Deep Probabilistic Generative Models

Dear Students,

After reviewing your paper preferences, we have finalized the list of paper assignments. You can find your matriculation number listed before each reference. Presentations will take place during the presentation session corresponding to each block. Remember that your presentation should be 15 minutes long, followed by a Q&A with questions from both the audience and the instructors.

If you notice that you’ve been assigned a paper you marked as “I would not present” or find any other error, please let us know as soon as possible.

Additionally, we’ve noticed that only three of you have registered on LSF so far. Please remember that registration is required by November 4, one week before the first presentation session.

• BLOCK 1:
  • [7058394] Tomczak, J. & Welling, M.. (2018). VAE with a VampPrior. Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 84:1214-1223. Link: https://proceedings.mlr.press/v84/tomczak18a.html.
  • [7070291] NVAE: A Deep Hierarchical Variational Autoencoder. Advances in Neural Information Processing Systems, 33, 19667-19679. Link: https://proceedings.neurips.cc/paper_files/paper/2020/file/e3b21256183cf7c2c7a66be163579d37-Paper.pdf
  • [7075715] Kingma, D. P., & Dhariwal, P. (2018). Glow: Generative Flow with Invertible 1x1 Convolutions. Advances in Neural Information Processing Systems, 31. Link: https://proceedings.neurips.cc/paper_files/paper/2018/file/d139db6a236200b21cc7f752979132d0-Paper.pdf
  • [7076153] Zhai, S., ZHANG, R., Nakkiran, P., Berthelot, D., Gu, J., Zheng, H., ... & Susskind, J. M. Normalizing Flows are Capable Generative Models. In Forty-second International Conference on Machine Learning. Link: https://openreview.net/pdf?id=2uheUFcFsM
• BLOCK 2:
  • [7062300] Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-Resolution Image Synthesis with Latent Diffusion Models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10684-10695). Link: https://openaccess.thecvf.com/content/CVPR2022/papers/Rombach_High-Resolution_Image_Synthesis_With_Latent_Diffusion_Models_CVPR_2022_paper.pdf
  • [7076165] Ho, J., & Salimans, T. Classifier-Free Diffusion Guidance. In NeurIPS 2021 Workshop on Deep Generative Models and Downstream Applications. Link: https://openreview.net/pdf?id=qw8AKxfYbI
  • [7026925] 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. Link: https://proceedings.neurips.cc/paper_files/paper/2016/file/7c9d0b1f96aebd7b5eca8c3edaa19ebb-Paper.pdf
  • [7074899] Casanova, A., Careil, M., Verbeek, J., Drozdzal, M., & Romero Soriano, A. (2021). Instance-Conditioned GAN. Advances in Neural Information Processing Systems, 34, 27517-27529. Link: https://proceedings.neurips.cc/paper_files/paper/2021/file/e7ac288b0f2d41445904d071ba37aaff-Paper.pdf
• BLOCK 3:
  • [7086510] Palumbo, E., Daunhawer, I., & Vogt, J. E. (2023). MMVAE+: Enhancing the Generative Quality of Multimodal VAEs without Compromises. In The Eleventh International Conference on Learning Representations. Link: https://openreview.net/pdf?id=sdQGxouELX
  • [7073034] Campbell, A., Yim, J., Barzilay, R., Rainforth, T., & Jaakkola, T. (2024, July). Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design. In International Conference on Machine Learning (pp. 5453-5512). PMLR. Link: https://proceedings.mlr.press/v235/campbell24a.html
  • [7057757] Kang, M., Zhu, J. Y., Zhang, R., Park, J., Shechtman, E., Paris, S., & Park, T. (2023). Scaling up GANs for Text-to-Image Synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10124-10134). Link: https://openaccess.thecvf.com/content/CVPR2023/papers/Kang_Scaling_Up_GANs_for_Text-to-Image_Synthesis_CVPR_2023_paper.pdf
  • [7081324] Chun, S., Kim, W., Park, S., & Yun, S. (2025) Probabilistic Language-Image Pre-Training. In The Thirteenth International Conference on Learning Representations. Link: https://openreview.net/pdf?id=D5X6nPGFUY

If you have any questions, don’t hesitate to reach out. 

Best regards,
Your seminar team