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The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone.
The use of multiple measurements in taxonomic problems
Ronald A Fisher · 1936
Earlier work this paper cites.
The pascal recognising textual entailment challenge
Ido Dagan, Oren Glickman, and Bernardo Magnini · 2005
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Automatically constructing a corpus of sentential paraphrases
Bill Dolan and Chris Brockett · 2005
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The second pascal recognising textual entailment challenge
Roy Bar-Haim, Ido Dagan, Bill Dolan, Lisa Ferro, and Danilo Giampiccolo · 2006
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The third pascal recognizing textual entailment challenge
Danilo Giampiccolo, Bernardo Magnini, Ido Dagan, and William B Dolan · 2007
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The fifth pascal recognizing textual entailment challenge
Luisa Bentivogli, Peter Clark, Ido Dagan, and Danilo Giampiccolo · 2009
Earlier work this paper cites.
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.
The winograd schema challenge
Hector Levesque, Ernest Davis, and Leora Morgenstern · 2012
Earlier work this paper cites.
Hidden factors and hidden topics: Understanding rating dimensions with review text
Julian McAuley and Jure Leskovec · 2013
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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 Ng, and Christopher Potts · 2013
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Obtaining well calibrated probabilities using bayesian binning
Mahdi Pakdaman Naeini, Gregory Cooper, and Milos Hauskrecht · 2015
Earlier work this paper cites.
SemEval-2016 task 4: Sentiment analysis in Twitter
Preslav Nakov, Alan Ritter, Sara Rosenthal, Fabrizio Sebastiani, and Veselin Stoyanov · 2016
Earlier work this paper cites.
Pubmed 200k rct: a dataset for sequential sentence classification in medical abstracts, 2017
Franck Dernoncourt and Ji Young Lee · 2017
Earlier work this paper cites.
On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger · 2017
Earlier work this paper cites.
Adam: A method for stochastic optimization, 2017
Diederik P. Kingma and Jimmy Ba · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Neural tangent kernel: Convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
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The implicit bias of gradient descent on separable data
Daniel Soudry, Elad Hoffer, and Nathan Srebro · 2018
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Glue: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman · 2018
Earlier work this paper cites.
On exact computation with an infinitely wide neural net
Sanjeev Arora, Simon S Du, Wei Hu, Zhiyuan Li, Russ R Salakhutdinov, and Ruosong Wang · 2019
Earlier work this paper cites.
Proceedings of the 2019 conference of the north american chapter of the association for computational linguistics: Human language technologies, volume 1 (long and short papers)
Jill Burstein, Christy Doran, and Thamar Solorio · 2019
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BoolQ: Exploring the surprising difficulty of natural yes/no questions
Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristina Toutanova · 2019
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The commitmentbank: Investigating projection in naturally occurring discourse
Marie-Catherine De Marneffe, Mandy Simons, and Judith Tonhauser · 2019
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Parameter-efficient transfer learning for NLP
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly · 2019
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Haoming Jiang, Pengcheng He, Weizhu Chen, Xiaodong Liu, Jianfeng Gao, and Tuo Zhao · 2019
functorch: Jax-like composable function transforms for pytorch
Richard Zou Horace He · 2021
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DynaSent: A dynamic benchmark for sentiment analysis
Christopher Potts, Zhengxuan Wu, Atticus Geiger, and Douwe Kiela · 2021
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Revisiting parameter-efficient tuning: Are we really there yet?
Guanzheng Chen, Fangyu Liu, Zaiqiao Meng, and Shangsong Liang · 2022
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Towards a unified view of parameter-efficient transfer learning
Junxian He, Chunting Zhou, Xuezhe Ma, Taylor Berg-Kirkpatrick, and Graham Neubig · 2022
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LoRA: Low-rank adaptation of large language models
Edward J Hu, yelong shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2022
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Fine-tuning can distort pretrained features and underperform out-of-distribution
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Do better imagenet models transfer better?
Simon Kornblith, Jonathon Shlens, and Quoc V Le · 2019
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Wide neural networks of any depth evolve as linear models under gradient descent
Jaehoon Lee, Lechao Xiao, Samuel Schoenholz, Yasaman Bahri, Roman Novak, Jascha Sohl-Dickstein, and Jeffrey Pennington · 2019
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Pytorch: An imperative style, high-performance deep learning library, 2019
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Superglue: A stickier benchmark for general-purpose language understanding systems
Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman · 2019
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Neural network acceptability judgments
Alex Warstadt, Amanpreet Singh, and Samuel R. Bowman · 2019
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Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the neural tangent kernel, 2020
Stanislav Fort, Gintare Karolina Dziugaite, Mansheej Paul, Sepideh Kharaghani, Daniel M. Roy, and Surya Ganguli · 2020
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Adjusting decision boundary for class imbalanced learning
Byungju Kim and Junmo Kim · 2020
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Ananya Kumar, Aditi Raghunathan, Robbie Jones, Tengyu Ma, and Percy Liang · 2022
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Fast finite width neural tangent kernel
Roman Novak, Jascha Sohl-Dickstein, and Samuel S Schoenholz · 2022
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Better supervisory signals by observing learning paths, 2022
Yi Ren, Shangmin Guo, and Danica J. Sutherland · 2022
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More than a toy: Random matrix models predict how real-world neural representations generalize
Alexander Wei, Wei Hu, and Jacob Steinhardt · 2022
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Last layer re-training is sufficient for robustness to spurious correlations
Polina Kirichenko, Pavel Izmailov, and Andrew Gordon Wilson · 2023
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Surgical fine-tuning improves adaptation to distribution shifts
Yoonho Lee, Annie S Chen, Fahim Tajwar, Ananya Kumar, Huaxiu Yao, Percy Liang, and Chelsea Finn · 2023
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A kernel-based view of language model fine-tuning
Sadhika Malladi, Alexander Wettig, Dingli Yu, Danqi Chen, and Sanjeev Arora · 2023
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A fast, well-founded approximation to the empirical neural tangent kernel, 2023
Mohamad Amin Mohamadi and Danica J. Sutherland · 2023
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How to prepare your task head for finetuning
Yi Ren, Shangmin Guo, Wonho Bae, and Danica J. Sutherland · 2023
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A closer look at model adaptation using feature distortion and simplicity bias
Puja Trivedi, Danai Koutra, and Jayaraman J. Thiagarajan · 2023
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Revisiting out-of-distribution robustness in nlp: Benchmark, analysis, and llms evaluations
Lifan Yuan, Yangyi Chen, Ganqu Cui, Hongcheng Gao, Fangyuan Zou, Xingyi Cheng, Heng Ji, Zhiyuan Liu, and Maosong Sun · 2023
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Domain-aware fine-tuning: Enhancing neural network adaptability
Seokhyeon Ha, Sunbeom Jeong, and Jungwoo Lee · 2024
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Lora training in the ntk regime has no spurious local minima, 2024
Uijeong Jang, Jason D. Lee, and Ernest K. Ryu · 2024
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Fine-tuning language models with just forward passes
Sadhika Malladi, Tianyu Gao, Eshaan Nichani, Alex Damian, Jason D Lee, Danqi Chen, and Sanjeev Arora · 2024
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The expressive power of low-rank adaptation
Yuchen Zeng and Kangwook Lee · 2024
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