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@@ -128,17 +128,17 @@ <h2 style="text-align: center">Selected References</h2>
               Bayesian Neural Networks for uncertainty quantification. IEEE TPAMI, 2023.</li>
             <li><b>Franchi, G.</b>, Yu, X., <b>Bursuc, A.</b>, Aldea, E., Dubuisson,
               S., & Filliat, D. (2022, October). Latent Discriminant
-              deterministic Uncertainty. ECCV 2022</li>
+              deterministic Uncertainty. In ECCV 2022.</li>
             <li><b>Laurent, O.</b>, Lafage, A., Tartaglione, E., Daniel, G.,
               Martinez, J. M., <b>Bursuc, A., & Franchi, G.</b>
-              Packed-Ensembles for Efficient Uncertainty Estimation. In ICLR 2023</li>
-            <li><b></b>Izmailov, P.</b>, Vikram, S., Hoffman, M. D., & Wilson, A. G. What are Bayesian neural network
+              Packed-Ensembles for Efficient Uncertainty Estimation. In ICLR 2023.</li>
+            <li><b>Izmailov, P.</b>, Vikram, S., Hoffman, M. D., & Wilson, A. G. What are Bayesian neural network
               posteriors really like? In ICML, 2021.</li>
             <li><b>Izmailov, P.</b>, Maddox, W. J., Kirichenko, P., Garipov, T., Vetrov, D., & Wilson, A. G. Sub-
               space inference for Bayesian deep learning. In UAI, 2020.</li>
             <li><b>Franchi, G., Bursuc, A.</b>, Aldea, E., Dubuisson, S., &
               Bloch, I. (2020, August). TRADI: Tracking deep neural
-              network weight distributions. In ECCV 2020</li>
+              network weight distributions. In ECCV 2020.</li>
             <li>Wilson, A. G., & <b>Izmailov, P</b>. Bayesian deep learning and a probabilistic perspective of gen-
               eralization. In NeurIPS, 2020.</li>
             <li>Hendrycks, D., Dietterich, T. Benchmarking Neural Network Robustness to Common Corruptions and