Parallel Tempering for Training of Restricted Boltzmann Machines

By Guillaume Desjardins, Aaron C. Courville, Yoshua Bengio, Pascal Vincent, Olivier Delalleau. 2010

85 citations recorded by the source corpus.

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Citations in the corpus

The flip-the-state transition operator for restricted Boltzmann machines (2013, methodology): “, Neal 1993) methods usually based on Gibbs sampling (e.g., Hinton 2002; Tieleman and Hinton 2009; Desjardins et al. 2010).”
Training restricted Boltzmann machines: An introduction (2014, background): “3 Parallel tempering One of the most promising sampling techniques used for RBM training so far is parallel tempering (PT) [44, 12, 10].”
An Introduction to Restricted Boltzmann Machines (2012, methodology): “3 Parallel Tempering One of the most promising sampling technique used for RBM-training so far is parallel tempering (PT, [33,10,8]).”
Learning Deep Energy Models (2011, methodology): “We implemented Parzen estimation using the protocol described by Desjardins et al. (2010), with 10,000 samples per model.”
Tikhonov-Type Regularization for Restricted Boltzmann Machines (2012, methodology): “We used parallel tempering to sample from the model distribution [7, 3], and used the enhanced gradient and the adaptive learning rate, with both an initial learning rate and an upper-bound set to 0 .”
A Two-Stage Pretraining Algorithm for Deep Boltzmann Machines (2013, methodology): “some work in applying more advanced sampling methods [32, 31, 14, 8].”
Understanding Representations Learned in Deep Architectures (2010, methodology): “There has also been some progress in better understanding the effect of unsupervised pre-training and its role as a regularizer (Erhan et al., 2010).”