题目： Evolving Normalization-Activation Layers
归一化层和激活函数是深度神经网络中经常采用的关键组件。 除了将它们分开设计之外，我们将它们统一为一个计算图，并从低级开始发展其结构。 我们的层搜索算法EvoNorms发现，EvoNorms是超越现有设计模式的一组新的进化激活层。 这些层中的几个层具有独立于批量处理统计信息的属性。 我们的实验表明，EvoNorms不仅在包括ResNets，MobileNets和EfficientNets在内的各种图像分类模型上表现出色，而且还可以很好地转移到Mask R-CNN进行实例分割和BigGAN进行图像合成，从而大大优于基于BatchNorm和GroupNorm的图层。
While machine learning techniques have been successfully applied in several fields, the black-box nature of the models presents challenges for interpreting and explaining the results. We develop a new framework called Adaptive Explainable Neural Networks (AxNN) for achieving the dual goals of good predictive performance and model interpretability. For predictive performance, we build a structured neural network made up of ensembles of generalized additive model networks and additive index models (through explainable neural networks) using a two-stage process. This can be done using either a boosting or a stacking ensemble. For interpretability, we show how to decompose the results of AxNN into main effects and higher-order interaction effects. The computations are inherited from Google's open source tool AdaNet and can be efficiently accelerated by training with distributed computing. The results are illustrated on simulated and real datasets.