Search results
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
-
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 -
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
Page 3 of 6