Switch Spaces：稀疏选通的乘积空间学习
Switch Spaces: Learning Product Spaces with Sparse Gating
摘要：
学习合适的几何嵌入空间是表示学习的关键。为了使学习的表示有效和高效，理想的是几何归纳偏差与数据的底层结构很好地对齐。在本文中，我们提出了开关空间，一个数据驱动的方法学习表示产品空间。具体地说，乘积空间（或流形）是混合曲率的空间，即，多个欧几里德和非欧几里德（双曲，球面）流形的组合。为此，我们引入稀疏门控机制，学习选择，组合和切换空间，使它们能够根据特定的输入数据进行切换。此外，所提出的方法也是有效的，并具有恒定的计算复杂性的模型大小无关。在知识图谱补全和项目推荐上的实验表明，所提出的开关空间实现了新的最先进的性能，优于纯产品空间和最近提出的特定任务模型.
英文摘要：
Learning embedding spaces of suitable geometry is critical for representation learning. In order for learned representations to be effective and efficient, it is ideal that the geometric inductive bias aligns well with the underlying structure of the data. In this paper, we propose Switch Spaces, a data-driven approach for learning representations in product space. Specifically, product spaces (or manifolds) are spaces of mixed curvature, i.e., a combination of multiple euclidean and non-euclidean (hyperbolic, spherical) manifolds. To this end, we introduce sparse gating mechanisms that learn to choose, combine and switch spaces, allowing them to be switchable depending on the input data with specialization. Additionally, the proposed method is also efficient and has a constant computational complexity regardless of the model size. Experiments on knowledge graph completion and item recommendations show that the proposed switch space achieves new state-of-the-art performances, outperforming pure product spaces and recently proposed task-specific models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
