Search Results for "dig"

The key difference with current graph deep learning libraries, such as PyTorch Geometric (PyG) and Deep Graph Library (DGL), is that, while PyG and DGL support basic graph deep learning operations, DIG provides a unified testbed for higher level, research-oriented graph deep learning tasks, such as graph generation, self-supervised learning, explainability, 3D graphs, and graph out-of-distribution. If you are working or plan to work on research in graph deep learning, DIG enables you to develop your own methods within our extensible framework, and compare with current baseline methods using common datasets and evaluation metrics without extra efforts. ...

...The sooner you understand that language learning has no skills, the less time and money will be wasted on various materials and experiences, and the fewer detours you will make. This guide will dig deep into the essence of language learning (mainly in Chinese for example) and think in a way that programmers can understand easily, and provide their own deductions based on the essence. Training methods and experience are for reference. The most important thing is that you have to design your own training methods based on the essence.