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More and more companies have deployed machine learning (ML) clusters, where deep learning (DL) models are trained for providing various AI-driven services.
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The Cross Entropy Method for Classification
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Rectified Linear Units Improve Restricted Boltzmann Machines
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Mesos: A Platform for Fine-Grained Resource Sharing in the Data Center
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Speech Recognition with Deep Recurrent Neural Networks
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Apache Hadoop YARN: Yet Another Resource Negotiator
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Multi-Resource Packing for Cluster Schedulers
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Convolutional Neural Networks for Sentence Classification
Y. Kim · 2014
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Scaling Distributed Machine Learning with the Parameter Server
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A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval
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Neural Machine Translation by Jointly Learning to Align and Translate
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Adam: A Method for Stochastic Optimization
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Human-level Control Through Deep Reinforcement Learning
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Very Deep Convolutional Networks for Large-scale Image Recognition
K. Simonyan and A. Zisserman · 2015
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Large-Scale Cluster Management at Google with Borg
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Petuum: A New Platform for Distributed Machine Learning on Big Data
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TensorFlow: A System for Large-Scale Machine Learning
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MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
Aggregated Residual Transformations for Deep Neural Networks
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SLAQ: Quality-Driven Scheduling for Distributed Machine Learning
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Online Job Scheduling in Distributed Machine Learning Clusters
Y. Bao, Y. Peng, C. Wu, and Z. Li · 2018
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Bert: Pre-training of Deep Bidirectional Transformers for Language Understanding
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2018
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Learning Graph-based Cluster Scheduling Algorithms
H. Mao, M. Schwarzkopf, S. Venkatakrishnan, and M. Alizadeh · 2018
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T. Chen, M. Li, Y. Li, M. Lin, N. Wang, M. Wang, T. Xiao, B. Xu, C. Zhang, and Z. Zhang · 2016
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Deep Learning
I. Goodfellow, Y. Bengio, A. Courville, and Y. Bengio · 2016
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Deep Residual Learning for Image Recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Resource Management with Deep Reinforcement Learning
H. Mao, M. Alizadeh, I. Menache, and S. Kandula · 2016
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Asynchronous Methods for Deep Reinforcement Learning
V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu · 2016
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Rethinking the Inception Architecture for Computer Vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2016
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Ako: Decentralised Deep Learning with Partial Gradient Exchange
P. Watcharapichat, V. L. Morales, R. C. Fernandez, and P. Pietzuch · 2016
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A Hierarchical Model for Device Placement
A. Mirhoseini, A. Goldie, H. Pham, B. Steiner, Q. V. Le, and J. Dean · 2018
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Optimus: an Efficient Dynamic Resource Scheduler for Deep Learning Clusters
Y. Peng, Y. Bao, Y. Chen, C. Wu, and C. Guo · 2018
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Litz: Elastic Framework for High-Performance Distributed Machine Learning
A. Qiao, A. Aghayev, W. Yu, H. Chen, Q. Ho, G. A. Gibson, and E. P. Xing · 2018
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Gandiva: Introspective Cluster Scheduling for Deep Learning
W. Xiao, R. Bhardwaj, R. Ramjee, M. Sivathanu, N. Kwatra, Z. Han, P. Patel, X. Peng, H. Zhao, Q. Zhang, F. Yang, and L. Zhou · 2018
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Experience-driven Networking: A Deep Reinforcement Learning based Approach
Z. Xu, J. Tang, J. Meng, W. Zhang, Y. Wang, C. H. Liu, and D. Yang · 2018
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https://caffe2.ai/
Caffe2 · 2019
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https://github.com/Microsoft/CNTK
CNTK · 2019
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https://www.docker.com/
Docker · 2019
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http://www.image-net.org
ImageNet Dataset · 2019
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https://kubernetes.io
Kubernetes · 2019
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https://github.com/apache/incubator-mxnet/tree/master/example
MXNet Official Examples · 2019
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http://www.paddlepaddle.org/
PaddlePaddle · 2019
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https://catalog.ldc.upenn.edu/ldc99t42
Penn Tree Bank Dataset · 2019
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http://tflearn.org/objectives/
Tflearn Objectives · 2019
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https://github.com/apache/incubator-mxnet/tree/master/example/gluon/word_language_model
Word Language Model · 2019
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Deep Learning-based Job Placement in Distributed Machine Learning Clusters
Y. Bao, Y. Peng, and C. Wu · 2019
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Learning Scheduling Algorithms for Data Processing Clusters
H. Mao, M. Schwarzkopf, S. B. Venkatakrishnan, Z. Meng, and M. Alizadeh · 2019
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