TY - GEN
T1 - Parallel tempering is efficient for learning restricted Boltzmann machines
AU - Cho, Kyunghyun
AU - Raiko, Tapani
AU - Ilin, Alexander
PY - 2010
Y1 - 2010
N2 - A new interest towards restricted Boltzmann machines (RBMs) has risen due to their usefulness in greedy learning of deep neural networks. While contrastive divergence learning has been considered an efficient way to learn an RBM, it has a drawback due to a biased approximation in the learning gradient. We propose to use an advanced Monte Carlo method called parallel tempering instead, and show experimentally that it works efficiently.
AB - A new interest towards restricted Boltzmann machines (RBMs) has risen due to their usefulness in greedy learning of deep neural networks. While contrastive divergence learning has been considered an efficient way to learn an RBM, it has a drawback due to a biased approximation in the learning gradient. We propose to use an advanced Monte Carlo method called parallel tempering instead, and show experimentally that it works efficiently.
UR - http://www.scopus.com/inward/record.url?scp=79959388970&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79959388970&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2010.5596837
DO - 10.1109/IJCNN.2010.5596837
M3 - Conference contribution
AN - SCOPUS:79959388970
SN - 9781424469178
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
T2 - 2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
Y2 - 18 July 2010 through 23 July 2010
ER -