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Relying on the fact that not all inputs require the same amount of computation to yield a confident prediction, multi-exit networks are gaining attention as a prominent approach for pushing the limits of efficient deployment.
Remarks on Some Nonparametric Estimates of a Density Function
Murray Rosenblatt · 1956
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Least Squares Quantization in PCM
Stuart P. Lloyd · 1982
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A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise
Martin Ester, Hans-Peter Kriegel, Jörg Sander, and Xiaowei Xu · 1996
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FIPS 180-2: Secure Hash Standard
NIST · 2002
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An Efficient K Nearest Neighbors Searching Algorithm for A Query Line
Subhas C. Nandy, Sandip Das, and Partha P. Goswami · 2003
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K-Isomorphism: Privacy Preserving Network Publication against Structural Attacks
James Cheng, Ada Wai chee Fu, and Jia Liu · 2010
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HMAC-based Extract-and-Expand Key Derivation Function (HKDF)
Hugo Krawczyk and Pasi Eronen · 2010
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Evasion Attacks against Machine Learning at Test Time
Battista Biggio, Igino Corona, Davide Maiorca, Blaine Nelson, Nedim Srndic, Pavel Laskov, Giorgio Giacinto, and Fabio Roli · 2013
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Generative Adversarial Nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Intriguing Properties of Neural Networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
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Optimal Randomized Classification in Adversarial Settings
Yevgeniy Vorobeychik and Bo Li · 2014
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Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
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Explaining and Harnessing Adversarial Examples
Ian Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Distilling the Knowledge in a Neural Network
Geoffrey E. Hinton, Oriol Vinyals, and Jeffrey Dean · 2015
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Scalable Optimization of Randomized Operational Decisions in Adversarial Classification Settings
Bo Li and Yevgeniy Vorobeychik · 2015
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FitNets: Hints for Thin Deep Nets
Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio · 2015
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Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan and Andrew Zisserman · 2015
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Deep Learning with Differential Privacy
Martin Abadi, Andy Chu, Ian Goodfellow, Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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The Limitations of Deep Learning in Adversarial Settings
Nicolas Papernot, Patrick D. McDaniel, Somesh Jha, Matt Fredrikson, Z. Berkay Celik, and Ananthram Swami · 2016
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BranchyNet: Fast Inference via Early Exiting from Deep Neural Networks
Surat Teerapittayanon, Bradley McDanel, and H. T. Kung · 2016
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Stealing Machine Learning Models via Prediction APIs
Florian Tramèr, Fan Zhang, Ari Juels, Michael K. Reiter, and Thomas Ristenpart · 2016
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Wide Residual Networks
Sergey Zagoruyko and Nikos Komodakis · 2016
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Towards Evaluating the Robustness of Neural Networks
Nicholas Carlini and David Wagner · 2017
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Badnets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain
Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Grag · 2017
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Neurosurgeon: Collaborative Intelligence Between the Cloud and Mobile Edge
Yiping Kang, Johann Hauswald, Cao Gao, Austin Rovinski, Trevor Mudge, Jason Mars, and Lingjia Tang · 2017
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Practical Black-Box Attacks Against Machine Learning
Nicolas Papernot, Patrick D. McDaniel, Ian Goodfellow, Somesh Jha, Z. Berkay Celik, and Ananthram Swami · 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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Machine Learning Models that Remember Too Much
Congzheng Song, Thomas Ristenpart, and Vitaly Shmatikov · 2017
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Ensemble Adversarial Training: Attacks and Defenses
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel · 2017
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Automated Crowdturfing Attacks and Defenses in Online Review Systems
Yuanshun Yao, Bimal Viswanath, Jenna Cryan, Haitao Zheng, and Ben Y. Zhao · 2017
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Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring
Yossi Adi, Carsten Baum, Moustapha Cisse, Benny Pinkas, and Joseph Keshet · 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.
Property Inference Attacks on Fully Connected Neural Networks using Permutation Invariant Representations
Karan Ganju, Qi Wang, Wei Yang, Carl A. Gunter, and Nikita Borisov · 2018
Cited alongside, same era.
LEMNA: Explaining Deep Learning based Security Applications
Wenbo Guo, Dongliang Mu, Jun Xu, Purui Su, and Gang Wang abd Xinyu Xing · 2018
Cited alongside, same era.
HopSkipJumpAttack: A Query-Efficient Decision-Based Attack
Jianbo Chen, Michael I. Jordan, and Martin J. Wainwright · 2020
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DynaBERT: Dynamic BERT with Adaptive Width and Depth
Lu Hou, Zhiqi Huang, Lifeng Shang, Xin Jiang, Xiao Chen, and Qun Liu · 2020
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Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference
Ting-Kuei Hu, Tianlong Chen, Haotao Wang, and Zhangyang Wang · 2020
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Defending Against Model Stealing Attacks With Adaptive Misinformation
Sanjay Kariyappa and Moinuddin K. Qureshi · 2020
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SPINN: Synergistic Progressive Inference of Neural Networks over Device and Cloud
Stefanos Laskaridis, Stylianos I. Venieris, Mário Almeida, Ilias Leontiadis, and Nicholas D. Lane · 2020
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Multi-Scale Dense Networks for Resource Efficient Image Classification
Gao Huang, Danlu Chen, Tianhong Li, Felix Wu, Laurens van der Maaten, and Kilian Q. Weinberger · 2018
Cited alongside, same era.
Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning
Matthew Jagielski, Alina Oprea, Battista Biggio, Chang Liu, Cristina Nita-Rotaru, and Bo Li · 2018
Cited alongside, same era.
Edge Intelligence: On-Demand Deep Learning Model Co-Inference with Device-Edge Synergy
En Li, Zhi Zhou, and Xu Chen · 2018
Cited alongside, same era.
Machine Learning with Membership Privacy using Adversarial Regularization
Milad Nasr, Reza Shokri, and Amir Houmansadr · 2018
Cited alongside, same era.
SoK: Towards the Science of Security and Privacy in Machine Learning
Nicolas Papernot, Patrick McDaniel, Arunesh Sinha, and Michael Wellman · 2018
Cited alongside, same era.
DeepSigns: A Generic Watermarking Framework for IP Protection of Deep Learning Models
Bita Darvish Rouhani, Huili Chen, and Farinaz Koushanfar · 2018
Cited alongside, same era.
MobileNetV2: Inverted Residuals and Linear Bottlenecks
Mark Sandler, Andrew G. Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
Cited alongside, same era.
Klas Leino and Matt Fredrikson · 2020
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QEBA: Query-Efficient Boundary-Based Blackbox Attack
Huichen Li, Xiaojun Xu, Xiaolu Zhang, Shuang Yang, and Bo Li · 2020
Later among the works it cites.
FastBERT: a Self-distilling BERT with Adaptive Inference Time
Weijie Liu, Peng Zhou, Zhiruo Wang, Zhe Zhao, Haotang Deng, and Qi Ju · 2020
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Differential Privacy Defenses and Sampling Attacks for Membership Inference
Shadi Rahimian, Tribhuvanesh Orekondy, and Mario Fritz · 2020
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Green AI
Roy Schwartz, Jesse Dodge, Noah A. Smith, and Oren Etzioni · 2020
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Dual Dynamic Inference: Enabling More Efficient, Adaptive, and Controllable Deep Inference
Yue Wang, Jianghao Shen, Ting-Kuei Hu, Pengfei Xu, Tan M. Nguyen, Richard G. Baraniuk, Zhangyang Wang, and Yingyan Lin · 2020
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DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference
Ji Xin, Raphael Tang, Jaejun Lee, Yaoliang Yu, and Jimmy Lin · 2020
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BERT Loses Patience: Fast and Robust Inference with Early Exit
Wangchunshu Zhou, Canwen Xu, Tao Ge, Julian J. McAuley, Ke Xu, and Furu Wei · 2020
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Label-Only Membership Inference Attacks
Christopher A. Choquette Choo, Florian Tramèr, Nicholas Carlini, and Nicolas Papernot · 2021
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Fingerprinting Multi-exit Deep Neural Network Models via Inference Time
Tian Dong, Han Qiu, Tianwei Zhang, Jiwei Li, Hewu Li, and Jialiang Lu · 2021
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Node-Level Membership Inference Attacks Against Graph Neural Networks
Xinlei He, Rui Wen, Yixin Wu, Michael Backes, Yun Shen, and Yang Zhang · 2021
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Quantifying and Mitigating Privacy Risks of Contrastive Learning
Xinlei He and Yang Zhang · 2021
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A Panda? No, It’s a Sloth: Slowdown Attacks on Adaptive Multi-Exit Neural Network Inference
Sanghyun Hong, Yigitcan Kaya, Ionut-Vlad Modoranu, and Tudor Dumitras · 2021
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Stealing Machine Learning Models: Attacks and Countermeasures for Generative Adversarial Networks
Hailong Hu and Jun Pang · 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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Multi-Exit Semantic Segmentation Networks
Alexandros Kouris, Stylianos I. Venieris, Stefanos Laskaridis, and Nicholas D. Lane · 2021
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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 Leakage in Label-Only Exposures
Zheng Li and Yang Zhang · 2021
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Membership Privacy for Machine Learning Models Through Knowledge Transfer
Virat Shejwalkar and Amir Houmansadr · 2021
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Systematic Evaluation of Privacy Risks of Machine Learning Models
Liwei Song and Prateek Mittal · 2021
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Harmonized Dense Knowledge Distillation Training for Multi-Exit Architectures
Xinglu Wang and Yingming Li · 2021
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Membership-Doctor: Comprehensive Assessment of Membership Inference Against Machine Learning Models
Xinlei He, Zheng Li, Weilin Xu, Cory Cornelius, and Yang Zhang · 2022
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Can’t Steal? Cont-Steal! Contrastive Stealing Attacks Against Image Encoders
Zeyang Sha, Xinlei He, Ning Yu, Michael Backes, and Yang Zhang · 2022
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Property Inference Attacks Against GANs
Junhao Zhou, Yufei Chen, Chao Shen, and Yang Zhang · 2022
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