Section outline
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We close the gap between neural networks and probabilistic learning by discussing generative deep learning models. We introduce a taxonomy of the existing generative deep learning approaches and study in-depth relevant families of models for each element of the taxonomy, including: autoregressive generation, variational autoencoders, generative adversarial networks, diffusion models, flow-based methods and score matching-
Date Topic References Additional Material 23 16/04/2026
(11-13)Neural Autoencoders
Introduction to the generative deep learning module; generative models taxonomy; undercomplete neural autoencoders; deep autoencoders.[SD] Coverage of the Prince book on this lecture is inadequate but you can use the lecture slides and complement with the additional material if necessary. (e.g. chapter 14 of the deep learning book). Additional Readings
[24] DBN: the paper that started deep learning
[25] Deep Boltzmann machines paper
[26] Review paper on deep generative models
[27] Long review paper on autoencoders from the perspective of representation learning24 21/04/2026
(11-13)Variational Autoencoders
explicit distribution models; score learning in DAE; neural ELBO; variational approximation; reparameterization trick; latent space properties
[SD] Chapter 14
[SD] Chapter 17
Additional Readings
[28] Tutorial on VAE
Sofware- A tutorial on VAE with code
25 22/04/2026
(16-18)Generative Adversarial Networks
learning a sampling process; adversarial learning principles; wasserstein GANs; conditional generation; notable GANs; adversarial autoencoders
[SD] Chapter 15 26 23/04/2026
(11-13)Coding practice III - Lecture by Riccardo Massidda
27 28/04/2026
(11-13)Normalizing flow models I
tractable explicit likelihood; autoregressive generative learning; probabilistic change of variable, forward/normalization pass; from 1D to multidimensional flows.
[SD] Chapter 16 29/04/2026
(16-18)LECTURE CANCELLED DUE TO STUDENTS' ASSEMBLY
28 30/04/2026
(11-13)Normalizing flow models II
coupling flows; masking & squeezing; invertible convolutions; autoregressive flows; residual & continous normalizing flows
[SD] Chapter 16 05/05/2026
(11-13)LECTURE CANCELLED DUE TO LECTURER UNAVAILABILITY
29 06/05/2026
(16-18)Diffusion models
noising-denoising processes; kernelized diffusion; latent space diffusion;