From 19252912d28b01cc43b6eef66cbe40de33f668d1 Mon Sep 17 00:00:00 2001
From: Thibaut Horel <thibaut.horel@gmail.com>
Date: Fri, 11 Dec 2015 20:10:16 -0500
Subject: Fix typos in intro

---
 finale/sections/intro.tex | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

(limited to 'finale/sections/intro.tex')

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}
-- 
cgit v1.2.3-70-g09d2

