L10 Representation learning and deep generative modelling

Lecture slides (Lect10a Download Lect10a, Lect 10b Download Lect 10b)

Date and venue: (Oct 3, kl. 8:15-10:00, K1)

                               Oct 7, kl. 10:15-12:00, M2

                               Oct 11, kl. 8:15-10:00, D2

                               Oct 15, kl. 15:15-17:00, D2

Session format:
Building on the pre-recorded introduction to deep learning fundamentals (L9) and representation learning (L10a), a more detailed account of selected aspects will be given first and then combined with group discussions and Q&A.
In the session L10b, the scope of representation learning will be extended towards deep generative modelling. 

Before the lecture

Pre-recorded video on representation learning (and lab 4 DBN, if possible) Please make sure you have watched also pre-recorded material for the previous session L9.

Discussions 10a Download 10a, 10b Download 10b

After the lecture

Recorded Zoom session Recorded L10a and 10b sessions

 

Scope, topics covered

• Data representations
• Learning representations
• Generative vs discriminative models
• Deep Autoencoders, Variational Autoencoders
• Generative Adversarial Networks

 

Reading material:

• I. Goodfellow et al.: ch.14, 15, 18 (18.1-18.2), 20 (20.1-20.5)

• tutorials/survey papers, e.g.:
  • Salakhutdinov, R. (2015) Learning deep generative models. Annual Reviews of Statistics and Its Application, 2, p.361–385.
  • Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence, 35 (8), p.1798-1828.
  • Bengio, Y. (2009) Learning deep architectures for AI. Foundations and trends® in Machine Learning, 2.1. p.1-127.