TAPAS：Softmax的两遍近似自适应采样
TAPAS: Two-pass Approximate Adaptive Sampling for Softmax
摘要：
TAPAS是softmax模型的一种新的自适应采样方法。它使用两次采样策略，其中用于近似分区函数梯度的示例首先根据压缩的总体分布进行采样，然后使用上下文和当前模型自适应地重新采样。我们描述了一个有效的分布式实现TAPAS。我们表明，在合成数据和一个大的真实数据集，TAPAS具有较低的计算开销，并能很好地用于最小化具有非常大的标签空间的多类分类问题的秩损失。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
