Deep learning for tabular data has garnered increasing attention in recent
years, yet employing deep models for structured data remains challenging. While
these models excel with unstructured data, their efficacy with structured data
has been limited. Recent research has introduced retrieval-augmented models to
address this gap, demonstrating promising results in supervised tasks such as
classification and regression. In this work, we investigate using
retrieval-augmented models for anomaly detection on tabular data. We propose a
reconstruction-based approach in which a transformer model learns to
reconstruct masked features of \textit{normal} samples. We test the
effectiveness of KNN-based and attention-based modules to select relevant
samples to help in the reconstruction process of the target sample. Our
experiments on a benchmark of 31 tabular datasets reveal that augmenting this
reconstruction-based anomaly detection (AD) method with non-parametric
relationships via retrieval modules may significantly boost performance.

使用检索增强模型进行表格数据的异常检测，通过选择相关样本来帮助目标样本的重建过程，以非参数关系提高性能。

将表格数据上的参数异常检测重新变为非参数化

Making Parametric Anomaly Detection on Tabular Data Non-Parametric Again

Pronouns are frequently omitted in pro-drop languages, such as Chinese,
generally leading to significant challenges with respect to the production of
complete translations. To date, very little attention has been paid to the
dropped pronoun (DP) problem within neural machine translation (NMT). In this
work, we propose a novel reconstruction-based approach to alleviating DP
translation problems for NMT models. Firstly, DPs within all source sentences
are automatically annotated with parallel information extracted from the
bilingual training corpus. Next, the annotated source sentence is reconstructed
from hidden representations in the NMT model. With auxiliary training
objectives, in terms of reconstruction scores, the parameters associated with
the NMT model are guided to produce enhanced hidden representations that are
encouraged as much as possible to embed annotated DP information. Experimental
results on both Chinese-English and Japanese-English dialogue translation tasks
show that the proposed approach significantly and consistently improves
translation performance over a strong NMT baseline, which is directly built on
the training data annotated with DPs.

本文提出了一种新颖的基于重构的方法来解决神经机器翻译中省略代词问题，自动提取并重构源语句使得神经机器翻译模型能够更好地嵌入被注释的代词信息，该方法显著和一致地提高了中英和日英对话翻译任务的翻译性能。