论文标题
火星：张量分解中的蒙版自动等级选择
MARS: Masked Automatic Ranks Selection in Tensor Decompositions
论文作者
论文摘要
张量分解方法已被证明在各种应用中有效，包括神经网络的压缩和加速。同时，确定最佳分解等级的问题（列出控制压缩 - 准确性权衡取舍的关键参数）仍然很急切。在本文中，我们介绍了火星 - 一种新的有效方法，用于在一般张量分解中自动选择等级。在训练过程中，该过程通过“选择”最佳张量结构的分解核心学习二进制面具。学习是通过特定贝叶斯模型中的后验（MAP）估计进行的，可以自然地嵌入到标准的神经网络训练中。不同的实验表明，与以前的各种任务中的作品相比，火星取得了更好的结果。
Tensor decomposition methods have proven effective in various applications, including compression and acceleration of neural networks. At the same time, the problem of determining optimal decomposition ranks, which present the crucial parameter controlling the compression-accuracy trade-off, is still acute. In this paper, we introduce MARS -- a new efficient method for the automatic selection of ranks in general tensor decompositions. During training, the procedure learns binary masks over decomposition cores that "select" the optimal tensor structure. The learning is performed via relaxed maximum a posteriori (MAP) estimation in a specific Bayesian model and can be naturally embedded into the standard neural network training routine. Diverse experiments demonstrate that MARS achieves better results compared to previous works in various tasks.
