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Results: 160
Number of items: 160
  • Open Access
    Bakker, T. B. (2024). Learning adaptive sensing and active learning. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Igashov, I., Stärk, H., Vignac, C., Schneuing, A., Satorras, V. G., Frossard, P., Welling, M., Bronstein, M., & Correia, B. (2024). Equivariant 3D-conditional diffusion model for molecular linker design. Nature Machine Intelligence, 6(4), 417–427. https://doi.org/10.1038/s42256-024-00815-9
  • Open Access
    Weiler, M. (2024). Equivariant and coordinate independent convolutional networks: A gauge field theory of neural networks. [Thesis, fully internal, Universiteit van Amsterdam].
  • van den Dool, W., Blankevoort, T., Welling, M., & Asano, Y. M. (2023). Efficient Neural PDE-Solvers using Quantization Aware Training. In 2023 IEEE/CVF International Conference on Computer Vision Workshops: proceedings: ICCVW 2023 : Paris, France, 2-6 October 2023 (pp. 1415-1424). IEEE Computer Society. https://doi.org/10.1109/ICCVW60793.2023.00154
  • Open Access
    van der Pol, E. (2023). Symmetry and structure in deep reinforcement learning. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Löwe, S., Lippe, P., Locatello, F., & Welling, M. (2023). Rotating Features for Object Discovery. 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. 59606-59635). (Advances in Neural Information Processing Systems; Vol. 36). Neural Information Processing Systems Foundation. https://doi.org/10.48550/arXiv.2306.00600, https://doi.org/10.52202/075280-2605
  • Open Access
    Bakker, T., van Hoof, H., & Welling, M. (2023). Learning Objective-Specific Active Learning Strategies with Attentive Neural Processes. In D. Koutra, C. Plant, M. Gomez Rodriguez, E. Baralis, & F. Bonchi (Eds.), Machine Learning and Knowledge Discovery in Databases : Research Track : European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023 : proceedings (Vol. I, pp. 3-19). (Lecture Notes in Computer Science; Vol. 14169), (Lecture Notes in Artificial Intelligence). Springer. https://doi.org/10.48550/arXiv.2309.05477, https://doi.org/10.1007/978-3-031-43412-9_1
  • 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
  • Open Access
    Keller, T. A. (2023). Natural inductive biases for artificial intelligence. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Hoogeboom, E. (2023). Normalizing flows and diffusion models for discrete and geometric data. [Thesis, fully internal, Universiteit van Amsterdam].
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