New technologies have led to vast troves of large and complex datasets across
many scientific domains and industries. People routinely use machine learning
techniques to not only process, visualize, and make predictions from this big
data, but also to make data-driven discoveries. These discoveries are often
made using Interpretable Machine Learning, or machine learning models and
techniques that yield human understandable insights. In this paper, we discuss
and review the field of interpretable machine learning, focusing especially on
the techniques as they are often employed to generate new knowledge or make
discoveries from large data sets. We outline the types of discoveries that can
be made using Interpretable Machine Learning in both supervised and
unsupervised settings. Additionally, we focus on the grand challenge of how to
validate these discoveries in a data-driven manner, which promotes trust in
machine learning systems and reproducibility in science. We discuss validation
from both a practical perspective, reviewing approaches based on data-splitting
and stability, as well as from a theoretical perspective, reviewing statistical
results on model selection consistency and uncertainty quantification via
statistical inference. Finally, we conclude by highlighting open challenges in
using interpretable machine learning techniques to make discoveries, including
gaps between theory and practice for validating data-driven-discoveries.

通过解释性机器学习，可以从大数据集中生成新的知识或进行发现，验证这些发现有助于提高机器学习系统的可信度和科学的可重复性。从实践和理论两个角度出发，我们讨论解释性机器学习的技术与挑战。

可解释的机器学习对于发现的统计挑战与机遇

Interpretable Machine Learning for Discovery: Statistical Challenges \&  Opportunities

We present a mechanism for detecting adversarial examples based on data
representations taken from the hidden layers of the target network. For this
purpose, we train individual autoencoders at intermediate layers of the target
network. This allows us to describe the manifold of true data and, in
consequence, decide whether a given example has the same characteristics as
true data. It also gives us insight into the behavior of adversarial examples
and their flow through the layers of a deep neural network. Experimental
results show that our method outperforms the state of the art in supervised and
unsupervised settings.

本研究提出了一种基于目标网络隐藏层数据表示的机制来检测对抗性样本，该机制训练了目标网络中间层的单独的自编码器，可以描述真实数据的流形，从而判断一个给定的例子是否具有与真实数据相同的特征，并探究对抗性样本在深度神经网络层中的行为。实验结果表明，我们的方法在监督和无监督环境中表现优于现有技术水平。