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Supervised neural networks, which first map an input $x$ to a single representation $z$, and then map $z$ to the output label $y$, have achieved remarkable success in a wide range of natural language processing (NLP) tasks.
Roberta: A robustly optimized bert pretraining approach
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Achieving verified robustness to symbol substitutions via interval bound propagation
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Hal Daume III and Daniel Marcu. 2006 · 2006
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Yi Zhou, Xiaoqing Zheng, Cho-Jui Hsieh, Kai-wei Chang, and Xuanjing Huang. 2020 · 2006
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Frustratingly easy domain adaptation
Hal Daumé III. 2009 · 2009
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Dumitru Erhan, Yoshua Bengio, Aaron Courville, and Pascal Vincent. 2009 · 2009
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Songbo Tan, Xueqi Cheng, Yuefen Wang, and Hongbo Xu. 2009 · 2009
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Yoshua Bengio, Aaron Courville, and Pascal Vincent. 2013 · 2013
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Auto-encoding variational bayes
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Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2013 · 2013
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. 2013 · 2013
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A convolutional neural network for modelling sentences
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Towards a general rule for identifying deceptive opinion spam
Jiwei Li, Myle Ott, Claire Cardie, and Eduard Hovy. 2014 · 2014
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Conditional generative adversarial nets
Mehdi Mirza and Simon Osindero. 2014 · 2014
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Visual domain adaptation: An overview of recent advances
Vishal M Patel, Raghuraman Gopalan, Ruonan Li, and Rama Chellappa. 2014 · 2014
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Sequence to sequence learning with neural networks
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How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson. 2014 · 2014
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Visualizing and understanding convolutional networks
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A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
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New transfer learning techniques for disparate label sets
Young-Bum Kim, Karl Stratos, Ruhi Sarikaya, and Minwoo Jeong. 2015 · 2015
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Visualizing and understanding neural models in nlp
Jiwei Li, Xinlei Chen, Eduard Hovy, and Dan Jurafsky. 2015 · 2015
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Stanford neural machine translation systems for spoken language domain
Minh-Thang Luong and Christopher D. Manning. 2015 · 2015
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Anh Nguyen, Jason Yosinski, and Jeff Clune. 2015 · 2015
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Deep text classification can be fooled
Bin Liang, Hongcheng Li, Miaoqiang Su, Pan Bian, Xirong Li, and Wenchang Shi. 2017 · 2017
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Practical black-box attacks against machine learning
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami. 2017 · 2017
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Opening the black box of deep neural networks via information
Ravid Shwartz-Ziv and Naftali Tishby. 2017 · 2017
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Unsupervised learning via total correlation explanation
Greg Ver Steeg. 2017 · 2017
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Data noising as smoothing in neural network language models
Ziang Xie, Sida I Wang, Jiwei Li, Daniel Lévy, Aiming Nie, Dan Jurafsky, and Andrew Y Ng. 2017 · 2017
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Naftali Tishby and Noga Zaslavsky. 2015 · 2015
Cited alongside, same era.
Maximally informative hierarchical representations of high-dimensional data
Greg Ver Steeg and Aram Galstyan. 2015 · 2015
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Deep variational information bottleneck
Alexander A Alemi, Ian Fischer, Joshua V Dillon, and Kevin Murphy. 2016 · 2016
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Infogan: Interpretable representation learning by information maximizing generative adversarial nets
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel. 2016 · 2016
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Fast domain adaptation for neural machine translation
Markus Freitag and Yaser Al-Onaizan. 2016 · 2016
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Rationalizing neural predictions
Tao Lei, Regina Barzilay, and Tommi Jaakkola. 2016 · 2016
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Understanding neural networks through representation erasure
Jiwei Li, Will Monroe, and Dan Jurafsky. 2016 · 2016
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Generating natural adversarial examples
Zhengli Zhao, Dheeru Dua, and Sameer Singh. 2017 · 2017
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Generating natural language adversarial examples
Moustafa Alzantot, Yash Sharma, Ahmed Elgohary, Bo-Jhang Ho, Mani Srivastava, and Kai-Wei Chang. 2018 · 2018
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Understanding disentangling in β \beta -vae
Christopher P Burgess, Irina Higgins, Arka Pal, Loic Matthey, Nick Watters, Guillaume Desjardins, and Alexander Lerchner. 2018 · 2018
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Isolating sources of disentanglement in variational autoencoders
Ricky TQ Chen, Xuechen Li, Roger B Grosse, and David K Duvenaud. 2018 · 2018
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The multitarget ted talks task
Kevin Duh. 2018 · 2018
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Auto-encoding total correlation explanation
Shuyang Gao, Rob Brekelmans, Greg Ver Steeg, and Aram Galstyan. 2018 · 2018
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Meta-learning for low-resource neural machine translation
Jiatao Gu, Yong Wang, Yun Chen, Kyunghyun Cho, and Victor OK Li. 2018 · 2018
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Learning deep representations by mutual information estimation and maximization
R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio. 2018 · 2018
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Adversarial example generation with syntactically controlled paraphrase networks
Mohit Iyyer, John Wieting, Kevin Gimpel, and Luke Zettlemoyer. 2018 · 2018
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Hyunjik Kim and Andriy Mnih. 2018 · 2018
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Variational inference of disentangled latent concepts from unlabeled observations
Abhishek Kumar, Prasanna Sattigeri, and Avinash Balakrishnan. 2018 · 2018
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Transfer Learning for Named-Entity Recognition with Neural Networks
Ji Young Lee, Franck Dernoncourt, and Peter Szolovits. 2018 · 2018
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Delete, retrieve, generate: A simple approach to sentiment and style transfer
Juncen Li, Robin Jia, He He, and Percy Liang. 2018 · 2018
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Semantically equivalent adversarial rules for debugging nlp models
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2018 · 2018
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Interpretable adversarial perturbation in input embedding space for text
Motoki Sato, Jun Suzuki, Hiroyuki Shindo, and Yuji Matsumoto. 2018 · 2018
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Design challenges and misconceptions in neural sequence labeling
Jie Yang, Shuailong Liang, and Yue Zhang. 2018 · 2018
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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
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Transferable end-to-end aspect-based sentiment analysis with selective adversarial learning
Zheng Li, Xin Li, Ying Wei, Lidong Bing, Yu Zhang, and Qiang Yang. 2019b · 2019
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Challenging common assumptions in the unsupervised learning of disentangled representations
Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Raetsch, Sylvain Gelly, Bernhard Schölkopf, and Olivier Bachem. 2019 · 2019
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Neural Transfer Learning for Natural Language Processing
Sebastian Ruder. 2019 · 2019
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Adversarial examples: Attacks and defenses for deep learning
Xiaoyong Yuan, Pan He, Qile Zhu, and Xiaolin Li. 2019 · 2019
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Adversarial and domain-aware bert for cross-domain sentiment analysis
Chunning Du, Haifeng Sun, Jingyu Wang, Qi Qi, and Jianxin Liao. 2020 · 2020
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Spanbert: Improving pre-training by representing and predicting spans
Mandar Joshi, Danqi Chen, Yinhan Liu, Daniel S Weld, Luke Zettlemoyer, and Omer Levy. 2020 · 2020
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Robustness verification for transformers
Zhouxing Shi, Huan Zhang, Kai-Wei Chang, Minlie Huang, and Cho-Jui Hsieh. 2020 · 2020
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Improving document-level neural machine translation with domain adaptation
Sami Ul Haq, Sadaf Abdul Rauf, Arslan Shoukat, and Noor-e Hira. 2020 · 2020
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Neural adaptation layers for cross-domain named entity recognition
Bill Yuchen Lin and Wei Lu. 2018 · 2022
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