Title: Robust Neural ODEs via Contractivity-promoting Regularization. Abstract: Neural networks can be fragile to input noise and adversarial attacks. In this work, we consider Neural Ordinary Differential Equations (NODEs) – a family of continuous-depth neural networks represented by dynamical systems - and propose to use contraction theory to improve their robustness. A dynamical system is contractive if two trajectories starting from different initial conditions converge to each other exponentially fast. Contractive NODEs can enjoy increased robustness as slight perturbations of the features do not cause a significant change in the output. Contractivity can be induced during training by using a regularization term involving the Jacobian of the system dynamics. To reduce the computational burden, we show that it can also be promoted using carefully selected weight regularization terms for a class of NODEs with slope-restricted activation functions, including convolutional networks commonly used in image classification. The performance of the proposed regularizers is illustrated through benchmark image classification tasks on MNIST and FashionMNIST datasets, where images are corrupted by different kinds of noise and attacks.