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Zero-shot learning in prompted vision-language models, the practice of crafting prompts to build classifiers without an explicit training process, has achieved impressive performance in many settings.
Generalization in deep networks: The role of distance from initialization
Vaishnavh Nagarajan and J Zico Kolter · 1901
Earlier work this paper cites.
Chervonenkis: On the uniform convergence of relative frequencies of events to their probabilities
Vladimir Naumovich Vapnik · 1971
Earlier work this paper cites.
Theory of pattern recognition, 1974
Vladimir Vapnik and Alexey Chervonenkis · 1974
Earlier work this paper cites.
Principles of risk minimization for learning theory
Vladimir Vapnik · 1991
Earlier work this paper cites.
Pac-bayesian model averaging
David A McAllester · 1999
Earlier work this paper cites.
(not) bounding the true error
John Langford and Rich Caruana · 2001
Earlier work this paper cites.
In search of the real inductive bias: On the role of implicit regularization in deep learning
Behnam Neyshabur, Ryota Tomioka, and Nathan Srebro · 2014
Earlier work this paper cites.
Understanding machine learning: From theory to algorithms
Shai Shalev-Shwartz and Shai Ben-David · 2014
Earlier work this paper cites.
Multiclass learnability and the erm principle
Amit Daniely, Sivan Sabato, Shai Ben-David, and Shai Shalev-Shwartz · 2015
Earlier work this paper cites.
Norm-based capacity control in neural networks
Behnam Neyshabur, Ryota Tomioka, and Nathan Srebro · 2015
Earlier work this paper cites.
Spectrally-normalized margin bounds for neural networks
Peter L. Bartlett, Dylan J. Foster, and Matus Telgarsky · 2017
Earlier work this paper cites.
Gintare Karolina Dziugaite and Daniel M Roy · 2017
Earlier work this paper cites.
Risk bounds for transferring representations with and without fine-tuning
Daniel McNamara and Maria-Florina Balcan · 2017
Earlier work this paper cites.
Deep hashing network for unsupervised domain adaptation
Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, and Sethuraman Panchanathan · 2017
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Functional map of the world
Gordon Christie, Neil Fendley, James Wilson, and Ryan Mukherjee · 2018
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Fantastic generalization measures and where to find them
Yiding Jiang, Behnam Neyshabur, Hossein Mobahi, Dilip Krishnan, and Samy Bengio · 2019
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Non-vacuous generalization bounds at the imagenet scale: A pac-bayesian compression approach
Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P Adams, and Peter Orbanz · 2019
Cited alongside, same era.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Cited alongside, same era.
The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Zero-shot image-to-text generation for visual-semantic arithmetic
Yoad Tewel, Yoav Shalev, Idan Schwartz, and Lior Wolf · 2021
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A general framework for the disintegration of pac-bayesian bounds
Paul Viallard, Pascal Germain, Amaury Habrard, and Emilie Morvant · 2021
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Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
Cited alongside, same era.
In search of robust measures of generalization
Gintare Karolina Dziugaite, Alexandre Drouin, Brady Neal, Nitarshan Rajkumar, Ethan Caballero, Linbo Wang, Ioannis Mitliagkas, and Daniel M Roy · 2020
Cited alongside, same era.
Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
Cited alongside, same era.
On the role of data in pac-bayes bounds
Gintare Karolina Dziugaite, Kyle Hsu, Waseem Gharbieh, Gabriel Arpino, and Daniel Roy · 2021
Cited alongside, same era.
Making pre-trained language models better few-shot learners
Tianyu Gao, Adam Fisch, and Danqi Chen · 2021
Cited alongside, same era.
Scaling up visual and vision-language representation learning with noisy text supervision
Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig · 2021
Cited alongside, same era.
How many data points is a prompt worth?
Teven Le Scao and Alexander M Rush · 2021
Cited alongside, same era.
Understanding deep learning (still) requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2021
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Promptsource: An integrated development environment and repository for natural language prompts
Stephen Bach, Victor Sanh, Zheng Xin Yong, Albert Webson, Colin Raffel, Nihal V Nayak, Abheesht Sharma, Taewoon Kim, M Saiful Bari, Thibault Févry, et al · 2022
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Pac-bayes compression bounds so tight that they can explain generalization
Sanae Lotfi, Marc Finzi, Sanyam Kapoor, Andres Potapczynski, Micah Goldblum, and Andrew G Wilson · 2022
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Robust fine-tuning of zero-shot models
Mitchell Wortsman, Gabriel Ilharco, Jong Wook Kim, Mike Li, Simon Kornblith, Rebecca Roelofs, Raphael Gontijo Lopes, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, et al · 2022
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample · 2023
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Hard prompts made easy: Gradient-based discrete optimization for prompt tuning and discovery
Yuxin Wen, Neel Jain, John Kirchenbauer, Micah Goldblum, Jonas Geiping, and Tom Goldstein · 2023
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