Code will be released upon acceptance.

In this study, we theoretically explain the counterintuitive success of perturbation learning. We assume wide two-layer networks and the results hold for any data distribution.

However, generative models have rarely been considered for semi-supervised classification recently.

This title draws inspiration from the chameleon's ability to adapt and blend into its surroundings, which parallels

For these systems, a common issue is the difficulty of collecting sufficiently refined timescale data.