Summary of Tensor Networks For Explainable Machine Learning in Cybersecurity, by Borja Aizpurua et al.
Tensor Networks for Explainable Machine Learning in Cybersecurity
by Borja Aizpurua, Samuel Palmer, Roman Orus
First submitted to arxiv on: 29 Dec 2023
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Quantum Physics (quant-ph)
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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 demonstrates the power of tensor networks in explaining machine learning algorithms. By developing an unsupervised clustering algorithm based on Matrix Product States (MPS), researchers show that MPS can rival traditional deep learning models like autoencoders and GANs in performance while providing richer model interpretability. The approach enables the extraction of feature-wise probabilities, Von Neumann Entropy, and mutual information, leading to a compelling narrative for anomaly classification and unprecedented transparency and interpretability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning algorithms can be tricky to understand. This paper shows how tensor networks can help make them more transparent. Researchers created an algorithm that groups similar data points together without being taught beforehand. They tested it on real-world data about potential threats, and it performed just as well as other popular methods while providing a clear explanation of why it made certain decisions. |
Keywords
* Artificial intelligence * Classification * Clustering * Deep learning * Machine learning * Unsupervised
