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Results: 17
Number of items: 17
  • Open Access
    Cornet, F., Bartosh, G., Schmidt, M. N., & Naesseth, C. A. (2025). Equivariant Neural Diffusion for Molecule Generation. In A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomczak, & C. Zhang (Eds.), 38th Conference on Neural Information Processing Systems (NeurIPS 2024): 10-15 December 2024, Vancouver, Canada (pp. 49429-49460). (Advances in Neural Information Processing Systems; Vol. 37). Neural Information Processing Systems Foundation. https://doi.org/10.52202/079017-1564
  • Open Access
    Bartosh, G., Vetrov, D., & Naesseth, C. A. (2024). Neural Diffusion Models. Proceedings of Machine Learning Research, 235, 3073-3095. https://proceedings.mlr.press/v235/bartosh24a.html
  • Open Access
    Yang, H., Moretti, A. K., Macaluso, S., Chlenski, P., Naesseth, C. A., & Pe’er, I. (2024). Variational Pseudo Marginal Methods for Jet Reconstruction in Particle Physics. Transactions on Machine Learning Research, 2024, Article 2926. https://doi.org/10.48550/arXiv.2406.03242
  • Open Access
    Zhang, L., Blei, D., & Naesseth, C. A. (2023). Transport Score Climbing: Variational Inference Using Forward KL and Adaptive Neural Transport. Transactions on Machine Learning Research, 2023, Article 1118. https://openreview.net/forum?id=7KW7zvKd7J
  • Open Access
    Wu, L., Trippe, B. L., Naesseth, C. A., Blei, D. M., & Cunningham, J. P. (2023). Practical and Asymptotically Exact Conditional Sampling in Diffusion Models. 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 (pp. 31372-31403). (Advances in Neural Information Processing Systems; Vol. 36). Neural Information Processing Systems Foundation. https://doi.org/10.48550/arXiv.2306.17775, https://doi.org/10.52202/075280-1363
  • Open Access
    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
  • Open Access
    Pandeva, T., Bakker, T., Naesseth, C. A., & Forré, P. (2022). E-Valuating Classifier Two-Sample Tests. ArXiv. https://doi.org/10.48550/arXiv.2210.13027
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