In this thesis, we focused on recent developments in variational inference. We saw how these methods can be married with ideas from deep learning to give life to the example of variational autoencoders (VAEs), models that learn how to generate new realistic-looking data. Along the way, we provided parallels with other statistical methods.
In this thesis, we focused on recent developments in variational inference. We saw how these methods can be married with ideas from deep learning to give life to the example of variational autoencoders (VAEs), models that learn how to generate new realistic-looking data. Along the way, we provided parallels with other statistical methods.
Auto-Encoding Variational Bayes
BRUNO, MATTIA
2021/2022
Abstract
In this thesis, we focused on recent developments in variational inference. We saw how these methods can be married with ideas from deep learning to give life to the example of variational autoencoders (VAEs), models that learn how to generate new realistic-looking data. Along the way, we provided parallels with other statistical methods.| File | Dimensione | Formato | |
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Bruno_Mattia.pdf
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https://hdl.handle.net/20.500.12608/38806