论文标题
MCFLOW：蒙特卡洛流量模型
MCFlow: Monte Carlo Flow Models for Data Imputation
论文作者
论文摘要
我们考虑数据插补的主题，这是机器学习中的基础任务，该任务解决了缺少数据的问题。为此，我们提出了McFlow，这是一个深入的插入框架，它利用了正常的流量生成模型和蒙特卡洛采样。我们通过引入一种迭代学习方案来交替更新培训数据中的密度估算和丢失条目的值，从而解决了使用不完整数据的训练模型时产生的因果难题。我们对拟议方法对标准多元和图像数据集的有效性提供了广泛的经验验证，并根据最新的替代方案进行了基准测试。我们证明，就估算数据的质量以及其保留数据的语义结构的能力而言，MCFLOW优于竞争方法。
We consider the topic of data imputation, a foundational task in machine learning that addresses issues with missing data. To that end, we propose MCFlow, a deep framework for imputation that leverages normalizing flow generative models and Monte Carlo sampling. We address the causality dilemma that arises when training models with incomplete data by introducing an iterative learning scheme which alternately updates the density estimate and the values of the missing entries in the training data. We provide extensive empirical validation of the effectiveness of the proposed method on standard multivariate and image datasets, and benchmark its performance against state-of-the-art alternatives. We demonstrate that MCFlow is superior to competing methods in terms of the quality of the imputed data, as well as with regards to its ability to preserve the semantic structure of the data.
