Discussion on the challenges and promises of deep learning for outlier detection in various data modalities, including image and tabular data, with a focus on self-supervised learning techniques.
This article discusses how traditional machine learning methods, particularly outlier detection, can be used to improve the precision and efficiency of Retrieval-Augmented Generation (RAG) systems by filtering out irrelevant queries before document retrieval.
Outlier treatment is a necessary step in data analysis. This article, part 3 of a four-part series, eases the process and provides insights on effective methods and tools for outlier detection.
