Joint Training of Deep Boltzmann Machines
By Ian J. Goodfellow, Aaron C. Courville, Yoshua Bengio. 2012
27 citations recorded by the source corpus.
We introduce a new method for training deep Boltzmann machines jointly. Prior methods require an initial learning pass that trains the deep Boltzmann machine greedily, one layer at a time, or do not perform well on classifi- cation tasks.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Generative Models, Probabilistic & Bayesian Methods
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Yoshua Bengio
- Kyunghyun Cho
- Quoc V. Le
- Aaron C. Courville
- Mohit Bansal
- Jianfeng Gao
- Graham Neubig
- Ruslan Salakhutdinov
In Computer Vision and Pattern Recognition
- Yoshua Bengio
- Luc Van Gool
- Kyunghyun Cho
- Dacheng Tao
- Trevor Darrell
- Xiaogang Wang
- Jitendra Malik
- Andrew Zisserman
In Generative Models
- Yoshua Bengio
- Stefano Ermon
- Aaron C. Courville
- Igor Mordatch
- Ying Nian Wu
- Yilun Du
- Jiaming Song
- Yang Song
In Probabilistic & Bayesian Methods
- Yoshua Bengio
- Pascal Vincent
- Aaron C. Courville
- Kyunghyun Cho
- L. Yao
- Asja Fischer
- Ruslan Salakhutdinov
- Stefano Ermon
Citations in the corpus
- Deep Learning of Representations: Looking Forward (2013, methodology): “Note also that sparse coding can be generalized into the spike-and-slab sparse coding algorithm (Goodfellow et al., 2012), in which MAP inference is replaced by variational inference, and that was used to win the NIPS 2011 transfer learning challenge (Goodfellow et al., 2011).”