"Some links for quick reference: <br><br>* 1995:  Hinton et al:  The wake-sleep algorithm for unsupervised neural networks:  <a href=""http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.51.215&amp;rep=rep1&amp;type=pdf"">http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.51.215&amp;rep=rep1&amp;type=pdf</a><br>* 1996:  Olshausen &amp; Field:  Natural image statistics and efficient coding: <a href=""https://pdfs.semanticscholar.org/4435/2b35791ceaad3439b8ccf165cc9b4978d801.pdf"">https://pdfs.semanticscholar.org/4435/2b35791ceaad3439b8ccf165cc9b4978d801.pdf</a><br>* 1996:  Olshausen &amp; Field:  Sparse coding of natural images produces localized", oriented," bandpass receptive fields:  <a href=""http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.38.6079&amp;rep=rep1&amp;type=pdf"">http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.38.6079&amp;rep=rep1&amp;type=pdf</a><br>* 2002:  Hinton:  Training Products of Experts by Minimizing Contrastive Divergence:  <a href=""http://www.cs.utoronto.ca/~hinton/absps/nccd.pdf"">http://www.cs.utoronto.ca/~hinton/absps/nccd.pdf</a><br>* 2006:  Hinton et al:  A Fast Learning Algorithm for Deep Belief Nets:  <a href=""https://www.mitpressjournals.org/doi/pdfplus/10.1162/neco.2006.18.7.1527"">https://www.mitpressjournals.org/doi/pdfplus/10.1162/neco.2006.18.7.1527</a><br><br>* 2006:  Hinton &amp; Salakhutdinov:  Reducing the dimensionality of data with neural networks:  <a href=""https://www.semanticscholar.org/paper/Reducing-the-dimensionality-of-data-with-neural-Hinton-Salakhutdinov/46eb79e5eec8a4e2b2f5652b66441e8a4c921c3e"">https://www.semanticscholar.org/paper/Reducing-the-dimensionality-of-data-with-neural-Hinton-Salakhutdinov/46eb79e5eec8a4e2b2f5652b66441e8a4c921c3e</a><br>* 2006:  Lee et al:  Efficient sparse coding algorithms:  <a href=""http://papers.nips.cc/paper/2979-efficient-sparse-coding-algorithms.pdf"">http://papers.nips.cc/paper/2979-efficient-sparse-coding-algorithms.pdf</a><br>* 2007:  Bengio et al:  Greedy Layer-Wise Training of Deep Networks:  <a href=""http://papers.nips.cc/paper/3048-greedy-layer-wise-training-of-deep-networks.pdf"">http://papers.nips.cc/paper/3048-greedy-layer-wise-training-of-deep-networks.pdf</a><br>* 2007:  Salakhutdinov et al:  Restricted Boltzmann Machines for Collaborative Filtering:  <a href=""http://www.utstat.toronto.edu/~rsalakhu/papers/rbmcf.pdf"">http://www.utstat.toronto.edu/~rsalakhu/papers/rbmcf.pdf</a><br>* 2008:  Salakhutdinov:  Learning and Evaluating Boltzmann Machines:  <a href=""http://www.cs.toronto.edu/~rsalakhu/papers/bm.pdf"">http://www.cs.toronto.edu/~rsalakhu/papers/bm.pdf</a><br>* 2008:  Torralba et al:  Small Codes and Large Image Databases for Recognition:  <a href=""http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.229.3256&amp;rep=rep1&amp;type=pdf"">http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.229.3256&amp;rep=rep1&amp;type=pdf</a><br>* 2009:  Bengio:  Learning Deep Architectures for AI:  <a href=""http://axon.cs.byu.edu/~martinez/classes/678/Papers/ftml.pdf"">http://axon.cs.byu.edu/~martinez/classes/678/Papers/ftml.pdf</a><br>* 2009:  Kavukcuoglu et al:  Learning Invariant Features through Topographic Filter Maps:  <a href=""http://yann.lecun.org/exdb/publis/pdf/koray-cvpr-09.pdf"">http://yann.lecun.org/exdb/publis/pdf/koray-cvpr-09.pdf</a><br>* 2009:  Kulis &amp; Darrell:  Learning to Hash with Binary Reconstructive Embeddings:  <a href=""http://papers.nips.cc/paper/3667-learning-to-hash-with-binary-reconstructive-embeddings.pdf"">http://papers.nips.cc/paper/3667-learning-to-hash-with-binary-reconstructive-embeddings.pdf</a><br>* 2009:  Larochelle et al:  Exploring Strategies for Training Deep Neural Networks:  <a href=""http://www.jmlr.org/papers/volume10/larochelle09a/larochelle09a.pdf"">http://www.jmlr.org/papers/volume10/larochelle09a/larochelle09a.pdf</a><br>* 2009:  Weiss et al:  Spectral Hashing:  <a href=""https://papers.nips.cc/paper/3383-spectral-hashing.pdf"">https://papers.nips.cc/paper/3383-spectral-hashing.pdf</a><br>* 2010:  Salakhutdinov &amp; Hinton:  Deep Boltzmann Machines:  <a href=""http://proceedings.mlr.press/v5/salakhutdinov09a/salakhutdinov09a.pdf"">http://proceedings.mlr.press/v5/salakhutdinov09a/salakhutdinov09a.pdf</a><br>* 2010:  Salakhutdinov &amp; Larochelle:  Efficient Learning of Deep Boltzmann Machines:  <a href=""http://proceedings.mlr.press/v9/salakhutdinov10a/salakhutdinov10a.pdf"">http://proceedings.mlr.press/v9/salakhutdinov10a/salakhutdinov10a.pdf</a><br>* 2011:  Larochelle et al:  The Neural Autoregressive Distribution Estimator (NADE):  <a href=""http://proceedings.mlr.press/v15/larochelle11a/larochelle11a.pdf"">http://proceedings.mlr.press/v15/larochelle11a/larochelle11a.pdf</a><br>* 2012:  Hinton &amp; Salakhutdinov:  A Better Way to Pretrain Deep Boltzmann Machines:  <a href=""http://papers.nips.cc/paper/4610-a-better-way-to-pretrain-deep-boltzmann-machines"">http://papers.nips.cc/paper/4610-a-better-way-to-pretrain-deep-boltzmann-machines</a><br>* 2012:  Srivastava &amp; Salakhutdinov:  Multimodal Learning with Deep Boltzmann Machines:  <a href=""https://papers.nips.cc/paper/4683-multimodal-learning-with-deep-boltzmann-machines.pdf"">https://papers.nips.cc/paper/4683-multimodal-learning-with-deep-boltzmann-machines.pdf</a><br>* 2012:  Srivastava &amp; Salakhutdinov:  Learning Representations for Multimodal Data with Deep Belief Nets:  <a href=""https://pdfs.semanticscholar.org/5555/b28607cada5474bca772e1cc553b624415c9.pdf"">https://pdfs.semanticscholar.org/5555/b28607cada5474bca772e1cc553b624415c9.pdf</a><br>* 2013:  Tang &amp; Salakhutdinov:  Learning Stochastic Feedforward Neural Networks:  <a href=""http://papers.nips.cc/paper/5026-learning-stochastic-feedforward-neural-networks"">http://papers.nips.cc/paper/5026-learning-stochastic-feedforward-neural-networks</a><br>* 2013:  Uria et al:  RNADE: The real-valued neural autoregressive density-estimator:  <a href=""http://papers.nips.cc/paper/5060-rnade-the-real-valued-neural-autoregressive-density-estimator"">http://papers.nips.cc/paper/5060-rnade-the-real-valued-neural-autoregressive-density-estimator</a><br>* 2014:  Bornschein &amp; Bengio:  Reweighted Wake-Sleep:  <a href=""https://arxiv.org/abs/1406.2751"">https://arxiv.org/abs/1406.2751</a><br>* 2014:  Goodfellow et al:  Generative Adversarial Nets:  <a href=""http://papers.nips.cc/paper/5423-generative-adversarial-nets"">http://papers.nips.cc/paper/5423-generative-adversarial-nets</a><br>* 2014:  Kingma &amp; Welling:  Stochastic Gradient VB and the Variational Auto-Encoder ("reparameterization trick"):  <a href=""https://pdfs.semanticscholar.org/eaa6/bf5334bc647153518d0205dca2f73aea971e.pdf"">https://pdfs.semanticscholar.org/eaa6/bf5334bc647153518d0205dca2f73aea971e.pdf</a><br>* 2014:  Kiros et al:  Multimodal Neural Language Models:  <a href=""http://proceedings.mlr.press/v32/kiros14.pdf"">http://proceedings.mlr.press/v32/kiros14.pdf</a><br>* 2014:  Kiros et al:  Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models:  <a href=""https://arxiv.org/abs/1411.2539"">https://arxiv.org/abs/1411.2539</a><br>* 2014:  Mnih &amp; Gregor:  Neural Variational Inference and Learning in Belief Networks:  <a href=""https://arxiv.org/abs/1402.0030"">https://arxiv.org/abs/1402.0030</a><br>* 2014:  Rezende et al:  Stochastic Backpropagation and Approximate Inference in Deep Generative Models:  <a href=""https://arxiv.org/abs/1401.4082"">https://arxiv.org/abs/1401.4082</a><br>* 2014:  Uria et al:  A Deep and Tractable Density Estimator:  <a href=""http://proceedings.mlr.press/v32/uria14.pdf"">http://proceedings.mlr.press/v32/uria14.pdf</a><br>* 2015:  Burda et al:  Importance Weighted Autoencoders:  <a href=""https://arxiv.org/abs/1509.00519"">https://arxiv.org/abs/1509.00519</a><br>* 2015:  Denton et al:  Deep Generative Image Models using a ￼Laplacian Pyramid of Adversarial Networks (LaPGAN):  <a href=""http://papers.nips.cc/paper/5773-deep-generative-image-models-using-a-5"">http://papers.nips.cc/paper/5773-deep-generative-image-models-using-a-5</a>  <br>* 2015:  Gregor et al:  DRAW: A Recurrent Neural Network For Image Generation:  <a href=""https://arxiv.org/abs/1502.04623"">https://arxiv.org/abs/1502.04623</a><br>* 2015:  Lake et al:  Human-Level Concept Learning through Probabilistic Program Induction:  <a href=""https://www.sas.upenn.edu/~astocker/lab/teaching-files/PSYC739-2016/Lake_etal2015.pdf"">https://www.sas.upenn.edu/~astocker/lab/teaching-files/PSYC739-2016/Lake_etal2015.pdf</a><br>* 2015:  Mansimov et al:  Generating Images from Captions with Attention:  <a href=""https://arxiv.org/abs/1511.02793"">https://arxiv.org/abs/1511.02793</a><br>* 2015:  Radford et al:  Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks:  <a href=""https://arxiv.org/abs/1511.06434"">https://arxiv.org/abs/1511.06434</a><br>* 2016:  Salimans et al:  Improved Techniques for Training GANs:  <a href=""http://papers.nips.cc/paper/6124-improved-techniques-for-training-gans"">http://papers.nips.cc/paper/6124-improved-techniques-for-training-gans</a><br>* 2016:  van den Oord:  Conditional Image Generation with PixelCNN Decoders:  <a href=""http://papers.nips.cc/paper/6527-conditional-image-generation-with-pixelcnn-decoders"">http://papers.nips.cc/paper/6527-conditional-image-generation-with-pixelcnn-decoders</a><br>* 2016:  van den Oord:  Pixel Recurrent Neural Networks:  <a href=""https://arxiv.org/abs/1601.06759"">https://arxiv.org/abs/1601.06759</a>"