LSH Microbatches for Stochastic Gradients: Value in Rearrangement

ICLR 2019 Eliav BuchnikEdith CohenAvinatan HassidimYossi Matias

Metric embeddings are immensely useful representations of associations between entities (images, users, search queries, words, and more). Embeddings are learned by optimizing a loss objective of the general form of a sum over example associations... (read more)

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