用JAX实现的开源工具，快速估算深度网络权重不确定性。
laplax -- Laplace Approximations with JAX
- 基于JAX构建纯函数式模块化架构，支持灵活实验
- 提供高效贝叶斯神经网络不确定性量化方法
- 适合研究不确定性和改进拉普拉斯近似的人群
拉普拉斯近似为深度神经网络中的权重空间不确定性提供了可扩展且高效的量化手段，使预测不确定性与基于奥卡姆剃刀的模型选择等贝叶斯工具得以应用。本文介绍 laplax，一个基于 JAX 的新开源 Python 包，用于执行拉普拉斯近似。该包采用模块化、纯函数式架构，依赖极小，提供灵活且便于研究的快速原型开发框架。其目标是推动贝叶斯神经网络、深度学习不确定性量化及改进拉普拉斯近似技术的研究。
原文摘要 · Abstract (English)
The Laplace approximation provides a scalable and efficient means of quantifying weight-space uncertainty in deep neural networks, enabling the application of Bayesian tools such as predictive uncertainty and model selection via Occam's razor. In this work, we introduce laplax, a new open-source Python package for performing Laplace approximations with jax. Designed with a modular and purely functional architecture and minimal external dependencies, laplax offers a flexible and researcher-friendly framework for rapid prototyping and experimentation. Its goal is to facilitate research on Bayesian neural networks, uncertainty quantification for deep learning, and the development of improved Laplace approximation techniques.
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