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Results: 157
Number of items: 157
  • Open Access
    Bondesan, R., Gavves, E., Oh, C., & Welling, M. (2023). Batch Bayesian Optimization on Permutations using Acquisition Weighted Kernels. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (Eds.), 36th Conference on Neural Information Processing Systems (NeurIPS 2022): New Orleans, Louisiana, USA, 28 November-9 December 2022 (Vol. 10, pp. 6843-6858). (Advances in Neural Information Processing Systems; Vol. 35). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper_files/paper/2022/hash/2d779258dd899505b56f237de66ae470-Abstract-Conference.html
  • Kadambi, S., Behboodi, A., Soriaga, J. B., Welling, M., Amiri, R., Yerramalli, S., & Yoo, T. (2022). Neural RF SLAM for unsupervised positioning and mapping with channel state information. In ICC 2022 - IEEE International Conference on Communications: Seoul, South Korea, 16-20 May 2022 (pp. 3238-3244). IEEE. https://doi.org/10.1109/ICC45855.2022.9838367
  • Open Access
    Kool, W. (2022). Learning and optimization in combinatorial spaces: With a focus on deep learning for vehicle routing. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Ilse, M. (2022). Invariance in deep representations. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Keller, T. A., & Welling, M. (2022). Topographic VAEs learn Equivariant Capsules. 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. 34, pp. 28585-28597). (Advances in Neural Information Processing Systems; Vol. 34). Neural Information Processing Systems Foundation. https://doi.org/10.48550/arXiv.2109.01394
  • Open Access
    Wang, Q. (2022). Functional representation learning for uncertainty quantification and fast skill transfer. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Bongers, S. R. (2022). Causal modeling & dynamical systems: A new perspective on feedback. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Louizos, C. (2022). Probabilistic reasoning for uncertainty & compression in deep learning. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Löwe, S., Lippe, P., Rudolph, M., & Welling, M. (2022). Complex-Valued Autoencoders for Object Discovery. Transactions on Machine Learning Research, 2022, Article 428. https://openreview.net/forum?id=1PfcmFTXoa
  • Open Access
    Forre, P., Hoogeboom, E., Jaini, P., Nielsen, D., & Welling, M. (2022). Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions. 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. 15, pp. 12454-12465). (Advances in Neural Information Processing Systems; Vol. 34). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2021/hash/67d96d458abdef21792e6d8e590244e7-Abstract.html
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