论文标题
贝叶斯神经网络通过随机梯度下降
Bayesian Neural Network via Stochastic Gradient Descent
论文作者
论文摘要
用于变异推断的贝叶斯方法的目标是最大程度地减少变异分布和未知后分布之间的KL差异。这是通过最大化证据下限（ELBO）来完成的。神经网络用于使用随机梯度下降来参数这些分布。这项工作通过得出变异推理模型来扩展他人完成的工作。我们展示了如何通过梯度估计技术将SGD应用于贝叶斯神经网络。为了进行验证，我们已经在5个UCI数据集上测试了我们的模型，并且选择用于评估的指标是均方根误差（RMSE）误差（RMSE）误差和负模可能性。我们的工作大大击败了先前使用贝叶斯神经网络回归的艺术方法。
The goal of bayesian approach used in variational inference is to minimize the KL divergence between variational distribution and unknown posterior distribution. This is done by maximizing the Evidence Lower Bound (ELBO). A neural network is used to parametrize these distributions using Stochastic Gradient Descent. This work extends the work done by others by deriving the variational inference models. We show how SGD can be applied on bayesian neural networks by gradient estimation techniques. For validation, we have tested our model on 5 UCI datasets and the metrics chosen for evaluation are Root Mean Square Error (RMSE) error and negative log likelihood. Our work considerably beats the previous state of the art approaches for regression using bayesian neural networks.
