Recent work on training neural networks to have robust attributions, to improve their trustworthiness and resilience to adversarial attacks.

The wild success of Deep Neural Network (DNN) models in a variety of domains has created considerable excitement in the machine learning community. Despite this success, a deep understanding of why DNNs perform so well, and whether their performance is somehow brittle, has been lacking.