At the dated check, the references listed below either did not resolve in
Crossref or DataCite, or carried a retraction notice. Each one is shown with the
registry record that put it there.
The 97 references without a DOI — listed, not checked
no DOI — not checkedMartín Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg S. Corrado Andy Davis Jeffrey Dean Matthieu Devin Sanjay Ghemawat Ian Goodfellow Andrew Harp Geoffrey Irving Michael Isard Yangqing Jia Rafal Jozefowicz Lukasz Kaiser Manjunath Kudlur Josh Levenberg Dandelion Mané Rajat Monga Sherry Moore Derek Murray Chris Olah Mike Schuster Jonathon Shlens Benoit Steiner Ilya Sutskever Kunal Talwar Paul Tucker Vincent Vanhoucke Vijay Vasudevan Fernanda Viégas Oriol Vinyals Pete Warden Martin Wattenberg Martin Wicke Yuan Yu and Xiaoqiang Zheng. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Retrieved from https://www.tensorflow.org/. Martín Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg S. Corrado Andy Davis Jeffrey Dean Matthieu Devin Sanjay Ghemawat Ian Goodfellow Andrew Harp Geoffrey Irving Michael Isard Yangqing Jia Rafal Jozefowicz Lukasz Kaiser Manjunath Kudlur Josh Levenberg Dandelion Mané Rajat Monga Sherry Moore Derek Murray Chris Olah Mike Schuster Jonathon Shlens Benoit Steiner Ilya Sutskever Kunal Talwar Paul Tucker Vincent Vanhoucke Vijay Vasudevan Fernanda Viégas Oriol Vinyals Pete Warden Martin Wattenberg Martin Wicke Yuan Yu and Xiaoqiang Zheng. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Retrieved from https://www.tensorflow.org/.
no DOI — not checkedProceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI’16)
no DOI — not checkedAdapteva Inc. 2017. E64G401 Epiphany 64-core Microprocessor Datasheet. Retrieved from http://www.adapteva.com/docs/e64g401_datasheet.pdf. Adapteva Inc. 2017. E64G401 Epiphany 64-core Microprocessor Datasheet. Retrieved from http://www.adapteva.com/docs/e64g401_datasheet.pdf.
no DOI — not checkedWhat does fault tolerant deep learning need from MPI? CoRR abs/1709.03316
no DOI — not checkedAmazon Web Services. 2018. Amazon SageMaker. Retrieved from https://aws.amazon.com/sagemaker/developer-resources/. Amazon Web Services. 2018. Amazon SageMaker. Retrieved from https://aws.amazon.com/sagemaker/developer-resources/.
no DOI — not checkedProceedings of the 33rd International Conference on Machine Learning, Maria Florina Balcan and Kilian Q. Weinberger (Eds.)
no DOI — not checkedProceedings of the ACM/IEEE Conference on Supercomputing. IEEE Computer Society Press, 2--11
no DOI — not checkedApple. 2017. Core ML Model Format Specification. Retrieved from https://apple.github.io/coremltools/coremlspecification/. Apple. 2017. Core ML Model Format Specification. Retrieved from https://apple.github.io/coremltools/coremlspecification/.
no DOI — not checkedApple. 2018. A12 Bionic. Retrieved from https://www.apple.com/iphone-xs/a12-bionic/. Apple. 2018. A12 Bionic. Retrieved from https://www.apple.com/iphone-xs/a12-bionic/.
no DOI — not checkedHow to backdoor federated learning. arXiv preprint arXiv:1807.00459
no DOI — not checkedProceedings of the International Conference on Advances in Neural Information Processing Systems. 91--98
no DOI — not checkedProceedings of the Conference on Learning Theory. 26--1.
no DOI — not checkedRandom search for hyper-parameter optimization. J. Mach. Learn. Res. 13 (Feb
no DOI — not checkedBernstein and Eric Newcomer
no DOI — not checkedLatent Dirichlet allocation
no DOI — not checkedDavide Del Testa
no DOI — not checkedRandom forests. Mach. Learn. 45, 1 (1
no DOI — not checkedRajkumar Buyya et al. 1999. High Performance Cluster Computing: Architectures and Systems. Prentice Hall Upper SaddleRiver NJ 999. Rajkumar Buyya et al. 1999. High Performance Cluster Computing: Architectures and Systems. Prentice Hall Upper SaddleRiver NJ 999.
no DOI — not checkedSibyl: A system for large scale supervised machine learning
no DOI — not checkedRevisiting distributed synchronous SGD. CoRR abs/1604.00981
no DOI — not checkedMXNet: A flexible and efficient machine learning library for heterogeneous distributed systems. CoRR abs/1512.01274
no DOI — not checkedProceedings of the 11th USENIX Symposium on Operating Systems Design and Implementation (OSDI’14)
no DOI — not checkedFrançois Chollet et al. 2015. Keras. Retrieved from https://keras.io/. François Chollet et al. 2015. Keras. Retrieved from https://keras.io/.
no DOI — not checkedProceedings of the International Conference on Advances in Neural Information Processing Systems. 281--288
no DOI — not checkedBuchanan
no DOI — not checkedProceedings of the International Conference on Machine Learning. 1337--1345
no DOI — not checkedDistributed Systems: Concepts and Design. Pearson Education.
no DOI — not checkedProceedings of the USENIX Annual Technical Conference. 37--48
no DOI — not checkedProceedings of the 25th International Conference on Neural Information Processing Systems
no DOI — not checkedProceedings of the 6th Conference on Operating Systems Design 8 Implementation
no DOI — not checkedAdaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12 (July
no DOI — not checkedFacebook. 2017. Gloo. Retrieved from https://github.com/facebookincubator/gloo. Facebook. 2017. Gloo. Retrieved from https://github.com/facebookincubator/gloo.
no DOI — not checkedAnastasia Ailamaki, and Babak Falsafi.
no DOI — not checkedErmias Gebremeskel. 2018. Analysis and comparison of distributed training techniques for deep neural networks in a dynamic environment. (2018). Ermias Gebremeskel. 2018. Analysis and comparison of distributed training techniques for deep neural networks in a dynamic environment. (2018).
no DOI — not checkedAndrew Gibiansky. 2017. Bringing HPC Techniques to Deep Learning. Retrieved from http://research.baidu.com/bringing-hpc-techniques-deep-learning/. Andrew Gibiansky. 2017. Bringing HPC Techniques to Deep Learning. Retrieved from http://research.baidu.com/bringing-hpc-techniques-deep-learning/.
no DOI — not checkedProceedings of the International Conference on Advances in Neural Information Processing Systems
no DOI — not checkedGoogle. 2017. Google Cloud TPU. Retrieved from https://cloud.google.com/tpu. Google. 2017. Google Cloud TPU. Retrieved from https://cloud.google.com/tpu.
no DOI — not checkedlarge minibatch SGD: Training ImageNet in 1 hour. CoRR abs/1706.02677
no DOI — not checkedUsing MPI: Portable Parallel Programming with the Message-passing Interface
no DOI — not checkedBreeze: Numerical Processing Library for Scala.
no DOI — not checkedProceedings of the 4th Workshop on General Purpose Processing on Graphics Processing Units. 3 (Mar. 2011)
no DOI — not checkedElmar Haußmann. 2018. Accelerating I/O Bound Deep Learning on Shared Storage. Retrieved from https://blog.riseml.com/accelerating-io-bound-deep-learning-e0e3f095fd0. Elmar Haußmann. 2018. Accelerating I/O Bound Deep Learning on Shared Storage. Retrieved from https://blog.riseml.com/accelerating-io-bound-deep-learning-e0e3f095fd0.
no DOI — not checkedDeep residual learning for image recognition. CoRR abs/1512.03385
no DOI — not checkedSalakhutdinov
no DOI — not checkedProceedings of the 15th Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann Publishers Inc., 289--296
no DOI — not checkedProceedings of the 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI’17)
no DOI — not checkedIBM Cloud. 2018. IBM Watson Machine Learning. Retrieved from https://www.ibm.com/cloud/machine-learning. IBM Cloud. 2018. IBM Watson Machine Learning. Retrieved from https://www.ibm.com/cloud/machine-learning.
no DOI — not checkedSylvain Jeaugey. 2017. NCCL 2.0. Retrieved from http://on-demand.gputechconf.com/gtc/2017/presentation/s7155-jeaugey-nccl.pdf. Sylvain Jeaugey. 2017. NCCL 2.0. Retrieved from http://on-demand.gputechconf.com/gtc/2017/presentation/s7155-jeaugey-nccl.pdf.
no DOI — not checkedMitchell
no DOI — not checkedKim
no DOI — not checkedProceedings of the International Conference on Machine Learning
no DOI — not checkedProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. ACM, 8.
no DOI — not checkedProceedings of the 27th International Conference on Neural Information Processing Systems (NIPS’14)
no DOI — not checkedBig Learning NIPS Workshop
no DOI — not checkedA. R. Mamidala G. Kollias C. Ward and F. Artico. 2018. MXNET-MPI: Embedding MPI parallelism in parameter server task model for scaling deep learning. ArXiv e-prints (Jan. 2018). arxiv:cs.DC/1801.03855. A. R. Mamidala G. Kollias C. Ward and F. Artico. 2018. MXNET-MPI: Embedding MPI parallelism in parameter server task model for scaling deep learning. ArXiv e-prints (Jan. 2018). arxiv:cs.DC/1801.03855.
no DOI — not checkedH. Brendan McMahan Eider Moore Daniel Ramage and Blaise Agüera y Arcas. 2016. Federated learning of deep networks using model averaging. (2016). H. Brendan McMahan Eider Moore Daniel Ramage and Blaise Agüera y Arcas. 2016. Federated learning of deep networks using model averaging. (2016).
no DOI — not checkedCade Metz. 2018. Big bets on AI open a new frontier for chip start-ups too. The New York Times 14 Jan. (2018). Retrieved from https://www.nytimes.com/2018/01/14/technology/artificial-intelligence-chip-start-ups.html. Cade Metz. 2018. Big bets on AI open a new frontier for chip start-ups too. The New York Times 14 Jan. (2018). Retrieved from https://www.nytimes.com/2018/01/14/technology/artificial-intelligence-chip-start-ups.html.
no DOI — not checkedMicrosoft. 2018. Microsoft Azure Machine Learning. Retrieved from https://azure.microsoft.com/en-us/overview/machine-learning/. Microsoft. 2018. Microsoft Azure Machine Learning. Retrieved from https://azure.microsoft.com/en-us/overview/machine-learning/.
no DOI — not checkedMicrosoft Inc. 2015. Distributed Machine Learning Toolkit (DMTK). Retrieved from http://www.dmtk.io. Microsoft Inc. 2015. Distributed Machine Learning Toolkit (DMTK). Retrieved from http://www.dmtk.io.
no DOI — not checkedDistributed algorithms for topic models
no DOI — not checkedNVIDIA Corporation. 2015. NVIDIA Collective Communications Library (NCCL). Retrieved from https://developer.nvidia.com/nccl. NVIDIA Corporation. 2015. NVIDIA Collective Communications Library (NCCL). Retrieved from https://developer.nvidia.com/nccl.
no DOI — not checkedNVIDIA Corporation. 2017. Nvidia Tesla V100. Retrieved from https://www.nvidia.com/en-us/data-center/tesla-v100/. NVIDIA Corporation. 2017. Nvidia Tesla V100. Retrieved from https://www.nvidia.com/en-us/data-center/tesla-v100/.
no DOI — not checkedAndreas Olofsson. 2016. Epiphany-V: A 1024 processor 64-bit RISC system-on-chip. (2016). Andreas Olofsson. 2016. Epiphany-V: A 1024 processor 64-bit RISC system-on-chip. (2016).
no DOI — not checkedKickstarting high-performance energy-efficient manycore architectures with epiphany. arXiv preprint arXiv:1412.5538
no DOI — not checkedOpitz and Richard Maclin
no DOI — not checkedChung
no DOI — not checkedGenomic big data hitting the storage bottleneck. EMBnet. J. 24
no DOI — not checkedAdam Paszke Sam Gross Soumith Chintala Gregory Chanan Edward Yang Zachary DeVito Zeming Lin Alban Desmaison Luca Antiga and Adam Lerer. 2017. Automatic differentiation in PyTorch. (2017). Adam Paszke Sam Gross Soumith Chintala Gregory Chanan Edward Yang Zachary DeVito Zeming Lin Alban Desmaison Luca Antiga and Adam Lerer. 2017. Automatic differentiation in PyTorch. (2017).
no DOI — not checkedMachine learning and cloud computing: Survey of distributed and SaaS solutions. arXiv preprint arXiv:1603.08767
no DOI — not checkedProceedings of the TeraGrid Conference. 12--15
no DOI — not checkedProceedings of the 26th International Conference on Machine Learning. ACM, 873--880
no DOI — not checkedAVX-512 instructions
no DOI — not checkedAn in-depth look at Google’s first Tensor Processing Unit (TPU). Google Cloud Big Data Mach. Learn. Blog 12
no DOI — not checkedHorovod: Fast and easy distributed deep learning in TensorFlow.
no DOI — not checkedProceedings of the 21st International Conference on Pattern Recognition (ICPR’12)
no DOI — not checkedAmazon Web Services. 2016. Introducing Amazon EC2 P2 Instances the Largest GPU-Powered Virtual Machine in the Cloud. Retrieved from https://aws.amazon.com/about-aws/whats-new/2016/09/introducing-amazon-ec2-p2-instances-the-largest-gpu-powered-virtual-machine-in-the-cloud/. Amazon Web Services. 2016. Introducing Amazon EC2 P2 Instances the Largest GPU-Powered Virtual Machine in the Cloud. Retrieved from https://aws.amazon.com/about-aws/whats-new/2016/09/introducing-amazon-ec2-p2-instances-the-largest-gpu-powered-virtual-machine-in-the-cloud/.
no DOI — not checkedAmazon Web Services. 2017. Amazon EC2 F1 Instances. Retrieved from https://aws.amazon.com/ec2/instance-types/f1/. Amazon Web Services. 2017. Amazon EC2 F1 Instances. Retrieved from https://aws.amazon.com/ec2/instance-types/f1/.
no DOI — not checkedShai Shalev-Shwartz and Tong Zhang. 2013. Stochastic dual coordinate ascent methods for regularized loss minimization. (2013). Shai Shalev-Shwartz and Tong Zhang. 2013. Stochastic dual coordinate ascent methods for regularized loss minimization. (2013).
no DOI — not checkedProceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2323--2324
no DOI — not checkedPerformance modeling and evaluation of distributed deep learning frameworks on GPUs. CoRR abs/1711.05979
no DOI — not checkedProceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security. ACM, 1310--1321
no DOI — not checkedProceedings of the IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP’17)
no DOI — not checkedApplication-specific Integrated Circuits
no DOI — not checkedProceedings of the International Conference on Advances in Neural Information Processing Systems
no DOI — not checkedGoing deeper with convolutions. CoRR abs/1409.4842
no DOI — not checkedRethinking the inception architecture for computer vision. CoRR abs/1512.00567
no DOI — not checkedProceedings of the International Conference on Machine Learning (ICML’13)
no DOI — not checkedThe Khronos Group. 2018. Neural Network Exchange Format (NNEF). Retrieved from https://www.khronos.org/registry/NNEF/specs/1.0/nnef-1.0.pdf. The Khronos Group. 2018. Neural Network Exchange Format (NNEF). Retrieved from https://www.khronos.org/registry/NNEF/specs/1.0/nnef-1.0.pdf.
no DOI — not checkedProceedings of the International Conference on Advances in Neural Information Processing Systems.
no DOI — not checkedNo peek: A survey of private distributed deep learning. arXiv preprint arXiv:1812.03288
no DOI — not checkedXing
no DOI — not checkedP. Xie J. K. Kim Y. Zhou Q. Ho A. Kumar Y. Yu and E. Xing. 2015. Distributed machine learning via sufficient factor broadcasting. (2015). P. Xie J. K. Kim Y. Zhou Q. Ho A. Kumar Y. Yu and E. Xing. 2015. Distributed machine learning via sufficient factor broadcasting. (2015).
no DOI — not checkedPetuum: A new platform for distributed machine learning on big data. ArXiv e-prints (Dec.
no DOI — not checkedLearning to compose words into sentences with reinforcement learning. CoRR abs/1611.09100
no DOI — not checkedProceedings of the 9th USENIX Conference on Networked Systems Design and Implementation. USENIX Association, 2--2.
no DOI — not checkedProceedings of the 2nd USENIX Conference on Hot Topics in Cloud Computing (HotCloud’10)
no DOI — not checkedXing
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