**arXiv ID:** 1203.4416 **Authors:** Guillaume Desjardins, Aaron Courville, Yoshua Bengio **Published:** 2012-03-20T12:59:15Z **Abstract:** The deep Boltzmann machine (DBM) has been an important development in the quest for powerful "deep" probabilistic models. To date, simultaneous or joint training of all layers of the DBM has been largely unsuccessful with existing training methods. We introduce a simple regularization scheme that encourages the weight vectors associated with each hidden u...
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# On Training Deep Boltzmann Machines
**arXiv ID:** 1203.4416
**Authors:** Guillaume Desjardins, Aaron Courville, Yoshua Bengio
**Published:** 2012-03-20T12:59:15Z
**Abstract:**
The deep Boltzmann machine (DBM) has been an important development in the quest for powerful "deep" probabilistic models. To date, simultaneous or joint training of all layers of the DBM has been largely unsuccessful with existing training methods. We introduce a simple regularization scheme that encourages the weight vectors associated with each hidden unit to have similar norms. We demonstrate that this regularization can be easily combined with standard stochastic maximum likelihood to yield an effective training strategy for the simultaneous training of all layers of the deep Boltzmann machine.
## Skill Description
This skill is generated from the arXiv paper: On Training Deep Boltzmann Machines (1203.4416).
## How to Use
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## References
- [arXiv:1203.4416](http://arxiv.org/abs/1203.4416v1)
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