置信网络：小数据集上回归神经网络更好预测区间的一步
Confidence-Nets: A Step Towards better Prediction Intervals for regression Neural Networks on small datasets
摘要：
近十年来，深度学习和神经网络的流行程度大幅上升。这些算法打破了许多以前的记录，取得了显著的效果。他们的出色表现大大加快了人工智能的进展，到目前为止，各种里程碑都比预期的要早。然而，在相对小的数据集的情况下，与其他机器学习模型相比，深度神经网络（DNN）的性能可能会受到准确性降低的影响。此外，在处理回归任务时，很难构建预测区间或评估预测的不确定性。在本文中，我们提出了一种集成方法，试图估计预测的不确定性，提高其准确性，并提供一个预期的变化的区间。与仅提供预测的传统DNN相比，我们提出的方法可以通过结合DNN，极端梯度提升（XGBoost）和相异度计算技术来输出预测区间。尽管设计简单，但这种方法显著提高了小数据集的准确性，并且不会给神经网络的架构带来太多复杂性。所提出的方法在各种数据集上进行了测试，并在神经网络模型的性能显着改善。该模型的预测区间可以在90%和55%的训练大小上分别以71%和78%的平均比率包括地面真值。最后，我们重点介绍了该方法在实验误差估计中的其他方面和应用，以及迁移学习的应用。
英文摘要：
The recent decade has seen an enormous rise in the popularity of deep learning and neural networks. These algorithms have broken many previous records and achieved remarkable results. Their outstanding performance has significantly sped up the progress of AI, and so far various milestones have been achieved earlier than expected. However, in the case of relatively small datasets, the performance of Deep Neural Networks (DNN) may suffer from reduced accuracy compared to other Machine Learning models. Furthermore, it is difficult to construct prediction intervals or evaluate the uncertainty of predictions when dealing with regression tasks. In this paper, we propose an ensemble method that attempts to estimate the uncertainty of predictions, increase their accuracy and provide an interval for the expected variation. Compared with traditional DNNs that only provide a prediction, our proposed method can output a prediction interval by combining DNNs, extreme gradient boosting (XGBoost) and dissimilarity computation techniques. Albeit the simple design, this approach significantly increases accuracy on small datasets and does not introduce much complexity to the architecture of the neural network. The proposed method is tested on various datasets, and a significant improvement in the performance of the neural network model is seen. The model's prediction interval can include the ground truth value at an average rate of 71% and 78% across training sizes of 90% and 55%, respectively. Finally, we highlight other aspects and applications of the approach in experimental error estimation, and the application of transfer learning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
