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Results: 62
Number of items: 62
  • Miller, B. K., Cole, A., Forré, P., Louppe, G., & Weniger, C. (2021). Truncated Marginal Neural Ratio Estimation - Data [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5592427
  • Open Access
    Keller, T. A., Peters, J. W. T., Jaini, P., Hoogeboom, E., Forré, P., & Welling, M. (2021). Self Normalizing Flows. Proceedings of Machine Learning Research, 139, 5378-5387. https://arxiv.org/abs/2011.07248
  • Open Access
    Ilse, M., Tomczak, J. M., & Forré, P. (2021). Selecting Data Augmentation for Simulating Interventions. Proceedings of Machine Learning Research, 139, 4555-4562. https://proceedings.mlr.press/v139/ilse21a.html
  • Open Access
    Boelrijk, J., Pirok, B., Ensing, B., & Forré, P. (2021). Bayesian optimization of comprehensive two-dimensional liquid chromatography separations. Journal of Chromatography A, 1659, Article 462628. https://doi.org/10.1016/j.chroma.2021.462628
  • Open Access
    Forré, P. (2021). Quasi-Measurable Spaces. (v1 ed.) ArXiv. https://doi.org/10.48550/arXiv.2109.11631
  • Open Access
    Ruhe, D., & Forré, P. (2021). Self-Supervised Inference in State-Space Models. (v1 ed.) ArXiv. https://doi.org/10.48550/arXiv.2107.13349
  • Open Access
    Forré, P. (2021). Transitional Conditional Independence. ( v1 ed.) ArXiv. https://doi.org/10.48550/arXiv.2104.11547
  • Open Access
    Bongers, S., Forré, P., Peters, J., & Mooij, J. M. (2021). Foundations of structural causal models with cycles and latent variables. The Annals of Statistics, 49(5), 2885-2915. https://doi.org/10.1214/21-AOS2064
  • Open Access
    Weiler, M., Forré, P., Verlinde, E., & Welling, M. (2021). Coordinate Independent Convolutional Networks: Isometry and Gauge Equivariant Convolutions on Riemannian Manifolds. (v1 ed.) ArXiv. https://doi.org/10.48550/arXiv.2106.06020
  • Open Access
    Apostol, A. C., Stol, M. C., & Forré, P. (2021). FlipOut: Uncovering Redundant Weights via Sign Flipping. In M. Baratchi, L. Cao, W. A. Kosters, J. Lijffijt, J. N. van Rijn, & F. W. Takes (Eds.), Artificial Intelligence and Machine Learning: 32nd Benelux Conference, BNAIC/Benelearn 2020, Leiden, The Netherlands, November 19–20, 2020 : revised selected papers (pp. 15-29). (Communications in Computer and Information Science; Vol. 1398). Springer. https://doi.org/10.1007/978-3-030-76640-5_2
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