Date | Description | Deadlines |
---|---|---|
Week 1 15 Aug |
Motivation / Likelihood-based Models Part I: Autoregressive Models
[ « Recording @ YouTube ] |
|
Week 2 22 Aug |
Likelihood-based Models: Autoregressive Models / Flow Models
[ « Recording @ YouTube ] |
|
Week 3 29 Aug |
Lossless Compression / Flow Models
[ « Recording @ YouTube ] |
|
Week 4 5 Sep |
Lecture 3a: Likelihood-based Models Part II: Flow Models (ctd) (same slides as week 2) / Lecture 3b: Latent Variable Models - part 1 | |
Week 5 12 Sep |
Lecture 4a: Latent Variable Models - part 2 / Lecture 4b: Bits-Back Coding | |
Week 6 19 Sep |
Lecture 5a: Latent Variable Models - wrap-up (same slides as Latent Variable Models - part 2) / Lecture 5b: ANS coding (same slides as bits-back coding) / Lecture 5c: Implicit Models / Generative Adversarial Networks | Preliminary project titles and team members due on Slack's #projects |
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Recess Week 26 Sep |
Lecture 6a: Implicit Models / Generative Adversarial Networks (ctd) (same slides as 5c) / Lecture 6b: Non-Generative Representation Learning [UPDATED 3/24] | |
Week 7 3 Oct |
Lecture 7: Non-Generative Representation Learning (same slides as 6b) | Preliminary abstracts due to #projects
|
Week 8 10 Oct |
Lecture 8a: Strengths/Weaknesses of Unsupervised Learning Methods Covered Thus Far / Lecture 8b: Semi-Supervised Learning / Lecture 8c: Guest Lecture by Ilya Sutskever | |
Week 9 17 Oct |
Lecture 9a: Unsupervised Distribution Alignment / Lecture 9b: Guest Lecture by Alyosha Efros | |
Week 10 24 Oct |
Lecture 10: Language Models (Alec Radford) | |
Week 11 31 Oct |
No lecture due to the Singapore Symposium on Natural Language Processing (SSNLP '19). | |
Week 12 7 Nov |
Lecture 11: Representation Learning in Reinforcement Learning | |
Week 13 14 Nov |
TBA | Participation on evening of 15th STePS |