An adversarial attack that recovers supposedly unlearned multi-modality knowledge from MLLMs via prompt-suffix optimization and fine-tuning, exposing vulnerabilities in machine unlearning defenses.
In classification tasks, classifiers trained with finite examples might generalize poorly to new data with unknown variance. For this issue, data augmentation is a successful solution where numerous artificial examples are added to training sets. In …
Linear dynamical system (LDS) offers a convenient way to reveal the unobservable structure behind the data. This makes it useful for data representation and explanatory analysis. An immediate limitation with this model is that most training …