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Results: 5
Number of items: 5
  • Open Access
    Federici, M. (2025). Information theory for representation learning. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Arts, M., García Satorras, V., Huang, C.-W., Zügner, D., Federici, M., Clementi, C., Noé, F., Pinsler, R., & van den Berg, R. (2023). Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics. Journal of Chemical Theory and Computation, 19(18), 6151-6159. https://doi.org/10.1021/acs.jctc.3c00702
  • Open Access
    Alaniz, S., Federici, M., & Akata, Z. (2022). Compositional Mixture Representations for Vision and Text. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops: Proceedings : New Orleans, Louisiana, 19-24 June 2022 (pp. 4201-4210). (CVPRW). IEEE Computer Society. https://doi.org/10.48550/arXiv.2206.06404, https://doi.org/10.1109/CVPRW56347.2022.00465
  • 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
    Federici, M., Ullrich, K., & Welling, M. (2017). Improved Bayesian Compression. Paper presented at Bayesian Deep Learning Workshop NIPS 2017, Long Beach, United States. http://bayesiandeeplearning.org/2017/papers/16.pdf
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