Fetching the paper…
Reading the bibliography…
Adversarial examples highlight model vulnerabilities and are useful for evaluation and interpretation.
Framewise phoneme classification with bidirectional LSTM and other neural network architectures
Alex Graves and Jürgen Schmidhuber. 2005 · 2005
Earlier work this paper cites.
Evasion attacks against machine learning at test time
Battista Biggio, Igino Corona, Davide Maiorca, Blaine Nelson, Nedim Šrndić, Pavel Laskov, Giorgio Giacinto, and Fabio Roli. 2013 · 2013
Earlier work this paper cites.
Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Ng, and Christopher Potts. 2013 · 2013
Earlier work this paper cites.
GloVe: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014 · 2014
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus. 2014 · 2014
Earlier work this paper cites.
A large annotated corpus for learning natural language inference
Samuel R Bowman, Gabor Angeli, Christopher Potts, and Christopher D Manning. 2015 · 2015
Earlier work this paper cites.
Understanding neural networks through representation erasure
Jiwei Li, Will Monroe, and Dan Jurafsky. 2016 · 2016
Earlier work this paper cites.
Crafting adversarial input sequences for recurrent neural networks
Nicolas Papernot, Patrick D. McDaniel, Ananthram Swami, and Richard E. Harang. 2016 · 2016
Earlier work this paper cites.
A decomposable attention model for natural language inference
Ankur P Parikh, Oscar Täckström, Dipanjan Das, and Jakob Uszkoreit. 2016 · 2016
Earlier work this paper cites.
SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
Earlier work this paper cites.
Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
Earlier work this paper cites.
Adversarial attacks on deep learning models in natural language processing: A survey
Wei Emma Zhang, Quan Z Sheng, and Ahoud Abdulrahmn F Alhazmi. 2019 · 2016
Earlier work this paper cites.
Adversarial patch
Tom B Brown, Dandelion Mané, Aurko Roy, Martín Abadi, and Justin Gilmer. 2017 · 2017
Cited alongside, same era.
Enhanced LSTM for natural language inference
Qian Chen, Xiaodan Zhu, Zhenhua Ling, Si Wei, Hui Jiang, and Diana Inkpen. 2017 · 2017
Cited alongside, same era.
Adversarial examples for evaluating reading comprehension systems
Robin Jia and Percy Liang. 2017 · 2017
Cited alongside, same era.
Universal adversarial perturbations
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, and Pascal Frossard. 2017 · 2017
Cited alongside, same era.
Bidirectional attention flow for machine comprehension
Min Joon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. 2017 · 2017
Cited alongside, same era.
Synthetic and natural noise both break neural machine translation
Yonatan Belinkov and Yonatan Bisk. 2018 · 2018
Cited alongside, same era.
Deep contextualized word representations
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
Later among the works it cites.
Hypothesis only baselines in natural language inference
Adam Poliak, Jason Naradowsky, Aparajita Haldar, Rachel Rudinger, and Benjamin Van Durme. 2018 · 2018
Later among the works it cites.
Semantically equivalent adversarial rules for debugging NLP models
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2018 · 2018
Later among the works it cites.
What makes reading comprehension questions easier?
Saku Sugawara, Kentaro Inui, Satoshi Sekine, and Akiko Aizawa. 2018 · 2018
Later among the works it cites.
QANet: Combining local convolution with global self-attention for reading comprehension
Adams Wei Yu, David Dohan, Minh-Thang Luong, Rui Zhao, Kai Chen, Mohammad Norouzi, and Quoc V. Le. 2018 · 2018
Later among the works it cites.
Universal adversarial attacks on text classifiers
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Seq2sick: Evaluating the robustness of sequence-to-sequence models with adversarial examples
Minhao Cheng, Jinfeng Yi, Huan Zhang, Pin-Yu Chen, and Cho-Jui Hsieh. 2018 · 2018
Cited alongside, same era.
Pathologies of neural models make interpretations difficult
Shi Feng, Eric Wallace, Alvin Grissom II, Mohit Iyyer, Pedro Rodriguez, and Jordan Boyd-Graber. 2018 · 2018
Cited alongside, same era.
Annotation artifacts in natural language inference data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel R. Bowman, and Noah A. Smith. 2018 · 2018
Cited alongside, same era.
Facebook translates “good morning” into “attack them”, leading to arrest
Alex Hern. 2018 · 2018
Cited alongside, same era.
Adversarial example generation with syntactically controlled paraphrase networks
Mohit Iyyer, John Wieting, Kevin Gimpel, and Luke Zettlemoyer. 2018 · 2018
Cited alongside, same era.
Advances in pre-training distributed word representations
Tomas Mikolov, Edouard Grave, Piotr Bojanowski, Christian Puhrsch, and Armand Joulin. 2018 · 2018
Cited alongside, same era.
Melika Behjati, Seyed-Mohsen Moosavi-Dezfooli, Mahdieh Soleymani Baghshah, and Pascal Frossard. 2019 · 2019
Closest in time.
Detecting egregious responses in neural sequence-to-sequence models
Tianxing He and James Glass. 2019 · 2019
Closest in time.
Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference
R Thomas McCoy, Ellie Pavlick, and Tal Linzen. 2019 · 2019
Closest in time.
On evaluation of adversarial perturbations for sequence-to-sequence models
Paul Michel, Xian Li, Graham Neubig, and Juan Miguel Pino. 2019 · 2019
Closest in time.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Closest in time.
Trick me if you can: Human-in-the-loop generation of adversarial examples for question answering
Eric Wallace, Pedro Rodriguez, Shi Feng, Ikuya Yamada, and Jordan Boyd-Graber. 2019 · 2019
Closest in time.