Our main algorithmic result is the first query-efficient algorithm for learning halfspaces under the popular Massart noise model.

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.

But spectral clustering remains a frequently used baseline, and a serious contender to state-of-the-art graphembeddingmethods,e.g.[20,11,28,22].

Similarly, a model that predicts the time it will take a customer to grocery shop should decrease in the number of cashiers, but each addedcashierreduces average wait time by less. In both cases, we would like to be able to incorporate this prior knowledge by constraining the machine learned model's output to have a diminishing returns response to the size of the apartment or number of cashiers.

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

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

To fix ideas, consider ECG recordings from healthy patients during rest.

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

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.