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Dropout是一种广泛使用的正则化技术，通常需要为许多体系结构获得最先进的技术。这项工作表明，dropout引入了两种截然不同但相互纠缠的正则化效应:由于dropout修改了预期的训练目标而产生的显式效应(在之前的工作中也研究过)，以及可能令人惊讶的是，dropout训练更新中的随机性带来的另一种隐式效应。这种隐式正则化效应类似于小批量随机梯度下降中的随机度效应。我们通过控制实验把这两种效应分开。然后，我们推导出分析的简化，用模型的导数和损失来描述每个影响，对于深度神经网络。我们证明了这些简化的、解析的正则化器准确地捕获了辍学的重要方面，表明它们在实践中忠实地替代了dropout。
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The Implicit and Explicit Regularization Effects of Dropout
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Countless research works of deep neural networks (DNNs) in the task of credit card fraud detection have focused on improving the accuracy of point predictions and mitigating unwanted biases by building different network architectures or learning models. Quantifying uncertainty accompanied by point estimation is essential because it mitigates model unfairness and permits practitioners to develop trustworthy systems which abstain from suboptimal decisions due to low confidence. Explicitly, assessing uncertainties associated with DNNs predictions is critical in real-world card fraud detection settings for characteristic reasons, including (a) fraudsters constantly change their strategies, and accordingly, DNNs encounter observations that are not generated by the same process as the training distribution, (b) owing to the time-consuming process, very few transactions are timely checked by professional experts to update DNNs. Therefore, this study proposes three uncertainty quantification (UQ) techniques named Monte Carlo dropout, ensemble, and ensemble Monte Carlo dropout for card fraud detection applied on transaction data. Moreover, to evaluate the predictive uncertainty estimates, UQ confusion matrix and several performance metrics are utilized. Through experimental results, we show that the ensemble is more effective in capturing uncertainty corresponding to generated predictions. Additionally, we demonstrate that the proposed UQ methods provide extra insight to the point predictions, leading to elevate the fraud prevention process.
