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diff --git a/paper/sections/intro.tex b/paper/sections/intro.tex index f369e3c..7688aeb 100644 --- a/paper/sections/intro.tex +++ b/paper/sections/intro.tex @@ -133,8 +133,8 @@ the recovery of the graph's edges and the estimation of the model's parameters, and achieve close to optimal bounds. The work of~\cite{du2014influence} is slightly orthogonal to ours since they -suggest learning the \emph{influence} function, rather than the networks -parameters directly. +suggest learning the \emph{influence} function, rather than the +parameters of the network directly. %\begin{comment} %Their work has the merit of studying a generalization of the discrete-time %independent cascade model to continuous functions. Similarly to |