论文标题
有关摊销优化的教程
Tutorial on amortized optimization
论文作者
论文摘要
优化是一种普遍存在的建模工具，通常被部署在反复解决相同问题的类似实例的设置中。摊销优化方法使用学习来预测这些设置中问题的解决方案，从而利用了相似的问题实例之间的共享结构。这些方法对于变异推理和增强学习至关重要，并且能够比不使用不使用摊销的传统优化方法更快地解决优化问题。该教程在这些进步背后的摊销优化基础上介绍了介绍，并概述了其在变异推理，稀疏编码，基于梯度的元学习，控制，加强学习，凸出优化，最佳运输和深层平衡网络中的应用。本教程的源代码可在https://github.com/facebookresearch/amortized-optimization-tutorial中获得。
Optimization is a ubiquitous modeling tool and is often deployed in settings which repeatedly solve similar instances of the same problem. Amortized optimization methods use learning to predict the solutions to problems in these settings, exploiting the shared structure between similar problem instances. These methods have been crucial in variational inference and reinforcement learning and are capable of solving optimization problems many orders of magnitudes times faster than traditional optimization methods that do not use amortization. This tutorial presents an introduction to the amortized optimization foundations behind these advancements and overviews their applications in variational inference, sparse coding, gradient-based meta-learning, control, reinforcement learning, convex optimization, optimal transport, and deep equilibrium networks. The source code for this tutorial is available at https://github.com/facebookresearch/amortized-optimization-tutorial.
