The word embedding space in neural models is skewed, and correcting this can improve task performance.

While 3D Gaussians efficiently approximate an object's surface, they fail to capture the volumetric properties of subsurface scattering. We propose a framework for optimizing an object's shape together with the radiance transfer field given multi-view OLA T (one light at a time) data.

Much remains to be understood, however, in statistical learning under distribution shifts. This paper focuses on a distribution shift setting where train and test distributions can be related by classes of (data) transformation maps.

We present an alternative approach to decompose non-negative tensors, called many-body approximation .