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2nd MLHPC@SC 2016: Salt Lake City, UT, USA

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2nd Workshop on Machine Learning in HPC Environments, MLHPC@SC, Salt Lake City, UT, USA, November 14, 2016. IEEE Computer Society 2016, ISBN 978-1-5090-3882-4
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  - conf/sc/DrydenMJE16
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Nikoli Dryden, Tim Moon, Sam Ade Jacobs, Brian Van Essen:
Communication Quantization for Data-Parallel Training of Deep Neural Networks. 1-8
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  - conf/sc/TsaiLKD16
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Yaohung M. Tsai, Piotr Luszczek, Jakub Kurzak, Jack J. Dongarra:
Performance-Portable Autotuning of OpenCL Kernels for Convolutional Layers of Deep Neural Networks. 9-18
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  - conf/sc/KeuperP16
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Janis Keuper, Franz-Josef Pfreundt:
Distributed Training of Deep Neural Networks: Theoretical and Practical Limits of Parallel Scalability. 19-26
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  - conf/sc/CameloFL16
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Miguel Camelo, Jeroen Famaey, Steven Latré:
A Scalable Parallel Q-Learning Algorithm for Resource Constrained Decentralized Computing Environments. 27-35
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  - conf/sc/SchumanDSBMKP16
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Catherine D. Schuman, Adam Disney, Susheela P. Singh, Grant Bruer, J. Parker Mitchell, Aleksander Klibisz, James S. Plank:
Parallel Evolutionary Optimization for Neuromorphic Network Training. 36-46
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  - conf/sc/PotokSYPSLYRC16
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Thomas E. Potok, Catherine D. Schuman, Steven R. Young, Robert M. Patton, Federico M. Spedalieri, Jeremy Liu, Ke-Thia Yao, Garrett S. Rose, Gangotree Chakma:
A Study of Complex Deep Learning Networks on High Performance, Neuromorphic, and Quantum Computers. 47-55
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Onkar Bhardwaj, Guojing Cong:
Practical Efficiency of Asynchronous Stochastic Gradient Descent. 56-62

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