A complete and statistically consistent uncertainty quantification for deep
learning is provided, including the sources of uncertainty arising from (1) the
new input data, (2) the training and testing data (3) the weight vectors of the
neural network, and (4) the neural network because it is not a perfect
predictor. Using Bayes Theorem and conditional probability densities, we
demonstrate how each uncertainty source can be systematically quantified. We
also introduce a fast and practical way to incorporate and combine all sources
of errors for the first time. For illustration, the new method is applied to
quantify errors in cloud autoconversion rates, predicted from an artificial
neural network that was trained by aircraft cloud probe measurements in the
Azores and the stochastic collection equation formulated as a two-moment bin
model. For this specific example, the output uncertainty arising from
uncertainty in the training and testing data is dominant, followed by
uncertainty in the input data, in the trained neural network, and uncertainty
in the weights. We discuss the usefulness of the methodology for machine
learning practice, and how, through inclusion of uncertainty in the training
data, the new methodology is less sensitive to input data that falls outside of
the training data set.

提供了深度学习的完整且具有统计一致性的不确定性量化，包括源自（1）新的输入数据，（2）训练和测试数据，（3）神经网络的权重向量和（4）神经网络本身的不确定性。

深度学习的不确定性量化

Uncertainty Quantification for Deep Learning

In this work, we present a model for document-grounded response generation in
dialog that is decomposed into two components according to Bayes theorem. One
component is a traditional ungrounded response generation model and the other
component models the reconstruction of the grounding document based on the
dialog context and generated response. We propose different approximate
decoding schemes and evaluate our approach on multiple open-domain and
task-oriented document-grounded dialog datasets. Our experiments show that the
model is more factual in terms of automatic factuality metrics than the
baseline model. Furthermore, we outline how introducing scaling factors between
the components allows for controlling the tradeoff between factuality and
fluency in the model output. Finally, we compare our approach to a recently
proposed method to control factuality in grounded dialog, CTRL
(arXiv:2107.06963), and show that both approaches can be combined to achieve
additional improvements.

本文提出了一种模型，通过 Bayes 定理将其分解为两个部分，其中一个是传统的非基础回应生成模型，另一个是基于对话上下文和生成的回应重建基础文档的模型。我们提出了不同的近似解码方案，并在多个开放域和面向任务的基础对话数据集上评估我们的方法，实验表明该模型在自动事实度指标方面比基线模型更为准确。此外，我们概述了如何介绍组件之间的缩放因子以控制模型输出中的事实度和流畅性之间的权衡。最后，我们将我们的方法与最近提出的用于控制基础对话中事实度的方法 CTRL 进行了比较，并表明这两种方法可以结合起来实现额外的改进。