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Results: 156
Number of items: 156
  • Bertone, G., Deisenroth, M. P., Kim, J. S., Liem, S., Ruiz de Austri, R., & Welling, M. (2019). Accelerating the BSM interpretation of LHC data with machine learning. Physics of the Dark Universe, 24, Article 100293. https://doi.org/10.1016/j.dark.2019.100293
  • Hu, S., Worrall, D., Knegt, S., Veeling, B., Huisman, H., & Welling, M. (2019). Supervised Uncertainty Quantification for Segmentation with Multiple Annotations. In D. Shen, T. Liu, T. M. Peters, L. H. Staib, C. Essert, S. Zhou, P.-T. Yap, & A. Khan (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019 : proceedings (Vol. 2, pp. 137-145). (Lecture Notes in Computer Science; Vol. 11765). Springer. https://doi.org/10.1007/978-3-030-32245-8_16
  • Open Access
    O'Connor, P., Gavves, E., & Welling, M. (2019). Initialized Equilibrium Propagation for Backprop-Free Training. In ICLR 2019: International Conference on Learning Representations : New Orleans, Louisiana, United States, May 6-May 9, 2019 OpenReview. https://openreview.net/forum?id=B1GMDsR5tm
  • Open Access
    Kipf, T., van der Pol, E., & Welling, M. (2019). Contrastive Learning of Structured World Models. (v1 ed.) University of Amsterdam. https://arxiv.org/abs/1911.12247v1
  • Open Access
    Shiarlis, K. C. (2019). Detaching the strings: Practical algorithms for Learning from Demonstration. [Thesis, fully internal, Universiteit van Amsterdam].
  • 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
  • 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
    Weiler, M., Boomsma, W., Geiger, M., Welling, M., & Cohen, T. (2019). 3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, & R. Garnett (Eds.), 32nd Conference on Neural Information Processing Systems 2018 : Montreal, Canada, 3-8 December 2018 (Vol. 15, pp. 10381-10392). (Advances in Neural Information Processing Systems; Vol. 31). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2018/hash/488e4104520c6aab692863cc1dba45af-Abstract.html
  • Open Access
    O'Connor, P., Gavves, E., & Welling, M. (2019). Training a Spiking Neural Network with Equilibrium Propagation. Proceedings of Machine Learning Research, 89, 1516-1523. http://proceedings.mlr.press/v89/o-connor19a.html
  • Open Access
    Hoogeboom, E., van den Berg, R., & Welling, M. (2019). Emerging Convolutions for Generative Normalizing Flows. Proceedings of Machine Learning Research, 97, 2771-2780. http://proceedings.mlr.press/v97/hoogeboom19a.html
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