@@ -170,21 +167,21 @@

Relation to prior tutorials and short courses

Selected References

    -
  1. Franchi, G., Bursuc, A., Aldea, E., Dubuisson, S., +
  2. Franchi, G., Bursuc, A., Aldea, E., Dubuisson, S., & Bloch, I. (2020). Encoding the latent posterior of Bayesian Neural Networks for uncertainty quantifica- tion. arXiv preprint arXiv:2012.02818.
  3. -
  4. Franchi, G., Bursuc, A., Aldea, E., Dubuisson, S., & +
  5. Franchi, G., Bursuc, A., Aldea, E., Dubuisson, S., & Bloch, I. (2020). One versus all for deep neural network incertitude (OVNNI) quantification. IEEE Access
  6. -
  7. Franchi, G., Bursuc, A., Aldea, E., Dubuisson, S., & +
  8. Franchi, G., Bursuc, A., Aldea, E., Dubuisson, S., & Bloch, I. (2020, August). TRADI: Tracking deep neural network weight distributions. ECCV 2020
  9. -
  10. Franchi, G., Yu, X., Bursuc, A., Aldea, E., Dubuisson, +
  11. Franchi, G., Yu, X., Bursuc, A., Aldea, E., Dubuisson, S., & Filliat, D. (2022, October). Latent Discriminant deterministic Uncertainty. ECCV 2022
  12. Laurent, O., Lafage, A., Tartaglione, E., Daniel, G., - Martinez, J. M., Bursuc, A., & Franchi, G. + Martinez, J. M., Bursuc, A., & Franchi, G. Packed-Ensembles for Efficient Uncertainty Estimation. ICLR 2023
  13. Yu, X., Franchi, G., & Aldea, E. (2022, October). On Monocular Depth Estimation and Uncertainty Quantifi- @@ -201,304 +198,6 @@

    Selected References