Popular GitHub Repositories for Outliers

A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques

[ICLR2025 Spotlight] SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models

? Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams

Text analytics for LLM apps. Cluster messages to detect use cases, outliers, power users. Detect intents and run evals with LLM (OpenAI, MistralAI, Ollama, etc.)

Text analytics for LLM apps. Cluster messages to detect use cases, outliers, power users. Detect intents and run evals with LLM (OpenAI, MistralAI, Ollama, etc.)

Anomaly detection using LoOP: Local Outlier Probabilities, a local density based outlier detection method providing an outlier score in the range of [0,1].

Streaming Anomaly Detection Framework in Python (Outlier Detection for Streaming Data)

[NeurIPS 2024 Oral?] DuQuant: Distributing Outliers via Dual Transformation Makes Stronger Quantized LLMs.

Certifiable Outlier-Robust Geometric Perception

[ICML 2024] Outlier-Efficient Hopfield Layers for Large Transformer-Based Models

Statistics, signal processing, finance, econometrics, manufacturing, networking[disambiguation needed] and data mining, anomaly detection (also outlier detection) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions.

One-class classifiers for anomaly detection (outlier detection)

Detect EEG artifacts, outliers, or anomalies using supervised machine learning.

Package implements a number local outlier factor algorithms for outlier detection and finding anomalous data

Beyond outliers and on to micro-clusters: Vision-guided Anomaly Detection

This repository contains the code, data, and models from the paper Vladan Stojni?, Zakaria Laskar, Giorgos Tolias, "Training Ensembles with Inliers and Outliers for Semi-supervised Active Learning", In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024.

Anomaly detection algorithms implementations

[ICLR 2025] Systematic Outliers in Large Language Models.

Data Mining and Machine Learning APS Failure at Scania Trucks Data Set.

KeepCoding Bootcamp Big Data & Machine Learning - Proyecto Final Team Outliers - Batiendo a la bolsa