Stochastic gradient descent (SGD) algorithm is the method of choice in many machine learning tasks thanks to its scalability and efficiency in dealing with large-scale problems. In this paper, we focus on the shuffling version of SGD which matches the mainstream practical heuristics. We show the convergence to a global solution of shuffling SGD for a class of non-convex functions under over-parameterized settings.

Short-horizon tasks require few steps: e.g., go to a tree and chop it down, dig a hole.

Adversarial patches have shown to be highly effective in causing DNNs to misclassify by distorting a localized area (patch) of the image.