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Matlab Environment for Deep Architecture Learning
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------------------------------------------------------------------------------- Matlab Environment for Deep Architecture Learning (MEDAL) - version 0.1 ------------------------------------------------------------------------------- o o / \ / \ EDAL o o o Model Objects: mlnn.m -- Multi-layer neural network mlcnn.m -- Multi-layer convolutional neural network rbm.m -- Restricted Boltzmann machine (RBM) mcrbm.m -- Mean-covariance (3-way Factored) RBM drbm.m -- Dynamic/conditional RBM dbn.m -- Deep Belief Network crbm.m -- Convolutional RBM ae.m -- Shallow autoencoder dae.m -- Deep Autoencoder ------------------------------------------------------------------------------- To begin type: >> startLearning in the medal directory To get an idea of how the model objects work, check out the demo script: >> deepLearningExamples('all') These examples are by no means optimized, but are for getting familiar with the code.If you have any questions or bugs, send them my way: [email protected] ------------------------------------------------------------------------------- References: *Neural Networks/Backpropagations: Rumelhart, D. et al. "Learning representations by back-propagating errors". Nature 323 (6088): 533–536. 1986. *Restricted Boltzmann Machines/Contrastive Divergence Hinton, G. E. "Training Products of Experts by Minimizing Contrastive Divergence". Neural Computation 14 (8): 1771–1800. 2002 *Deep Belief Networks: Bengio, Y., Lamblin, P., Popovici, P., Larochelle, H. "Greedy Layer-Wise Training of Deep Networks" NIPS 2006 *Deep & Denoising Autoencoders Hinton, G. E. and Salakhutdinov, R. R "Reducing the dimensionality of data with neural networks." Science, Vol. 313. no. 5786, pp. 504 - 507, 28 July 2006. *Pascal, V. et al. “Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion.“ The Journal of Machine Learning Research 11:3371-3408. 2010 *Mean-Covariance/3-way Factored RBMs: Ranzato M. et al. "Modeling Pixel Means and Covariances Using Factorized Third-Order Boltzmann Machines." CVPR 2012. *Dynamic/Conditional RBMs: Taylor G. et al. "Modeling Human Motion Using Binary Latent Variables" NIPS 2006. *Convolutional MLNNs: LeCun, Y., et al. "Gradient-based learning applied to document recognition". Proceedings of the IEEE, 86(11), 2278–2324. 2008 Krizhevsky, A et al. "ImageNet Classification with Deep Convolutional Neural Networks." NIPS 2012. *Convolutional RBMs: Lee, H. et al. “Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations.”, ICML 2009 *Rectified Linear Units Nair V., Hinton GE. (2010) Rectified Linear Units Improve Restricted Boltzmann Machines. IMCL 2010. Glorot, X. Bordes A. & Bengio Y. (2011). "Deep sparse rectifier neural networks". AISTATS 2011. *Dropout Regularization: Hinton GE et al. Technical Report, Univ. of Toronto, 2012. *General Hinton, G. E. "A practical guide to training restricted Boltzmann machines" Technical Report, Univ. of Toronto, 2010. -------------------------------------------------------------------------
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