The adoption of neural networks and deep learning in non-Euclidean domains has been hindered until recently by the lack of scalable and efficient learning frameworks. Existing toolboxes in this space were mainly motivated by research and education use cases, whereas practical aspects, such as deploying and maintaining machine learning models, were often overlooked. We attempt to bridge this gap by proposing TensorFlow ManOpt, a Python library for optimization on Riemannian manifolds in TensorFlow. The library is designed with the aim for a seamless integration with the TensorFlow ecosystem, targeting not only research, but also streamlining production machine learning pipelines.
翻译:直到最近,由于缺少可扩展和高效的学习框架,在非欧洲域采用神经网络和深层学习一直受到阻碍,这一空间的现有工具箱主要受研究和教育使用案例的驱动,而实际方面,例如部署和维护机器学习模式,往往被忽视。我们试图弥合这一差距,为此提议设立Python图书馆TensorFlow ManOpt,以优化TensorFlow的里曼多管。该图书馆的设计目的是与TensorFlow生态系统实现无缝融合,不仅针对研究,而且针对简化生产机器学习管道。