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Author: o-laurent

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@@ -207,8 +207,6 @@ <h2 class="title is-3">Abstract</h2>
                 LS degrades the uncertainty rank ordering of correct vs incorrect predictions by regularising the max logit more when a prediction is likely to be correct, and less when it is likely to be wrong. 
                 This elucidates previously reported experimental results where strong classifiers underperform in SC. We then demonstrate the empirical effectiveness of post-hoc logit normalisation for recovering lost SC performance caused by LS. 
                 Furthermore, linking back to our gradient analysis, we again provide an explanation for why such normalisation is effective. <br><br>
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-              <b>TLDR;</b> TBD
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@@ -323,18 +321,18 @@ <h2 class="subtitle has-text-justified">
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     <!--BibTex citation -->
-    <!-- <section class="section" id="BibTeX">
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           <h2 class="title">BibTeX</h2>
           <pre style="padding: 0px; margin: 0px"><code style="overflow-x: auto; padding: 0px; margin: 0px">
-            @article{farina2024frustratingly,
+            @inproceedings{xia2024understanding,
               title={Towards Understanding Why Label Smoothing Degrades Selective Classification and How to Fix It},
-              author={Farina, Matteo and Franchi, Gianni and Iacca, Giovanni and Mancini, Massimiliano and Ricci, Elisa},
-              journal={arXiv preprint arXiv:2405.18330},
-              year={2024}
+              author={Xia, Guoxuan and Laurent, Olivier and Franchi, Gianni and Bouganis, Christos-Savvas},
+              journal={ICLR},
+              year={2025}       
           }</code></pre>
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+    </section>
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