Towards Robust Deep Neural Networks

27 Oct 2018Timothy E. WangYiming GuDhagash MehtaXiaojun ZhaoEdgar A. Bernal

We investigate the topics of sensitivity and robustness in feedforward and convolutional neural networks. Combining energy landscape techniques developed in computational chemistry with tools drawn from formal methods, we produce empirical evidence indicating that networks corresponding to lower-lying minima in the optimization landscape of the learning objective tend to be more robust... (read more)

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