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| author | Thibaut Horel <thibaut.horel@gmail.com> | 2015-02-06 18:32:07 -0500 |
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| committer | Thibaut Horel <thibaut.horel@gmail.com> | 2015-02-06 18:32:07 -0500 |
| commit | ec7e1507af5bb359ac9455fee353e6b7f67bc5bb (patch) | |
| tree | dd89fc8b412bb8acac7b333010fd4ff46f4f2f72 /paper/sections/discussion.tex | |
| parent | 85d2de4ff3588b8059b2bb45ec9444097b996aa7 (diff) | |
| download | cascades-ec7e1507af5bb359ac9455fee353e6b7f67bc5bb.tar.gz | |
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| -rw-r--r-- | paper/sections/discussion.tex | 2 |
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diff --git a/paper/sections/discussion.tex b/paper/sections/discussion.tex index 10aa50b..52eb6c5 100644 --- a/paper/sections/discussion.tex +++ b/paper/sections/discussion.tex @@ -15,7 +15,7 @@ This model therefore falls into the 1-bit compressed sensing framework \cite{Boufounos:2008}. Several recent papers study the theoretical guarantees obtained for 1-bit compressed sensing with specific measurements \cite{Gupta:2010, Plan:2014}. Whilst they obtained bounds of the order -${\cal O}(n \log \frac{n}{s}$), no current theory exists for recovering +${\cal O}(n \log \frac{m}{s}$), no current theory exists for recovering positive bounded signals from biniary measurememts. This research direction may provide the first clues to solve the ``adaptive learning'' problem: if we are allowed to adaptively \emph{choose} the source nodes at the beginning of |