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Fix typo
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book/index.html

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@@ -210,7 +210,7 @@ <h3 id="label-robust-score-lrs"><a class="header" href="#label-robust-score-lrs"
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\]
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Then, the LRS is normalized to \([0, 1]\) as follows:
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\[
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\text{LRS} = 100% \times (e^{\alpha}-e^{-1}) / (e - e^{-1})
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\text{LRS} = (e^{\alpha}-e^{-1}) / (e - e^{-1}) \times 100 \%
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\]</p>
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<p>By default, we set \(w = 0.5\) on the leaderboard, meaning that both \(\text{IOR}\) and \(\text{HLR}\) are equally important. Users can adjust the weights to emphasize one aspect on the leaderboard.</p>
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<h3 id="augmentation-robust-score-ars"><a class="header" href="#augmentation-robust-score-ars">Augmentation-Robust Score (ARS)</a></h3>

book/introduction.html

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@@ -210,7 +210,7 @@ <h3 id="label-robust-score-lrs"><a class="header" href="#label-robust-score-lrs"
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\]
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Then, the LRS is normalized to \([0, 1]\) as follows:
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\[
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\text{LRS} = 100% \times (e^{\alpha}-e^{-1}) / (e - e^{-1})
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\text{LRS} = (e^{\alpha}-e^{-1}) / (e - e^{-1}) \times 100 \%
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\]</p>
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<p>By default, we set \(w = 0.5\) on the leaderboard, meaning that both \(\text{IOR}\) and \(\text{HLR}\) are equally important. Users can adjust the weights to emphasize one aspect on the leaderboard.</p>
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<h3 id="augmentation-robust-score-ars"><a class="header" href="#augmentation-robust-score-ars">Augmentation-Robust Score (ARS)</a></h3>

book/print.html

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@@ -211,7 +211,7 @@ <h3 id="label-robust-score-lrs"><a class="header" href="#label-robust-score-lrs"
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\]
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Then, the LRS is normalized to \([0, 1]\) as follows:
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\[
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\text{LRS} = 100% \times (e^{\alpha}-e^{-1}) / (e - e^{-1})
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\text{LRS} = (e^{\alpha}-e^{-1}) / (e - e^{-1}) \times 100 \%
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\]</p>
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<p>By default, we set \(w = 0.5\) on the leaderboard, meaning that both \(\text{IOR}\) and \(\text{HLR}\) are equally important. Users can adjust the weights to emphasize one aspect on the leaderboard.</p>
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<h3 id="augmentation-robust-score-ars"><a class="header" href="#augmentation-robust-score-ars">Augmentation-Robust Score (ARS)</a></h3>

book/searchindex.js

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book/searchindex.json

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doc/introduction.md

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@@ -61,7 +61,7 @@ LRS is defined as a weight sum of \\(\text{IOR}\\) and \\(-\text{HLR}\\) to rank
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\\]
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Then, the LRS is normalized to \\([0, 1]\\) as follows:
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\\[
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\text{LRS} = 100% \times (e^{\alpha}-e^{-1}) / (e - e^{-1})
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\text{LRS} = (e^{\alpha}-e^{-1}) / (e - e^{-1}) \times 100 \\%
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\\]
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By default, we set \\(w = 0.5\\) on the leaderboard, meaning that both \\(\text{IOR}\\) and \\(\text{HLR}\\) are equally important. Users can adjust the weights to emphasize one aspect on the leaderboard.

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