text:   Networks",6,"['[1/6] How tight can convex-relaxed robustness verification for neural networks be in practice? We thoroughly investigate this in our new paper <LINK>!\n\nIn collaboration w\\ @TheGregYang, Huan Zhang, Cho-Jui Hsieh, and Pengchuan Zhang. Special thanks to @ilyaraz2! <LINK>', '[2/6] We unify all existing LP-relaxed verifiers under a general convex relaxation framework.', '[3/6] We perform extensive experiments, amounting to more than 22 CPU-Years, to obtain exact solution to the convex-relaxed problem that is optimal within our framework for ReLU networks.', '[4/6] We find the exact solution does not significantly improve upon the gap between exact verifiers and existing relaxed verifiers for various networks trained normally or robustly on MNIST and CIFAR-10 datasets.', '[5/6] Our results suggest there is an inherent barrier to tight robustness verification for the large class of methods captured by our framework.', '[6/6] Finally, we discuss possible causes of this barrier and potential future directions for bypassing it.']",19,02,1018