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Article overview
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Quantum Adaptive Fourier Features for Neural Density Estimation | | Joseph A. Gallego M.
; Fabio A. González
; | | Date: |
1 Aug 2022 | | Abstract: | Density estimation is a fundamental task in statistics and machine learning
applications. Kernel density estimation is a powerful tool for non-parametric
density estimation in low dimensions; however, its performance is poor in
higher dimensions. Moreover, its prediction complexity scale linearly with more
training data points. This paper presents a method for neural density
estimation that can be seen as a type of kernel density estimation, but without
the high prediction computational complexity. The method is based on density
matrices, a formalism used in quantum mechanics, and adaptive Fourier features.
The method can be trained without optimization, but it could be also integrated
with deep learning architectures and trained using gradient descent. Thus, it
could be seen as a form of neural density estimation method. The method was
evaluated in different synthetic and real datasets, and its performance
compared against state-of-the-art neural density estimation methods, obtaining
competitive results. | | Source: | arXiv, 2208.00564 | | Services: | Forum | Review | PDF | Favorites | |
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