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Found 3 papers, 1 papers with code
Can Tree Based Approaches Surpass Deep Learning in Anomaly Detection? A Benchmarking Study
The paper contributes significantly by conducting an unbiased comparison of various anomaly detection algorithms, spanning classical machine learning including various tree-based approaches to deep learning and outlier detection methods.
Quantile LSTM: A Robust LSTM for Anomaly Detection In Time Series Data
In particular, we propose Parametric Elliot Function (PEF) as an activation function (AF) inside LSTM, which saturates lately compared to sigmoid and tanh.
Postulating Exoplanetary Habitability via a Novel Anomaly Detection Method
The algorithm is based on the postulate that Earth is an anomaly, with the possibility of existence of few other anomalies among thousands of data points.
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