暂无分享,去创建一个
[1] Ananthram Swami,et al. Crafting adversarial input sequences for recurrent neural networks , 2016, MILCOM 2016 - 2016 IEEE Military Communications Conference.
[2] Quoc V. Le,et al. QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension , 2018, ICLR.
[3] Jürgen Schmidhuber,et al. Framewise phoneme classification with bidirectional LSTM and other neural network architectures , 2005, Neural Networks.
[4] Danqi Chen,et al. A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task , 2016, ACL.
[5] Percy Liang,et al. Know What You Don’t Know: Unanswerable Questions for SQuAD , 2018, ACL.
[6] Xinlei Chen,et al. Visualizing and Understanding Neural Models in NLP , 2015, NAACL.
[7] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[8] Dejing Dou,et al. HotFlip: White-Box Adversarial Examples for NLP , 2017, ArXiv.
[9] Bin Yu,et al. Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs , 2018, ICLR.
[10] Jason Weston,et al. Reading Wikipedia to Answer Open-Domain Questions , 2017, ACL.
[11] Abubakar Abid,et al. Interpretation of Neural Networks is Fragile , 2017, AAAI.
[12] Finale Doshi-Velez,et al. A Roadmap for a Rigorous Science of Interpretability , 2017, ArXiv.
[13] Christopher Potts,et al. Learning Word Vectors for Sentiment Analysis , 2011, ACL.
[14] Carlos Guestrin,et al. Semantically Equivalent Adversarial Rules for Debugging NLP models , 2018, ACL.
[15] Shin Ishii,et al. Distributional Smoothing with Virtual Adversarial Training , 2015, ICLR 2016.
[16] Christopher Potts,et al. A large annotated corpus for learning natural language inference , 2015, EMNLP.
[17] Samy Bengio,et al. Adversarial examples in the physical world , 2016, ICLR.
[18] Vahid Kazemi,et al. Show, Ask, Attend, and Answer: A Strong Baseline For Visual Question Answering , 2017, ArXiv.
[19] Andrew Zisserman,et al. Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps , 2013, ICLR.
[20] Emily M. Bender,et al. Towards Linguistically Generalizable NLP Systems: A Workshop and Shared Task , 2017, Proceedings of the First Workshop on Building Linguistically Generalizable NLP Systems.
[21] Jian Zhang,et al. SQuAD: 100,000+ Questions for Machine Comprehension of Text , 2016, EMNLP.
[22] Dawn Xiaodong Song,et al. Delving into Transferable Adversarial Examples and Black-box Attacks , 2016, ICLR.
[23] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[24] Carlos Guestrin,et al. "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.
[25] Carlos Guestrin,et al. Anchors: High-Precision Model-Agnostic Explanations , 2018, AAAI.
[26] Kilian Q. Weinberger,et al. On Calibration of Modern Neural Networks , 2017, ICML.
[27] Jakob Uszkoreit,et al. A Decomposable Attention Model for Natural Language Inference , 2016, EMNLP.
[28] Omer Levy,et al. Annotation Artifacts in Natural Language Inference Data , 2018, NAACL.
[29] Sergey Ioffe,et al. Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[30] Daniel Jurafsky,et al. Understanding Neural Networks through Representation Erasure , 2016, ArXiv.
[31] Yonatan Belinkov,et al. Synthetic and Natural Noise Both Break Neural Machine Translation , 2017, ICLR.
[32] Been Kim,et al. Towards A Rigorous Science of Interpretable Machine Learning , 2017, 1702.08608.
[33] Regina Barzilay,et al. Rationalizing Neural Predictions , 2016, EMNLP.
[34] Zhiguo Wang,et al. Bilateral Multi-Perspective Matching for Natural Language Sentences , 2017, IJCAI.
[35] Jeffrey Pennington,et al. GloVe: Global Vectors for Word Representation , 2014, EMNLP.
[36] Ankur Taly,et al. Axiomatic Attribution for Deep Networks , 2017, ICML.
[37] Margaret Mitchell,et al. VQA: Visual Question Answering , 2015, International Journal of Computer Vision.
[38] Percy Liang,et al. Adversarial Examples for Evaluating Reading Comprehension Systems , 2017, EMNLP.
[39] Luke S. Zettlemoyer,et al. Adversarial Example Generation with Syntactically Controlled Paraphrase Networks , 2018, NAACL.
[40] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[41] Andrew M. Dai,et al. Adversarial Training Methods for Semi-Supervised Text Classification , 2016, ICLR.