论文标题
pygsl：图形结构学习工具包
pyGSL: A Graph Structure Learning Toolkit
论文作者
论文摘要
我们介绍了Pygsl，这是一个Python库，可提供最先进的图形结构学习模型以及各种数据集的有效实现，以对其进行评估。这些实现是以GPU友好的方式编写的，可以扩展到更大的网络任务。引入了用于算法展开方法的通用接口，统一了最新的最新技术的实现，并允许通过避免重建基础外观基础架构来快速开发新方法。可区分的图形结构学习模型的实现是用Pytorch编写的，使我们能够利用现有的富裕软件生态系统，例如，围绕日志记录，超参数搜索和GPU沟通。这也使得将这些模型纳入较大的基于梯度的学习系统中变得容易，在大梯度的学习系统中，图形结构的可区分估计可能很有用，例如在潜在图中学习。各种数据集和性能指标允许在这个快速增长的领域中对模型进行一致的比较。可以在https://github.com/maxwass/pygsl上找到完整的代码存储库。
We introduce pyGSL, a Python library that provides efficient implementations of state-of-the-art graph structure learning models along with diverse datasets to evaluate them on. The implementations are written in GPU-friendly ways, allowing one to scale to much larger network tasks. A common interface is introduced for algorithm unrolling methods, unifying implementations of recent state-of-the-art techniques and allowing new methods to be quickly developed by avoiding the need to rebuild the underlying unrolling infrastructure. Implementations of differentiable graph structure learning models are written in PyTorch, allowing us to leverage the rich software ecosystem that exists e.g., around logging, hyperparameter search, and GPU-communication. This also makes it easy to incorporate these models as components in larger gradient based learning systems where differentiable estimates of graph structure may be useful, e.g. in latent graph learning. Diverse datasets and performance metrics allow consistent comparisons across models in this fast growing field. The full code repository can be found on https://github.com/maxwass/pyGSL.
