This repo contains the code to run the experiments in "Making Parametric Anomaly Detection on Tabular Data Non-Parametric Again". Installation. Set up and ...

Jan 30, 2024 · In this work, we investigate using retrieval-augmented models for anomaly detection on tabular data. We propose a reconstruction-based approach.

Jul 22, 2024 · This paper proposes a new approach to make parametric anomaly detection on tabular data non-parametric again.

In this work, we investigate using retrieval-augmented models for anomaly detection on tabular data. We propose a reconstruction-based approach in which a ...

Jul 27, 2023 · In this chapter, we will demystify these techniques, beginning with an overview of parametric and non-parametric statistical tests.

Sep 4, 2024 · In this paper, we propose a novel deep anomaly detection method for tabular data that leverages Non-Parametric Transformers (NPTs), a model ...

Sep 4, 2024 · Beyond Individual Input for Deep Anomaly Detection on Tabular Data. ... Making Parametric Anomaly Detection on Tabular Data Non-Parametric Again.

Tree-based classifiers outperform DNNs in anomaly-based intrusion/error detection. DNNs do not outperform tree-based classifiers even with “big data” tabular ...

Keep in mind that this look-back window is always moving and the smaller the look-back window, the more sensitive your data will be to anomaly detection.

Jul 23, 2023 · In this work, we only assume access to contaminated data and present a diffusion-based probabilistic model effective for unsupervised anomaly detection.