Summary of On the Value Of Labeled Data and Symbolic Methods For Hidden Neuron Activation Analysis, by Abhilekha Dalal et al.
On the Value of Labeled Data and Symbolic Methods for Hidden Neuron Activation Analysis
by Abhilekha Dalal, Rushrukh Rayan, Adrita Barua, Eugene Y. Vasserman, Md Kamruzzaman Sarker, Pascal Hitzler
First submitted to arxiv on: 21 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 This paper tackles a crucial challenge in Explainable AI: accurately interpreting hidden neuron activations in deep learning systems. The state-of-the-art indicates that some cases of hidden node activations can be human-interpretable, but systematic automated methods for hypothesizing and verifying such interpretations are scarce, particularly for approaches combining background knowledge with symbolic, inherently explainable methods. This research proposes a novel framework to address this challenge, leveraging substantial background knowledge to generate explanations from hidden neuron activations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to understand what a super smart computer is thinking when it makes decisions on its own. This paper wants to help us figure out how deep learning systems “see” things in their internal workings. Right now, scientists have some ideas about what’s going on inside these systems, but they need better tools to really understand and explain what the computers are doing. The goal is to create a system that can take lots of information it’s learned from and use it to make sense of its own thinking. |
Keywords
» Artificial intelligence » Deep learning
