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Results: 63
Number of items: 63
  • 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
  • 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
    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
    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
    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
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