Summary of Identification and Uses Of Deep Learning Backbones Via Pattern Mining, by Michael Livanos and Ian Davidson
Identification and Uses of Deep Learning Backbones via Pattern Mining
by Michael Livanos, Ian Davidson
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the notion of identifying a backbone of deep learning for a given group of instances. The authors formulate this problem as a set cover style problem, which is shown to be intractable and presents a highly constrained integer linear programming (ILP) formulation. As an alternative, they explore a coverage-based heuristic approach related to pattern mining, which converges to a Pareto equilibrium point of the ILP formulation. The paper demonstrates 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how deep learning works by identifying a “backbone” of neurons that are activated when making predictions. The authors show that this backbone can be used to identify mistakes and improve performance, explanation, and visualization. They use several challenging datasets to test their approach and get promising results. This research could lead to better AI models that are more transparent and easier to understand. |
Keywords
» Artificial intelligence » Deep learning
