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

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)

Open-source framework to detect outliers in Elasticsearch events

Image Mosaicing or Panorama Creation

Deep Learning for Anomaly Deteection

Utility library for detecting and removing outliers from normally distributed datasets using the Smirnov-Grubbs test.

Beyond Outlier Detection: LookOut for Pictorial Explanation

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

A python package for robust statistics

[ICML 2025] Fast and Low-Cost Genomic Foundation Models via Outlier Removal.

One-class classifiers for anomaly detection (outlier detection)

This repository contains the code, data, and models from the paper Vladan Stojnić, 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.

Implementation of the Robust Random Cut Forest algorithm for anomaly detection

In statistics, an outlier is an observation point that is distant from other observations. How we can filter out these values using python?

Supervised machine learning methods for novel anomaly detection.

Single Cell Outlier Selector - quickly find outliers in your single-cell data

[ICLR 2026] FROST: Filtering Reasoning Outliers with Attention for Efficient Reasoning

Feature Engineering konulu bir kursun içeriğini ve materyallerini barındırmaktadır. Kurs, veri bilimi ve makine öğrenmesi alanında temel bir konu olan "özellik mühendisliği"ni ele almaktadır.

Python package with a class that allows pipeline-like specification and execution of regression workflows.