用于识别回归问题的熟练预测的受控预测神经网络
Controlled abstention neural networks for identifying skillful predictions for regression problems
摘要：
地球系统极其复杂，而且在本质上往往是混乱的，这使得预测变得非常具有挑战性：我们不能指望总是做出完美的预测。相反，我们寻找系统的特定状态，这些状态导致比其他状态更可预测的行为，通常被称为“机会预测”。当这些机会不存在时，科学家需要能够说“我不知道”的预测系统。“我们引入了一种新的损失函数，称为“预测损失”，它允许神经网络识别回归问题的机会预测。预防损失的工作原理是在网络的预测中加入不确定性，以识别更有信心的样本，并对不太有信心的样本弃权（说“我不知道”）。该控制器通过PID控制器设计出最佳的控制量，或放弃用户自定义的控制量。与许多在训练后将不确定性附加到神经网络预测的方法不同，在训练期间应用修正损失以优先从更自信的样本中学习。反射损失建立在标准计算机科学方法上。虽然标准方法本身是一种简单而强大的工具，可以将不确定性纳入回归问题，但我们证明，对于这里探讨的合成气候用例，反射损失优于这种更标准的方法。所提出的损失函数的实现在大多数为回归设计的网络架构中是直接的，因为它只需要修改输出层和损失函数。
英文摘要：
The earth system is exceedingly complex and often chaotic in nature, making prediction incredibly challenging: we cannot expect to make perfect predictions all of the time. Instead, we look for specific states of the system that lead to more predictable behavior than others, often termed "forecasts of opportunity". When these opportunities are not present, scientists need prediction systems that are capable of saying "I don't know." We introduce a novel loss function, termed "abstention loss", that allows neural networks to identify forecasts of opportunity for regression problems. The abstention loss works by incorporating uncertainty in the network's prediction to identify the more confident samples and abstain (say "I don't know") on the less confident samples. The abstention loss is designed to determine the optimal abstention fraction, or abstain on a user-defined fraction via a PID controller. Unlike many methods for attaching uncertainty to neural network predictions post-training, the abstention loss is applied during training to preferentially learn from the more confident samples. The abstention loss is built upon a standard computer science method. While the standard approach is itself a simple yet powerful tool for incorporating uncertainty in regression problems, we demonstrate that the abstention loss outperforms this more standard method for the synthetic climate use cases explored here. The implementation of proposed loss function is straightforward in most network architectures designed for regression, as it only requires modification of the output layer and loss function.
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。
