Density estimation using deep generative neural networks.
Density estimation using deep generative neural networks.
复制标题
使用深度生成神经网络进行密度估计
DOI：
10.1073/pnas.2101344118
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发表时间：
2021-04-13
影响因子：
11.1
通讯作者：
Wong WH
中科院分区：
文献类型：
--
作者：
Liu Q;Xu J;Jiang R;Wong WH
密度估计是统计学中最基本的问题之一。由于“维度诅咒”，估计高维数据的密度是出了名的困难。这里，我们介绍了一种新的基于深度生成神经网络的通用密度估计器。通过对降维流形上正态分布的数据建模，我们展示了如何利用双向生成神经网络(例如，循环GAN)的能力来显式地评估数据密度。仿真和真实数据实验表明，我们的方法在各种问题上都是有效的。这种方法在许多需要精确密度估计器的应用中应该是有帮助的。密度估计是统计学和机器学习中的基本问题之一。在这项研究中，我们提出了一种基于深度生成神经网络的通用密度估计计算框架--RourTrip。往返保留了深度生成模型的生成能力，例如生成性对抗网络(GANS)，同时它还提供了密度值估计，从而支持数据生成和密度估计。与以前对从潜在空间到数据空间的转换施加严格条件的神经密度估计器不同，往返允许使用更一般的映射，其中目标密度是通过学习从基本密度(例如，高斯分布)诱导的流形来建模的。往返为GaN模型提供了一个统计框架，在该模型中，可以显式评估密度值。在数值实验中，往返在不同范围的密度估计任务中的性能超过了最先进的性能。
Density estimation is among the most fundamental problems in statistics. It is notoriously difficult to estimate the density of high-dimensional data due to the “curse of dimensionality.” Here, we introduce a new general-purpose density estimator based on deep generative neural networks. By modeling data normally distributed around a manifold of reduced dimension, we show how the power of bidirectional generative neural networks (e.g., cycleGAN) can be exploited for explicit evaluation of the data density. Simulation and real data experiments suggest that our method is effective in a wide range of problems. This approach should be helpful in many applications where an accurate density estimator is needed. Density estimation is one of the fundamental problems in both statistics and machine learning. In this study, we propose Roundtrip, a computational framework for general-purpose density estimation based on deep generative neural networks. Roundtrip retains the generative power of deep generative models, such as generative adversarial networks (GANs) while it also provides estimates of density values, thus supporting both data generation and density estimation. 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 where target density is modeled by learning a manifold induced from a base density (e.g., Gaussian distribution). Roundtrip provides a statistical framework for GAN models where an explicit evaluation of density values is feasible. In numerical experiments, Roundtrip exceeds state-of-the-art performance in a diverse range of density estimation tasks.
影响因子：
--
作者：
PARZEN, E
通讯作者：
PARZEN, E
影响因子：
2.9
作者：
Schölkopf, B;Platt, JC;Williamson, RC
通讯作者：
Williamson, RC