You can use Infer.NET to solve many different kinds of machine learning problems, from standard problems like classification, recommendation or clustering through to customised solutions to domain-specific problems. Infer.NET has been used in a wide variety of domains including information retrieval, bioinformatics, epidemiology, vision, and many others.
Infer.NET is published as open source on GitHub under the MIT license and also available as NuGet packages.
Infer.NET alternatives and similar packages
Based on the "Machine Learning and Data Science" category.
Alternatively, view Infer.NET alternatives based on common mentions on social networks and blogs.
9.7 8.2 Infer.NET VS ML.NETML.NET is an open source and cross-platform machine learning framework for .NET.
8.0 9.1 Infer.NET VS TensorFlow.NET.NET Standard bindings for Google's TensorFlow for developing, training and deploying Machine Learning models in C# and F#.
6.3 5.2 Infer.NET VS DeedleEasy to use .NET library for data and time series manipulation and for scientific programming
4.5 0.0 Infer.NET VS SpreadsSeries and Panels for Real-time and Exploratory Analysis of Data Streams
3.9 6.3 Infer.NET VS Catalyst🚀 Catalyst is a C# Natural Language Processing library built for speed. Inspired by spaCy's design, it brings pre-trained models, out-of-the box support for training word and document embeddings, and flexible entity recognition models.
* Code Quality Rankings and insights are calculated and provided by Lumnify.
They vary from L1 to L5 with "L5" being the highest. Visit our partner's website for more details.
Do you think we are missing an alternative of Infer.NET or a related project?
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 for passing EP messages.
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" UAI, 2015
This project extends
Nicolas Heess, Daniel Tarlow, and John Winn. “Learning to Pass Expectation Propagation Messages.” NIPS, 2013. http://media.nips.cc/nipsbooks/nipspapers/paper_files/nips26/1493.pdf.
S. M. Ali Eslami, Daniel Tarlow, Pushmeet Kohli, and John Winn "Just-In-Time Learning for Fast and Flexible Inference." NIPS, 2014. http://papers.nips.cc/paper/5595-just-in-time-learning-for-fast-and-flexible-inference.pdf
KJIT software is under MIT license.
The KJIT software relies on Infer.NET (freely available for non-commercial use) which is not included in our software. Even though the license of KJIT software is permissive, Infer.NET's license is not. Please refer to its license for details.
The repository contains
- Matlab code for experimenting in a batch learning setting. Experiments on new
kernels, factors, random features, message operators are all done in Matlab
in the first stage. Once the methods are developed, they are reimplemented in
C# to be operable in Infer.NET framework. EP inference is implemented in C#
using Infer.NET, not in Matlab. All Matlab code is in the
- C# code for message operators in Infer.NET framework. The code for this
part is in
code/KernelEP.NETwhich contains a C# project developed with Monodevelop (free cross-platform IDE) on Ubuntu 14.04. You should be able to use Visual studio in Windows to open the project file if it is more preferable.
All the code is written in Matlab and C# and expected to be cross-platform.
The Matlab part of this project does not depend on the Infer.NET package. However, to use our KJIT message operator in the Infer.NET framework, you have to include Infer.NET package by taking the following steps.
- Download Infer.NET package from its Microsoft research
Upon extracting the zip archive, you will see subfolders including
Source, and its license. Carefully read its license.
Binfolder of the extracted archive into
code/KernelEP.NET/lib/Infer.NET/Bin/of this repository. Without this step, when you open the project in Monodevelop, it will not compile due to the missing dependency.
- Try to build the project. There should be no errors.
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. These functions might be useful for other works. These include
Incomplete Cholesky factorization. This is implemented in Matlab in such a way that any kernel and any type of data (not necessarily points from Euclidean space) can be used. The full kernel matrix is not pre-loaded. Only one row of the kernel matrix is computed at a time, allowing a large kernel matrix to be factorized. In this project, points are distributions and the kernel takes two distributions as input. See
Dynamic matrix in Matlab. This is a matrix whose entries are given by a function
f: (I, J) -> Mwhere
I, Jare index list and
Mis a submatrix specified by
I, J. The dynamic matrix is useful when the underlying matrix is too large to fit into memory but entries can be computed on the fly when needed. In this project, this object is used to represent the data matrix when a large number of random features are used. Multiplication (to a regular matrix or a dynamic matrix) operations are implemented. See
*Note that all licence references and agreements mentioned in the Infer.NET README section above are relevant to that project's source code only.