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Results: 58
Number of items: 58
  • Apostol, A. C., Stol, M. C., & Forré, P. (2022). Pruning by leveraging training dynamics. AI Communications, 35(2), 65-85. https://doi.org/10.3233/AIC-210127
  • 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
    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
    Bos, T. S., Boelrijk, J., Molenaar, S. R. A., Veer, B. V. ., Niezen, L. E., van Herwerden, D., Samanipour, S., Stoll, D. R., Forré, P., Ensing, B., Somsen, G. W., & Pirok, B. W. J. (2022). Chemometric Strategies for Fully Automated Interpretive Method Development in Liquid Chromatography. Analytical Chemistry, 94(46), 16060-16068. https://doi.org/10.1021/acs.analchem.2c03160
  • Open Access
    Lippert, F., Kranstauber, B., Forré, P. D., & van Loon, E. E. (2022). Learning to predict spatiotemporal movement dynamics from weather radar networks. Methods in Ecology and Evolution, 13(12), 2811-2826. https://doi.org/10.1111/2041-210X.14007
  • Open Access
    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
  • Open Access
    Federici, M., Forre, P., & Tomioka, R. (2022). An Information-theoretic Approach to Distribution Shifts. 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. 21, pp. 17628-17641). (Advances in Neural Information Processing Systems; Vol. 34). Neural Information Processing Systems Foundation. https://doi.org/10.48550/arXiv.2106.03783
  • 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
    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
    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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