  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.
