Summary of Explaining Deep Neural Networks by Leveraging Intrinsic Methods, By Biagio La Rosa
Explaining Deep Neural Networks by Leveraging Intrinsic Methods
by Biagio La Rosa
First submitted to arxiv on: 17 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The paper tackles the issue of opacity in deep neural networks (DNNs), which are often seen as “black-box” models due to their complex structures and lack of explanation for their decisions. This challenge hinders AI’s widespread adoption and trustworthiness. The thesis contributes to Explainable AI by introducing novel techniques to make DNNs more interpretable. It proposes designs for self-explanatory DNNs, investigates neuron activation values within trained networks, and analyzes the application of explanatory techniques in visual analytics. These contributions aim to enhance transparency and usability in AI systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep neural networks are super smart computers that can learn from data, but they’re often mysterious because we don’t understand why they make certain decisions. This is a problem for people who want to trust these networks. The research tries to solve this by making the networks more understandable. They propose new ways to design these networks so they explain their own decisions and also study what’s happening inside the networks when they’re learning. Additionally, they explore how these explanatory techniques can be used in fields like visual analytics. |
