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Pre-trained models (PTMs) have been widely used in various downstream tasks.
Spam filtering with naive bayes - which naive bayes?
Vangelis Metsis, Ion Androutsopoulos, and Georgios Paliouras · 2006
Earlier work this paper cites.
Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition
J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel · 2012
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Recursive deep models for semantic compositionality over a sentiment treebank
R. Socher, Alex Perelygin, J. Wu, Jason Chuang, Christopher D. Manning, A. Ng, and Christopher Potts · 2013
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Explaining and harnessing adversarial examples
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Aligning books and movies: Towards story-like visual explanations by watching movies and reading books
Yukun Zhu, Ryan Kiros, Richard S. Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler · 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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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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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens van der Maaten, and Kilian Q. Weinberger · 2017
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Badnets: Identifying vulnerabilities in the machine learning model supply chain
Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Garg · 2017
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A downsampled variant of imagenet as an alternative to the CIFAR datasets
Patryk Chrabaszcz, Ilya Loshchilov, and Frank Hutter · 2017
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Security risks in deep learning implementations
Qixue Xiao, Kang Li, Deyue Zhang, and Weilin Xu · 2018
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Model-reuse attacks on deep learning systems
Yujie Ji, Xinyang Zhang, Shouling Ji, Xiapu Luo, and Ting Wang · 2018
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Trojaning attack on neural networks
Yingqi Liu, Shiqing Ma, Yousra Aafer, Wen-Chuan Lee, Juan Zhai, Weihang Wang, and Xiangyu Zhang · 2018
Cited alongside, same era.
Fine-pruning: Defending against backdooring attacks on deep neural networks
Kang Liu, Brendan Dolan-Gavitt, and Siddharth Garg · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Revealing the dark secrets of BERT
Olga Kovaleva, Alexey Romanov, Anna Rogers, and Anna Rumshisky · 2019
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Analyzing multi-head self-attention: Specialized heads do the heavy lifting, the rest can be pruned
Elena Voita, David Talbot, Fedor Moiseev, Rico Sennrich, and Ivan Titov · 2019
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Albert: A lite bert for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut · 2020
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Is bert really robust? a strong baseline for natural language attack on text classification and entailment
Di Jin, Zhijing Jin, Joey Tianyi Zhou, and Peter Szolovits · 2020
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Word-level textual adversarial attacking as combinatorial optimization
Yuan Zang, Chenghao Yang, Fanchao Qi, Z. Liu, Meng Zhang, Qun Liu, and Maosong Sun · 2020
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Humpty dumpty: Controlling word meanings via corpus poisoning
Roei Schuster, Tal Schuster, Yoav Meri, and Vitaly Shmatikov · 2020
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Extracting training data from large language models
Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, et al · 2020
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Y. Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, M. Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
Cited alongside, same era.
Programmable neural network trojan for pre-trained feature extractor
Yu Ji, Zixin Liu, Xing Hu, Peiqi Wang, and Youhui Zhang · 2019
Cited alongside, same era.
A backdoor attack against lstm-based text classification systems
Jiazhu Dai, Chuanshuai Chen, and Yufeng Li · 2019
Cited alongside, same era.
Predicting the type and target of offensive posts in social media
Marcos Zampieri, Shervin Malmasi, Preslav Nakov, Sara Rosenthal, Noura Farra, and Ritesh Kumar · 2019
Cited alongside, same era.
Neural cleanse: Identifying and mitigating backdoor attacks in neural networks
Bolun Wang, Yuanshun Yao, Shawn Shan, Huiying Li, Bimal Viswanath, Haitao Zheng, and Ben Y. Zhao · 2019
Cited alongside, same era.
Shaofeng Li, Shiqing Ma, Minhui Xue, and Benjamin Zi Hao Zhao · 2020
Cited alongside, same era.
Weight poisoning attacks on pretrained models
Keita Kurita, Paul Michel, and Graham Neubig · 2020
Cited alongside, same era.
Badnl: Backdoor attacks against nlp models
Xiaoyi Chen, Ahmed Salem, Michael Backes, Shiqing Ma, and Yang Zhang · 2020
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Natural backdoor attack on text data
Lichao Sun · 2020
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Trojaning language models for fun and profit
Xinyang Zhang, Zheng Zhang, and Ting Wang · 2020
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Blind backdoors in deep learning models
Eugene Bagdasaryan and Vitaly Shmatikov · 2020
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A target-agnostic attack on deep models: Exploiting security vulnerabilities of transfer learning
Shahbaz Rezaei and Xin Liu · 2020
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Mlp-mixer: An all-mlp architecture for vision
Ilya O. Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, and Alexey Dosovitskiy · 2021
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Pay attention to mlps
Hanxiao Liu, Zihang Dai, David R. So, and Quoc V. Le · 2021
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Neural attention distillation: Erasing backdoor triggers from deep neural networks
Yige Li, Xixiang Lyu, Nodens Koren, Lingjuan Lyu, Bo Li, and Xingjun Ma · 2021
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