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ENSTA-U2IS-AI · May 7, 2024 · 92660f3 · 92660f3
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diff --git a/.gitignore/.gitignore → .gitignore b/.gitignore/.gitignore → .gitignore
diff --git a/README.md b/README.md
@@ -1,11 +1,3 @@
 ## <b>Webpage template</b>
 
-This webpage template was made for a [colorful ECCV paper](http://richzhang.github.io/colorization/). See how the webpage looks [here](https://richzhang.github.io/webpage-template).
-
-To use this template, clone the repo:
-
-```
-git clone https://github.com/richzhang/webpage-template.git
-```
-
-Copy the contents into a `gh-pages` branch of a GitHub repo. That will automatically make a webpage under address [GITHUB_USERNAME.github.io/REPO_NAME](GITHUB_USERNAME.github.io/REPO_NAME).
+This webpage template comes from https://github.com/AaltoPML/FoRDE/tree/gh-page.
diff --git a/_config.yml b/_config.yml
@@ -0,0 +1,10 @@
+theme: jekyll-theme-cayman
+openreview_url: https://openreview.net/forum?id=FOSBQuXgAq
+arxiv_url: https://arxiv.org/abs/2310.08287
+poster_url: ./assets/poster.pdf
+slides_url: ./assets/slides.pdf
+tu_url: https://github.com/ENSTA-U2IS-AI/torch-uncertainty
+code_url: https://github.com/o-laurent/Symmetries-BNN-Posteriors
+dataset_url: https://huggingface.co/datasets/torch-uncertainty/Checkpoints
+title: A Symmetry-Aware Exploration of Bayesian Neural Network Posteriors
+tagline: This website contains information regarding the paper A Symmetry-Aware Exploration of Bayesian Neural Network Posteriors.
diff --git a/_layouts/default.html b/_layouts/default.html
@@ -0,0 +1,102 @@
+<!DOCTYPE html>
+<html lang="{{ site.lang | default: "en-US" }}">
+  <head>
+    <meta charset="UTF-8">
+
+{% seo %}
+    <link rel="preconnect" href="https://fonts.gstatic.com">
+    <link rel="preload" href="https://fonts.googleapis.com/css?family=Open+Sans:400,700&display=swap" as="style" type="text/css" crossorigin>
+    <meta name="viewport" content="width=device-width, initial-scale=1">
+    <meta name="theme-color" content="#157878">
+    <meta name="apple-mobile-web-app-status-bar-style" content="black-translucent">
+    <link rel="stylesheet" href="{{ '/assets/css/style.css?v=' | append: site.github.build_revision | relative_url }}">
+    {% include head-custom.html %}
+    <!-- MathJax -->
+    <style>
+      body {
+        color: #344854;
+      }
+      img {
+        display: block;
+        margin: auto;
+      }
+      .MathJax {
+        padding: 0.25em;
+        min-width: 0 ! important;
+        overflow-x: auto;
+        overflow-y: hidden;
+      }
+      table {
+        display: table !important;
+      }
+      .my_blue {
+        color: #4472C4;
+      }
+      .my_red {
+        color: #FF0000;
+      }
+      .my_orange {
+        color: #ED7D31;
+      }
+      .my_deepred {
+        color: rgb(196, 78, 82)
+      }
+      .my_box {
+        border: 2px solid #159957;
+        border-radius: 20px;
+        padding: 10px;
+      }
+      blockquote p strong {
+        color: #ED7D31;
+      }
+      blockquote p {
+        color: #344854;
+      }
+    </style>
+    <script>
+      MathJax = {
+        tex: {
+          tags: 'ams',
+          autoload: {
+            boldsymbol: ['boldsymbol']
+          }
+        }
+      };
+    </script>
+    <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script>
+    <script type="text/javascript" id="MathJax-script" async
+        src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-svg.js">
+    </script>
+  </head>
+  <body>
+    <header class="page-header" role="banner">
+      <h1 class="project-name">{{ page.title | default: site.title | default: site.github.repository_name }}</h1>
+      <h2 class="project-tagline">{{ page.publication }}</h2>
+      <h2 class="project-tagline">{{ page.description | default: site.description | default: site.github.project_tagline }}</h2>
+      {% if site.github.is_project_page %}
+      <a href="{{ site.openreview_url }}" class="btn">OpenReview</a>
+      <a href="{{ site.arxiv_url }}" class="btn">ArXiv</a>
+      <a href="{{ site.tu_url }}" class="btn">TorchUncertainty</a>
+      <a href="{{ site.code_url }}" class="btn">Code</a>
+      <a href="{{ site.dataset_url }}" class="btn">Dataset</a>
+      <a href="{{ site.poster_url }}" class="btn">Poster</a>
+      <a href="{{ site.slides_url }}" class="btn">Slides</a>
+      <!-- <a href="{{ site.video_url }}" class="btn">Presentation</a> -->
+      {% endif %}
+      {% if site.show_downloads %}
+        <a href="{{ site.github.zip_url }}" class="btn">Download .zip</a>
+        <a href="{{ site.github.tar_url }}" class="btn">Download .tar.gz</a>
+      {% endif %}
+    </header>
+
+    <main id="content" class="main-content" role="main">
+      {{ content }}
+
+      <footer class="site-footer">
+        {% if site.github.is_project_page %}
+          <span class="site-footer-owner">Olivier Laurent • Emanuel Aldea • Gianni Franchi • 2024 • Template by Trung Trinh et al.</span>
+        {% endif %}
+      </footer>
+    </main>
+  </body>
+</html>
diff --git a/example_axis.png → assets/example_axis.png b/example_axis.png → assets/example_axis.png
diff --git a/assets/poster.pdf b/assets/poster.pdf
diff --git a/assets/slides.pdf b/assets/slides.pdf
diff --git a/index.md b/index.md
@@ -0,0 +1,30 @@
+---
+title: A Symmetry-Aware Exploration of Bayesian Neural Network Posteriors
+publication: Poster at the Twelfth International Conference on Learning Representations (ICLR) 2024
+description: Olivier Laurent, Emanuel Aldea, & Gianni Franchi
+---
+
+*This website contains information regarding the paper A Symmetry-Aware Exploration of Bayesian Neural Network Posteriors.*
+
+> **TL;DR**: We explore the Bayesian posterior of modern deep neural networks, highlighting the impact of weight-space symmetries.
+
+Please cite our work if you find it useful:
+
+```bibtex
+@inproceedings{laurent2024symmetry,
+  title={A symmetry-aware exploration of bayesian neural network posteriors},
+  author={Laurent, Olivier and Aldea, Emanuel and Franchi, Gianni},
+  booktitle={ICLR},
+  year={2024}
+}
+```
+
+<img src="./assets/example_axis.png" alt="drawing" width="100%" max-width="1000px">
+
+# Abstract
+
+The distribution of modern deep neural networks (DNNs) weights -- crucial for uncertainty quantification and robustness -- is an eminently complex object due to its extremely high dimensionality. This paper presents one of the first large-scale explorations of the posterior distribution of deep Bayesian Neural Networks (BNNs), expanding its study to real-world vision tasks and architectures. Specifically, we investigate the optimal approach for approximating the posterior, analyze the connection between posterior quality and uncertainty quantification, delve into the impact of modes on the posterior, and explore methods for visualizing the posterior. Moreover, we uncover weight-space symmetries as a critical aspect for understanding the posterior. To this extent, we develop an in-depth assessment of the impact of both permutation and scaling symmetries that tend to obfuscate the Bayesian posterior. While the first type of transformation is known for duplicating modes, we explore the relationship between the latter and L2 regularization, challenging previous misconceptions. Finally, to help the community improve our understanding of the Bayesian posterior, we release the <a href="https://huggingface.co/datasets/torch-uncertainty/Checkpoints">first large-scale checkpoint dataset</a>, including thousands of real-world models, along with our <a href="https://github.com/ENSTA-U2IS-AI/torch-uncertainty">code</a>.
+
+#
+
+The code consists of a library for training and evaluating the models - TorchUncertainty, a dataset available for download on <a href="https://huggingface.co/datasets/torch-uncertainty/Checkpoints">Hugging Face</a> and specific code that will be made available in <a href=" https://github.com/o-laurent/Symmetries-BNN-Posteriors">this repository</a>.
diff --git a/index.html → old/index.html b/index.html → old/index.html
@@ -172,19 +172,17 @@
 		</table>
 	</center>
 
-	<center>
+	<!-- <center>
 		<table align=center width=850px>
 		<hr>
-	<center>
+	<center> -->
 	<hr>
 	<center><h1>Talk</h1></center>
 	<p align="center">
 	<iframe width="900" height="400" src="example_axis.png" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
 	</p>
 	<p style="text-align:center;">Caption: Weight-space symmetries greatly impact the estimated Bayesian posterior. Permutation symmetries clearly increase the number of modes of the posterior distribution in the case of the last layer of a 2-hidden neuron perceptron.</p>
 
-
-
 	<hr>
 
 	<table align=center width=850px>
@@ -198,10 +196,10 @@
 	<br>
 
 	<hr>
-	<center><h1>Talk</h1></center>
+	<!-- <center><h1>Talk</h1></center>
 	<p align="center">
 		<iframe width="800" height="600" src="https://recorder-v3.slideslive.com/?share=90903&s=bbb2aecc-d3e2-474a-8dc3-a896a9b03584" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen align="center"></iframe>
-	</p>
+	</p> -->
 
 	<table align=center width=800px>
 		<br>

diff --git a/resources/bibtex.txt b/resources/bibtex.txt
diff --git a/resources/method_diagram.png b/resources/method_diagram.png
diff --git a/resources/paper.png b/resources/paper.png
diff --git a/resources/teaser.mp4 b/resources/teaser.mp4
diff --git a/resources/teaser.png b/resources/teaser.png