Reviews: Beyond the Single Neuron Convex Barrier for Neural Network Certification

Originality: The authors propose a novel relaxation (to the best of my knowledge) for networks with ReLU activations that tighten previously proposed relaxations that ignore the correlations between neurons in the network. The theoretical results are also novel (although unsurprising). However, it would be useful for the authors to better clarify the computational requirements and tightness of k-ReLU relative to DeepPoly and other similar relaxations and bound propagation methods like [13] and https://arxiv.org/abs/1805.12514, Quality: The theoretical results are accurate (albeit unsurprising) in my opinion. The experimental section is missing several important details in my opinion: 1) The authors say that experiments are performed on both MNIST and CIFAR-10, but the tables 2/3 only report numbers on MNIST.