Tensor decomposition methods are known to be efficient for compressing and accelerating neural networks. However, the problem of optimal decomposition structure determination is still not well studied while being quite important. Specifically, decomposition ranks present the crucial parameter controlling the compression-accuracy trade-off. 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. The proposed method achieves better results compared to previous works in various tasks.

翻译：已知电离分解方法在压缩和加速神经网络方面是有效的,然而,最佳分解结构确定问题虽然相当重要,但仍未得到很好研究。具体地说,分解等级是控制压缩-准确性交换的关键参数。在本文件中,我们引入了MARS,这是在一般高压分解中自动选择等级的一种新的有效方法。在培训期间,程序学习双面面罩,而不是“选择”最佳的振动结构的分解核心。在特定贝叶斯模型中,通过宽松的事后(IMAP)估计来进行学习。拟议方法比以往在各种任务中的工作取得更好的结果。