论文标题
贝叶斯机器学习的非线性MCMC
Nonlinear MCMC for Bayesian Machine Learning
论文作者
论文摘要
我们探讨了[1]中最初引入贝叶斯机器学习问题的非线性MCMC技术的应用。我们提供了总变化的收敛保证，该保证使用了新的结果，用于长期收敛和大颗粒（“混乱的传播”）收敛。我们将这种非线性MCMC技术应用于采样问题，包括CIFAR10上的贝叶斯神经网络。
We explore the application of a nonlinear MCMC technique first introduced in [1] to problems in Bayesian machine learning. We provide a convergence guarantee in total variation that uses novel results for long-time convergence and large-particle ("propagation of chaos") convergence. We apply this nonlinear MCMC technique to sampling problems including a Bayesian neural network on CIFAR10.
