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Deep neural networks (DNNs) have been shown to tolerate "brain damage": cumulative changes to the network's parameters (e.g., pruning, numerical perturbations) typically result in a graceful degradation of classification accuracy.
Optimal brain damage
Yann LeCun, John S Denker, and Sara A Solla · 1990
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Why tanh: Choosing a sigmoidal function
Barry L Kalman and Stan C Kwasny · 1992
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The effects of adding noise during backpropagation training on a generalization performance
G. An · 1996
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Exploiting machine learning to subvert your spam filter
Blaine Nelson, Marco Barreno, Fuching Jack Chi, Anthony D Joseph, Benjamin IP Rubinstein, Udam Saini, Charles A Sutton, J Doug Tygar, and Kai Xia · 2008
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Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Convolutional deep belief networks on cifar-10
Alex Krizhevsky and Geoff Hinton · 2010
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Poisoning attacks against support vector machines
Battista Biggio, Blaine Nelson, and Pavel Laskov · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition
Johannes Stallkamp, Marc Schlipsing, Jan Salmen, and Christian Igel · 2012
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Security implications of memory deduplication in a virtualized environment
Jidong Xiao, Zhang Xu, Hai Huang, and Haining Wang · 2013
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Densenet: Implementing efficient convnet descriptor pyramids
Forrest Iandola, Matt Moskewicz, Sergey Karayev, Ross Girshick, Trevor Darrell, and Kurt Keutzer · 2014
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Flipping bits in memory without accessing them: An experimental study of dram disturbance errors
Yoongu Kim, Ross Daly, Jeremie Kim, Chris Fallin, Ji Hye Lee, Donghyuk Lee, Chris Wilkerson, Konrad Lai, and Onur Mutlu · 2014
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Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Fixed point optimization of deep convolutional neural networks for object recognition
Sajid Anwar, Kyuyeon Hwang, and Wonyong Sung · 2015
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Deepdriving: Learning affordance for direct perception in autonomous driving
Chenyi Chen, Ari Seff, Alain Kornhauser, and Jianxiong Xiao · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Lenet-5, convolutional neural networks
Yann LeCun et al · 2015
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ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Empirical evaluation of rectified activations in convolutional network, 2015
Bing Xu, Naiyan Wang, Tianqi Chen, and Mu Li · 2015
Cited alongside, same era.
Anvil: Software-based protection against next-generation rowhammer attacks
Zelalem Birhanu Aweke, Salessawi Ferede Yitbarek, Rui Qiao, Reetuparna Das, Matthew Hicks, Yossi Oren, and Todd Austin · 2016
Cited alongside, same era.
Dedup est machina: Memory deduplication as an advanced exploitation vector
Erik Bosman, Kaveh Razavi, Herbert Bos, and Cristiano Giuffrida · 2016
Cited alongside, same era.
Rowhammer. js: A remote software-induced fault attack in javascript
Daniel Gruss, Clémentine Maurice, and Stefan Mangard · 2016
Cited alongside, same era.
Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding
Song Han, Huizi Mao, and William J Dally · 2016
Cited alongside, same era.
Turning your weakness into a strength: Watermarking deep neural networks by backdooring
Yossi Adi, Carsten Baum, Moustapha Cisse, Benny Pinkas, and Joseph Keshet · 2018
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Scalable methods for 8-bit training of neural networks
Ron Banner, Itay Hubara, Elad Hoffer, and Daniel Soudry · 2018
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Practical fault attack on deep neural networks
Jakub Breier, Xiaolu Hou, Dirmanto Jap, Lei Ma, Shivam Bhasin, and Yang Liu · 2018
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Hardware trojan attacks on neural networks, 2018
Joseph Clements and Yingjie Lao · 2018
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Sever: A robust meta-algorithm for stochastic optimization, 2018
Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Jacob Steinhardt, and Alistair Stewart · 2018
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Grand pwning unit: accelerating microarchitectural attacks with the gpu
Pietro Frigo, Cristiano Giuffrida, Herbert Bos, and Kaveh Razavi · 2018
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Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Pruning filters for efficient convnets
Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf · 2016
Cited alongside, same era.
Xnor-net: Imagenet classification using binary convolutional neural networks
Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi · 2016
Cited alongside, same era.
Flip feng shui: Hammering a needle in the software stack
Kaveh Razavi, Ben Gras, Erik Bosman, Bart Preneel, Cristiano Giuffrida, and Herbert Bos · 2016
Cited alongside, same era.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
Cited alongside, same era.
Stealing machine learning models via prediction apis
Florian Tramèr, Fan Zhang, Ari Juels, Michael K. Reiter, and Thomas Ristenpart · 2016
Cited alongside, same era.
Drammer: Deterministic rowhammer attacks on mobile platforms
Victor Van Der Veen, Yanick Fratantonio, Martina Lindorfer, Daniel Gruss, Clémentine Maurice, Giovanni Vigna, Herbert Bos, Kaveh Razavi, and Cristiano Giuffrida · 2016
Cited alongside, same era.
Later among the works it cites.
Tcmalloc : Thread-caching malloc, 2018
Sanjay Ghemawat · 2018
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Another flip in the wall of rowhammer defenses
Daniel Gruss, Moritz Lipp, Michael Schwarz, Daniel Genkin, Jonas Juffinger, Sioli O’Connell, Wolfgang Schoechl, and Yuval Yarom · 2018
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Zebram: Comprehensive and compatible software protection against rowhammer attacks
Radhesh Krishnan Konoth, Marco Oliverio, Andrei Tatar, Dennis Andriesse, Herbert Bos, Cristiano Giuffrida, and Kaveh Razavi · 2018
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Nethammer: Inducing rowhammer faults through network requests
Moritz Lipp, Misiker Tadesse Aga, Michael Schwarz, Daniel Gruss, Clémentine Maurice, Lukas Raab, and Lukas Lamster · 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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Ares: A framework for quantifying the resilience of deep neural networks
Brandon Reagen, Udit Gupta, Lillian Pentecost, Paul Whatmough, Sae Kyu Lee, Niamh Mulholland, David Brooks, and Gu-Yeon Wei · 2018
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Poison frogs! targeted clean-label poisoning attacks on neural networks
Ali Shafahi, W. Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, and Tom Goldstein · 2018
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When does machine learning FAIL? generalized transferability for evasion and poisoning attacks
Octavian Suciu, Radu Marginean, Yigitcan Kaya, Hal Daume III, and Tudor Dumitras · 2018
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Defeating software mitigations against rowhammer: a surgical precision hammer
Andrei Tatar, Cristiano Giuffrida, Herbert Bos, and Kaveh Razavi · 2018
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Defeating software mitigations against rowhammer: a surgical precision hammer
Andrei Tatar, Cristiano Giuffrida, Herbert Bos, and Kaveh Razavi · 2018
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Throwhammer: Rowhammer attacks over the network and defenses
Andrei Tatar, Radhesh Krishnan Konoth, Elias Athanasopoulos, Cristiano Giuffrida, Herbert Bos, and Kaveh Razavi · 2018
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With great training comes great vulnerability: Practical attacks against transfer learning
Bolun Wang, Yuanshun Yao, Bimal Viswanath, Haitao Zheng, and Ben Y. Zhao · 2018
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Training deep neural networks with 8-bit floating point numbers
Naigang Wang, Jungwook Choi, Daniel Brand, Chia-Yu Chen, and Kailash Gopalakrishnan · 2018
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Breaking transferability of adversarial samples with randomness, 2018
Yan Zhou, Murat Kantarcioglu, and Bowei Xi · 2018
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Daniel Gruss, Erik Kraft, Trishita Tiwari, Michael Schwarz, Ari Trachtenberg, Jason Hennessey, Alex Ionescu, and Anders Fogh · 2019
Closest in time.
Jemalloc: general purpose memory allocation functions
Jemalloc manual · 2019
Closest in time.