Augment and Reduce: Stochastic Inference for Large Categorical Distributions

ICML, pp. 4400-4409, 2018.

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Abstract:

Categorical distributions are ubiquitous in machine learning, e.g., in classification, language models, and recommendation systems. They are also at the core of discrete choice models. However, when the number of possible outcomes is very large, using categorical distributions becomes computationally expensive, as the complexity scales li...More

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