论文标题
通过渐进式级联网络解释的气候变化建模
Interpretable Climate Change Modeling With Progressive Cascade Networks
论文作者
论文摘要
建模高维数据的典型深度学习方法通常会导致复杂的模型，这些模型不容易揭示对数据的新理解。深度学习领域的研究非常积极地追求新的方法来解释深层神经网络并降低其复杂性。这里描述了一种从线性模型开始的方法，并逐步添加了仅根据数据支持的复杂性。显示了一个应用程序，其中训练了将全球温度和降水绘制为年度的模型，以研究与气候变化相关的模式。
Typical deep learning approaches to modeling high-dimensional data often result in complex models that do not easily reveal a new understanding of the data. Research in the deep learning field is very actively pursuing new methods to interpret deep neural networks and to reduce their complexity. An approach is described here that starts with linear models and incrementally adds complexity only as supported by the data. An application is shown in which models that map global temperature and precipitation to years are trained to investigate patterns associated with changes in climate.
