Fetching the paper…
Reading the bibliography…
Foundation models offer an exciting new paradigm for constructing models with out-of-the-box embeddings and a few labeled examples.
Combining labeled and unlabeled data with co-training
Avrim Blum and Tom Mitchell · 1998
Earlier work this paper cites.
Learning from measurements in exponential families
Percy Liang, Michael I Jordan, and Dan Klein · 2009
Earlier work this paper cites.
Generalized expectation criteria for semi-supervised learning with weakly labeled data
Gideon S Mann and Andrew McCallum · 2010
Earlier work this paper cites.
Reducing wrong labels in distant supervision for relation extraction
Shingo Takamatsu, Issei Sato, and Hiroshi Nakagawa · 2012
Earlier work this paper cites.
Probabilistic lipschitzness a niceness assumption for deterministic labels
Ruth Urner and Shai Ben-David · 2013
Earlier work this paper cites.
Improved pattern learning for bootstrapped entity extraction
Sonal Gupta and Christopher Manning · 2014
Earlier work this paper cites.
Tubespam: Comment spam filtering on youtube
Túlio C Alberto, Johannes V Lochter, and Tiago A Almeida · 2015
Earlier work this paper cites.
Activitynet: A large-scale video benchmark for human activity understanding
Fabian Caba Heilbron, Victor Escorcia, Bernard Ghanem, and Juan Carlos Niebles · 2015
Earlier work this paper cites.
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
Earlier work this paper cites.
Understanding intermediate layers using linear classifier probes
Guillaume Alain and Yoshua Bengio · 2016
Earlier work this paper cites.
What do a million news articles look like?
David Corney, Dyaa Albakour, Miguel Martinez-Alvarez, and Samir Moussa · 2016
Earlier work this paper cites.
Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Earlier work this paper cites.
Learning from noisy singly-labeled data
Ashish Khetan, Zachary C. Lipton, and Anima Anandkumar · 2018
Earlier work this paper cites.
Deep k-nearest neighbors: Towards confident, interpretable and robust deep learning
Nicolas Papernot and Patrick McDaniel · 2018
Earlier work this paper cites.
Snorkel: Rapid training data creation with weak supervision
Alexander Ratner, Stephen H. Bach, Henry Ehrenberg, Jason Fries, Sen Wu, and Christopher Ré · 2018
Earlier work this paper cites.
Snorkel drybell: A case study in deploying weak supervision at industrial scale
Stephen H Bach, Daniel Rodriguez, Yintao Liu, Chong Luo, Haidong Shao, Cassandra Xia, Souvik Sen, Alex Ratner, Braden Hancock, Houman Alborzi, et al · 2019
Cited alongside, same era.
Pairwise feedback for data programming
Benedikt Boecking and Artur Dubrawski · 2019
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
Cited alongside, same era.
Rekall: Specifying video events using compositions of spatiotemporal labels
Daniel Y. Fu, Will Crichton, James Hong, Xinwei Yao, Haotian Zhang, Anh Truong, Avanika Narayan, Maneesh Agrawala, Christopher Ré, and Kayvon Fatahalian · 2019
Cited alongside, same era.
Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2019
Fast and three-rious: Speeding up weak supervision with triplet methods
Daniel Y. Fu, Mayee F. Chen, Frederic Sala, Sarah M. Hooper, Kayvon Fatahalian, and Christopher Ré · 2020
Later among the works it cites.
Big transfer (bit): General visual representation learning
Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, and Neil Houlsby · 2020
Later among the works it cites.
Weakly supervised sequence tagging from noisy rules
Esteban Safranchik, Shiying Luo, and Stephen H Bach · 2020
Later among the works it cites.
Migrating a privacy-safe information extraction system to a software 2.0 design
Ying Sheng, Nguyen Ha Vo, James B. Wendt, Sandeep Tata, and Marc Najork · 2020
Later among the works it cites.
On the opportunities and risks of foundation models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
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
Cited alongside, same era.
Label propagation for deep semi-supervised learning
Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, and Ondrej Chum · 2019
Cited alongside, same era.
Generalization through memorization: Nearest neighbor language models
Urvashi Khandelwal, Omer Levy, Dan Jurafsky, Luke Zettlemoyer, and Mike Lewis · 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
Cited alongside, same era.
Training complex models with multi-task weak supervision
Alexander Ratner, Braden Hancock, Jared Dunnmon, Frederic Sala, Shreyash Pandey, and Christopher Ré · 2019
Cited alongside, same era.
Multi-resolution weak supervision for sequential data
Frederic Sala, Paroma Varma, Jason Fries, Daniel Y. Fu, Shiori Sagawa, Saelig Khattar, Ashwini Ramamoorthy, Ke Xiao, Kayvon Fatahalian, James Priest, and Christopher Ré · 2019
Cited alongside, same era.
Learning dependency structures for weak supervision models
Paroma Varma, Frederic Sala, Ann He, Alexander Ratner, and Christopher Re · 2019
Cited alongside, same era.
End-to-end weak supervision
Salva Rühling Cachay, Benedikt Boecking, and Artur Dubrawski · 2021
Later among the works it cites.
Comparing the value of labeled and unlabeled data in method-of-moments latent variable estimation
Mayee Chen, Benjamin Cohen-Wang, Stephen Mussmann, Frederic Sala, and Christopher Re · 2021
Later among the works it cites.
Analysis of faces in a decade of us cable tv news
James Hong, Will Crichton, Haotian Zhang, Daniel Y Fu, Jacob Ritchie, Jeremy Barenholtz, Ben Hannel, Xinwei Yao, Michaela Murray, Geraldine Moriba, et al · 2021
Later among the works it cites.
The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant · 2021
Later among the works it cites.
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
Later among the works it cites.
Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever · 2021
Later among the works it cites.
Want to reduce labeling cost? GPT-3 can help
Shuohang Wang, Yang Liu, Yichong Xu, Chenguang Zhu, and Michael Zeng · 2021
Later among the works it cites.
Wrench: A comprehensive benchmark for weak supervision
Jieyu Zhang, Yue Yu, Yinghao Li, Yujing Wang, Yaming Yang, Mao Yang, and Alexander Ratner · 2021
Later among the works it cites.
Training subset selection for weak supervision
Hunter Lang, Aravindan Vijayaraghavan, and David Sontag · 2022
Closest in time.
Text and code embeddings by contrastive pre-training
Arvind Neelakantan, Tao Xu, Raul Puri, Alec Radford, Jesse Michael Han, Jerry Tworek, Qiming Yuan, Nikolas Tezak, Jong Wook Kim, Chris Hallacy, et al · 2022
Closest in time.