Summary of Lean-dmkde: Quantum Latent Density Estimation For Anomaly Detection, by Joseph Gallego-mejia et al.
LEAN-DMKDE: Quantum Latent Density Estimation for Anomaly Detection
by Joseph Gallego-Mejia, Oscar Bustos-Brinez, Fabio A. González
First submitted to arxiv on: 15 Nov 2022
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Statistics Theory (math.ST); Quantum Physics (quant-ph)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposes an anomaly detection model that fuses the statistical strengths of density-estimation-based methods with deep learning’s representation capabilities. It combines an autoencoder and a density-estimation model using random Fourier features and density matrices in an end-to-end architecture optimized via gradient-based techniques. The method predicts normality degrees for new samples based on estimated densities. Experimental evaluations on various benchmark datasets show that the approach performs competitively or better than state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to find unusual data points by combining two different techniques. One part uses deep learning to learn about the patterns in the data, and the other part uses statistics to understand what’s normal. It then combines these two parts into one model that can be trained and tested on real-world datasets. The results show that this new approach works well and is as good or better than existing methods. |
Keywords
* Artificial intelligence * Anomaly detection * Autoencoder * Deep learning * Density estimation
