“asymmetric embeddings”

Asymmetric compressive learning guarantees with applications to quantized sketches

Abstract: The compressive learning framework reduces the computational cost of training on large-scale datasets. In a sketching phase, the data is first compressed to a lightweight sketch vector, obtained by mapping the data samples through a well-chosen feature map, and averaging those contributions.