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arXiv · 1707.03073

TAPAS: Two-pass Approximate Adaptive Sampling for Softmax

Yu Bai·Sally Goldman·Li Zhang
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Abstract

TAPAS is a novel adaptive sampling method for the softmax model. It uses a two pass sampling strategy where the examples used to approximate the gradient of the partition function are first sampled according to a squashed population distribution and then resampled adaptively using the context and current model. We describe an efficient distributed implementation of TAPAS. We show, on both synthetic data and a large real dataset, that TAPAS has low computational overhead and works well for minimizing the rank loss for multi-class classification problems with a very large label space.

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Yu Bai, Sally Goldman, Li Zhang. 2017-07-10. TAPAS: Two-pass Approximate Adaptive Sampling for Softmax. https://arxiv.org/abs/1707.03073

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