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

Such embeddings have been traditionally recovered by seeking isometric embeddings in lower dimensional Euclidean spaces, as studied in [Johnson and Lindenstrauss, 1984, Bourgain, 1985].

Huge scale machine learning problems are nowadays tackled by distributed optimization algorithms, i.e. algorithms that leverage the compute power of many

Statistical leverage scores emerged as a fundamental tool for matrix sketching and column sampling with applications to low rank approximation, regression, random feature learning and quadrature.

This paper focuses on the representational capabilities of generative networks.

By tailoring a multi-dimensional space (or multi-dimensional array) into a number of rectangular regions, the partition model can fit data using these "blocks" such that the data within each block

Learning problems of this scale are often referred to as extreme classification .

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

Understanding how humans perceive the likability of high-dimensional "objects"

Finally, we validated our method using a previously published dataset.