Topic: ml-safety Goto Github
Some thing interesting about ml-safety
Some thing interesting about ml-safety
ml-safety,PromptInject is a framework that assembles prompts in a modular fashion to provide a quantitative analysis of the robustness of LLMs to adversarial prompt attacks. 🏆 Best Paper Awards @ NeurIPS ML Safety Workshop 2022
Organization: agencyenterprise
ml-safety,This repository contains the project for the Advanced AI course @CentraleSupélec
User: arianedlns
ml-safety,📚 A curated list of papers & technical articles on AI Quality & Safety
Organization: giskard-ai
Home Page: https://giskard.ai
ml-safety,🐢 Open-Source Evaluation & Testing for LLMs and ML models
Organization: giskard-ai
Home Page: https://docs.giskard.ai
ml-safety,Code for the attack multiplicative filter attack MUFIA, from the paper "Frequency-based vulnerability analysis of deep learning models against image corruptions".
User: harsmac
Home Page: https://arxiv.org/abs/2306.07178
ml-safety,The Combined Anomalous Object Segmentation (CAOS) Benchmark
User: hendrycks
ml-safety,Aligning AI With Shared Human Values (ICLR 2021)
User: hendrycks
ml-safety,ImageNet-R(endition) and DeepAugment (ICCV 2021)
User: hendrycks
ml-safety,Jiminy Cricket Environment (NeurIPS 2021)
User: hendrycks
ml-safety,A Harder ImageNet Test Set (CVPR 2021)
User: hendrycks
ml-safety,Deep Anomaly Detection with Outlier Exposure (ICLR 2019)
User: hendrycks
ml-safety,Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)
User: hendrycks
ml-safety,Corruption and Perturbation Robustness (ICLR 2019)
User: hendrycks
ml-safety,Self-Supervised Learning for OOD Detection (NeurIPS 2019)
User: hendrycks
ml-safety,Repo for "Benchmarking Robustness of 3D Point Cloud Recognition against Common Corruptions" https://arxiv.org/abs/2201.12296
User: jiachens
Home Page: https://sites.google.com/umich.edu/modelnet40c
ml-safety,Deliver safe & effective language models
Organization: johnsnowlabs
Home Page: http://langtest.org/
ml-safety,Evaluation & testing framework for computer vision models
Organization: moonwatcher-ai
Home Page: https://www.moonwatcher.ai/
ml-safety,Predicting Out-of-Distribution Error with the Projection Norm
User: yaodongyu
ml-safety,[ICML 2019] ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation
User: yyzharry
Home Page: http://me-net.csail.mit.edu
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