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Wei Zhang | Daniel Brand | Ankur Agrawal | Kailash Gopalakrishnan | Jungwook Choi | Chia-Yu Chen | D. Brand | Wei Zhang | A. Agrawal | K. Gopalakrishnan | Chia-Yu Chen | Jungwook Choi
[1] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[2] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[3] Marc'Aurelio Ranzato,et al. Large Scale Distributed Deep Networks , 2012, NIPS.
[4] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[5] Seunghak Lee,et al. More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server , 2013, NIPS.
[6] Yoshua Bengio,et al. Training deep neural networks with low precision multiplications , 2014 .
[7] Dong Yu,et al. 1-bit stochastic gradient descent and its application to data-parallel distributed training of speech DNNs , 2014, INTERSPEECH.
[8] Trevor Darrell,et al. Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.
[9] Trishul M. Chilimbi,et al. Project Adam: Building an Efficient and Scalable Deep Learning Training System , 2014, OSDI.
[10] Nikko Strom,et al. Scalable distributed DNN training using commodity GPU cloud computing , 2015, INTERSPEECH.
[11] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[12] Pritish Narayanan,et al. Deep Learning with Limited Numerical Precision , 2015, ICML.
[13] Yijun Huang,et al. Asynchronous Parallel Stochastic Gradient for Nonconvex Optimization , 2015, NIPS.
[14] Timo Aila,et al. Pruning Convolutional Neural Networks for Resource Efficient Transfer Learning , 2016, ArXiv.
[15] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[16] Song Han,et al. Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding , 2015, ICLR.
[17] Sam Ade Jacobs,et al. Communication Quantization for Data-Parallel Training of Deep Neural Networks , 2016, 2016 2nd Workshop on Machine Learning in HPC Environments (MLHPC).
[18] George Kurian,et al. Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation , 2016, ArXiv.
[19] Bhuvana Ramabhadran,et al. Training variance and performance evaluation of neural networks in speech , 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[20] Kaiming He,et al. Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour , 2017, ArXiv.
[21] S. Gupta,et al. Wildfire: Approximate synchronization of parameters in distributed deep learning , 2017, IBM J. Res. Dev..
[22] Timo Aila,et al. Pruning Convolutional Neural Networks for Resource Efficient Inference , 2016, ICLR.
[23] Cong Xu,et al. TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning , 2017, NIPS.
[24] Suyog Gupta,et al. Model Accuracy and Runtime Tradeoff in Distributed Deep Learning: A Systematic Study , 2015, Industrial Conference on Data Mining.
[25] Geoffrey Zweig,et al. The microsoft 2016 conversational speech recognition system , 2016, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[26] Sameer Kumar,et al. PowerAI DDL , 2017, ArXiv.