Uncertainty-quantification methods are applied to estimate the confidence of
deep-neural-networks classifiers over their predictions. However, most widely
used methods are known to be overconfident. We address this problem by
developing an algorithm that exploits the latent-space representation of data
points fed into the network, to assess the accuracy of their prediction. Using
the latent-space representation generated by the fraction of training set that
the network classifies correctly, we build a statistical model that is able to
capture the likelihood of a given prediction. We show on a synthetic dataset
that commonly used methods are mostly overconfident. Overconfidence occurs also
for predictions made on data points that are outside the distribution that
generated the training data. In contrast, our method can detect such
out-of-distribution data points as inaccurately predicted, thus aiding in the
automatic detection of outliers.

本文提出了一种新的算法，通过利用深度神经网络分类器中数据点的潜在空间表示来评估其预测的准确性，该方法可以检测出分布之外的数据点被不正确地预测，从而有助于自动检测异常值。

深度神经网络中基于潜空间统计推断的不确定性量化

Uncertainty Quantification in Deep Neural Networks through Statistical  Inference on Latent Space

Auto-encoders compress input data into a latent-space representation and
reconstruct the original data from the representation. This latent
representation is not easily interpreted by humans. In this paper, we propose
training an auto-encoder that encodes input text into human-readable sentences,
and unpaired abstractive summarization is thereby achieved. The auto-encoder is
composed of a generator and a reconstructor. The generator encodes the input
text into a shorter word sequence, and the reconstructor recovers the generator
input from the generator output. To make the generator output human-readable, a
discriminator restricts the output of the generator to resemble human-written
sentences. By taking the generator output as the summary of the input text,
abstractive summarization is achieved without document-summary pairs as
training data. Promising results are shown on both English and Chinese corpora.

本文介绍了一种基于自编码器的无配对抽象摘要生成方法，通过生成器和复原器对输入文本进行编码和解码，再通过鉴别器确保生成的文本与人类写作的句子相似，从而获得可读性强的人类摘要，无需文档摘要对作为训练数据。该方法在英文和中文语料库上取得了良好的表现。