Fetching the paper…
Reading the bibliography…
We show that a generative random field model, which we call generative ConvNet, can be derived from the commonly used discriminative ConvNet, by assuming a ConvNet for multi-category classification and assuming one of the categories is a base category generated by a reference distribution.
Neural networks and physical systems with emergent collective computational abilities
Hopfield, John J · 1982
Earlier work this paper cites.
Sparse coding with an overcomplete basis set: A strategy employed by v1?
Olshausen, Bruno A and Field, David J · 1997
Earlier work this paper cites.
Minimax entropy principle and its application to texture modeling
Zhu, Song-Chun, Wu, Ying Nian, and Mumford, David · 1997
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Yann, Bottou, Léon, Bengio, Yoshua, and Haffner, Patrick · 1998
Earlier work this paper cites.
Grade: Gibbs reaction and diffusion equations
Zhu, Song-Chun and Mumford, David · 1998
Earlier work this paper cites.
On the convergence of markovian stochastic algorithms with rapidly decreasing ergodicity rates
Younes, Laurent · 1999
Earlier work this paper cites.
Training products of experts by minimizing contrastive divergence
Hinton, Geoffrey E · 2002
Earlier work this paper cites.
Energy-based models for sparse overcomplete representations
Teh, Yee-Whye, Welling, Max, Osindero, Simon, and Hinton, Geoffrey E · 2003
Earlier work this paper cites.
Estimation of non-normalized statistical models by score matching
Hyvärinen, Aapo · 2005
Earlier work this paper cites.
Fields of experts: A framework for learning image priors
Roth, Stefan and Black, Michael J · 2005
Cited alongside, same era.
A tutorial on energy-based learning
LeCun, Yann, Chopra, Sumit, Hadsell, Rata, Ranzato, Mare’Aurelio, and Huang, Fu Jie · 2006
Cited alongside, same era.
Connections between score matching, contrastive divergence, and pseudolikelihood for continuous-valued variables
Hyvärinen, Aapo · 2007
Cited alongside, same era.
From information scaling of natural images to regimes of statistical models
Wu, Ying Nian, Zhu, Song-Chun, and Guo, Cheng-En · 2008
Cited alongside, same era.
Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
Lee, Honglak, Grosse, Roger, Ranganath, Rajesh, and Ng, Andrew Y · 2009
Cited alongside, same era.
Deep boltzmann machines
Salakhutdinov, Ruslan and Hinton, Geoffrey E · 2009
Imagenet classification with deep convolutional neural networks
Krizhevsky, Alex, Sutskever, Ilya, and Hinton, Geoffrey E · 2012
Later among the works it cites.
What regularized auto-encoders learn from the data-generating distribution
Alain, Guillaume and Bengio, Yoshua · 2014
Later among the works it cites.
On the number of linear regions of deep neural networks
Montufar, Guido F, Pascanu, Razvan, Cho, Kyunghyun, and Bengio, Yoshua · 2014
Later among the works it cites.
Learning sparse FRAME models for natural image patterns
Xie, Jianwen, Hu, Wenze, Zhu, Song-Chun, and Wu, Ying Nian · 2014
Later among the works it cites.
Visualizing and understanding convolutional neural networks
Zeiler, Matthew D and Fergus, Rob · 2014
Later among the works it cites.
Generative modeling of convolutional neural networks
Dai, Jifeng, Lu, Yang, and Wu, Ying Nian · 2015
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Learning deep energy models
Ngiam, Jiquan, Chen, Zhenghao, Koh, Pang Wei, and Ng, Andrew Y · 2011
Cited alongside, same era.
On autoencoders and score matching for energy based models
Swersky, Kevin, Ranzato, Marc’Aurelio, Buchman, David, Marlin, Benjamin, and Freitas, Nando · 2011
Cited alongside, same era.
A connection between score matching and denoising autoencoders
Vincent, Pascal · 2011
Cited alongside, same era.
A fast learning algorithm for deep belief nets
Hinton, Geoffrey E., Osindero, Simon, and Teh, Yee-Whye
Cited in the paper.
Unsupervised discovery of nonlinear structure using contrastive backpropagation
Hinton, Geoffrey E, Osindero, Simon, Welling, Max, and Teh, Yee-Whye
Cited in the paper.
Later among the works it cites.
Matconvnet – convolutional neural networks for matlab
Vedaldi, A. and Lenc, K · 2015
Later among the works it cites.
Learning FRAME models using CNN filters
Lu, Yang, Zhu, Song-Chun, and Wu, Ying Nian · 2016
Closest in time.
Inducing wavelets into random fields via generative boosting
Xie, Jianwen, Lu, Yang, Zhu, Song-Chun, and Wu, Ying Nian · 2016
Closest in time.