Tensor Decompositions in Deep Learning
It provides a review for researchers in machine learning, but is incremental as it summarizes existing work without novel contributions.
The paper surveys the use of tensor decompositions in deep learning, focusing on compressing model parameters and enhancing data representations, but does not present new experimental results or concrete numbers.
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.