Due to their inherent complexity, these tasks pose significant challenges for current machine learning systems.

The ability of a brain or a neural network to efficiently learn depends crucially on both the task structure and the learning rule.

Certified adversarial robustness of large-scale deep networks has progressed substantially after the introduction of randomized smoothing.

The conventional approach to imputing longitudinal data is to employ a parametric model with mixed-effects, such as the linear mixed-effects model.

To address the error propagation problem, we identify image fusion as the key point for improvement.