3 accepted papers
The robustness certification of deep neural networks (DNNs) is crucial in many safety-critical domains. Randomized Smoothing (RS) has emerged as the current state-of-the-art method for DNN robustness verification that successfully scales on large DNNs used in practice, has achieved excellent results
Randomized smoothing (RS) is one of the prominent techniques to ensure the correctness of machine learning models, where point-wise robustness certificates can be derived analytically. While RS is well understood for classification, its application to generative models is unclear, since their output
Rather than performing inefficient local iterative routing between adjacent capsule layers, we propose an alternative global view based on representing the inherent uncertainty in part-object assignment. In our formulation, the local routing iterations are replaced with variational inference of part…