Summary of Varade: a Variational-based Autoregressive Model For Anomaly Detection on the Edge, by Alessio Mascolini et al.
VARADE: a Variational-based AutoRegressive model for Anomaly Detection on the Edge
by Alessio Mascolini, Sebastiano Gaiardelli, Francesco Ponzio, Nicola Dall’Ora, Enrico Macii, Sara Vinco, Santa Di Cataldo, Franco Fummi
First submitted to arxiv on: 23 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 abstract proposes VARADE, a novel deep learning solution for detecting complex anomalies in massive datasets. Traditional methods are computationally demanding, requiring cloud architectures that can suffer from latency and bandwidth issues. VARADE addresses this challenge by implementing a light autoregressive framework based on variational inference, making it suitable for real-time execution on the edge. The approach is validated on a robotic arm, part of a pilot production line, and compared to several state-of-the-art algorithms. VARADE achieves the best trade-off between anomaly detection accuracy, power consumption, and inference frequency on two different edge platforms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary VARADE is a new way to find unusual patterns in very big datasets. Right now, finding these patterns takes a lot of computer power and requires using cloud computing services that can be slow and have connectivity issues. VARADE solves this problem by creating a light framework that can run quickly on devices at the edge. This means it can be used for real-time detection without slowing down or needing to upload data to the cloud. The approach was tested on a robotic arm and compared to other state-of-the-art methods. VARADE did the best job of finding anomalies while using less power and processing information faster. |
Keywords
» Artificial intelligence » Anomaly detection » Autoregressive » Deep learning » Inference
