论文标题
复杂预测的拓扑结构
Topological structure of complex predictions
论文作者
论文摘要
诸如深度学习之类的复杂预测模型是拟合机器学习，神经网络或AI模型到一组培训数据的输出。这些现在是科学的标准工具。当前一代模型的一个关键挑战是它们是高度参数化的，这使得和解释预测策略变得困难。我们使用拓扑数据分析将这些复杂的预测模型转换为代表拓扑视图的图片。结果是可以进行检查的预测的地图。这些方法扩展到跨不同领域的大型数据集，使我们能够检测训练数据中的错误，了解图像分类中的概括，并检查BRCA1基因中可能致病突变的预测。
Complex prediction models such as deep learning are the output from fitting machine learning, neural networks, or AI models to a set of training data. These are now standard tools in science. A key challenge with the current generation of models is that they are highly parameterized, which makes describing and interpreting the prediction strategies difficult. We use topological data analysis to transform these complex prediction models into pictures representing a topological view. The result is a map of the predictions that enables inspection. The methods scale up to large datasets across different domains and enable us to detect labeling errors in training data, understand generalization in image classification, and inspect predictions of likely pathogenic mutations in the BRCA1 gene.
