Inspired by multi-modal models that align different modalities with natural language, the text has recently been adopted to provide a unified feature space for diverse graphs.

To handle the issue, we propose a new framework, coined robust model adaptation (RoMA), based on gradient-based optimization of inputs over the DNN. Specifically, it consists oftwosteps: (a)apre-training strategytorobustly train theproxy model and (b) a novel adaptation procedure of the proxy model to have robust estimates for a specific set of candidate solutions. At ahigh level, our scheme utilizes thelocal smoothness priorto overcome the brittleness of the DNN.

To handle the issue, we propose a new framework, coined robust model adaptation (RoMA), based on gradient-based optimization of inputs over the DNN. Specifically, it consists oftwosteps: (a)apre-training strategytorobustly train theproxy model and (b) a novel adaptation procedure of the proxy model to have robust estimates for aspecific set ofcandidate solutions. Atahigh level, our scheme utilizes thelocal smoothness priorto overcome the brittleness of the DNN.

Augmentation strategies The augmentation idea of ANODEs (Dupont et al., 2019) is taken further and generalized to novel dynamical system-inspired and parameter efficient alternatives, relyingondifferentchoicesofhx in(1).

In such a setting, distribution-free risk bounds are impossible, and the best one could hope for is a fully empirical data-dependent bound.

In such a setting, distribution-free risk bounds are impossible, and the best one could hope for is a fully empirical data-dependent bound.