Roundtrip: A Deep Generative Neural Density Estimator
This addresses the fundamental problem of density estimation in statistics and machine learning, offering a general-purpose solution with broad applicability.
The authors tackled the problem of density estimation by proposing Roundtrip, a deep generative neural density estimator that retains generative power while providing density estimates, achieving state-of-the-art performance across diverse tasks.
Density estimation is a fundamental problem in both statistics and machine learning. In this study, we proposed Roundtrip as a general-purpose neural density estimator based on deep generative models. Roundtrip retains the generative power of generative adversarial networks (GANs) but also provides estimates of density values. Unlike previous neural density estimators that put stringent conditions on the transformation from the latent space to the data space, Roundtrip enables the use of much more general mappings. In a series of experiments, Roundtrip achieves state-of-the-art performance in a diverse range of density estimation tasks.