README
November 12, 2013 · View on GitHub
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:
stan_s_bury@berkeley.edu
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.