Interpretable Mixture Density Estimation by use of Differentiable Tree-module

• URL: http://arxiv.org/abs/2105.03616v1
• Date: Sat, 8 May 2021 07:29:58 GMT
• Title: Interpretable Mixture Density Estimation by use of Differentiable Tree-module
• Authors: Ryuichi Kanoh, Tomu Yanabe
• Abstract summary: We propose a method for mixture density estimation that utilizes an interpretable tree structure. A fast inference procedure based on time-invariant information cache achieves both high speed and interpretability.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In order to develop reliable services using machine learning, it is important to understand the uncertainty of the model outputs. Often the probability distribution that the prediction target follows has a complex shape, and a mixture distribution is assumed as a distribution that uncertainty follows. Since the output of mixture density estimation is complicated, its interpretability becomes important when considering its use in real services. In this paper, we propose a method for mixture density estimation that utilizes an interpretable tree structure. Further, a fast inference procedure based on time-invariant information cache achieves both high speed and interpretability.

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