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Results: 3
Number of items: 3
  • Open Access
    Jazbec, M., Allingham, J. U., Zhang, D., & Nalisnick, E. (2023). Towards Anytime Classification in Early-Exit Architectures by Enforcing Conditional Monotonicity. 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://papers.nips.cc/paper_files/paper/2023/hash/af2d9fb5bcee19ef2dfa70d843520c97-Abstract-Conference.html
  • Open Access
    Antorán, J., Janz, D., Allingham, J. U., Daxberger, E., Barbano, R., Nalisnick, E., & Hernández-Lobato, J. M. (2022). Adapting the Linearised Laplace Model Evidence for Modern Deep Learning. Proceedings of Machine Learning Research, 162, 796-821. https://doi.org/10.48550/arXiv.2206.08900
  • Open Access
    Daxberger, E., Nalisnick, E., Allingham, J. U., Antorán, J., & Hernández-Lobato, J. M. (2021). Bayesian Deep Learning via Subnetwork Inference. Proceedings of Machine Learning Research, 139, 2510-2521. https://proceedings.mlr.press/v139/daxberger21a.html
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