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| author | jeanpouget-abadie <jean.pougetabadie@gmail.com> | 2015-05-15 18:54:54 +0200 |
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| committer | jeanpouget-abadie <jean.pougetabadie@gmail.com> | 2015-05-15 18:54:54 +0200 |
| commit | 2586d50b4ce7c932656b8f144784511f08692e14 (patch) | |
| tree | 9fcaf074ece2abcd71decf78cf63129e9e7ffe86 /paper/sections/discussion.tex | |
| parent | 0f6b315caf29f67d89b876ee14178dc7b1db6254 (diff) | |
| download | cascades-2586d50b4ce7c932656b8f144784511f08692e14.tar.gz | |
fixing small typos + adding style file
Diffstat (limited to 'paper/sections/discussion.tex')
| -rw-r--r-- | paper/sections/discussion.tex | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/paper/sections/discussion.tex b/paper/sections/discussion.tex index 03e7ff2..2f0fd36 100644 --- a/paper/sections/discussion.tex +++ b/paper/sections/discussion.tex @@ -17,7 +17,7 @@ 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 -${\cal O}(n \log \frac{m}{s}$), no current theory exists for recovering +${\cal O}(s \log \frac{m}{s}$), no current theory exists for recovering positive bounded signals from binary 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 |