remove infer.net package. Its license does not allow redistribution.

LICENSE

@@ -1,6 +1,6 @@
 The MIT License (MIT)
 
-Copyright (c) 2014 Wittawat Jitkrittum
+Copyright (c) 2014-2015 Wittawat Jitkrittum
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal
@@ -18,4 +18,4 @@ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
+SOFTWARE.

README.md

@@ -1,20 +1,23 @@
-kernel-ep
-=========
-This project is an attempt to learn a kernel-based operator which takes as
-input all incoming messages to a factor and produces a projected outgoing EP
-message. The projected outgoing message is constrained to be a certain
-parametric form e.g., Gaussian. In ordinary expectation propagation, computing
-an outgoing message may involve solving a difficult (potentially
-multdimensional) integral for minimizing the KL divergence between the tilted
-distribution and the approximate posterior. Such operator allows one to bypass
-the computation of the integral by directly mapping all incoming messages into
-an outgoing message. Learning of such mapping is done offline with the aid of
-importance sampling for computing ground truth projected output messages. A
-learned operator is useful in an application such as tracking where inference
-has to be done in real time and numerically computing the integral is
-infeasible due to time constraint. 
-
-This project extends the following work
+# KJIT 
+
+The goal of this project is to learn a kernel-based message operator which
+takes as input all incoming messages to a factor and produces a projected
+outgoing expectation propagation (EP) message. In ordinary EP, computing an
+outgoing message may involve solving a difficult integral for minimizing the KL
+divergence between the tilted distribution and the approximate posterior. Such
+operator allows one to bypass the computation of the integral by directly
+mapping all incoming messages into an outgoing message. Learning of such an
+operator is done online during EP.  The operator is termed **KJIT** for
+Kernel-based Just-In-Time learning to pass EP messages.
+
+Paper:
+
+    Wittawat Jitkrittum, Arthur Gretton, Nicolas Heess, S. M. Ali Eslami, Balaji Lakshminarayanan, Dino Sejdinovic, and Zoltán Szabó
+    Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages
+    [arXiv:1503.02551](http://arxiv.org/abs/1503.02551), 2015
+
+
+This project extends 
 
     Heess, Nicolas, Daniel Tarlow, and John Winn. 
     “Learning to Pass Expectation Propagation Messages.” 
@@ -23,12 +26,25 @@ This project extends the following work
     3219–27, 2013. 
     http://media.nips.cc/nipsbooks/nipspapers/paper_files/nips26/1493.pdf.
 
+and 
+
     Eslami, S. M. A.; Tarlow, D.; Kohli, P. & Winn, 
     "Just-In-Time Learning for Fast and Flexible Inference." 
     In Advances in Neural Information Processing Systems 27, 2014, 154-162
     http://papers.nips.cc/paper/5595-just-in-time-learning-for-fast-and-flexible-inference.pdf
 
-### Useful Functions
+## License
+MIT license.
+
+The KJIT software relies on
+[Infer.NET](http://research.microsoft.com/en-us/um/cambridge/projects/infernet/download.aspx)
+which is not distributed here. Even though license of KJIT software is permissive, 
+Infer.NET is not. Please refer to [its
+license](http://research.microsoft.com/en-us/downloads/710cd61f-3587-44f4-b12d-a2c75722c4f6/InferNetLicense.rtf)
+for details.
+
+
+## Useful components
 
 In the development of the code for learning an EP message operator, some commonly 
 used functions are reimplemented to better suit the need of this project. 

code/KernelEP.NET/KernelEP.NET/KernelEP.csproj

@@ -42,10 +42,10 @@
     <Reference Include="System" />
     <Reference Include="System.Xml" />
     <Reference Include="Infer.Compiler">
-      <HintPath>..\lib\Infer.NET 2.6\Bin\Infer.Compiler.dll</HintPath>
+      <HintPath>..\lib\Infer.NET\Bin\Infer.Compiler.dll</HintPath>
     </Reference>
     <Reference Include="Infer.Runtime">
-      <HintPath>..\lib\Infer.NET 2.6\Bin\Infer.Runtime.dll</HintPath>
+      <HintPath>..\lib\Infer.NET\Bin\Infer.Runtime.dll</HintPath>
     </Reference>
     <Reference Include="System.Core" />
     <Reference Include="MathNet.Numerics">