Although numerous studies have been emerged on adversarial attacks and defenses in fields such as face recognition, person re-identification, and pedestrian detection, there is currently a lack of research on the security of VIReID systems.

We observe that MFM outperforms the Euclidean baselines, particularly achieving SOT A on single-cell trajectory prediction.

Out-of-Distribution (OoD) detection is vital for the reliability of Deep Neural Networks (DNNs).

Lasso performance, with the added benefit of providing multiple solutions.

Despite its ease of implementation, the EF approximation has its theoretical and practical limitations.

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.

Despite its broad applications in fields such as computer vision, graph learning, and natural language processing, the development of a data projection model that can be effectively used to visualize data in the context of FL is crucial yet remains heavily under-explored. Neighbor embedding (NE) is an essential technique for visualizing complex high-dimensional data, but collab-oratively learning a joint NE model is difficult.

Off-policy estimation: Oftentimes, one needs to estimate the linear functional based on observational data collected from a behavior policy.