We present Fortuna, an open-source library for uncertainty quantification in deep learning. Fortuna supports a range of calibration techniques, such as conformal prediction that can be applied to any trained neural network to generate reliable uncertainty estimates, and scalable Bayesian inference methods that can be applied to Flax-based deep neural networks trained from scratch for improved uncertainty quantification and accuracy. By providing a coherent framework for advanced uncertainty quantification methods, Fortuna simplifies the process of benchmarking and helps practitioners build robust AI systems.

翻译：本文介绍Fortuna，一个用于深度学习不确定性量化的开源库。Fortuna支持多种校准技术，例如可应用于任意已训练神经网络以生成可靠不确定性估计的共形预测，以及可应用于基于Flax的从零训练深度神经网络以提升不确定性量化与准确性的可扩展贝叶斯推理方法。通过为先进的不确定性量化方法提供连贯框架，Fortuna简化了基准测试流程，助力从业者构建稳健的人工智能系统。