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authorThibaut Horel <thibaut.horel@gmail.com>2015-12-11 20:10:16 -0500
committerThibaut Horel <thibaut.horel@gmail.com>2015-12-11 20:10:16 -0500
commit19252912d28b01cc43b6eef66cbe40de33f668d1 (patch)
tree4aaa622423479d4b9ded3001f59a8b7d99cf7ab2 /finale
parentfb13f68c3e0901b0e876c602fee556566b7ed6ce (diff)
downloadcascades-19252912d28b01cc43b6eef66cbe40de33f668d1.tar.gz
Fix typos in intro
Diffstat (limited to 'finale')
-rw-r--r--finale/sections/intro.tex12
1 files changed, 6 insertions, 6 deletions
diff --git a/finale/sections/intro.tex b/finale/sections/intro.tex
index 0806c98..4d59ac6 100644
--- a/finale/sections/intro.tex
+++ b/finale/sections/intro.tex
@@ -22,18 +22,18 @@ Specifically:
\begin{itemize}
\item we propose a Bayesian Inference formulation of the NIP problem in the
Generalized Linear Cascade (GLC) Model of \cite{pouget} and show how to apply
- MCMC and variationel inference to it.
- \item we show how to leverage this Bayesian formulation to design active
- learning heuristics where the experimenter is able to dynamically
+ MCMC and Variational Inference to it.
+ \item we show how to leverage this Bayesian formulation to design Active
+ Learning heuristics where the experimenter is able to dynamically
choose the source node at which the observe cascades originate.
- \item we show empirically that active learning greatly improves the speed
- of learning compared to i.i.d observations.
+ \item we give empirical evidence that Active Learning greatly improves the
+ speed of learning compared to i.i.d observations.
\end{itemize}
The organization of the paper is as follows: we conclude this introduction by
a review of the related works. Section 2 introduces the notations and the
Generalized Linear Model, Section 3 presents our Bayesian Inference
-formulation. The active learning approach is described in Section 4. Section
+formulation. The Active Learning approach is described in Section 4. Section
5 gives our experimental results. Finally we conclude by a discussion in
Section 6.
\input{sections/related.tex}