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Matlab Environment for Deep Architecture Learning (MEDAL) - version 0.1
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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
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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:
stan_s_bury@berkeley.edu
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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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