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Results: 62
Number of items: 62
  • Open Access
    Ilse, M., Tomczak, J. M., & Forré, P. (2020). Selecting Data Augmentation for Simulating Interventions. (v4 ed.) ArXiv. https://doi.org/10.48550/arXiv.2005.01856
  • Open Access
    Ullrich, K. (2020). A coding perspective on deep latent variable models. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Verdenius, S., Stol, M., & Forré, P. (2020). Pruning via Iterative Ranking of Sensitivity Statistics. (v2 ed.) ArXiv. https://doi.org/10.48550/arXiv.2006.00896
  • Open Access
    Apostol, A. C., Stol, M. C., & Forré, P. (2020). FlipOut: Uncovering Redundant Weights via Sign Flipping. In L. Cao, W. Kosters, & J. Lijffijt (Eds.), BNAIC/BeNeLearn 2020: proceedings : Leiden, the Netherlands, November 19-20, 2020 (pp. 15-29). Universiteit Leiden. http://bnaic.liacs.leidenuniv.nl/wordpress/wp-content/uploads/papers/BNAICBENELEARN_2020_Final_paper_25.pdf
  • Open Access
    Helwegen, R., Louizos, C., & Forré, P. (2020). Improving Fair Predictions Using Variational Inference In Causal Models. (v1 ed.) ArXiv. https://doi.org/10.48550/arXiv.2008.10880
  • Open Access
    Bongers, S., Forré, P., Peters, J., Schölkopf, B., & Mooij, J. M. (2020). Foundations of Structural Causal Models with Cycles and Latent Variables. (v4 ed.) ArXiv. https://doi.org/10.48550/arXiv.1611.06221
  • Open Access
    Forré, P., & Mooij, J. M. (2019). Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias. 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 15 AUAI Press. http://auai.org/uai2019/proceedings/papers/15.pdf
  • Open Access
    Falorsi, L., de Haan, P., Davidson, T. R., & Forré, P. (2019). Reparameterizing Distributions on Lie Groups. Proceedings of Machine Learning Research, 89, 3244-3253. https://arxiv.org/abs/1903.02958
  • 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
    Falorsi, L., de Haan, P., Davidson, T. R., De Cao, N., Weiler, M., Forré, P., & Cohen, T. S. (2018). Explorations in Homeomorphic Variational Auto-Encoding. Paper presented at ICML18 Workshop on Theoretical Foundations and Applications
    of Deep Generative Models, Stockholm, Sweden. https://arxiv.org/abs/1807.04689
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