Graph Representational Learning (GRL) have come at the cost of significant computational resource requirements for training, e.g., for calculating gradients

Human-AI shared control allows human to interact and collaborate with autonomous agents to accomplish control tasks in complex environments.

In dynamical systems, the states of an agent are affected by the interactions, and the states are usually recorded as a set of continuous variables, which make it difficult to uncover the interactions based on the similarity between the agents.

Precise and accurate predictions over boundary areas are essential for semantic segmentation.

Neural networks are powerful surrogates for numerous forward processes. The inversion of such surrogates is extremely valuable in science and engineering.

Neural networks have impressive performance on a variety of datasets (LeCun et al., 1998; He et al., 2015; Krizhevsky et al., 2017; Everingham et al., 2010) but can be fooled by imperceptible