Popular GitHub Repositories for Failure

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.

A machine learning toolkit for log-based anomaly detection [ISSRE'16]

WTTE-RNN a framework for churn and time to event prediction

Failure archive for ChatGPT and similar models

log anomaly detection toolkit including DeepLog

A unified framework for tabular probabilistic regression, time-to-event prediction, and probability distributions in python

Nvidia DLI workshop on AI-based predictive maintenance techniques to identify anomalies and failures in time-series data, estimate the remaining useful life of the corresponding parts, and map anomalies to failure conditions.

This project is about predictive maintenance with machine learning. It's a final project of my Computer Science AP degree.

To address some of the failure modes in training of physics informed neural networks, a Lagrangian architecture is designed to conform to the direction of travel of information in convection-diffusion equations, i.e., method of characteristic; The repository includes a pytorch implementation of PINN and proposed LPINN with periodic boundary conditions

Perform a survival analysis based on the time-to-event (death event) for the subjects. Compare machine learning models to assess the likelihood of a death by heart failure condition. This can be used to help hospitals in assessing the severity of patients with cardiovascular diseases and heart failure condition.

故障诊断研究的优秀资源

ML Approaches for RUL Prediction, Anomaly Detection, Survival Analysis and Failure Classification

Aircraft engine failure prediction model

Anomaly detection and failure prognosis applied to industrial machines

Physics-informed neural networks for highly compressible flows ??

The official implementation of paper "Learning From Failure: Integrating Negative Examples when Fine-tuning Large Language Models as Agents"

ARLIN is a research library written in Python that provides explainability outputs and vulnerability detection for DRL models, specifically designed to increase model assurance and identify vulnerabilities and potential points of failure within a trained model. https://mitre.github.io/arlin/

Official repository of "FashionFail: Addressing Failure Cases in Fashion Object Detection and Segmentation".

Detecting Failure Modes in Image Reconstructions with Interval Neural Network Uncertainty

MetroPT-3 Anomaly Detection using Machine Learning and Deep Learning