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Results: 6
Number of items: 6
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Eijkelboom, F., Zimmermann, H., Vadgama, S., Bekkers, E. J., Welling, M., Naesseth, C. A., & van de Meent, J.-W. (2025). Controlled Generation with Equivariant Variational Flow Matching. Proceedings of Machine Learning Research, 267, 15066-15078. https://proceedings.mlr.press/v267/eijkelboom25a.html -
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, 2023, Article 612. https://openreview.net/forum?id=AZ4GobeSLq
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Esmaeili, B., Walters, R., Zimmermann, H., & van de Meent, J.-W. (2023). Topological Obstructions and How to Avoid Them. In A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, & S. Levine (Eds.), 37th Conference on Neural Information Processing Systems (NeurIPS 2023): 10-16 December 2023, New Orleans, Louisana, USA (Advances in Neural Information Processing Systems; Vol. 36). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper_files/paper/2023/hash/1c12ccfc7720f6b680edea17300bfc2b-Abstract-Conference.html
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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
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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
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