Identification and Uses of Deep Learning Backbones via Pattern Mining
<|reference_start|>Identification and Uses of Deep Learning Backbones via Pattern Mining: 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.<|reference_end|>
@article{livanos2024identification,
    title={Identification and Uses of Deep Learning Backbones via Pattern Mining},
    author={Michael Livanos and Ian Davidson},
    journal={arXiv preprint arXiv:2403.18278},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.18278},
    primaryClass={cs.AI}
}