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Results: 56
Number of items: 56
  • Open Access
    Shang, W., van der Wal, D., van Hoof, H., & Welling, M. (2020). Stochastic Activation Actor Critic Methods. In U. Brefeld, E. Fromont, A. Hotho, A. Knobbe, M. Maathuis, & C. Robardet (Eds.), Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019 : proceedings (Vol. III, pp. 103-117). (Lecture Notes in Computer Science; Vol. 11908), (Lecture Notes in Artificial Intelligence). Springer. https://doi.org/10.1007/978-3-030-46133-1_7
  • 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
    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
    van der Heiden, T., Mirus, F., & van Hoof, H. (2020). Social Navigation with Human Empowerment Driven Deep Reinforcement Learning. In I. Farkaš, P. Masulli, & S. Wermter (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2020: 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020 : proceedings (Vol. II, pp. 395-407). (Lecture Notes in Computer Science; Vol. 12397). Springer. https://doi.org/10.1007/978-3-030-61616-8_32
  • Open Access
    Wöhlke, J., Schmitt, F., & van Hoof, H. (2020). A Performance-Based Start State Curriculum Framework for Reinforcement Learning. In AAMAS'20: proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems : May 9-13, 2020, Auckland, New Zealand (pp. 1503-1511). International Foundation for Autonomous Agents and Multiagent Systems. https://dl.acm.org/doi/10.5555/3398761.3398934
  • Open Access
    Wang, Q., & van Hoof, H. (2020). Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variables. Proceedings of Machine Learning Research, 119, 10018-10028. http://proceedings.mlr.press/v119/wang20s.html
  • Open Access
    Manjanna, S., Van Hoof, H., & Dudek, G. (2020). Policy Search on Aggregated State Space for Active Sampling. In J. Xiao, T. Kröger, & O. Khatib (Eds.), Proceedings of the 2018 International Symposium on Experimental Robotics (pp. 211-221). (Springer Proceedings in Advanced Robotics; Vol. 11). Springer. https://doi.org/10.1007/978-3-030-33950-0_19
  • 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
  • Open Access
    Thakur, S., van Hoof, H., Gamboa Higuera, J. C., Precup, D., & Meger, D. (2019). Uncertainty Aware Learning from Demonstrations in Multiple Contexts using Bayesian Neural Networks. In 2019 International Conference on Robotics and Automation (ICRA) : Montreal, Quebec, Canada, 20-24 May 2019 (Vol. 1, pp. 768-774). IEEE. https://doi.org/10.1109/ICRA.2019.8794328
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