Until now, no tabular method has consistently outperformed classical supervised learning, which ignores these shifts.

Diffusion models (DMs) have recently gained attention with state-of-the-art performance in text-to-image synthesis.

That is, we propose to formulate continual learning as a sequence modeling problem, allowing advanced sequence models to be utilized for continual learning.

In Table 1, we report the cross-task testbed results in two transfer learning settings, i.e., (a) node classification to link prediction (Table 1a) and (b) link prediction to node classification (Table 1b).

Multivariate time series (MTS) forecasting has shown great importance in numerous industries.

Automated seizure detection is of great importance to epilepsy diagnosis and treatment.

The discovery of dynamical systems models plays a fundamental role across various domains, including pharmacology, epidemiology, and physical systems.

A vision model with general-purpose object-level 3D understanding should be capable of inferring both 2D ( e.g., class name and bounding box) and 3D information ( e.g., 3D location and 3D viewpoint) for arbitrary rigid objects in natural