Fetching the paper…
Reading the bibliography…
Verification of machine learning models used in Natural Language Processing (NLP) is known to be a hard problem.
Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
Earlier work this paper cites.
Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning · 2014
Earlier work this paper cites.
Sequence to sequence learning with neural networks, 2014
Ilya Sutskever, Oriol Vinyals, and Quoc V. Le · 2014
Earlier work this paper cites.
Advances in natural language processing
Julia Hirschberg and Christopher D. Manning · 2015
Earlier work this paper cites.
Faster R-CNN: Towards real-time object detection with region proposal networks, 2016
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2016
Earlier work this paper cites.
Attention is all you need, 2017
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
BERT: Pre-training of deep bidirectional transformers for language understanding, 2018
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Earlier work this paper cites.
Senate bill no. 1001, chapter 892, chapter 6.bots, paragraph 17941, 2018
California State Legislature · 2018
Earlier work this paper cites.
Onnx: Open neural network exchange
Junjie Bai, Fang Lu, Ke Zhang, et al · 2019
Earlier work this paper cites.
Achieving verified robustness to symbol substitutions via interval bound propagation, 2019
Po-Sen Huang, Robert Stanforth, Johannes Welbl, Chris Dyer, Dani Yogatama, Sven Gowal, Krishnamurthy Dvijotham, and Pushmeet Kohli · 2019
Earlier work this paper cites.
The Marabou Framework for Verification and Analysis of Deep Neural Networks
Guy Katz, Derek Huang, Duligur Ibeling, Kyle Julian, Christopher Lazarus, Rachel Lim, Parth Shah, Shantanu Thakoor, Haoze Wu, Aleksandar Zeljić, David Dill, Mykel Kochenderfer, and Clark Barrett · 2019
Earlier work this paper cites.
Towards deep learning models resistant to adversarial attacks, 2019
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2019
Earlier work this paper cites.
Sentence-BERT: Sentence embeddings using siamese BERT-networks, 2019
Nils Reimers and Iryna Gurevych · 2019
Earlier work this paper cites.
An abstract domain for certifying neural networks
Gagandeep Singh, Timon Gehr, Markus Püschel, and Martin Vechev · 2019
Cited alongside, same era.
Freelb: Enhanced adversarial training for natural language understanding
Chen Zhu, Yu Cheng, Zhe Gan, Siqi Sun, Tom Goldstein, and Jingjing Liu · 2019
Cited alongside, same era.
Continuous verification of machine learning: a declarative programming approach
Ekaterina Komendantskaya, Wen Kokke, and Daniel Kienitz · 2020
Cited alongside, same era.
Language models are few-shot learners
Ben Mann, N Ryder, M Subbiah, J Kaplan, P Dhariwal, A Neelakantan, P Shyam, G Sastry, A Askell, S Agarwal, et al · 2020
Cited alongside, same era.
Robustness verification for transformers, 2020
Zhouxing Shi, Huan Zhang, Kai-Wei Chang, Minlie Huang, and Cho-Jui Hsieh · 2020
Cited alongside, same era.
Searching for an effective defender: Benchmarking defense against adversarial word substitution
Zongyi Li, Jianhan Xu, Jiehang Zeng, Linyang Li, Xiaoqing Zheng, Qi Zhang, Kai-Wei Chang, and Cho-Jui Hsieh · 2021
Later among the works it cites.
Evaluating the robustness of neural language models to input perturbations
Milad Moradi and Matthias Samwald · 2021
Later among the works it cites.
Beta-crown: Efficient bound propagation with per-neuron split constraints for neural network robustness verification
Shiqi Wang, Huan Zhang, Kaidi Xu, Xue Lin, Suman Jana, Cho-Jui Hsieh, and J Zico Kolter · 2021
Later among the works it cites.
Towards a robust deep neural network against adversarial texts: A survey
Wenqi Wang, Run Wang, Lina Wang, Zhibo Wang, and Aoshuang Ye · 2021
Later among the works it cites.
Measure and improve robustness in nlp models: A survey, 2021
Xuezhi Wang, Haohan Wang, and Diyi Yang · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Safer: A structure-free approach for certified robustness to adversarial word substitutions, 2020
Mao Ye, Chengyue Gong, and Qiang Liu · 2020
Cited alongside, same era.
Adversarial attacks on deep-learning models in natural language processing: A survey
Wei Emma Zhang, Quan Z Sheng, Ahoud Alhazmi, and Chenliang Li · 2020
Cited alongside, same era.
On the dangers of stochastic parrots: Can language models be too big?
Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell · 2021
Cited alongside, same era.
Anticipating safety issues in E2E conversational AI: Framework and tooling, 2021
Emily Dinan, Gavin Abercrombie, A. Stevie Bergman, Shannon Spruit, Dirk Hovy, Y-Lan Boureau, and Verena Rieser · 2021
Cited alongside, same era.
Towards robustness against natural language word substitutions
Xinshuai Dong, Anh Tuan Luu, Rongrong Ji, and Hong Liu · 2021
Cited alongside, same era.
The R-U-A-Robot dataset: Helping avoid chatbot deception by detecting user questions about human or non-human identity, 2021
David Gros, Yu Li, and Zhou Yu · 2021
Cited alongside, same era.
Eu artificial intelligence act: The european approach to ai, 2021
Mauritz Kop · 2021
Cited alongside, same era.
Later among the works it cites.
Polyjuice: Generating counterfactuals for explaining, evaluating, and improving models
Tongshuang Wu, Marco Tulio Ribeiro, Jeffrey Heer, and Daniel S Weld · 2021
Later among the works it cites.
Certified robustness to programmable transformations in LSTMs, 2021
Yuhao Zhang, Aws Albarghouthi, and Loris D’Antoni · 2021
Later among the works it cites.
Defense against synonym substitution-based adversarial attacks via dirichlet neighborhood ensemble
Yi Zhou, Xiaoqing Zheng, Cho-Jui Hsieh, Kai-Wei Chang, and Xuanjing Huan · 2021
Later among the works it cites.
Accessed on 01.12.2022
VNNLib format: https://vnnlib.org/ · 2022
Later among the works it cites.
Neural network robustness as a verification property: A principled case study
Marco Casadio, Ekaterina Komendantskaya, Matthew L. Daggitt, Wen Kokke, Guy Katz, Guy Amir, and Idan Refaeli · 2022
Later among the works it cites.
Mark Niklas Müller, Christopher Brix, Stanley Bak, Changliu Liu, and Taylor T Johnson · 2022
Later among the works it cites.
Logic of differentiable logics: Towards a uniform semantics of DL
Natalia Slusarz, Ekaterina Komendantskaya, Matthew L. Daggitt, Robert J. Stewart, and Kathrin Stark · 2023
Closest in time.