Horn: A System for Parallel Training and Regularizing of Large-Scale   Neural Networks
  I introduce a new distributed system for effective training and regularizing of Large-Scale Neural Networks on distributed computing architectures. The experiments demonstrate the effectiveness of flexible model partitioning and parallelization strategies based on neuron-centric computation model, with an implementation of the collective and parallel dropout neural networks training. Experiments are performed on MNIST handwritten digits classification including results. 
Edward J. Yoon
cs.DC cs.LG cs.NE