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Explores non-parametric estimation using kernel density estimators to estimate distribution functions and parameters, emphasizing bandwidth selection for optimal accuracy.
Covers Variational Autoencoders, a probabilistic approach to autoencoders for data generation and feature representation, with applications in Natural Language Processing.
