论文标题
饲料向前神经网络的不确定性识别量子上下文性
Uncertainty of Feed Forward Neural Networks Recognizing Quantum Contextuality
论文作者
论文摘要
表征量子域问题的神经网络表现的表征的表征的通常数字是它们的准确性，这是对以前看不见的输入正确答案的概率。在这里，我们以预测的不确定性附加了此参数，表征了答案的信心程度。贝叶斯神经网络（BNNS）提供了一种估计准确性和不确定性的强大技术。我们首先给出了简单的说明性示例，即BNN带来的优势，即使在使用有偏见的数据集培训后，我们也希望强调它们的可靠不确定性估计能力。然后，我们将BNN应用于识别量子上下文性的问题，这表明不确定性本身是一个独立的参数，识别错误分类情境性的机会。
The usual figure of merit characterizing the performance of neural networks applied to problems in the quantum domain is their accuracy, being the probability of a correct answer on a previously unseen input. Here we append this parameter with the uncertainty of the prediction, characterizing the degree of confidence in the answer. A powerful technique for estimating both the accuracy and the uncertainty is provided by Bayesian neural networks (BNNs). We first give simple illustrative examples of advantages brought forward by BNNs, out of which we wish to highlight their ability of reliable uncertainty estimation even after training with biased data sets. Then we apply BNNs to the problem of recognition of quantum contextuality which shows that the uncertainty itself is an independent parameter identifying the chance of misclassification of contextuality.
