Fetching the paper…
Reading the bibliography…
Deep generative models (DGMs) are effective on learning multilayered representations of complex data and performing inference of input data by exploring the generative ability.
The laplacian pyramid as a compact image code
P. Burt and Edward A · 1983
Earlier work this paper cites.
Statistical analysis with missing data
R. J. Little and D. B. Rubin · 1987
Earlier work this paper cites.
Support-vector networks
C. Cortes and V. Vapnik · 1995
Earlier work this paper cites.
Mean field theory for sigmoid belief networks
L. Saul, T. Jaakkola, and M. Jordan · 1996
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
Statistical learning theory
V. Vapnik · 1998
Earlier work this paper cites.
Hidden Markov support vector machines
Y. Altun, I. Tsochantaridis, and T. Hofmann · 2003
Earlier work this paper cites.
Max-margin Markov networks
B. Taskar, C. Guestrin, and D. Koller · 2003
Earlier work this paper cites.
Learning methods for generic object recognition with invariance to pose and lighting
Y. LeCun, F. J. Huang, and L. Bottou · 2004
Earlier work this paper cites.
Support vector machine learning for interdependent and structured output spaces
I. Tsochantaridis, T. Hofmann, T. Joachims, and Y. Altun · 2004
Earlier work this paper cites.
Visualizing data using t-SNE
L. V. Matten and G. Hinton · 2008
Earlier work this paper cites.
Partially observed maximum entropy discrimination Markov networks
J. Zhu, E.P. Xing, and B. Zhang · 2008
Earlier work this paper cites.
Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
H. Lee, R. Grosse, R. Ranganath, and A. Y. Ng · 2009
Earlier work this paper cites.
Deep Boltzmann machines
R. Salakhutdinov and G. E. Hinton · 2009
Earlier work this paper cites.
Learning structural SVMs with latent variables
C. J. Yu and T. Joachims · 2009
Earlier work this paper cites.
Introduction to semi-supervised learning
X. Zhu and A. Goldberg · 2009
Earlier work this paper cites.
Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P. A. Manzagol · 2010
Earlier work this paper cites.
The neural autoregressive distribution estimator
H. Larochelle and I. Murray · 2011
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng · 2011
Earlier work this paper cites.
On deep generative models with applications to recognition
M. Ranzato, J. Susskind, V. Mnih, and G. E. Hinton · 2011
Earlier work this paper cites.
Pegasos: Primal estimated sub-gradient solver for SVM
S. Shalev-Shwartz, Y. Singer, N. Srebro, and A. Cotter · 2011
Earlier work this paper cites.
Theano: new features and speed improvements
F. Bastien, P. Lamblin, R. Pascanu, J. Bergstra, I. Goodfellow, A. Bergeron, N. Bouchard, D. Warde-Farley, and Y. Bengio · 2012
Cited alongside, same era.
Large-margin predictive latent subspace learning for multi-view data analysis
N. Chen, J. Zhu, F. Sun, and E. P. Xing · 2012
Cited alongside, same era.
Max-margin min-entropy models
K. Miller, M. P. Kumar, B. Packer, D. Goodman, and D. Koller · 2012
Cited alongside, same era.
Convolutional neural networks applied to house numbers digit classification
P. Sermanet, S. Chintala, and Y. LeCun · 2012
Cited alongside, same era.
MedLDA: Maximum margin supervised topic models
J. Zhu, A. Ahmed, and E. P. Xing · 2012
Cited alongside, same era.
Generalized denoising auto-encoders as generative models
Y. Bengio, L. Yao, G.e Alain, and P. Vincent · 2013
Cited alongside, same era.
Importance weighted autoencoders
Y. Burda, R. Grosse, and R. Salakhutdinov · 2015
Later among the works it cites.
Deep generative image models using a laplacian pyramid of adversarial networks
E. Denton, S. Chintala, S. Arthur, and R. Fergus · 2015
Later among the works it cites.
Learning to generate chairs with convolutional neural networks
A. Dosovitskiy, J. T. Springenberg, and T. Brox · 2015
Later among the works it cites.
Learning deep generative models with doubly stochastic mcmc
C. Du, J. Zhu, and B. Zhang · 2015
Later among the works it cites.
Training generative neural networks via maximum mean discrepancy optimization
G. K. Dziugaite, D. M. Roy, and Z. Ghahramani · 2015
Later among the works it cites.
Draw: A recurrent neural network for image generation
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Maxout networks
I. J. Goodfellow, D.Warde-Farley, M. Mirza, A. C. Courville, and Y. Bengio · 2013
Cited alongside, same era.
Deep learning using linear support vector machines
Y. Tang · 2013
Cited alongside, same era.
Stochastic pooling for regularization of deep convolutional neural networks
M. D. Zeiler and R. Fergus · 2013
Cited alongside, same era.
Deep generative stochastic networks trainable by backprop
Y. Bengio, E. Laufer, G. Alain, and J. Yosinski · 2014
Cited alongside, same era.
Generative adversarial nets
I. J. Goodfellow, J. P. Abadie, M. Mirza, B. Xu, D. W. Farley, S.ozair, A. Courville, and Y. Bengio · 2014
Cited alongside, same era.
Deep autoregressive networks
K. Gregor, I. Danihelka, A. Mnih, C. Blundell, and D. Wierstra · 2014
Cited alongside, same era.
K. Gregor, I. Danihelka, A. Graves, D. J. Rezende, and D. Wierstra · 2015
Later among the works it cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Later among the works it cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. L. Ba · 2015
Later among the works it cites.
Deeply-supervised nets
C. Lee, S. Xie, P. Gallagher, Z. Zhang, and Z. Tu · 2015
Later among the works it cites.
Max-margin deep generative models
C. Li, J. Zhu, T. Shi, and B. Zhang · 2015
Later among the works it cites.
Generative moment matching networks
Y. Li, K. Swersky, and R. Zemel · 2015
Later among the works it cites.
Distributional smoothing with virtual adversarial training
T. Miyato, S. Maeda, M. Koyama, K. Nakae, and S. Ishii · 2015
Later among the works it cites.
Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2015
Later among the works it cites.
Semi-supervised learning with ladder networks
A. Rasmus, M. Berglund, M. Honkala, H. Valpola, and T. Raiko · 2015
Later among the works it cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Closest in time.
Learning to generate with memory
C. Li, J. Zhu, and B. Zhang · 2016
Closest in time.
Auxiliary deep generative models
L. Maaløe, C. K. Sønderby, S. K. Sønderby, and O. Winther · 2016
Closest in time.
Conditional generative moment-matching networks
Y. Ren, J. Li, Y. Luo, and J. Zhu · 2016
Closest in time.
Improved techniques for training gans
T. Salimans, I. J. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen · 2016
Closest in time.
Unsupervised and semi-supervised learning with categorical generative adversarial networks
J. T. Springenberg · 2016
Closest in time.