👕 References are now links to the papers

index.html

@@ -187,30 +187,38 @@ <h2 style="text-align: center">Relation to prior tutorials and short courses</h2
         <div class="containertext" style="max-width:50rem">
           <h2 style="text-align: center">Selected References</h2>
           <ol>
-            <li><b>Immer, A.</b>, Palumbo, E., Marx, A., & Vogt, J. E. Effective Bayesian Heteroscedastic Regres-
-              sion with Deep Neural Networks. In NeurIPS, 2023.</li>
+            <li><b>Immer, A.</b>, Palumbo, E., Marx, A., & Vogt, J. E. E<a
+                href="https://proceedings.neurips.cc/paper_files/paper/2023/file/a901d5540789a086ee0881a82211b63d-Paper-Conference.pdf">
+                Effective Bayesian Heteroscedastic Regres-
+                sion with Deep Neural Networks</a>. In NeurIPS, 2023.</li>
             <li><b>Franchi, G., Bursuc, A.,</b> Aldea, E., Dubuisson, S.,
-              & Bloch, I. Encoding the latent posterior of
-              Bayesian Neural Networks for uncertainty quantification. IEEE TPAMI, 2023.</li>
+              & Bloch, I. <a href="https://arxiv.org/pdf/2012.02818">Encoding the latent posterior of
+                Bayesian Neural Networks for uncertainty quantification</a>. 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. In ECCV 2022.</li>
+              S., & Filliat, D. <a href="https://arxiv.org/pdf/2207.10130">Latent Discriminant
+                deterministic Uncertainty</a>. In ECCV 2022.</li>
             <li><b>Laurent, O.</b>, <b>Lafage, A.</b>, Tartaglione, E., Daniel, G.,
               Martinez, J. M., <b>Bursuc, A.</b>, & <b></b>Franchi, G.</b>
-              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>
+              <a href="https://arxiv.org/pdf/2210.09184">Packed-Ensembles for Efficient Uncertainty Estimation</a>. In
+              ICLR 2023.
+            </li>
+            <li><b>Izmailov, P.</b>, Vikram, S., Hoffman, M. D., & Wilson, A. G. <a
+                href="https://arxiv.org/pdf/2104.14421">What are Bayesian neural network
+                posteriors really like?</a> In ICML, 2021.</li>
+            <li><b>Izmailov, P.</b>, Maddox, W. J., Kirichenko, P., Garipov, T., Vetrov, D., & Wilson, A. G. <a
+                href="https://arxiv.org/pdf/1907.07504">Subspace inference for Bayesian deep learning</a>. In UAI, 2020.
+            </li>
             <li><b>Franchi, G.</b>, <b>Bursuc, A.</b>, Aldea, E., Dubuisson, S., &
-              Bloch, I. (2020, August). TRADI: Tracking deep neural
-              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
-              Perturbations. In ICLR 2019.</li>
-            <li><b> Izmailov, P.</b>, Podoprikhin, D., Garipov, T., Vetrov, D., & Wilson, A. G. Averaging weights
-              leads to wider optima and better generalization. In UAI, 2018. </li>
+              Bloch, I. <a href="https://arxiv.org/pdf/1912.11316">TRADI: Tracking deep neural
+                network weight distributions</a>. In ECCV 2020.</li>
+            <li>Wilson, A. G., & <b>Izmailov, P</b>. <a href="https://arxiv.org/pdf/2002.08791">Bayesian deep
+                learning and a probabilistic perspective of generalization</a>. In NeurIPS, 2020.</li>
+            <li>Hendrycks, D., Dietterich, T. <a href="https://arxiv.org/pdf/1903.12261">Benchmarking Neural Network
+                Robustness to Common Corruptions and
+                Perturbations</a>. In ICLR 2019.</li>
+            <li><b> Izmailov, P.</b>, Podoprikhin, D., Garipov, T., Vetrov, D., & Wilson, A. G. <a
+                href="https://arxiv.org/pdf/1803.05407">Averaging weights
+                leads to wider optima and better generalization</a>. In UAI, 2018. </li>
           </ol>
           You will find more references in the <a
             href="https://github.com/ensta-u2is-ai/awesome-uncertainty-deeplearning">Awesome Uncertainty in deep