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Results: 160
Number of items: 160
  • Open Access
    Akata, Z., Balliet, D., de Rijke, M., Dignum, F., Dignum, V., Eiben, G., Fokkens, A., Grossi, D., Hindriks, K., Hoos, H., Hung, H., Jonker, C., Monz, C., Neerincx, M., Oliehoek, F., Prakken, H., Schlobach, S., van der Gaag, L., van Harmelen, F., ... Welling, M. (2020). A Research Agenda for Hybrid Intelligence: Augmenting Human Intellect With Collaborative, Adaptive, Responsible, and Explainable Artificial Intelligence. Computer, 53(8), 18-28. https://doi.org/10.1109/MC.2020.2996587
  • Open Access
    O'Connor, P. (2020). Biologically plausible deep learning: Should airplanes flap their wings? [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Hoogeboom, E., Peters, J. W. T., van den Berg, R., & Welling, M. (2020). Integer Discrete Flows and Lossless Compression. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, & R. Garnett (Eds.), 32nd Conference on Neural Information Processing Systems (NeurIPS 2019): Vancouver, Canada, 8-14 December 2019 (Vol. 16, pp. 12114-12124). (Advances in Neural Information Processing Systems; Vol. 32). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2019/hash/9e9a30b74c49d07d8150c8c83b1ccf07-Abstract.html
  • Open Access
    Kipf, T. N. (2020). Deep learning with graph-structured representations. [Thesis, fully internal, Universiteit van Amsterdam].
  • 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
  • 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
  • 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
    Patrini, G., van den Berg, R., Forré, P., Carioni, M., Bhargav, S., Welling, M., Genewein, T., & Nielsen, F. (2019). Sinkhorn AutoEncoders. In A. Globerson, & R. Silva (Eds.), Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence: UAI 2019, Tel Aviv, Israel, July 22-25, 2019 Article 253 AUAI Press. https://arxiv.org/abs/1810.01118
  • 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
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