location-Optimized Adversarial Patches

Research Internship at the University of Tokyo in the summer of 2018

Research Internship at Robert Bosch in the summer of 2017

Implementation of the model described in "Training deep neural nets to aggregate crowdsourced responses." by Gaunt et al.