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diff --git a/paper/sections/discussion.tex b/paper/sections/discussion.tex index c4c70a3..25d9c53 100644 --- a/paper/sections/discussion.tex +++ b/paper/sections/discussion.tex @@ -11,11 +11,6 @@ Solving the Graph Inference problem with sparse recovery techniques opens new ve This model therefore falls into the 1-bit compressed sensing model \cite{Boufounos:2008} framework. Several recent papers study the theoretical guarantees obtained for 1-bit compressed sensing with specific measurements \cite{Gupta:2010}, \cite{Plan:2014}. Whilst they obtained bounds of the order ${\cal O}(n \log \frac{n}{s}$), no current theory exists for recovering positive bounded signals from bernoulli hyperplanes. This research direction may provide the first clues to solve the ``active learning'' problem: if we are allowed to adaptively \emph{choose} the source nodes at the beginning of each cascade, can we improve on current results? - - - - - \begin{comment} The Linear Threshold model can \emph{also} be cast a generalized linear cascade model. However, as we show below, its link function is non-differentiable and necessitates a different analysis. In the Linear Threshold Model, each node $j\in V$ has a threshold $t_j$ from the interval $[0,1]$ and for each node, the sum of incoming weights is less than $1$: $\forall j\in V$, $\sum_{i=1}^m \Theta_{i,j} \leq 1$. |