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Ortho-ODE: Enhancing Robustness and of Neural ODEs against Adversarial Attacks. (arXiv:2305.09179v1 [cs.LG])
cs.CR updates on arXiv.org arxiv.org
Neural Ordinary Differential Equations (NODEs) probed the usage of numerical
solvers to solve the differential equation characterized by a Neural Network
(NN), therefore initiating a new paradigm of deep learning models with infinite
depth. NODEs were designed to tackle the irregular time series problem.
However, NODEs have demonstrated robustness against various noises and
adversarial attacks. This paper is about the natural robustness of NODEs and
examines the cause behind such surprising behaviour. We show that by
controlling the Lipschitz constant …
adversarial adversarial attacks attacks deep learning equation network neural network nodes paradigm problem robustness series