论文标题
深度学习中的张量分解
Tensor Decompositions in Deep Learning
论文作者
论文摘要
本文调查了现代机器学习应用中张量分解的话题。它重点介绍了与社区重要相关性的三个积极研究主题。在对多路数据分析的合并作品进行了简要审查之后，我们考虑使用张量分解来压缩深度学习模型的参数空间。最后，我们讨论如何利用张量方法来产生复杂数据的更丰富的适应性表示，包括结构化信息。本文以讨论有趣的开放研究挑战的讨论结束。
The paper surveys the topic of tensor decompositions in modern machine learning applications. It focuses on three active research topics of significant relevance for the community. After a brief review of consolidated works on multi-way data analysis, we consider the use of tensor decompositions in compressing the parameter space of deep learning models. Lastly, we discuss how tensor methods can be leveraged to yield richer adaptive representations of complex data, including structured information. The paper concludes with a discussion on interesting open research challenges.
