Popular GitHub Repositories for Root Cause

UpTrain is an open-source unified platform to evaluate and improve Generative AI applications. We provide grades for 20+ preconfigured checks (covering language, code, embedding use-cases), perform root cause analysis on failure cases and give insights on how to resolve them.

AIOps学习资料汇总，欢迎一起补全这个仓库，欢迎star

ARCH models in Python

ML powered analytics engine for outlier detection and root cause analysis.

Nonlinear optimisation (root-finding, least squares, ...) in JAX+Equinox. https://docs.kidger.site/optimistix/

AI-powered key driver analysis tool that pinpoints root cause behind metrics fluctuation in one minute.

Timeseries Anomaly detection and Root Cause Analysis on data in SQL data warehouses and databases

?? RumbleDB 1.22.0 "Pyrenean oak" ? for Apache Spark | Run queries on your large-scale, messy JSON-like data (JSON, text, CSV, Parquet, ROOT, AVRO, SVM...) | No install required (just a jar to download) | Declarative Machine Learning and more

收集AIOPS（智能运维），时间序列，异常检测，关联分析，告警收敛，根因分析，数据挖掘，机器学习，深度学习的学习资源。欢迎star。Collect learning resources for AIOPS (Intelligent Operation and Maintenance), time series, anomaly detection, correlation analysis, alarm convergence, root cause analysis, data mining, machine learning, and deep learning. Welcome star.

This repository contains the dataset and the PyTorch implementations of the models from the paper Recognizing Emotion Cause in Conversations.

Following master Karpathy with GPT-2 implementation and training, writing lots of comments cause I have memory of a goldfish

WebUI X from MMRL but portable!

[ICLR'25] OpenRCA: Can Large Language Models Locate the Root Cause of Software Failures?

Real-time object detection is one of the key applications of deep neural networks (DNNs) for real-world mission-critical systems. While DNN-powered object detection systems celebrate many life-enriching opportunities, they also open doors for misuse and abuse. This project presents a suite of adversarial objectness gradient attacks, coined as TOG, which can cause the state-of-the-art deep object detection networks to suffer from untargeted random attacks or even targeted attacks with three types of specificity: (1) object-vanishing, (2) object-fabrication, and (3) object-mislabeling. Apart from tailoring an adversarial perturbation for each input image, we further demonstrate TOG as a universal attack, which trains a single adversarial perturbation that can be generalized to effectively craft an unseen input with a negligible attack time

Find the Root cause in your Code's Trace

? Code for our EMNLP 2021 paper: "Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes"

RootPainter: Deep Learning Segmentation of Biological Images with Corrective Annotation

Collection of papers on Emotion Cause Analysis

[ASE'24][WWW'25] RCAEval: A Benchmark for Root Cause Analysis. https://doi.org/10.1145/3691620.3695065

Can ChatGPT really understand the opinions, sentiments, and emotions contained in the text? We provide a preliminary evaluation.