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Deep learning models can take weeks to train on a single GPU-equipped machine, necessitating scaling out DL training to a GPU-cluster.
Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A · 2009
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Torch7: A Matlab-like Environment for Machine Learning
Collobert, R., Kavukcuoglu, K., and Farabet, C · 2011
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Large Scale Distributed Deep Networks
Dean, J., Corrado, G. S., Monga, R., Chen, K., Devin, M., Le, Q. V., Mao, M. Z., Ranzato, M., Senior, A., Tucker, P., Yang, K., and Ng, A. Y · 2012
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ImageNet Classification with Deep Convolutional Neural Networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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Building High-level Features Using Large Scale Unsupervised Learning
Le, Q. V., Monga, R., Devin, M., Chen, K., Corrado, G. S., Dean, J., and Ng, A. Y · 2012
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Deep Learning with COTS HPC Systems
Coates, A., Huval, B., Wang, T., Wu, D. J., Ng, A. Y., and Catanzaro, B · 2013
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Recent Advances in Deep Learning for Speech Research at Microsoft
Deng, L., Li, J., Huang, J.-T., Yao, K., Yu, D., Seide, F., Seltzer, M. L., Zweig, G., He, X., Williams, J., Gong, Y., and Acero, A · 2013
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More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server
Ho, Q., Cipar, J., Cui, H., Kim, J. K., Lee, S., Gibbons, P. B., Gibson, G. A., Ganger, G. R., and Xing, E. P · 2013
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Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., and Dean, J · 2013
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Project Adam: Building an Efficient and Scalable Deep Learning Training System
Chilimbi, T., Apacible, Y. S. J., and Kalyanaraman, K · 2014
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Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., and Darrell, T · 2014
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Scaling distributed machine learning with the parameter server
Li, M., Andersen, D. G., Park, J. W., Smola, A. J., Ahmed, A., Josifovski, V., Long, J., Shekita, E. J., and Su, B.-Y · 2014
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1-bit stochastic gradient descent and its application to data-parallel distributed training of speech dnns
Seide, F., Fu, H., Droppo, J., Li, G., and Yu, D · 2014
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On parallelizability of stochastic gradient descent for speech dnns
Seide, F., Fu, H., Droppo, J., Li, G., and Yu, D · 2014
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Mariana: Tencent Deep Learning Platform and its Applications
Zou, Y., Jin, X., Li, Y., Guo, Z., Wang, E., and Xiao, B · 2014
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Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems
Chen, T., Li, M., Li, Y., Lin, M., Wang, N., Wang, M., Xiao, T., Xu, B., Zhang, C., and Zhang, Z · 2015
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Analysis of high-performance distributed ml at scale through parameter server consistency models
Dai, W., Kumar, A., Wei, J., Ho, Q., Gibson, G., and Xing, E. P · 2015
SINGA: Putting Deep Learning in the Hands of Multimedia Users
Wang, W., Chen, G., Dinh, T. T. A., Gao, J., Ooi, B. C., Tan, K.-L., and Wang, S · 2015
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Managed Communication and Consistency for Fast Data-parallel Iterative Analytics
Wei, J., Dai, W., Qiao, A., Ho, Q., Cui, H., Ganger, G. R., Gibbons, P. B., Gibson, G. A., and Xing, E. P · 2015
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Distributed Machine Learning via Sufficient Factor Broadcasting
Xie, P., Kim, J. K., Zhou, Y., Ho, Q., Kumar, A., Yu, Y., and Xing, E · 2015
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Hd-cnn: Hierarchical deep convolutional neural network for image classification
Yan, Z., Zhang, H., Jagadeesh, V., DeCoste, D., Di, W., and Piramuthu, R · 2015
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Poseidon: A system architecture for efficient gpu-based deep learning on multiple machines
Zhang, H., Hu, Z., Wei, J., Xie, P., Kim, G., Ho, Q., and Xing, E · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2015
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Malt: distributed data-parallelism for existing ml applications
Li, H., Kadav, A., Kruus, E., and Ungureanu, C · 2015
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Sparknet: Training deep networks in spark
Moritz, P., Nishihara, R., Stoica, I., and Jordan, M. I · 2015
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ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L · 2015
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Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., and Zisserman, A · 2015
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Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A · 2015
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Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2015
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Tensorflow: A system for large-scale machine learning
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al · 2016
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Revisiting distributed synchronous sgd
Chen, J., Monga, R., Bengio, S., and Jozefowicz, R · 2016
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Geeps: Scalable deep learning on distributed gpus with a gpu-specialized parameter server
Cui, H., Zhang, H., Ganger, G. R., Gibbons, P. B., and Xing, E. P · 2016
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Firecaffe: near-linear acceleration of deep neural network training on compute clusters
Iandola, F. N., Moskewicz, M. W., Ashraf, K., and Keutzer, K · 2016
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Ako: Decentralised deep learning with partial gradient exchange
Watcharapichat, P., Morales, V. L., Fernandez, R. C., and Pietzuch, P · 2016
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Automatic photo adjustment using deep neural networks
Yan, Z., Zhang, H., Wang, B., Paris, S., and Yu, Y · 2016
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Recurrent topic-transition gan for visual paragraph generation
Liang, X., Hu, Z., Zhang, H., Gan, C., and Xing, E. P · 2017
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