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Add to SafariWhen comparing Torch vs Infer.NET, the Slant community recommends Infer.NET for most people. In the question“What are the best artificial intelligence frameworks?” Infer.NET is ranked 6th while Torch is ranked 7th. The most important reason people chose Infer.NET is:
Infer.NET supports expectation propagation (including belief propagation as a special case), variational message passing (also known as variational Bayes), max product (for discrete models), and block Gibbs sampling.
Question Ranking
Pros
Pro Easy switch between CPU and GPU
It takes little more than a type cast of your inputs to go from CPU to GPU computation.
Pro Lots of easy to combine modular pieces
Torch is a very modular framework. As such, you can choose which modules you need to implement and which modules to eliminate from your solution.
Pro Supports multiple inference algorithms
Infer.NET supports expectation propagation (including belief propagation as a special case), variational message passing (also known as variational Bayes), max product (for discrete models), and block Gibbs sampling.
Pro Versatile
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.
Pro Cross platform
Can be used on Windows (.NET), OSX or Linux (using mono)
Cons
Con Not easily accessible to the academic community
Being written in Lua instead of the more widely used Python, it's not as accessible to academics as other solutions which are implemented in Python. With Python being one of the most widely used languages in scientific computing.
Con No commercial license
Infer.NET is free for academic use. However, at this time, commercial use of Infer.NET is limited to Microsoft. No other commercial licenses are available.
Con Closed source
