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Detecting out-of-distribution (OOD) inputs is crucial for the safe deployment of natural language processing (NLP) models.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
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An evaluation dataset for intent classification and out-of-scope prediction
Stefan Larson, Anish Mahendran, Joseph J Peper, Christopher Clarke, Andrew Lee, Parker Hill, Jonathan K Kummerfeld, Kevin Leach, Michael A Laurenzano, Lingjia Tang, et al. 2019 · 1909
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Newsweeder: Learning to filter netnews
Ken Lang. 1995 · 1995
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
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Lof: identifying density-based local outliers
Markus M Breunig, Hans-Peter Kriegel, Raymond T Ng, and Jörg Sander. 2000 · 2000
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Learning question classifiers
Xin Li and Dan Roth. 2002 · 2002
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Ranking a stream of news
Gianna M. Del Corso, Antonio Gulli, and Francesco Romani. 2005 · 2005
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The pascal recognising textual entailment challenge
Ido Dagan, Oren Glickman, and Bernardo Magnini. 2005 · 2005
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Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton. 2008 · 2008
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Learning word vectors for sentiment analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. 2011 · 2011
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Y. Ng, and Christopher Potts. 2013 · 2013
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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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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
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Concrete problems in ai safety
Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané. 2016 · 2016
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Findings of the 2016 conference on machine translation
Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Varvara Logacheva, Christof Monz, et al. 2016 · 2016
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Multi30k: Multilingual english-german image descriptions
Desmond Elliott, Stella Frank, Khalil Sima’an, and Lucia Specia. 2016 · 2016
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani. 2016 · 2016
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel. 2017 · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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SentEval: An evaluation toolkit for universal sentence representations
Alexis Conneau and Douwe Kiela. 2018 · 2018
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What you can cram into a single \$&!#* vector: Probing sentence embeddings for linguistic properties
Alexis Conneau, Germán Kruszewski, Guillaume Lample, Loïc Barrault, and Marco Baroni. 2018 · 2018
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Large scale crowdsourcing and characterization of twitter abusive behavior
Antigoni-Maria Founta, Constantinos Djouvas, Despoina Chatzakou, Ilias Leontiadis, Jeremy Blackburn, Gianluca Stringhini, Athena Vakali, Michael Sirivianos, and Nicolas Kourtellis. 2018 · 2018
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A simple unified framework for detecting out-of-distribution samples and adversarial attacks
Kimin Lee, Kibok Lee, Honglak Lee, and Jinwoo Shin. 2018 · 2018
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Enhancing the reliability of out-of-distribution image detection in neural networks
Shiyu Liang, Yixuan Li, and R. Srikant. 2018 · 2018
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Likelihood regret: An out-of-distribution detection score for variational auto-encoder
Zhisheng Xiao, Qing Yan, and Yali Amit. 2020 · 2020
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Types of out-of-distribution texts and how to detect them
Udit Arora, William Huang, and He He. 2021 · 2021
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Exploring the limits of out-of-distribution detection
Stanislav Fort, Jie Ren, and Balaji Lakshminarayanan. 2021 · 2021
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Feature space singularity for out-of-distribution detection
Haiwen Huang, Zhihan Li, Lulu Wang, Sishuo Chen, Xinyu Zhou, and Bin Dong. 2021 · 2021
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kfolden: k-fold ensemble for out-of-distribution detection
Xiaoya Li, Jiwei Li, Xiaofei Sun, Chun Fan, Tianwei Zhang, Fei Wu, Yuxian Meng, and Jun Zhang. 2021 · 2021
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Deep autoencoding gaussian mixture model for unsupervised anomaly detection
Bo Zong, Qi Song, Martin Renqiang Min, Wei Cheng, Cristian Lumezanu, Dae-ki Cho, and Haifeng Chen. 2018 · 2018
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Deep unknown intent detection with margin loss
Ting-En Lin and Hua Xu. 2019 · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019 · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Cited alongside, same era.
Likelihood ratios for out-of-distribution detection
Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark A. DePristo, Joshua V. Dillon, and Balaji Lakshminarayanan. 2019 · 2019
Cited alongside, same era.
Detecting semantic anomalies
Faruk Ahmed and Aaron C. Courville. 2020 · 2020
Cited alongside, same era.
Rishabh Misra and Jigyasa Grover. 2021 · 2021
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Revisiting mahalanobis distance for transformer-based out-of-domain detection
Alexander Podolskiy, Dmitry Lipin, Andrey Bout, Ekaterina Artemova, and Irina Piontkovskaya. 2021 · 2021
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Sentry: Selective entropy optimization via committee consistency for unsupervised domain adaptation
Viraj Prabhu, Shivam Khare, Deeksha Kartik, and Judy Hoffman. 2021 · 2021
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Enhancing the generalization for intent classification and out-of-domain detection in SLU
Yilin Shen, Yen-Chang Hsu, Avik Ray, and Hongxia Jin. 2021 · 2021
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Whitening sentence representations for better semantics and faster retrieval
Jianlin Su, Jiarun Cao, Weijie Liu, and Yangyiwen Ou. 2021 · 2021
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Unsupervised out-of-domain detection via pre-trained transformers
Keyang Xu, Tongzheng Ren, Shikun Zhang, Yihao Feng, and Caiming Xiong. 2021 · 2021
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Generalized out-of-distribution detection: A survey
Jingkang Yang, Kaiyang Zhou, Yixuan Li, and Ziwei Liu. 2021 · 2021
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Contrastive out-of-distribution detection for pretrained transformers
Wenxuan Zhou, Fangyu Liu, and Muhao Chen. 2021 · 2021
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Holistic sentence embeddings for better out-of-distribution detection
Sishuo Chen, Wenkai Yang, Xiaohan Bi, and Xu Sun. 2022 · 2022
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Barle: Background-aware representation learning for background shift out-of-distribution detection
Hanyu Duan, Yi Yang, Ahmed Abbasi, and Kar Yan Tam. 2022 · 2022
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Fine-tuning can distort pretrained features and underperform out-of-distribution
Ananya Kumar, Aditi Raghunathan, Robbie Matthew Jones, Tengyu Ma, and Percy Liang. 2022 · 2022
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Out-of-distribution detection with deep nearest neighbors
Yiyou Sun, Yifei Ming, Xiaojin Zhu, and Yixuan Li. 2022 · 2022
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KNN-contrastive learning for out-of-domain intent classification
Yunhua Zhou, Peiju Liu, and Xipeng Qiu. 2022 · 2022
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D2U: Distance-to-uniform learning for out-of-scope detection
Eyup Yilmaz and Cagri Toraman. 2022 · 2093
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