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Results: 10
Number of items: 10
  • van der Pol, E., Kipf, T., Oliehoek, F. A., & Welling, M. (2020). Plannable Approximations to MDP Homomorphisms: Equivariance under Actions. In AAMAS'20: proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems : May 9-13, 2020, Auckland, New Zealand (pp. 1431–1439). International Foundation for Autonomous Agents and Multiagent Systems. https://dl.acm.org/doi/10.5555/3398761.3398926
  • Open Access
    Kipf, T. N. (2020). Deep learning with graph-structured representations. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Kipf, T., Li, Y., Dai, H., Zambaldi, V., Sanchez-Gonzalez, A., Grefenstette, E., Kohli, P., & Battaglia, P. (2019). CompILE: Compositional Imitation Learning and Execution. Proceedings of Machine Learning Research, 97, 3418-3428. http://proceedings.mlr.press/v97/kipf19a.html
  • 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
    Schlichtkrull, M., Kipf, T. N., Bloem, P., van den Berg, R., Titov, I., & Welling, M. (2018). Modeling Relational Data with Graph Convolutional Networks. In A. Gangemi, R. Navigli, M.-E. Vidal, P. Hitzler, R. Troncy, L. Hollink, A. Tordai, & M. Alam (Eds.), The Semantic Web: 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3–7, 2018 : proceedings (pp. 593-607). (Lecture Notes in Computer Science; Vol. 10843). Springer. https://doi.org/10.1007/978-3-319-93417-4_38
  • Open Access
    Davidson, T. R., Falorsi, L., De Cao, N., Kipf, T., & Tomczak, J. M. (2018). Hyperspherical Variational Auto-Encoders. In A. Globerson, & R. Silva (Eds.), Uncertainty in Artificial Intelligence: proceedings of the Thirty-Fourth Concerence (2018) : August 6-10, 2018, Monterey, California, USA (pp. 856-865). AUAI Press. http://auai.org/uai2018/proceedings/papers/309.pdf
  • Open Access
    Kipf, T., Fetaya, E., Wang, K.-C., Welling, M., & Zemel, R. (2018). Neural Relational Inference for Interacting Systems. Proceedings of Machine Learning Research, 80, 2688-2697. http://proceedings.mlr.press/v80/kipf18a.html
  • Open Access
    De Cao, N., & Kipf, T. (2018). MolGAN: An implicit generative model for small molecular graphs. Paper presented at ICML18 Workshop on Theoretical Foundations and Applications
    of Deep Generative Models, Stockholm, Sweden. https://arxiv.org/abs/1805.11973
  • Open Access
    van den Berg, R., Kipf, T. N., & Welling, M. (2017). Graph Convolutional Matrix Completion. ArXiv. https://arxiv.org/abs/1706.02263
  • Open Access
    Kipf, T. N., & Welling, M. (2016). Variational Graph Auto-Encoders. Paper presented at Bayesian Deep Learning Workshop NIPS 2016, Barcelona, Spain. https://doi.org/10.48550/arXiv.1611.07308
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