Lect. | Lecturer | Topic | Pdf | Additional reading |
01 | BF | Markov Random Fields & Gibbs Random Fields | | |
02 | BF | The most probable realisation of a GRF | | [Savchynskyy19] |
03 | BF | Belief Networks & Stochastic Neural Networks | | [Neal92], Rezende, 2014, Kingma, 2013 |
04 | VF | Empirical Risk Minimization | , print | [Domke10] [Agarwal11] [Balcan11] |
05 | VF | Structured Output Linear Classifier and Perceptron | , print | [Collins02] Peceptron proof |
06 | VF | Learning max-sum classifier by Perceptron | , print | [Franc08] |
07 | VF | Structured Output Support Vector Machines | , print | [Tsochantaridis05] |
08 | VF | Cutting Plane Algorithm | , print | [Teo09] |
09 | VF | Learning Max-Sum classifier by SO-SVM | , print | [Taskar04] [Taskar04] [Franc08] |
10 | BF | Maximum Likelihood learning for MRFs I | | [Fujishige (book)], [Zhang2015] |
11 | BF | Maximum Likelihood learning for MRFs II | | |
12 | BF | Variational Bayesian inference for DNNs | | |
13 | BF | Variational Autoencoders | | [Doersch2016] |
14 | BF | Generative adversarial networks | | [Arjovsky2017] |