论文标题
使用DIRICHLET流程可能方法将功能性摘要信息合并到贝叶斯神经网络中
Incorporating functional summary information in Bayesian neural networks using a Dirichlet process likelihood approach
论文作者
论文摘要
贝叶斯神经网络（BNN）可以解释核心和认知不确定性。但是，在BNNS中，先验通常是在重量上指定的，这些权重很少反映出大型和复杂的神经网络体系结构中的真实知识。我们提出了一种简单的方法，可以基于有关给定数据集的预测分类概率的外部摘要信息将先验知识纳入BNN。可用的摘要信息被合并为增强数据，并以Dirichlet过程进行建模，我们得出相应的\ Emph {摘要证据下限}。该方法建立在贝叶斯原理上，所有超参数都有适当的概率解释。我们展示了该方法如何为模型提供有关任务难度和阶级失衡的信息。广泛的实验表明，通过可忽略不计的计算开销，我们的方法相似，在许多情况下，在准确性，不确定性校准和稳健性方面的替代方案都超过了对腐败的稳健性，并且具有平衡和不平衡的数据。
Bayesian neural networks (BNNs) can account for both aleatoric and epistemic uncertainty. However, in BNNs the priors are often specified over the weights which rarely reflects true prior knowledge in large and complex neural network architectures. We present a simple approach to incorporate prior knowledge in BNNs based on external summary information about the predicted classification probabilities for a given dataset. The available summary information is incorporated as augmented data and modeled with a Dirichlet process, and we derive the corresponding \emph{Summary Evidence Lower BOund}. The approach is founded on Bayesian principles, and all hyperparameters have a proper probabilistic interpretation. We show how the method can inform the model about task difficulty and class imbalance. Extensive experiments show that, with negligible computational overhead, our method parallels and in many cases outperforms popular alternatives in accuracy, uncertainty calibration, and robustness against corruptions with both balanced and imbalanced data.
