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Results: 6
Number of items: 6
  • Huijben, I. A. M., Kool, W., Paulus, M. B., & van Sloun, R. J. G. (2023). A review of the Gumbel-max trick and its extensions for discrete stochasticity in machine learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(2), 1353-1371. https://doi.org/10.1109/TPAMI.2022.3157042
  • 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
    Kool, W., van Hoof, H., Gromicho, J., & Welling, M. (2022). Deep Policy Dynamic Programming for Vehicle Routing Problems. In P. Schaus (Ed.), Integration of Constraint Programming, Artificial Intelligence, and Operations Research: 19th International Conference, CPAIOR 2022, Los Angeles, CA, USA, June 20-23, 2022 : proceedings (pp. 190–213). (Lecture Notes in Computer Science; Vol. 13292). Springer. https://doi.org/10.48550/arXiv.2102.11756, https://doi.org/10.1007/978-3-031-08011-1_14
  • Open Access
    Kool, W., van Hoof, H., & Welling, M. (2020). Ancestral Gumbel-Top-k Sampling for Sampling Without Replacement. Journal of Machine Learning Research, 21, Article 47. https://jmlr.csail.mit.edu/papers/v21/19-985.html
  • Open Access
    Kool, W., van Hoof, H., & Welling, M. (2019). Stochastic Beams and Where To Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement. Proceedings of Machine Learning Research, 97, 3499-3508. http://proceedings.mlr.press/v97/kool19a.html
  • Open Access
    Kool, W., van Hoof, H., & Welling, M. (2019). Attention, learn to solve routing problems! In ICLR 2019: International Conference on Learning Representations : New Orleans, Louisiana, United States, May 6-May 9, 2019 OpenReview. https://arxiv.org/abs/1803.08475
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