Summary of Metric As Transform: Exploring Beyond Affine Transform For Interpretable Neural Network, by Suman Sapkota
Metric as Transform: Exploring beyond Affine Transform for Interpretable Neural Network
by Suman Sapkota
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Neural and Evolutionary Computing (cs.NE)
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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 In this paper, researchers investigate the effectiveness of using dot product neurons with global influence in artificial neural networks. They find that these neurons are less interpretable compared to those with local influence, used in radial basis function networks. To address this issue, they generalize dot product neurons to l^p-norm metrics and beyond. The results show that metrics as transform performs similarly to affine transformation when used in multi-layer perceptrons or convolutional neural networks. Furthermore, the researchers compare metrics with affine and present cases where metrics provide better interpretability. They also develop an interpretable local dictionary-based neural network and use it to understand and reject adversarial examples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial Neural Networks (ANNs) are powerful tools used in many applications. But did you know that some ANNs are less transparent than others? That’s what researchers tried to fix by looking at a special type of neuron called the dot product neuron. They found that this type of neuron makes it harder to understand how the network is making decisions. To solve this problem, they came up with new ways to use these neurons and tested them on different types of networks. Surprisingly, some of their ideas worked just as well as older methods! They also developed a new way to make neural networks more understandable and used it to reject fake examples that could harm the network. |
Keywords
» Artificial intelligence » Dot product » Neural network
