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Training machine learning (ML) models typically involves expensive iterative optimization.
“Robust Watermarking of Neural Network with Exponential Weighting”
Ryota Namba and Jun Sakuma · 1901
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Soham Pal et al · 1905
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“High-Fidelity Extraction of Neural Network Models”
Matthew Jagielski et al · 1909
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“Piracy Resistant Watermarks for Deep Neural Networks”
Huiying Li, Emily Wenger, Ben. Zhao and Haitao Zheng · 1910
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“On the Intrinsic Privacy of Stochastic Gradient Descent”
Stephanie. Hyland and Shruti Tople · 1912
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“A mathematical theory of communication”
Claude Shannon · 1948
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“A stochastic approximation method”
Herbert Robbins and Sutton Monro · 1951
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“The Kolmogorov-Smirnov Test for Goodness of Fit”
Frank. Jr · 1951
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“Learning representations by back-propagating errors”
David Rumelhart, Geoffrey Hinton and Ronald Williams · 1986
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“A digital signature based on a conventional encryption function”
Ralph Merkle · 1987
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“Pricing via Processing or Combatting Junk Mail”
Cynthia Dwork and Moni Naor · 1992
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“Comparison of learning algorithms for handwritten digit recognition”
Yann LeCun et al · 1995
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“The MNIST database of handwritten digits”
Yann LeCun · 1998
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“Proofs of Work and Bread Pudding Protocols(Extended Abstract)”
Markus Jakobsson and Ari Juels · 1999
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“Hashcash - A Denial of Service Counter-Measure”, 2002
Adam Back · 2002
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“Entangled Watermarks as a Defense against Model Extraction”
Hengrui Jia, Christopher. Choquette-Choo and Nicolas Papernot · 2002
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“Entangled Watermarks as a Defense against Model Extraction”
Hengrui Jia, Christopher. Choquette-Choo, Varun Chandrasekaran and Nicolas Papernot · 2002
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“New client puzzle outsourcing techniques for DoS resistance”
Brent Waters, Ari Juels, J. Halderman and Edward Felten · 2004
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“Moderately Hard, Memory-bound Functions”
Martín Abadi, Michael Burrows, Mark Manasse and Ted Wobber · 2005
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“Exponential Memory-Bound Functions for Proof of Work Protocols” https://eprint.iacr.org/2005/356 , Cryptology ePrint Archive, Report 2005/356, 2005
Fabien Coelho · 2005
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“Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)”
Thomas. Cover and Joy. Thomas · 2006
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“Efficient arguments without short PCPs”
Yuval Ishai, Eyal Kushilevitz and Rafail Ostrovsky · 2007
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“An (Almost) Constant-Effort Solution-Verification Proof-of-Work Protocol based on Merkle Trees” https://eprint.iacr.org/2007/433 , Cryptology ePrint Archive, Report 2007/433, 2007
Fabien Coelho · 2007
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“A Guided Tour Puzzle for Denial of Service Prevention”
M. Abliz and T. Znati · 2009
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“Bitcoin: A Peer-to-Peer Electronic Cash System”
Satoshi Nakamoto · 2009
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“A Survey on Model Watermarking Neural Networks”
Franziska Boenisch · 2009
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“Learning Multiple Layers of Features from Tiny Images”, 2009
Alex Krizhevsky · 2009
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“Understanding the difficulty of training deep feedforward neural networks”
Xavier Glorot and Yoshua Bengio · 2010
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“Adversarial machine learning”
Ling Huang et al · 2011
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“Deep sparse rectifier neural networks”
Xavier Glorot, Antoine Bordes and Yoshua Bengio · 2011
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“Parallel random numbers: as easy as 1, 2, 3”
John Salmon, Mark Moraes, Ron Dror and David Shaw · 2011
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“Making argument systems for outsourced computation practical (sometimes).”
Srinath Setty, Richard McPherson, Andrew Blumberg and Michael Walfish · 2012
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“Poisoning attacks against support vector machines”
Battista Biggio, Blaine Nelson and Pavel Laskov · 2012
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“Large scale distributed deep networks”
Jeffrey Dean et al · 2012
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“Verifying computations with state”
Benjamin Braun et al · 2013
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“Self-Normalizing Neural Networks”
Günter Klambauer, Thomas Unterthiner, Andreas Mayr and Sepp Hochreiter · 2017
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“Embedded Proofs for Verifiable Neural Networks”
Hervé Chabanne, Julien Keuffer and Refik Molva · 2017
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“SecureML: A System for Scalable Privacy-Preserving Machine Learning”
P. Mohassel and Y. Zhang · 2017
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“UCI Machine Learning Repository”, 2017
Dheeru Dua and Casey Graff · 2017
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“Nvidia tensor core programmability, performance & precision”
Stefano Markidis et al · 2018
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“Copycat CNN: Stealing Knowledge by Persuading Confession with Random Non-Labeled Data”
Jacson Correia-Silva et al · 2018
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“Intriguing properties of neural networks”
Christian Szegedy et al · 2013
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“Evasion attacks against machine learning at test time”
Battista Biggio et al · 2013
Cited alongside, same era.
“Stochastic gradient descent with differentially private updates”
Shuang Song, Kamalika Chaudhuri and Anand Sarwate · 2013
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“The nature of statistical learning theory”
Vladimir Vapnik · 2013
Cited alongside, same era.
“Exact solutions to the nonlinear dynamics of learning in deep linear neural networks”
Andrew. Saxe, James. McClelland and Surya Ganguli · 2013
Cited alongside, same era.
“Splittable pseudorandom number generators using cryptographic hashing”
Koen Claessen and Michał Pałka · 2013
Cited alongside, same era.
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“SoK: Towards the Science of Security and Privacy in Machine Learning”
Nicolas Papernot, Patrick McDaniel, Arunesh Sinha and Michael Wellman · 2018
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“Wild patterns: Ten years after the rise of adversarial machine learning”
Battista Biggio and Fabio Roli · 2018
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“Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring”
Yossi Adi et al · 2018
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“Protecting Intellectual Property of Deep Neural Networks with Watermarking”
Jialong Zhang et al · 2018
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“Model Extraction and Active Learning”
Varun Chandrasekaran et al · 2018
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“Defending Against Machine Learning Model Stealing Attacks Using Deceptive Perturbations”
Taesung Lee, Benjamin Edwards, Ian Molloy and Dong Su · 2018
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“Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks”
K. Liu, Brendan Dolan-Gavitt and Siddharth Garg · 2018
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“How to Start Training: The Effect of Initialization and Architecture”
Boris Hanin and David Rolnick · 2018
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“Stochastic backward Euler: an implicit gradient descent algorithm for k-means clustering”
Penghang Yin, Minh Pham, Adam Oberman and Stanley Osher · 2018
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“Fine-pruning: Defending against backdooring attacks on deep neural networks”
Kang Liu, Brendan Dolan-Gavitt and Siddharth Garg · 2018
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“The lottery ticket hypothesis: Finding sparse, trainable neural networks”
Jonathan Frankle and Michael Carbin · 2018
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“Knockoff nets: Stealing functionality of black-box models”
Tribhuvanesh Orekondy, Bernt Schiele and Mario Fritz · 2019
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“CSI NN: Reverse Engineering of Neural Network Architectures Through Electromagnetic Side Channel”
Lejla Batina, Shivam Bhasin, Dirmanto Jap and Stjepan Picek · 2019
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“Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks”
Bolun Wang et al · 2019
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“The Byzantine generals problem”
Leslie Lamport, Robert Shostak and Marshall Pease · 2019
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“Leveraging zero-knowledge succinct arguments of knowledge for efficient verification of outsourced training of artificial neural networks”, 2019
M.J. van Zande · 2019
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“Pytorch: An imperative style, high-performance deep learning library”
Adam Paszke et al · 2019
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“OpenAI’s GPT-3 Language Model: A Technical Overview” Library Catalog: lambdalabs.com, 2020
Chuan Li · 2020
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“vCNN: Verifiable Convolutional Neural Network”
Seunghwa Lee, Hankyung Ko, Jihye Kim and Hyunok Oh · 2020
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“High Accuracy and High Fidelity Extraction of Neural Networks”
Matthew Jagielski et al · 2020
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“SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python”
Pauli Virtanen et al · 2020
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“Dataset Inference: Ownership Resolution in Machine Learning”
Pratyush Maini, Mohammad Yaghini and Nicolas Papernot · 2021
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