✨ Add ELSA

index.html

@@ -97,19 +97,22 @@ <h2 style="text-align: center">Outline</h2>
           <h3 style="text-align: left">Introduction: Why & where is UQ helpful?</h3>
           <p>
             Initial exploration into the critical role of uncertainty quantification (UQ) within the realm
-            of computer vision (CV): participants will gain an understanding of why it’s essential to consider uncertainty in CV, especially concerning decision-making in complex
+            of computer vision (CV): participants will gain an understanding of why it’s essential to consider
+            uncertainty in CV, especially concerning decision-making in complex
             environments. We will introduce real-world scenarios where uncertainty can profoundly
             impact model performance and safety, setting the stage for deeper exploration through out the tutorial.
           </p>
           <h3 style="text-align: left">From maximum a posteriori to BNNs.</h3>
           <p>
             In this part, we will journey through the evolution of UQ techniques, starting
-            from classic approaches such as maximum a posteriori estimation to the more ellaborate Bayesian Neural Networks. The participants will grasp the conceptual foundations
+            from classic approaches such as maximum a posteriori estimation to the more ellaborate Bayesian Neural
+            Networks. The participants will grasp the conceptual foundations
             of UQ, laying the groundwork for the subsequent discussions of Bayesian methods.
           </p>
           <h3 style="text-align: left">Strategies for BNN posterior inference.</h3>
           <p>
-            This is the core part, which will dive into the process of estimating the posterior distribution of BNNs. The participants
+            This is the core part, which will dive into the process of estimating the posterior distribution of BNNs.
+            The participants
             will gain insights into the computational complexities involved in modeling uncertainty
             through a comprehensive overview of techniques such as Variational Inference (VI),
             Hamiltonian Monte Carlo (HMC), and Langevin Dynamics. Moreover, we will explore
@@ -118,19 +121,21 @@ <h3 style="text-align: left">Strategies for BNN posterior inference.</h3>
           </p>
           <h3 style="text-align: left">Computationally-efficient BNNs for CV.</h3>
           <p>
-            Here, we will present recent techniques to improve the computational efficiency of BNNs for computer vision tasks.
+            Here, we will present recent techniques to improve the computational efficiency of BNNs for computer vision
+            tasks.
             We will present different forms of obtaining BNNs from a intermediate checkpoints,
             weight trajectories during a training run, different types of variational subnetworks,
             etc., along with their main strenghts and limitations.
           </p>
           <h3 style="text-align: left">Convert your DNN into a BNN: post-hoc BNN inference.</h3>
-          <p> 
-            This segment focuses on post-hoc inference techniques, with a focus on Laplace approximation. The participants
+          <p>
+            This segment focuses on post-hoc inference techniques, with a focus on Laplace approximation. The
+            participants
             will learn how Laplace approximation serves as a computationally efficient method for
             approximating the posterior distribution of Bayesian Neural Networks.
           </p>
           <h3 style="text-align: left">Quality of estimated uncertainty and practical examples.</h3>
-          <p> 
+          <p>
             In the final session, participants will learn how to evaluate the quality of UQ in practi-
             cal settings. We will develop multiple approaches to assess the reliability and calibra-
             tion of uncertainty estimates, equipping participants with the tools to gauge the robust-
@@ -142,9 +147,10 @@ <h3 style="text-align: left">Quality of estimated uncertainty and practical exam
           </p>
 
           <h3 style="text-align: left">Uncertainty Quantification Framework.</h3>
-          <p> 
-            This tutorial will also very quickly introduce the <a href="https://github.com/ensta-u2is-ai/torch-uncertainty">TorchUncertainty
-            library</a>, an uncertainty-aware open-source framework for training models in PyTorch.
+          <p>
+            This tutorial will also very quickly introduce the <a
+              href="https://github.com/ensta-u2is-ai/torch-uncertainty">TorchUncertainty
+              library</a>, an uncertainty-aware open-source framework for training models in PyTorch.
           </p>
         </div>
 
@@ -202,8 +208,21 @@ <h2 style="text-align: center">Selected References</h2>
             href="https://github.com/ensta-u2is-ai/awesome-uncertainty-deeplearning">Awesome Uncertainty in deep
             learning.</a>
         </div>
+
+        <br>
+
+        <div class="containertext">
+          <h3 style="text-align: center">Andrei Bursuc is supported by</h3>
+
+          <center>
+            <a href="https://elsa-ai.eu/" target="_blank"><img src="assets/elsa_logo.png" width="10%" hspace="2%" />
+          </center>
+          </a>
+        </div>
       </div>
     </div>
+
+
   </section>
 
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