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@@ -110,7 +110,7 @@ Theorem 1 (Approximation Error for Softmax-Kernel). Assume $\Vert \mathbf { q }
 
 We can see that the error bound of RF for approximating original softmax-kernel function depends on both the dimension of feature map $\phi$ and temperature $\tau$ . Notably, the error bound is independent of node number $N$ , which implies that the approximation ability is insensitive to dataset sizes.
 
-The second question is non-trivial since Eqn. $^ { 7 }$ involves randomness of Gumbel variables and random transformation in $\phi$ , which cannot be decoupled apart. We define $\begin{array} { r l } { c _ { u v } } & { { } = } \end{array}$ (qu/ p⌧)>(kv/ p⌧)egv/⌧PNw=1 (qu/ p⌧)>(kw/ p⌧)egw/⌧ as the result from the kernelized Gumbel-Softmax and cu = $\{ c _ { u v } \} _ { v = 1 } ^ { N }$ denotes the sampled edge vector for node $u$ . We can arrive at the result as follows.
+The second question is non-trivial since Eqn. $^ { 7 }$ involves randomness of Gumbel variables and random transformation in $\phi$ , which cannot be decoupled apart. We define $\begin{array} { r l } { c _ { u v } } & { { } = } \end{array}$ (qu/ p⌧)>(kv/ p⌧)egv/⌧PNw=1 (qu/ p⌧)>(kw/ p⌧)egw/⌧ as the result from the kernelized Gumbel-Softmax and cu = $\{ c _ { u v } \} _ { v = 1 } ^ { N }$ denotes the sampled edge vector for node $u$ . We can arrive at the result as follows.
 
 Theorem 2 (Property of Kernelized Gumbel-Softmax Random Variables). Suppose m is sufficiently large, we have the convergence property for the kernelized Gumbel-Softmax operator