A least-squares approach to anomaly detection in static and sequential data

By John Alexander Quinn, Masashi Sugiyama. 2014

56 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering, Computer Networks and Communications

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Joost R. van Amersfoort
2. Lewis Smith
3. Yee Whye Teh
4. Yarin Gal
5. Maurizio Filippone
6. Pietro Michiardi
7. Jihane Zouaoui
8. Rémi Domingues

In Biomedical Engineering

1. Lewis Smith
2. Yee Whye Teh
3. Yarin Gal
4. Joost R. van Amersfoort

In Computer Networks and Communications

1. Pietro Michiardi
2. Lewis Smith
3. Yee Whye Teh
4. Yarin Gal
5. Joost R. van Amersfoort

Citations in the corpus

• A comparative evaluation of outlier detection algorithms: Experiments and analyses (2018, background): “More recently, Least-squares anomaly detection (LSA) [25] developed by Quinn et al.”
• Uncertainty Estimation Using a Single Deep Deterministic Neural Network (2020, methodology): “Instead our approach is more related to pre-deep learning kernel methods (Quinn & Sugiyama, 2014; Sch¨olkopf et al., 2000), such as Gaussian processes which revert to a prior away from data, and Support Vector Machines, where the distance to the separating hyperplane is informative of the…”