CLONE - Autoencoder

Goal: Implement various types of autoencoders
Requirements:
1)Basic (deep) autoencoder that supports different types of inputs: binary, real in [0..1]. real in [-inf, +inf]
2)Sparse autoencoder i.e. L1 regularization. It should be added as a feature to the MLP and then used here
3)Denoising autoencoder
4)Stacked autoencoder for pre-training of deep networks. It should support arbitrary network layers

References:
1. Vincent, Pascal, et al. "Extracting and composing robust features with denoising autoencoders." Proceedings of the 25th international conference on Machine learning. ACM, 2008. http://www.iro.umontreal.ca/~vincentp/Publications/denoising_autoencoders_tr1316.pdf
2. http://machinelearning.wustl.edu/mlpapers/paper_files/ICML2011Rifai_455.pdf,
3. Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., and Manzagol, P.-A. (2010). Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of Machine Learning Research, 11(3371–3408). http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.297.3484&rep=rep1&type=pdf
4, 5, 6. Bengio, Yoshua, et al. "Greedy layer-wise training of deep networks." Advances in neural information processing systems 19 (2007): 153. http://www.iro.umontreal.ca/~lisa/pointeurs/dbn_supervised_tr1282.pdf