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index.html
CHANGED
@@ -158,11 +158,11 @@ gradient norm and then apply soft removal on them to mitigate the potential jail
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$$
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\displaystyle
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\begin{aligned}
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x^\prime_i
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\beta \times \mathtt{embed}(q_i), \text{ if $q_i$ in $\mathcal{Q}$}\\
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\mathtt{embed}(q_i), \text{
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\end{cases} \\
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r_\theta(q_{1:n})\sim
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\end{aligned}
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$$
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</span>
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$$
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\displaystyle
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\begin{aligned}
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&x^\prime_i=\begin{cases}
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\beta \times \mathtt{embed}(q_i), \text{ if $q_i$ in $\mathcal{Q}$}\\
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\mathtt{embed}(q_i), \text{ otherwise}
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\end{cases} \\
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&r_\theta(q_{1:n})\sim P_\theta(\cdot|x^\prime_{1:n})
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\end{aligned}
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$$
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</span>
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