Identification and Uses of Deep Learning Backbones via Pattern Mining
Proceedings of the 2024 SIAM International Conference on Data Mining (SDM)(2024)
Abstract
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.
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