Fetching the paper…
Reading the bibliography…
Deep generative models parameterized by neural networks have recently achieved state-of-the-art performance in unsupervised and semi-supervised learning.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998) · 1998
Earlier work this paper cites.
An Auxiliary Variational Method
Agakov, F. and Barber, D. (2004) · 2004
Earlier work this paper cites.
Learning methods for generic object recognition with invariance to pose and lighting
LeCun, Y., Huang, F. J., and Bottou, L. (2004) · 2004
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks
Glorot, X. and Bengio, Y. (2010) · 2010
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A. Y. (2011) · 2011
Earlier work this paper cites.
Theano: new features and speed improvements
Bastien, F., Lamblin, P., Pascanu, R., Bergstra, J., Goodfellow, I. J., Bergeron, A., Bouchard, N., and Bengio, Y. (2012) · 2012
Earlier work this paper cites.
Auto-Encoding Variational Bayes
Kingma, Diederik P; Welling, M. (2013) · 2013
Earlier work this paper cites.
Rnade: The real-valued neural autoregressive density-estimator
Uria, B., Murray, I., and Larochelle, H. (2013) · 2013
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization
Kingma, D. and Ba, J. (2014) · 2014
Cited alongside, same era.
Semi-Supervised Learning with Deep Generative Models
Kingma, D. P., Rezende, D. J., Mohamed, S., and Welling, M. (2014) · 2014
Cited alongside, same era.
Ranganath, R., Tang, L., Charlin, L., and Blei, D. M. (2014) · 2014
Cited alongside, same era.
Stochastic Backpropagation and Approximate Inference in Deep Generative Models
Rezende, D. J., Mohamed, S., and Wierstra, D. (2014) · 2014
Cited alongside, same era.
From neural pca to deep unsupervised learning
Valpola, H. (2014) · 2014
Cited alongside, same era.
Distributional Smoothing with Virtual Adversarial Training
Miyato, T., Maeda, S.-i., Koyama, M., Nakae, K., and Ishii, S. (2015) · 2015
Later among the works it cites.
Hierarchical variational models
Ranganath, R., Tran, D., and Blei, D. M. (2015) · 2015
Later among the works it cites.
Semi-supervised learning with ladder networks
Rasmus, A., Berglund, M., Honkala, M., Valpola, H., and Raiko, T. (2015) · 2015
Later among the works it cites.
Variational Inference with Normalizing Flows
Rezende, D. J. and Mohamed, S. (2015) · 2015
Later among the works it cites.
Tran, D., Ranganath, R., and Blei, D. M. (2015) · 2015
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Importance Weighted Autoencoders
Burda, Y., Grosse, R., and Salakhutdinov, R. (2015) · 2015
Cited alongside, same era.
Lasagne: First release
Dieleman, S., Schlüter, J., Raffel, C., Olson, E., Sønderby, S. K., Nouri, D., van den Oord, A., and and, E. B. (2015) · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C. (2015) · 2015
Cited alongside, same era.
Sønderby, C. K., Raiko, T., Maaløe, L., Sønderby, S. K., and Winther, O. (2016) · 2016
Closest in time.
Pixel recurrent neural networks
van den Oord, A., Nal, K., and Kavukcuoglu, K. (2016) · 2016
Closest in time.