用神经元胞自动机实现表格数据缺失值填补，效果优于现有方法。
Missing Data Imputation using Neural Cellular Automata
- 基于神经元胞自动机设计新型填补方法，利用局部规则迭代优化数据。
- 在多个真实数据集上，填补误差和后续任务表现均优于当前最优方法。
- 适合处理复杂依赖关系的表格数据缺失问题，尤其适用于高维场景。
表格数据中缺失值始终是核心难题。尽管多年来研究者不断探索更优填补方法，近年随着生成模型的发展，利用生成模型解决填补问题成为新趋势。已有工作研究了变分自编码器、生成对抗网络等模型的填补版本，但忽略了神经元胞自动机（NCA）这一强大计算模型。本文提出一种受NCA启发的新型填补方法，通过适当改造，证明其可有效解决缺失数据填补问题。实验表明，该方法在多个数据集上均优于现有最先进方法，在填补误差和填补后下游任务性能方面均有显著提升。
原文摘要 · Abstract (English)
When working with tabular data, missingness is always one of the most painful problems. Throughout many years, researchers have continuously explored better and better ways to impute missing data. Recently, with the rapid development evolution in machine learning and deep learning, there is a new trend of leveraging generative models to solve the imputation task. While the imputing version of famous models such as Variational Autoencoders or Generative Adversarial Networks were investigated, prior work has overlooked Neural Cellular Automata (NCA), a powerful computational model. In this paper, we propose a novel imputation method that is inspired by NCA. We show that, with some appropriate adaptations, an NCA-based model is able to address the missing data imputation problem. We also provide several experiments to evidence that our model outperforms state-of-the-art methods in terms of imputation error and post-imputation performance.
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