Search results
Results: 63
Number of items: 63
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Ruhe, D., Kuiack, M., Rowlinson, A., Wijers, R., & Forré, P. (2022). Detecting dispersed radio transients in real time using convolutional neural networks. Astronomy and Computing, 38, Article 100512. https://doi.org/10.1016/j.ascom.2021.100512 -
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
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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
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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
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Forré, P. (2021). Quasi-Measurable Spaces. (v1 ed.) ArXiv. https://doi.org/10.48550/arXiv.2109.11631
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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
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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
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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
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Forré, P. (2021). Transitional Conditional Independence. ( v1 ed.) ArXiv. https://doi.org/10.48550/arXiv.2104.11547
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Ruhe, D., & Forré, P. (2021). Self-Supervised Inference in State-Space Models. (v1 ed.) ArXiv. https://doi.org/10.48550/arXiv.2107.13349
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