CaST, which consistently outperforms existing methods with good interpretability.

Notably, most real-world homophilic and heterophilic graphs are comprised of a mixture of nodes in both homophilic and heterophilic structural patterns, exhibiting a structural disparity.

The line, triangular and square shapes represent the joint distribution of two, three and four variables respectively.

The design of a reward function often poses a major practical challenge to realworld applications of reinforcement learning.

First we propose a two-stage architecture for ourgenerator,consisting ofamask generator,andanimage in-painter which cooperate toachieve removal.

Neural Information Processing Systems http://nips.cc/

Recent language models have shown impressive multilingual performance, even when not explicitly trained for it. Despite this, there are concerns about the quality of their outputs across different languages. In this paper, we show how disparity in the treatment of different languages arises at the tokenization stage, well before a model is even invoked. The same text translated into different languages can have drastically different tok-enization lengths, with differences up to 15 times in some cases. These disparities persist even for tokenizers that are intentionally trained for multilingual support.

We introduce OpenIllumination, a real-world dataset containing over 108K images of 64 objects with diverse materials, captured under 72 camera views and a large number of different illuminations. For each image in the dataset, we provide accurate camera parameters, illumination ground truth, and foreground segmentation masks. Our dataset enables the quantitative evaluation of most inverse rendering and material decomposition methods for real objects.