Tentative list of papers:

Choose one of the papers and write your Full Name and Matrikelnummer in the
following google sheet: LINK TO GOOGLE SHEET

Session 1: 04.11.22
• GAN I: GAN, f-GAN
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., &
Bengio, Y. (2020). Generative adversarial networks. Communications of the ACM, 63 (11), 139–144
Nowozin, S., Cseke, B., & Tomioka, R. (2016). F-gan: Training generative neural samplers using
variational divergence minimization. Advances in neural information processing systems, 29
• GAN I: W-GAN
Arjovsky, M., Chintala, S., & Bottou, L. (2017). Wasserstein generative adversarial networks. International
conference on machine learning, 214–223

Session 2: 11.11.22
• Mutual Information Estimation I:
Belghazi, M. I., Baratin, A., Rajeshwar, S., Ozair, S., Bengio, Y., Courville, A., & Hjelm, D. (2018).
Mutual information neural estimation. International conference on machine learning, 531–540
• Mutual Information Estimation II:
Poole, B., Ozair, S., Van Den Oord, A., Alemi, A., & Tucker, G. (2019). On variational bounds of
mutual information. International Conference on Machine Learning, 5171–5180

Session 3: 18.11.22
• Representation Learning:
Hjelm, R. D., Fedorov, A., Lavoie-Marchildon, S., Grewal, K., Bachman, P., Trischler, A., & Bengio,
Y. (2018). Learning deep representations by mutual information estimation and maximization. arXiv
preprint arXiv:1808.06670
• Paired Representations:
Sanchez, E. H., Serrurier, M., & Ortner, M. (2020). Learning disentangled representations via mutual
information estimation. European Conference on Computer Vision, 205–221

Session 4: 25.11.22
• GAN II: InfoGAN
Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., & Abbeel, P. (2016). Infogan: Interpretable
representation learning by information maximizing generative adversarial nets. Advances in
neural information processing systems, 29
• GAN II: BiGAN?
Donahue, J., Kr¨ahenb¨uhl, P., & Darrell, T. (2016). Adversarial feature learning. arXiv preprint
arXiv:1605.09782

Session 5: 02.12.22
• Information Bottleneck
Tishby, N., Pereira, F. C., & Bialek, W. (2000). The information bottleneck method. arXiv preprint
physics/0004057
• Deep Information Bottleneck
Tishby, N., & Zaslavsky, N. (2015). Deep learning and the information bottleneck principle. 2015
ieee information theory workshop (itw), 1–5

Session 6: 09.12.22
• VAE I: VAE
Kingma, D. P., &Welling, M. (2013). Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114
• VAE I: β-VAE
Burgess, C. P., Higgins, I., Pal, A., Matthey, L., Watters, N., Desjardins, G., & Lerchner, A. (2018).
Understanding disentangling in \beta-vae. arXiv preprint arXiv:1804.03599

Session 7: 16.12.22
• Conditional mutual information estimation
Mukherjee, S., Asnani, H., & Kannan, S. (2020). Ccmi: Classifier based conditional mutual information
estimation. Uncertainty in artificial intelligence, 1083–1093
• Neural Estimation of the Rate-Distrortion Function
Lei, E., Hassani, H., & Bidokhti, S. S. (2022). Neural estimation of the rate-distortion function for
massive datasets. 2022 IEEE International Symposium on Information Theory (ISIT), 608–613

Session 8: 06.01.23
• VAE II: Rate-Distortion Problem and VAE Alemi, A., Poole, B., Fischer, I., Dillon, J., Saurous,
R. A., & Murphy, K. (2018). Fixing a broken elbo. International Conference on Machine Learning,
159–168
• VAE II: Unified perspective on generative models Zhao, S., Song, J., & Ermon, S. (2018). The
information autoencoding family: A lagrangian perspective on latent variable generative models.
arXiv preprint arXiv:1806.06514

Session 9: 13.01.23
• Diffusion Models I: DDPM
Ho, J., Jain, A., & Abbeel, P. (2020). Denoising diffusion probabilistic models. Advances in Neural
Information Processing Systems, 33, 6840–6851
• Diffusion Models II: SDE
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., & Poole, B. (2020). Score-based
generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456

Session 10: 20.01.23
• Visualizing high dimensional data using ML: t-SNE
Van der Maaten, L., & Hinton, G. (2008). Visualizing data using t-sne. Journal of machine learning
research, 9 (11)