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Esmaeili, B., Walters, R., Zimmermann, H., & van de Meent, J. W. (2023). Topological Obstructions and How to Avoid Them. In Conference on Neural Information Processing Systems Neural Information Processing Systems Foundation.
Zimmermann, H., Lindsten, F., van de Meent, J.-W., & Naesseth, C. A. (2023). A Variational Perspective on Generative Flow Networks. Transactions on Machine Learning Research, (4), Article 612. https://openreview.net/forum?id=AZ4GobeSLq[details]
Esmaeili, B., Wu, H., Zimmermann, H., & Van De Meent, J-W. (2022). Nested Variational Inference. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P. S. Liang, & J. Wortman Vaughan (Eds.), 35th Conference on Neural Information Processing Systems (NeurIPS 2021) : online, 6-14 December 2021 (Vol. 25, pp. 20423-20435). (Advances in Neural Information Processing Systems; Vol. 34). Neural Information Processing Systems Foundation. https://proceedings.neurips.cc/paper_files/paper/2021/hash/ab49b208848abe14418090d95df0d590-Abstract.html[details]
Smedemark-Margulies, N., Walters, R., Zimmermann, H., Laird, L., van der Loo, C., Kaushik, N., Caceres, R., & van de Meent, J. W. (2022). Probabilistic program inference in network-based epidemiological simulations. PLoS Computational Biology, 18(11), Article e1010591. https://doi.org/10.1371/journal.pcbi.1010591[details]
Esmaeili, B., Walters, R., Zimmermann, H., & van de Meent, J. W. (2023). Topological Obstructions and How to Avoid Them. Paper presented at 37th Conference on Neural Information Processing Systems (NeurIPS 2023), New Orleans, Louisiana, United States. https://openreview.net/attachment?id=1tviRBNxI9&name=pdf
2024
Esmaeili, B. (2024). Learning useful representations with variational autoencoders. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
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