Tag: anomaly
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Mitigating Long-Tailed Anomaly Score Distributions with Importance-Weighted Loss
Mitigating Long-Tailed Anomaly Score Distributions with Importance-Weighted Loss arXiv:2601.02440v1 Announce Type: new Abstract: Anomaly detection is crucial in industrial applications for identifying rare and unseen patterns to ensure system reliability. Traditional models, trained on a single class of normal data, struggle with real-world distributions where normal data exhibit diverse patterns, leading to class imbalance and…
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Boosting Your Anomaly Detection With LLMs
Boosting Your Anomaly Detection With LLMs The 7 emerging application patterns you should know The post Boosting Your Anomaly Detection With LLMs appeared first on Towards Data Science. Shuai Guo Go to original source
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Don’t Waste Your Labeled Anomalies: 3 Practical Strategies to Boost Anomaly Detection Performance
Don’t Waste Your Labeled Anomalies: 3 Practical Strategies to Boost Anomaly Detection Performance A few labels go a long way in anomaly detection The post Don’t Waste Your Labeled Anomalies: 3 Practical Strategies to Boost Anomaly Detection Performance appeared first on Towards Data Science. Shuai Guo Go to original source
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Explainable Anomaly Detection with RuleFit: An Intuitive Guide
Explainable Anomaly Detection with RuleFit: An Intuitive Guide Creating interpretable rules to characterize the identified anomalies The post Explainable Anomaly Detection with RuleFit: An Intuitive Guide appeared first on Towards Data Science. Shuai Guo Go to original source
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Explainable Unsupervised Anomaly Detection with Random Forest
Explainable Unsupervised Anomaly Detection with Random Forest arXiv:2504.16075v1 Announce Type: new Abstract: We describe the use of an unsupervised Random Forest for similarity learning and improved unsupervised anomaly detection. By training a Random Forest to discriminate between real data and synthetic data sampled from a uniform distribution over the real data bounds, a distance measure…
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A Review on Self-Supervised Learning for Time Series Anomaly Detection: Recent Advances and Open Challenges
A Review on Self-Supervised Learning for Time Series Anomaly Detection: Recent Advances and Open Challenges arXiv:2501.15196v1 Announce Type: new Abstract: Time series anomaly detection presents various challenges due to the sequential and dynamic nature of time-dependent data. Traditional unsupervised methods frequently encounter difficulties in generalization, often overfitting to known normal patterns observed during training and…