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1. Robust-kernels-for-robust-location-estimation Robust-kernels-for-robust-location-estimation Public

   This work shows that least-square estimation (mean calculation) in a reproducing kernel Hilbert space (RKHS) F corresponds to different M-estimators in the original space depending on the kernel fu…

   MATLAB 7 4

2. INQMAD INQMAD Public

   We present a new incremental anomaly detection method that performs continuous density estimation based on random Fourier features and the mechanism of quantum measurements and density matrices. T…

   Python 4 1

3. lean-dmkde lean-dmkde Public

   We present anomaly detection model that combines the strong statistical foundation of density-estimation-based anomaly detection methods with the representation-learning ability of deep-learning mo…

   Jupyter Notebook 4 1

4. Fast-Kernel-Density-Estimation-with-Density-Matrices-and-Random-Fourier-Features Fast-Kernel-Density-Estimation-with-Density-Matrices-and-Random-Fourier-Features Public

   Kernel Density Estimation (KDE) is a powerful non-parametric method to estimate continuous probability density functions from data. However, traditional KDE scales poorly with dataset size since it…

   Jupyter Notebook 3

5. demande demande Public

   This paper presents a novel method for neural density estimation based on density matrices and adaptive Fourier features.

   Jupyter Notebook 2

6. Learning-with-Density-Matrices-and-Random-Features Learning-with-Density-Matrices-and-Random-Features Public

   This paper explores how density matrices can be used as a building block to build machine learning models exploiting their ability to straightforwardly combine linear algebra and probability.

   Python 2