论文标题
通过广义张量轨迹规范进行深度多任务学习
Deep Multi-Task Learning via Generalized Tensor Trace Norm
论文作者
论文摘要
痕量规范在多任务学习中广泛使用，因为它可以在模型参数方面发现任务之间的低级结构。如今，随着大数据集的出现以及深度学习技术的普及，张量痕量规范已用于深度任务模型。但是，现有的张量轨迹规范无法发现所有低级结构，他们要求用户手动确定其组件的重要性。为了共同解决这两个问题，在本文中，我们提出了一个广义张量痕量标准（GTTN）。 GTTN定义为所有可能的张量平坦的矩阵跟踪规范的凸组合，因此可以发现所有可能的低级结构。在诱导的目标函数中，我们将学习GTTN中的组合系数，以自动确定重要性。对现实世界数据集的实验证明了拟议的GTTN的有效性。
The trace norm is widely used in multi-task learning as it can discover low-rank structures among tasks in terms of model parameters. Nowadays, with the emerging of big datasets and the popularity of deep learning techniques, tensor trace norms have been used for deep multi-task models. However, existing tensor trace norms cannot discover all the low-rank structures and they require users to manually determine the importance of their components. To solve those two issues together, in this paper, we propose a Generalized Tensor Trace Norm (GTTN). The GTTN is defined as a convex combination of matrix trace norms of all possible tensor flattenings and hence it can discover all the possible low-rank structures. In the induced objective function, we will learn combination coefficients in the GTTN to automatically determine the importance. Experiments on real-world datasets demonstrate the effectiveness of the proposed GTTN.
