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Deep learning methods have shown great promise in many practicalapplications, ranging from speech recognition, visual object recognition, totext processing. However, most of the current deep learning methods suffer fromscalability problems for large-scale applications, forcing researchers or usersto focus on small-scale problems with fewer parameters.  In this paper, we consider a well-known machine learning model, deep beliefnetworks (DBNs) that have yielded impressive classification performance on alarge number of benchmark machine learning tasks. To scale up DBN, we proposean approach that can use the computing clusters in a distributed environment totrain large models, while the dense matrix computations within a single machineare sped up using graphics processors (GPU). When training a DBN, each machinerandomly drops out a portion of neurons in each hidden layer, for each trainingcase, making the remaining neurons only learn to detect features that aregenerally helpful for producing the correct answer. Within our approach, wehave developed four methods to combine outcomes from each machine to form aunified model. Our preliminary experiment on the mnst handwritten digitdatabase demonstrates that our approach outperforms the state of the art testerror rate.

Partitioning Large Scale Deep Belief Networks Using Dropout