Creating high-quality scientific figures can be time-consuming and challenging, even though sketching ideas on paper is relatively easy. Furthermore, recreating existing figures that are not stored in formats preserving semantic information is equally complex.

Stein V ariational Gradient Descent (SVGD) is a nonparametric particle-based deterministic sampling algorithm.

It is desirable to restrict the number of components (i.e., encourage sparsity) for

Further, we are interested in algorithms that enjoy sample efficiency while leveraging (value) function approximation.

Accurately modeling weather variation pattern from large amount of meteorological variables sequences is increasingly vital for providing efficient weather analysis support for disaster warning.

AR baseline by improving Fréchet inception distance (FID) from 18.65 to 1.73, inception score (IS) from 80.4 to 350.2, with 20

However, recent works reveal that it is non-trivial to find a proper initialization of the prompt tokens.