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- W3025526632 abstract "Restricted Boltzmann machine (RBM) is a generative stochastic neural network that can be applied to collaborative filtering technique used by recommendation systems. Prediction accuracy of the RBM model is usually better than that of other models for recommendation systems. However, training the RBM model involves Markov-Chain Monte Carlo method, which is computationally expensive. In this paper, we have successfully applied distributed parallel training using Horovod framework to improve the training time of the RBM model. Our tests show that the distributed training approach of the RBM model has a good scaling efficiency. We also show that this approach effectively reduces the training time to little over 12 min on 64 CPU nodes compared to 5 h on a single CPU node. This will make RBM models more practically applicable in recommendation systems." @default.
- W3025526632 created "2020-05-21" @default.
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- W3025526632 date "2020-05-01" @default.
- W3025526632 modified "2023-10-16" @default.
- W3025526632 title "Parallelized Training of Restricted Boltzmann Machines Using Markov-Chain Monte Carlo Methods" @default.
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- W3025526632 doi "https://doi.org/10.1007/s42979-020-00170-7" @default.
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