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Due to their great performance and scalability properties neural networks have become ubiquitous building blocks of many applications.
A simple procedure for pruning back-propagation trained neural networks
E. D. Karnin · 1990
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Second order derivatives for network pruning: Optimal brain surgeon
B. Hassibi and D. G. Stork · 1993
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An iterative pruning algorithm for feedforward neural networks
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Online learning and stochastic approximations
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Secure multi-party computation
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Applications of neural networks to digital communications–a survey
M. Ibnkahla · 2000
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A highly efficient multiplication-free binary arithmetic coder and its application in video coding
D. Marpe and T. Wiegand · 2003
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Framewise phoneme classification with bidirectional lstm and other neural network architectures
A. Graves and J. Schmidhuber · 2005
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Foundations of parallel programming
D. B. Skillicorn · 2005
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Mapreduce: simplified data processing on large clusters
J. Dean and S. Ghemawat · 2008
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Hogwild: A lock-free approach to parallelizing stochastic gradient descent
B. Recht, C. Re, S. Wright, and F. Niu · 2011
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Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
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Managing dicom images: Tips and tricks for the radiologist
D. R. Varma · 2012
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Neural machine translation by jointly learning to align and translate
D. Bahdanau, K. Cho, and Y. Bengio · 2014
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The algorithmic foundations of differential privacy
C. Dwork, A. Roth, et al · 2014
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Sequence to sequence learning with neural networks
I. Sutskever, O. Vinyals, and Q. V. Le · 2014
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
S. Bach, A. Binder, G. Montavon, F. Klauschen, K.-R. Müller, and W. Samek · 2015
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Taming the wild: A unified analysis of hogwild-style algorithms
C. M. De Sa, C. Zhang, K. Olukotun, and C. Ré · 2015
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S. Han, H. Mao, and W. J. Dally · 2015
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Learning both weights and connections for efficient neural network
S. Han, J. Pool, J. Tran, and W. Dally · 2015
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Distilling the knowledge in a neural network
G. Hinton, O. Vinyals, and J. Dean · 2015
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Deep visual-semantic alignments for generating image descriptions
A. Karpathy and L. Fei-Fei · 2015
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Show, attend and tell: Neural image caption generation with visual attention
K. Xu, J. Ba, R. Kiros, K. Cho, A. Courville, R. Salakhudinov, R. Zemel, and Y. Bengio · 2015
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Staleness-aware async-sgd for distributed deep learning
W. Zhang, S. Gupta, X. Lian, and J. Liu · 2015
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Practical secure aggregation for federated learning on user-held data
K. Bonawitz, V. Ivanov, B. Kreuter, A. Marcedone, H. B. McMahan, S. Patel, D. Ramage, A. Segal, and K. Seth · 2016
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Revisiting distributed synchronous sgd
J. Chen, X. Pan, R. Monga, S. Bengio, and R. Jozefowicz · 2016
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Diannao family: energy-efficient hardware accelerators for machine learning
Y. Chen, T. Chen, Z. Xu, N. Sun, and O. Temam · 2016
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Towards the limit of network quantization
Y. Choi, M. El-Khamy, and J. Lee · 2016
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Character-aware neural language models
Y. Kim, Y. Jernite, D. Sontag, and A. M. Rush · 2016
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Federated learning: Strategies for improving communication efficiency
J. Konečnỳ, H. B. McMahan, F. X. Yu, P. Richtárik, A. T. Suresh, and D. Bacon · 2016
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Communication-efficient learning of deep networks from decentralized data
H. B. McMahan, E. Moore, D. Ramage, S. Hampson, et al · 2016
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Reducing the model order of deep neural networks using information theory
M. Tu, V. Berisha, Y. Cao, and J.-s. Seo · 2016
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Deep learning for identifying metastatic breast cancer
D. Wang, A. Khosla, R. Gargeya, H. Irshad, and A. H. Beck · 2016
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Tensor decomposition for compressing recurrent neural network
A. Tjandra, S. Sakti, and S. Nakamura · 2018
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Atomo: Communication-efficient learning via atomic sparsification
H. Wang, S. Sievert, S. Liu, Z. Charles, D. Papailiopoulos, and S. Wright · 2018
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J. Wang and G. Joshi · 2018
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Training deep neural networks with 8-bit floating point numbers
N. Wang, J. Choi, D. Brand, C.-Y. Chen, and K. Gopalakrishnan · 2018
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Federated learning with non-iid data
Y. Zhao, M. Li, L. Lai, N. Suda, D. Civin, and V. Chandra · 2018
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A. F. Aji and K. Heafield · 2017
Cited alongside, same era.
Qsgd: Communication-efficient sgd via gradient quantization and encoding
D. Alistarh, D. Grubic, J. Li, R. Tomioka, and M. Vojnovic · 2017
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Personalized and private peer-to-peer machine learning
A. Bellet, R. Guerraoui, M. Taziki, and M. Tommasi · 2017
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Distributed statistical machine learning in adversarial settings: Byzantine gradient descent
Y. Chen, L. Su, and J. Xu · 2017
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Differentially private federated learning: A client level perspective
R. C. Geyer, T. Klein, and M. Nabi · 2017
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Deep models under the gan: information leakage from collaborative deep learning
B. Hitaj, G. Ateniese, and F. Perez-Cruz · 2017
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Cloud computing for machine learning and cognitive applications
K. Hwang · 2017
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Deep gradient compression: Reducing the communication bandwidth for distributed training
Y. Lin, S. Han, H. Mao, Y. Wang, and W. J. Dally · 2017
Cited alongside, same era.
M. S. H. Abad, E. Ozfatura, D. Gunduz, and O. Ercetin · 2019
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Communication-efficient distributed sgd with sketching
N. Ivkin, D. Rothchild, E. Ullah, V. Braverman, I. Stoica, and R. Arora · 2019
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Improving federated learning personalization via model agnostic meta learning
Y. Jiang, J. Konečnỳ, K. Rush, and S. Kannan · 2019
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Advances and open problems in federated learning
P. Kairouz, H. B. McMahan, B. Avent, A. Bellet, M. Bennis, A. N. Bhagoji, K. Bonawitz, Z. Charles, G. Cormode, R. Cummings, et al · 2019
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Error feedback fixes signsgd and other gradient compression schemes
S. P. Karimireddy, Q. Rebjock, S. U. Stich, and M. Jaggi · 2019
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Decentralized deep learning with arbitrary communication compression
A. Koloskova, T. Lin, S. U. Stich, and M. Jaggi · 2019
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Decentralized stochastic optimization and gossip algorithms with compressed communication
A. Koloskova, S. U. Stich, and M. Jaggi · 2019
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Peer-to-peer federated learning on graphs
A. Lalitha, O. C. Kilinc, T. Javidi, and F. Koushanfar · 2019
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Privacy for free: Communication-efficient learning with differential privacy using sketches
T. Li, Z. Liu, V. Sekar, and V. Smith · 2019
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Federated learning: Challenges, methods, and future directions
T. Li, A. K. Sahu, A. Talwalkar, and V. Smith · 2019
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Edge-assisted hierarchical federated learning with non-iid data
L. Liu, J. Zhang, S. Song, and K. B. Letaief · 2019
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Robust and communication-efficient collaborative learning
A. Reisizadeh, H. Taheri, A. Mokhtari, H. Hassani, and R. Pedarsani · 2019
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F. Sattler, K.-R. Müller, and W. Samek · 2019
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Robust and communication-efficient federated learning from non-iid data
F. Sattler, S. Wiedemann, K.-R. Müller, and W. Samek · 2019
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Sparse binary compression: Towards distributed deep learning with minimal communication
F. Sattler, S. Wiedemann, K.-R. Müller, and W. Samek · 2019
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Megatron-lm: Training multi-billion parameter language models using gpu model parallelism
M. Shoeybi, M. Patwary, R. Puri, P. LeGresley, J. Casper, and B. Catanzaro · 2019
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Deepsqueeze: Parallel stochastic gradient descent with double-pass error-compensated compression
H. Tang, X. Lian, S. Qiu, L. Yuan, C. Zhang, T. Zhang, and J. Liu · 2019
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Powersgd: Practical low-rank gradient compression for distributed optimization
T. Vogels, S. P. Karimireddy, and M. Jaggi · 2019
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Deepcabac: A universal compression algorithm for deep neural networks
S. Wiedemann, H. Kirchoffer, S. Matlage, P. Haase, A. Marban, T. Marinc, D. Neumann, T. Nguyen, A. Osman, D. Marpe, et al · 2019
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Entropy-constrained training of deep neural networks
S. Wiedemann, A. Marban, K.-R. Müller, and W. Samek · 2019
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Compact and computationally efficient representation of deep neural networks
S. Wiedemann, K.-R. Müller, and W. Samek · 2019
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Fbnet: Hardware-aware efficient convnet design via differentiable neural architecture search
B. Wu, X. Dai, P. Zhang, Y. Wang, F. Sun, Y. Wu, Y. Tian, P. Vajda, Y. Jia, and K. Keutzer · 2019
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Pruning by explaining: A novel criterion for deep neural network pruning
S.-K. Yeom, P. Seegerer, S. Lapuschkin, S. Wiedemann, K.-R. Müller, and W. Samek · 2019
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Parallel restarted sgd with faster convergence and less communication: Demystifying why model averaging works for deep learning
H. Yu, S. Yang, and S. Zhu · 2019
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