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Results: 65
Number of items: 65
  • Open Access
    Lippert, F., Kranstauber, B., van Loon, E. E., & Forré, P. (2022). Physics-informed inference of aerial animal movements from weather radar data. Paper presented at Workshop AI for Science: Progress and Promises, New Orleans, Louisiana, United States. https://doi.org/10.48550/arXiv.2211.04539
  • Open Access
    Maile, K., Wilson, D. G., & Forré, P. (2022). Towards architectural optimization of equivariant neural networks over subgroups. Paper presented at NeurIPS 2022 Workshop: NeurReps, New Orleans, Louisiana, United States. https://openreview.net/forum?id=KJFpArxWe-g
  • Open Access
    Maile, K., Wilson, D. G., & Forré, P. (2022). Architectural Optimization over Subgroups for Equivariant Neural Networks. (v1 ed.) ArXiv. https://doi.org/10.48550/arXiv.2210.05484
  • Open Access
    Ruhe, D., Wong, K., Cranmer, M., & Forré, P. (2022). Normalizing Flows for Hierarchical Bayesian Analysis: A Gravitational Wave Population Study. In Machine Learning and the Physical Sciences: Workshop at the 36th conference on Neural Information Processing Systems (NeurIPS) : December 3, 2022 ML4PS. https://doi.org/10.48550/arXiv.2211.09008
  • Open Access
    Lang, L., Baudot, P., Quax, R., & Forré, P. (2022). Information Decomposition Diagrams Applied beyond Shannon Entropy: A Generalization of Hu's Theorem. (v1 ed.) ArXiv. https://doi.org/https://arxiv.org/abs/2202.09393v1
  • Open Access
    Pandeva, T., & Forré, P. (2022). Multi-View Independent Component Analysis with Shared and Individual Sources. (v1 ed.) ArXiv. https://doi.org/https://arxiv.org/abs/2210.02083v1
  • Open Access
    Boelrijk, J., Ensing, B., & Forré, P. (2022). Multi-Objective Optimization via Equivariant Deep Hypervolume Approximation. (v1 ed.) ArXiv. https://doi.org/https://arxiv.org/abs/2210.02177v1
  • Open Access
    Pandeva, T., Bakker, T., Naesseth, C. A., & Forré, P. (2022). E-Valuating Classifier Two-Sample Tests. ArXiv. https://doi.org/10.48550/arXiv.2210.13027
  • Open Access
    Ilse, M., Forré, P., Welling, M., & Mooij, J. M. (2022). Combining Observational and Interventional Data through Causal ductions. (v2 ed.) ArXiv. https://doi.org/10.48550/arXiv.2103.04786
  • Open Access
    Forre, P., Hoogeboom, E., Jaini, P., Nielsen, D., & Welling, M. (2022). Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P. S. Liang, & J. Wortman Vaughan (Eds.), 35th Conference on Neural Information Processing Systems (NeurIPS 2021) : online, 6-14 December 2021 (Vol. 15, pp. 12454-12465). (Advances in Neural Information Processing Systems; Vol. 34). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2021/hash/67d96d458abdef21792e6d8e590244e7-Abstract.html
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