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Neural network pruning has been an essential technique to reduce the computation and memory requirements for using deep neural networks for resource-constrained devices.
On information and sufficiency
Solomon Kullback and Richard A Leibler · 1951
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Skeletonization: A technique for trimming the fat from a network via relevance assessment
Michael Mozer and Paul Smolensky · 1988
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Online algorithms and stochastic approximations
David Saad · 1998
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Differential privacy: A survey of results
Cynthia Dwork · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
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Privacy-preserving deep learning
Reza Shokri and Vitaly Shmatikov · 2015
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Explaining and harnessing adversarial examples
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Nationtelescope: Monitoring and visualizing large-scale collective behavior in lbsns
Dingqi Yang, Daqing Zhang, Longbiao Chen, and Bingqing Qu · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding
Song Han, Huizi Mao, and William J. Dally · 2016
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam D. Smith · 2016
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Deep learning with differential privacy
Martín Abadi, Andy Chu, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Lei Jimmy Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton · 2016
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Multi-class texture analysis in colorectal cancer histology
Jakob Nikolas Kather, Cleo-Aron Weis, Francesco Bianconi, Susanne M Melchers, Lothar R Schad, Timo Gaiser, Alexander Marx, and Frank Gerrit Zöllner · 2016
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Participatory cultural mapping based on collective behavior data in location-based social networks
Dingqi Yang, Daqing Zhang, and Bingqing Qu · 2016
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Gaussian error linear units (gelus)
Dan Hendrycks and Kevin Gimpel · 2016
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Pruning filters for efficient convnets
Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf · 2017
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Learning efficient convolutional networks through network slimming
Zhuang Liu, Jianguo Li, Zhiqiang Shen, Gao Huang, Shoumeng Yan, and Changshui Zhang · 2017
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Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
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ZOO: zeroth order optimization based black-box attacks to deep neural networks without training substitute models
Pin-Yu Chen, Huan Zhang, Yash Sharma, Jinfeng Yi, and Cho-Jui Hsieh · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Machine learning with membership privacy using adversarial regularization
Milad Nasr, Reza Shokri, and Amir Houmansadr · 2018
Cited alongside, same era.
Privacy risk in machine learning: Analyzing the connection to overfitting
Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha · 2018
Cited alongside, same era.
Practical black-box attacks on deep neural networks using efficient query mechanisms
Arjun Nitin Bhagoji, Warren He, Bo Li, and Dawn Song · 2018
Cited alongside, same era.
Non-local neural networks
Effects of differential privacy and data skewness on membership inference vulnerability
Stacey Truex, Ling Liu, Mehmet Emre Gursoy, Wenqi Wei, and Lei Yu · 2019
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What is the state of neural network pruning?
Davis W. Blalock, Jose Javier Gonzalez Ortiz, Jonathan Frankle, and John V. Guttag · 2020
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Gan-leaks: A taxonomy of membership inference attacks against generative models
Dingfan Chen, Ning Yu, Yang Zhang, and Mario Fritz · 2020
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Membership inference attacks on sequence-to-sequence models: Is my data in your machine translation system?
Sorami Hisamoto, Matt Post, and Kevin Duh · 2020
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Yang Zou, Zhikun Zhang, Michael Backes, and Yang Zhang · 2020
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Xiaolong Wang, Ross B. Girshick, Abhinav Gupta, and Kaiming He · 2018
Cited alongside, same era.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David A. Wagner · 2018
Cited alongside, same era.
Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning
Milad Nasr, Reza Shokri, and Amir Houmansadr · 2019
Cited alongside, same era.
LOGAN: membership inference attacks against generative models
Jamie Hayes, Luca Melis, George Danezis, and Emiliano De Cristofaro · 2019
Cited alongside, same era.
Auditing data provenance in text-generation models
Congzheng Song and Vitaly Shmatikov · 2019
Cited alongside, same era.
The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2019
Cited alongside, same era.
What do compressed deep neural networks forget? arxiv e-prints, art
Sara Hooker, Aaron Courville, Gregory Clark, Yann Dauphin, and Andrea Frome · 2019
Cited alongside, same era.
Once-for-all: Train one network and specialize it for efficient deployment
Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, and Song Han · 2020
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Eagleeye: Fast sub-net evaluation for efficient neural network pruning
Bailin Li, Bowen Wu, Jiang Su, and Guangrun Wang · 2020
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Comparing rewinding and fine-tuning in neural network pruning
Alex Renda, Jonathan Frankle, and Michael Carbin · 2020
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Michela Paganini · 2020
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Stolen memories: Leveraging model memorization for calibrated white-box membership inference
Klas Leino and Matt Fredrikson · 2020
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Characterising bias in compressed models
Sara Hooker, Nyalleng Moorosi, Gregory Clark, Samy Bengio, and Emily Denton · 2020
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Membership inference attacks and defenses in classification models
Jiacheng Li, Ninghui Li, and Bruno Ribeiro · 2021
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Membership inference attack on graph neural networks
Iyiola E. Olatunji, Wolfgang Nejdl, and Megha Khosla · 2021
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Differential privacy protection against membership inference attack on machine learning for genomic data
Junjie Chen, Wendy Hui Wang, and Xinghua Shi · 2021
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Systematic evaluation of privacy risks of machine learning models
Liwei Song and Prateek Mittal · 2021
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Practical blind membership inference attack via differential comparisons
Bo Hui, Yuchen Yang, Haolin Yuan, Philippe Burlina, Neil Zhenqiang Gong, and Yinzhi Cao · 2021
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Differential privacy defenses and sampling attacks for membership inference
Shadi Rahimian, Tribhuvanesh Orekondy, and Mario Fritz · 2021
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Label-only membership inference attacks
Christopher A Choquette-Choo, Florian Tramer, Nicholas Carlini, and Nicolas Papernot · 2021
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Membership leakage in label-only exposures
Zheng Li and Yang Zhang · 2021
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