From 7e4692bf7bd3df8d260f723da4669297e1893be9 Mon Sep 17 00:00:00 2001 From: Olivier Laurent Date: Fri, 27 Sep 2024 11:22:58 +0200 Subject: [PATCH] :shirt: References are now links to the papers --- index.html | 46 +++++++++++++++++++++++++++------------------- 1 file changed, 27 insertions(+), 19 deletions(-) diff --git a/index.html b/index.html index 677df25..c3ffacf 100755 --- a/index.html +++ b/index.html @@ -187,30 +187,38 @@

Relation to prior tutorials and short courses

Selected References

    -
  1. Immer, A., Palumbo, E., Marx, A., & Vogt, J. E. Effective Bayesian Heteroscedastic Regres- - sion with Deep Neural Networks. In NeurIPS, 2023.
    2. +
  2. Immer, A., Palumbo, E., Marx, A., & Vogt, J. E. E + Effective Bayesian Heteroscedastic Regres- + sion with Deep Neural Networks. In NeurIPS, 2023.
  3. Franchi, G., Bursuc, A., Aldea, E., Dubuisson, S., - & Bloch, I. Encoding the latent posterior of - Bayesian Neural Networks for uncertainty quantification. IEEE TPAMI, 2023.
    4. + & Bloch, I. Encoding the latent posterior of + Bayesian Neural Networks for uncertainty quantification. IEEE TPAMI, 2023.
  4. Franchi, G., Yu, X., Bursuc, A., Aldea, E., Dubuisson, - S., & Filliat, D. (2022, October). Latent Discriminant - deterministic Uncertainty. In ECCV 2022.
    5. + S., & Filliat, D. Latent Discriminant + deterministic Uncertainty. In ECCV 2022.
  5. Laurent, O., Lafage, A., Tartaglione, E., Daniel, G., Martinez, J. M., Bursuc, A., & Franchi, G. - Packed-Ensembles for Efficient Uncertainty Estimation. In ICLR 2023.
    6. -
  6. Izmailov, P., Vikram, S., Hoffman, M. D., & Wilson, A. G. What are Bayesian neural network - posteriors really like? In ICML, 2021.
    7. -
  7. Izmailov, P., Maddox, W. J., Kirichenko, P., Garipov, T., Vetrov, D., & Wilson, A. G. Sub- - space inference for Bayesian deep learning. In UAI, 2020.
    8. + Packed-Ensembles for Efficient Uncertainty Estimation. In + ICLR 2023. + +
  8. Izmailov, P., Vikram, S., Hoffman, M. D., & Wilson, A. G. What are Bayesian neural network + posteriors really like? In ICML, 2021.
    9. +
  9. Izmailov, P., Maddox, W. J., Kirichenko, P., Garipov, T., Vetrov, D., & Wilson, A. G. Subspace inference for Bayesian deep learning. In UAI, 2020. +
  10. Franchi, G., Bursuc, A., Aldea, E., Dubuisson, S., & - Bloch, I. (2020, August). TRADI: Tracking deep neural - network weight distributions. In ECCV 2020.
    11. -
  11. Wilson, A. G., & Izmailov, P. Bayesian deep learning and a probabilistic perspective of gen- - eralization. In NeurIPS, 2020.
    12. -
  12. Hendrycks, D., Dietterich, T. Benchmarking Neural Network Robustness to Common Corruptions and - Perturbations. In ICLR 2019.
    13. -
  13. Izmailov, P., Podoprikhin, D., Garipov, T., Vetrov, D., & Wilson, A. G. Averaging weights - leads to wider optima and better generalization. In UAI, 2018.
    14. + Bloch, I. TRADI: Tracking deep neural + network weight distributions. In ECCV 2020. +
  14. Wilson, A. G., & Izmailov, P. Bayesian deep + learning and a probabilistic perspective of generalization. In NeurIPS, 2020.
    15. +
  15. Hendrycks, D., Dietterich, T. Benchmarking Neural Network + Robustness to Common Corruptions and + Perturbations. In ICLR 2019.
    16. +
  16. Izmailov, P., Podoprikhin, D., Garipov, T., Vetrov, D., & Wilson, A. G. Averaging weights + leads to wider optima and better generalization. In UAI, 2018.
You will find more references in the Awesome Uncertainty in deep