The code on this webpage was developed by researchers of the SELECT Lab. Permission is granted to use this code for research purposes, although we request that you acknowledge these researchers in any published work, or equivalent distribution, using the bibtex entries provided. We make no warranties as to the quality or accuracy of any of the code linked from this page.
Submodular Function Optimization
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This MATLAB toolbox provides implementations of algorithms for maximizing and minimizing submodular set functions. It includes Queyranne's algorithm, Fujishige's minimum norm algorithm, Zhao et al's recursive splitting procedure, Nemhauser et al's greedy algorithm, Krause et al's Saturate algorithm, Goldengorin et al's Data Correcting algorithm, Narasimhan and Bilmes' submodular-supermodular procedure etc. It also provides a detailed tutorial script explaining the application of the toolbox to several machine learning problems like image denoising, clustering and experimental design. See also our tutorial materials.
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Parallel MRF Inference
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This C++ library provides an efficient parallel implementation of the ResidualSplash algorithm described in Residual Splash for Optimally Parallelizing Belief Propagation, on discrete Markov Random Fields. In addition the library provides basic implementations of Synchronous, Round Robin, and Residual Belief Propagation (BP) along with supporting objects for representing factors, Markov Random Fields, and message scheduling. An easy to use Matlab interface is also provided. Read the README file for compilation and usage instructions.
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Distributed Factor Graph Inference
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This C++ library provides a distributed MPI implementation of the Belief Residual Splash algorithm described in Distributed Parallel Inference on Large Factor Graphs. It provides an inference program which takes as input a factor graph described in a text file, and outputs estimated variable marginals. In addition, we provide a lifted version which operates on lifted factor graphs. A thin Matlab wrapper is also included. Read the README file for compilation and usage instructions.