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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 (Advances in Neural Information Processing Systems; Vol. 36). Neural Information Processing Systems Foundation. https://doi.org/10.48550/arXiv.2306.08445
  • 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
    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
    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
    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
    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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