Popular GitHub Repositories for Fault Mitigation

Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database?.

Fault-tolerant, highly scalable GPU orchestration, and a machine learning framework designed for training models with billions to trillions of parameters

A Python package to assess and improve fairness of machine learning models.

UQLM: Uncertainty Quantification for Language Models, is a Python package for UQ-based LLM hallucination detection

:traffic_light: Traffic control for code.

[IJCAI-21] "Time-Series Representation Learning via Temporal and Contextual Contrasting"

PyTorch per step fault tolerance (actively under development)

智能故障诊断和寿命预测期刊（Journals of Intelligent Fault Diagnosis and Remaining Useful Life）

Bearing fault diagnosis model based on MCNN-LSTM

Tensor network based quantum software framework for the NISQ era

up-to-date curated list of state-of-the-art Large vision language models hallucinations research work, papers & resources

WEFE: The Word Embeddings Fairness Evaluation Framework. WEFE is a framework that standardizes the bias measurement and mitigation in Word Embeddings models. Please feel welcome to open an issue in case you have any questions or a pull request if you want to contribute to the project!

?? Blades: A Unified Benchmark Suite for Attacks and Defenses in Federated Learning

Modern circuit breaker for Ruby & Rails. Thread-safe, fiber-ready async support. Built-in fallbacks, rich introspection, clean DSL. Memory-efficient with jitter & monitoring.

This is an open-source tool to assess and improve the trustworthiness of AI systems.

Python package that provides predictive models for fault detection, soft sensing, and process condition monitoring.

Machine learning applied to wind turbines incipient fault detection.

CNN applied to bearing signals for analysis

Implement GANs to generate time-series signals for imbalanced learning problem. The experiments are conducted using CWRU bearing data.

An open-source knowledge base of defensive countermeasures to protect AI/ML systems. Features interactive views and maps defenses to known threats from frameworks like MITRE ATLAS, MAESTRO, and OWASP.