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As Natural Language Processing (NLP) algorithms continually achieve new milestones, out-of-distribution generalization remains a significant challenge.
A generative model for sampling high-performance and diverse weights for neural networks
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Martín Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz. 2019 · 1907
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Some statistical issues in the comparison of speech recognition algorithms
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2005
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Domain adaptation with structural correspondence learning
John Blitzer, Ryan T. McDonald, and Fernando Pereira. 2006 · 2006
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Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification
John Blitzer, Mark Dredze, and Fernando Pereira. 2007 · 2007
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Frustratingly easy domain adaptation
Hal Daumé III. 2007 · 2007
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Self-training for enhancement and domain adaptation of statistical parsers trained on small datasets
Roi Reichart and Ari Rappoport. 2007 · 2007
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Domain adaptation with multiple sources
Yishay Mansour, Mehryar Mohri, and Afshin Rostamizadeh. 2008 · 2008
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A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan. 2010 · 2010
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Cross-language text classification using structural correspondence learning
Peter Prettenhofer and Benno Stein. 2010 · 2010
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Domain adaptation for large-scale sentiment classification: A deep learning approach
Xavier Glorot, Antoine Bordes, and Yoshua Bengio. 2011 · 2011
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Domain adaptation by constraining inter-domain variability of latent feature representation
Ivan Titov. 2011 · 2011
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Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor S. Lempitsky. 2015 · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
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A dynamic convolutional layer for short rangeweather prediction
Benjamin Klein, Lior Wolf, and Yehuda Afek. 2015 · 2015
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Conditioned regression models for non-blind single image super-resolution
Gernot Riegler, Samuel Schulter, Matthias Rüther, and Horst Bischof. 2015 · 2015
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Diverse beam search: Decoding diverse solutions from neural sequence models
Ashwin K. Vijayakumar, Michael Cogswell, Ramprasaath R. Selvaraju, Qing Sun, Stefan Lee, David J. Crandall, and Dhruv Batra. 2016 · 2016
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Hypernetworks
David Ha, Andrew M. Dai, and Quoc V. Le. 2017 · 2017
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Domain attention with an ensemble of experts
Young-Bum Kim, Karl Stratos, and Dongchan Kim. 2017 · 2017
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David Krueger, Chin-Wei Huang, Riashat Islam, Ryan Turner, Alexandre Lacoste, and Aaron C. Courville. 2017 · 2017
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Implicit weight uncertainty in neural networks
Nick Pawlowski, Martin Rajchl, and Ben Glocker. 2017 · 2017
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Language modeling with recurrent highway hypernetworks
Joseph Suarez. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Neural structural correspondence learning for domain adaptation
Yftah Ziser and Roi Reichart. 2017 · 2017
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The hitchhiker’s guide to testing statistical significance in natural language processing
Rotem Dror, Gili Baumer, Segev Shlomov, and Roi Reichart. 2018 · 2018
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Multi-source domain adaptation with mixture of experts
Jiang Guo, Darsh Shah, and Regina Barzilay. 2018 · 2018
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Robust text classification under confounding shift
Virgile Landeiro and Aron Culotta. 2018 · 2018
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Task refinement learning for improved accuracy and stability of unsupervised domain adaptation
Yftah Ziser and Roi Reichart. 2019 · 2019
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Learning to few-shot learn across diverse natural language classification tasks
Trapit Bansal, Rishikesh Jha, and Andrew McCallum. 2020 · 2020
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PERL: pivot-based domain adaptation for pre-trained deep contextualized embedding models
Eyal Ben-David, Carmel Rabinovitz, and Roi Reichart. 2020 · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 2020
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Neural unsupervised domain adaptation in NLP - A survey
Alan Ramponi and Barbara Plank. 2020 · 2020
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Contextual parameter generation for universal neural machine translation
Emmanouil Antonios Platanios, Mrinmaya Sachan, Graham Neubig, and Tom M. Mitchell. 2018 · 2018
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Neural style transfer via meta networks
Falong Shen, Shuicheng Yan, and Gang Zeng. 2018 · 2018
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Hypernetwork-based implicit posterior estimation and model averaging of CNN
Kenya Ukai, Takashi Matsubara, and Kuniaki Uehara. 2018 · 2018
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel R. Bowman. 2018 · 2018
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Deep pivot-based modeling for cross-language cross-domain transfer with minimal guidance
Yftah Ziser and Roi Reichart. 2018 · 2018
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Cross-lingual language model pretraining
Alexis Conneau and Guillaume Lample. 2019 · 2019
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Shiori Sagawa, Pang Wei Koh, Tatsunori B. Hashimoto, and Percy Liang. 2020 · 2020
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Udapter: Language adaptation for truly universal dependency parsing
Ahmet Üstün, Arianna Bisazza, Gosse Bouma, and Gertjan van Noord. 2020 · 2020
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Continual learning with hypernetworks
Johannes von Oswald, Christian Henning, João Sacramento, and Benjamin F. Grewe. 2020 · 2020
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020 · 2020
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Transformer based multi-source domain adaptation
Dustin Wright and Isabelle Augenstein. 2020 · 2020
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Multi-source distilling domain adaptation
Sicheng Zhao, Guangzhi Wang, Shanghang Zhang, Yang Gu, Yaxian Li, Zhichao Song, Pengfei Xu, Runbo Hu, Hua Chai, and Kurt Keutzer. 2020 · 2020
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Dilbert: Customized pre-training for domain adaptation with category shift, with an application to aspect extraction
Entony Lekhtman, Yftah Ziser, and Roi Reichart. 2021 · 2021
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Parameter-efficient multi-task fine-tuning for transformers via shared hypernetworks
Rabeeh Karimi Mahabadi, Sebastian Ruder, Mostafa Dehghani, and James Henderson. 2021 · 2021
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Personalized federated learning using hypernetworks
Aviv Shamsian, Aviv Navon, Ethan Fetaya, and Gal Chechik. 2021 · 2021
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On calibration and out-of-domain generalization
Yoav Wald, Amir Feder, Daniel Greenfeld, and Uri Shalit. 2021 · 2021
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mt5: A massively multilingual pre-trained text-to-text transformer
Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, and Colin Raffel. 2021 · 2021
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Pada: Example-based prompt learning for on-the-fly adaptation to unseen domains
Eyal Ben-David, Nadav Oved, and Roi Reichart. 2022 · 2022
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Docogen: Domain counterfactual generation for low resource domain adaptation
Nitay Calderon, Eyal Ben-David, Amir Feder, and Roi Reichart. 2022 · 2022
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Hyper-x: A unified hypernetwork for multi-task multilingual transfer
Ahmet Üstün, Arianna Bisazza, Gosse Bouma, Gertjan van Noord, and Sebastian Ruder. 2022 · 2022
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Can we trust the evaluation on chatgpt?
Rachith Aiyappa, Jisun An, Haewoon Kwak, and Yong-Yeol Ahn. 2023 · 2023
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Does distributionally robust supervised learning give robust classifiers?
Weihua Hu, Gang Niu, Issei Sato, and Masashi Sugiyama. 2018 · 2042
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