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Results: 65
Number of items: 65
  • Open Access
    Ruhe, D., Brandstetter, J., & Forré, P. (2023). Clifford Group Equivariant Neural Networks. In A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, & S. Levine (Eds.), 37th Conference on Neural Information Processing Systems (NeurIPS 2023): 10-16 December 2023, New Orleans, Louisana, USA (pp. 62922-62990). (Advances in Neural Information Processing Systems; Vol. 36). Neural Information Processing Systems Foundation. https://doi.org/10.52202/075280-2748
  • Open Access
    Lippert, F., Kranstauber, B., van Loon, E. E., & Forré, P. (2023). Deep Gaussian Markov Random Fields for Graph-Structured Dynamical Systems. In A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, & S. Levine (Eds.), 37th Conference on Neural Information Processing Systems (NeurIPS 2023): 10-16 December 2023, New Orleans, Louisana, USA (pp. 76248-76271). (Advances in Neural Information Processing Systems; Vol. 36). Neural Information Processing Systems Foundation. https://doi.org/10.48550/arXiv.2306.08445, https://doi.org/10.52202/075280-3333
  • Open Access
    Boelrijk, J., van Herwerden, D., Ensing, B., Forré, P., & Samanipour, S. (2023). Predicting RP-LC retention indices of structurally unknown chemicals from mass spectrometry data. Journal of Cheminformatics, 15(1), Article 28. https://doi.org/10.1186/s13321-023-00699-8
  • Open Access
    Boelrijk, J., Ensing, B., Forré, P., & Pirok, B. W. J. (2023). Closed-loop automatic gradient design for liquid chromatography using Bayesian optimization. Analytica Chimica Acta, 1242, Article 340789. https://doi.org/10.1016/j.aca.2023.340789
  • Open Access
    Forré, P., Miller, B. K., & Weniger, C. (2023). Contrastive Neural Ratio Estimation. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (Eds.), 36th Conference on Neural Information Processing Systems (NeurIPS 2022): New Orleans, Louisiana, USA, 28 November-9 December 2022 (Vol. 5, pp. 3262-3278). (Advances in Neural Information Processing Systems; Vol. 35). Neural Information Processing Systems Foundation. https://doi.org/10.48550/arXiv.2210.06170
  • Lippert, F., Kranstauber, B., Forré, P., & van Loon, E. E. (2022). Data from: Learning to predict spatio-temporal movement dynamics from weather radar networks [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6874789
  • Lippert, F., Kranstauber, B., Forré, P., & van Loon, E. E. (2022). Data from: Learning to predict spatio-temporal movement dynamics from static sensor networks [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6364941
  • 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
    Cole, A., Forre, P., Louppe, G., Miller, B. K., & Weniger, C. (2022). Truncated Marginal Neural Ratio Estimation. 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. 1, pp. 129-143). (Advances in Neural Information Processing Systems; Vol. 34). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2021/hash/01632f7b7a127233fa1188bd6c2e42e1-Abstract.html
  • 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
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