Neural Information Processing Systems http://nips.cc/

In this paper, we present Rotating Features, a generalization of complex-valued features to higher dimensions, and a new evaluation procedure for extracting objects from distributed representations.

Many applications seek to recover low-rank approximations of noisy tensor data.

The data split details are presented in Tab 3.

However, existing music datasets suffer from limited size, accessibility, and lack of audio resources.

Standard few-shot learning methods employ geometric similarity metrics such as cosine similarity and negative Euclidean distance to gauge the semantic relatedness between two features.