paper url

Author: celrm

_bibliography/references.bib

@@ -5,7 +5,7 @@ @inproceedings{
 jamadandi2024spectral,
 title={Spectral Graph Pruning Against Over-Squashing and Over-Smoothing},
 author={Adarsh Jamadandi and Celia Rubio-Madrigal and Rebekka Burkholz},
-booktitle={Thirty-eigth Conference on Neural Information Processing Systems},
+booktitle={Thirty-eighth Conference on Neural Information Processing Systems},
 year={2024},
 url={https://openreview.net/forum?id=EMkrwJY2de},
 pdf={https://openreview.net/pdf?id=EMkrwJY2de},
@@ -16,8 +16,10 @@ @inproceedings{
 mustafa2024training,
 title={Training GNNs in Balance by Dynamic Rescaling},
 author={Nimrah Mustafa and Rebekka Burkholz},
-booktitle={Thirty-eigth Conference on Neural Information Processing Systems},
+booktitle={Thirty-eighth Conference on Neural Information Processing Systems},
 year={2024},
+url={https://openreview.net/forum?id=IfZwSRpqHl},
+pdf={https://openreview.net/pdf?id=IfZwSRpqHl},
 abstract={Graph neural networks exhibiting a rescale invariance, like GATs, obey a conservation law of its parameters, which has been exploited to derive a balanced state that induces good initial trainability. Yet, finite learning rates as used in practice topple the network out of balance during training. This effect is even more pronounced with larger learning rates that tend to induce improved generalization but make the training dynamics less robust. To support even larger learning rates, we propose to dynamically balance the network according to a different criterion, based on relative gradients, that promotes faster and better. In combination with large learning rates and gradient clipping, dynamic rebalancing significantly improves generalization on real-world data. We observe that rescaling provides us with the flexibility to control the order in which network layers are trained. This leads to novel insights into similar phenomena as grokking, which can further boost generalization performance.},
 }
 

_site/publications/index.html

@@ -103,7 +103,7 @@ <h1 id="publications">Publications</h1>
 
 
         <div class="periodical">
-          <em>In Thirty-eigth Conference on Neural Information Processing Systems</em>, 2024
+          <em>In Thirty-eighth Conference on Neural Information Processing Systems</em>, 2024
         </div>
         <div class="periodical">
 
@@ -124,7 +124,7 @@ <h1 id="publications">Publications</h1>
             <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span class="p">{</span><span class="nl">jamadandi2024spectral</span><span class="p">,</span>
   <span class="na">title</span> <span class="p">=</span> <span class="s">{Spectral Graph Pruning Against Over-Squashing and Over-Smoothing}</span><span class="p">,</span>
   <span class="na">author</span> <span class="p">=</span> <span class="s">{Jamadandi, Adarsh and Rubio-Madrigal, Celia and Burkholz, Rebekka}</span><span class="p">,</span>
-  <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{Thirty-eigth Conference on Neural Information Processing Systems}</span><span class="p">,</span>
+  <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{Thirty-eighth Conference on Neural Information Processing Systems}</span><span class="p">,</span>
   <span class="na">year</span> <span class="p">=</span> <span class="s">{2024}</span><span class="p">,</span>
   <span class="na">url</span> <span class="p">=</span> <span class="s">{https://openreview.net/forum?id=EMkrwJY2de}</span><span class="p">,</span>
 <span class="p">}</span></code></pre></figure>
@@ -140,7 +140,7 @@ <h1 id="publications">Publications</h1>
         <div id="mustafa2024training" class="col-sm-10">
         <!-- Title -->
 
-          <div class="title"><b>Training GNNs in Balance by Dynamic Rescaling</b></div>
+          <div class="title"><a href="https://openreview.net/forum?id=IfZwSRpqHl"><b>Training GNNs in Balance by Dynamic Rescaling</b></a></div>
 
         <!-- Author -->
         <div class="author">
@@ -152,7 +152,7 @@ <h1 id="publications">Publications</h1>
 
 
         <div class="periodical">
-          <em>In Thirty-eigth Conference on Neural Information Processing Systems</em>, 2024
+          <em>In Thirty-eighth Conference on Neural Information Processing Systems</em>, 2024
         </div>
         <div class="periodical">
 
@@ -161,6 +161,7 @@ <h1 id="publications">Publications</h1>
           <!-- Links/Buttons -->
         <div class="links"><a class="conf btn btn-sm z-depth-0">NeurIPS</a><a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a>
             <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
+            <a href="https://openreview.net/pdf?id=IfZwSRpqHl" class="btn btn-sm z-depth-0" role="button">PDF</a>
           </div>
 
 
@@ -172,8 +173,9 @@ <h1 id="publications">Publications</h1>
             <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span class="p">{</span><span class="nl">mustafa2024training</span><span class="p">,</span>
   <span class="na">title</span> <span class="p">=</span> <span class="s">{Training GNNs in Balance by Dynamic Rescaling}</span><span class="p">,</span>
   <span class="na">author</span> <span class="p">=</span> <span class="s">{Mustafa, Nimrah and Burkholz, Rebekka}</span><span class="p">,</span>
-  <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{Thirty-eigth Conference on Neural Information Processing Systems}</span><span class="p">,</span>
-  <span class="na">year</span> <span class="p">=</span> <span class="s">{2024}</span>
+  <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{Thirty-eighth Conference on Neural Information Processing Systems}</span><span class="p">,</span>
+  <span class="na">year</span> <span class="p">=</span> <span class="s">{2024}</span><span class="p">,</span>
+  <span class="na">url</span> <span class="p">=</span> <span class="s">{https://openreview.net/forum?id=IfZwSRpqHl}</span><span class="p">,</span>
 <span class="p">}</span></code></pre></figure>
           </div>
         </div>