
Improving Nonparametric Density Estimation with Tensor Decompositions
While nonparametric density estimators often perform well on low dimensi...
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Deep Anomaly Detection by Residual Adaptation
Deep anomaly detection is a difficult task since, in high dimensions, it...
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A Unifying Review of Deep and Shallow Anomaly Detection
Deep learning approaches to anomaly detection have recently improved the...
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Input Hessian Regularization of Neural Networks
Regularizing the input gradient has shown to be effective in promoting t...
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Explainable Deep OneClass Classification
Deep oneclass classification variants for anomaly detection learn a map...
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Consistent Estimation of Identifiable Nonparametric Mixture Models from Grouped Observations
Recent research has established sufficient conditions for finite mixture...
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Rethinking Assumptions in Deep Anomaly Detection
Though anomaly detection (AD) can be viewed as a classification problem ...
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Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion
Activity coefficients, which are a measure of the nonideality of liquid...
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Deep SemiSupervised Anomaly Detection
Deep approaches to anomaly detection have recently shown promising resul...
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An Operator Theoretic Approach to Nonparametric Mixture Models
When estimating finite mixture models, it is common to make assumptions ...
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On The Identifiability of Mixture Models from Grouped Samples
Finite mixture models are statistical models which appear in many proble...
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Robust Kernel Density Estimation by Scaling and Projection in Hilbert Space
While robust parameter estimation has been well studied in parametric de...
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Robert A. Vandermeulen
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