hopfield: Hopfield Networks, Boltzmann Machines and Clusters

[ ai, library, machine-learning, mit, program ] [ Propose Tags ]
Dependencies array (>=, base (>=4 && <=5), deepseq (>=, directory (>=, erf (>=, exact‑combinatorics (>=, hmatrix (>=, hopfield, JuicyPixels (>=2.0.0), monad‑loops (>=, MonadRandom (>=0.1.8), optparse‑applicative (>=, parallel (>=, probability (>=0.2.4), QuickCheck (>=2.4.2), random (>=, random‑fu (>=, rvar (>=, split (>=, vector (>=0.9.1) [details]
License MIT
Copyright Copyright: (c) 2012 Mihaela Rosca, Lukasz Severyn, Niklas Hambuechen, Razvan Marinescu, Wael Al Jisihi
Author Mihaela Rosca, Lukasz Severyn, Niklas Hambuechen, Razvan Marinescu, Wael Al Jisihi
Maintainer Niklas Hambuechen <mail@nh2.me>
Category AI, Machine Learning
Home page https://github.com/imperialhopfield/hopfield
Bug tracker https://github.com/imperialhopfield/hopfield/issues
Source repo head: git clone git://github.com/imperialhopfield/hopfield.git
Uploaded by NiklasHambuechen at Thu Dec 12 10:40:31 UTC 2013
Distributions NixOS:
Executables recognize, experiment
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Status Docs uploaded by user [build log]
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Attractor Neural Networks for Modelling Associative Memory

Report: https://github.com/imperialhopfield/hopfield/raw/master/report/report.pdf

A third year group project at Imperial College London, supervised by Prof. Abbas Edalat.

This projects implements:

and comes with a range of experiments to evaluate their properties.




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