Loss Decomposition for Fast Learning in Large Output Spaces

ICML 2018 Ian En-Hsu YenSatyen KaleFelix YuDaniel Holtmann-RiceSanjiv KumarPradeep Ravikumar

For problems with large output spaces, evaluation of the loss function and its gradient are expensive, typically taking linear time in the size of the output space. Recently, methods have been developed to speed up learning via efficient data structures for Nearest-Neighbor Search (NNS) or Maximum Inner-Product Search (MIPS)... (read more)

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