Deep learning is extensively used in many areas of data mining as a black-box
method with impressive results. However, understanding the core mechanism of
how deep learning makes predictions is a relatively understudied problem. Here
we explore the notion of identifying a backbone of deep learning for a given
group of instances. A group here can be instances of the same class or even
misclassified instances of the same class. We view each instance for a given
group as activating a subset of neurons and attempt to find a subgraph of
neurons associated with a given concept/group. We formulate this problem as a
set cover style problem and show it is intractable and presents a highly
constrained integer linear programming (ILP) formulation. As an alternative, we
explore a coverage-based heuristic approach related to pattern mining, and show
it converges to a Pareto equilibrium point of the ILP formulation.
Experimentally we explore these backbones to identify mistakes and improve
performance, explanation, and visualization. We demonstrate application-based
results using several challenging data sets, including Bird Audio Detection
(BAD) Challenge and Labeled Faces in the Wild (LFW), as well as the classic
MNIST data.

探索深度学习中的核心机制，通过识别给定实例的骨干，应用整数线性规划和模式挖掘的方法，优化性能、解释和可视化，并在相应数据集上进行实验验证。