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- W4317242571 abstract "Machine learning is a complicated course that contains many subjects which is known as Probability and Mathematical Statistic, approximation theory, convex analysis and the complexity of the algorithm. It concentrates on the way people’s learning behavior is simulated or achieved by the computers, so that they can get new knowledges and skills and reform the now available knowledge and improve their functions by learning this. So, at present, there are still many problems in machine learning research, one of which is the improvement of the generative model. Nowadays, discriminant models are mainly used. This paper introduces the concept of Variational autoencoder models. Besides, this paper discusses Some properties of Variational autoencoder models, provides a reference for improving generative models, and explains the principle of Variational autoencoder. It is hoped that in the future, generative models can also be developed as perfect as discriminant models to achieve significant progress in the field of artificial intelligence." @default.
- W4317242571 created "2023-01-18" @default.
- W4317242571 creator A5001598173 @default.
- W4317242571 date "2022-12-11" @default.
- W4317242571 modified "2023-10-18" @default.
- W4317242571 title "The Advance of Generative Model and Variational Autoencoder" @default.
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- W4317242571 doi "https://doi.org/10.1109/tocs56154.2022.10016057" @default.
- W4317242571 hasPublicationYear "2022" @default.
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