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Results: 157
Number of items: 157
  • 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
    Ilse, M., Forré, P., Welling, M., & Mooij, J. M. (2022). Combining Observational and Interventional Data through Causal ductions. (v2 ed.) ArXiv. https://doi.org/10.48550/arXiv.2103.04786
  • 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
    Hoogeboom, E., Garcia Satorras, V., Tomczak, J., & Welling, M. (2021). The Convolution Exponential and Generalized Sylvester Flows. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), 34th Concerence on Neural Information Processing Systems (NeurIPS 2020): online, 6-12 December 2020 (Vol. 22, pp. 18249-18248). (Advances in Neural Information Processing Systems; Vol. 33). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2020/hash/d3f06eef2ffac7faadbe3055a70682ac-Abstract.html
  • Open Access
    De Haan, P., Cohen, T. S., & Welling, M. (2021). Natural Graph Networks. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), 34th Concerence on Neural Information Processing Systems (NeurIPS 2020): online, 6-12 December 2020 (Vol. 5, pp. 3636-3646). (Advances in Neural Information Processing Systems; Vol. 33). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2020/hash/2517756c5a9be6ac007fe9bb7fb92611-Abstract.html
  • Open Access
    Van Der Pol, E., Worrall, D., Van Hoof, H., Oliehoek, F., & Welling, M. (2021). MDP homomorphic networks: Group symmetries in reinforcement learning. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), 34th Concerence on Neural Information Processing Systems (NeurIPS 2020): online, 6-12 December 2020 (Vol. 6, pp. 4199-4210). (Advances in Neural Information Processing Systems; Vol. 33). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2020/hash/2be5f9c2e3620eb73c2972d7552b6cb5-Abstract.html
  • Open Access
    Shang, W. (2021). Crafting deep learning models for reinforcement learning and computer vision applications. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Hu, S., Fridgeirsson, E. A., van Wingen, G., & Welling, M. (2021). Transformer-Based Deep Survival Analysis. Proceedings of Machine Learning Research, 146, 132-148. https://proceedings.mlr.press/v146/hu21a.html
  • Open Access
    Keller, T. A., Peters, J. W. T., Jaini, P., Hoogeboom, E., Forré, P., & Welling, M. (2021). Self Normalizing Flows. Proceedings of Machine Learning Research, 139, 5378-5387. https://arxiv.org/abs/2011.07248
  • Open Access
    Nielsen, D., Jaini, P., Hoogeboom, E., Winther, O., & Welling, M. (2021). SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), 34th Concerence on Neural Information Processing Systems (NeurIPS 2020): online, 6-12 December 2020 (Vol. 16, pp. 12685-12696). (Advances in Neural Information Processing Systems; Vol. 33). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2020/hash/9578a63fbe545bd82cc5bbe749636af1-Abstract.html
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