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Self-Supervised Learning (SSL) has emerged as the solution of choice to learn transferable representations from unlabeled data.
Estimate of the number of signals in error correcting codes
Rom Varshamov · 1957
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The computer as master mind
Donald Knuth · 1977
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Elements of Information Theory
Thomas Cover and Joy Thomas · 1991
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Convexity, classification, and risk bounds
Peter Bartlett, Michael Jordan, and Jon McAuliffe · 2006
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Greedy layer-wise training of deep networks
Yoshua Bengio, Pascal Lamblin, Dan Popovici, and Hugo Larochelle · 2006
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Incremental algorithms for hierarchical classification
Nicolò Cesa-Bianchi, Claudio Gentile, and Luca Zaniboni · 2006
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Universal kernels
Charles Micchelli, Yuesheng Xu, and Haizhang Zhang · 2006
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Extracting and composing robust features with denoising autoencoders
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol · 2008
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Fei-Fei Li · 2009
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Active learning for networked data
Mustafa Bilgic, Lilyana Mihalkova, and Lise Getoor · 2010
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A survey on transfer learning
Sinno Jialin Pan and Qiang Yang · 2010
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Active learning literature survey
Burr Settles · 2010
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Learning from partial labels
Timothée Cour, Benjamin Sapp, and Ben Taskar · 2011
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Two faces of active learning
Sanjoy Dasgupta · 2011
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From theories to queries: Active learning in practice
Burr Settles · 2011
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Theory of disagreement-based active learning
Steve Hanneke · 2014
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Convex optimization: Algorithms and complexity
Sébastien Bubeck · 2015
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Semi-supervised learning with graph embeddings
Zhilin Yang, William Cohen, , and Ruslan Salakhutdinov · 2016
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Machine teaching: A new paradigm for building machine learning systems
Patrice Simard, Saleema Amershi, David Chickering, Alicia Edelman Pelton, Soroush Ghorashi, Christopher Meek, Gonzalo Ramos, Jina Suh, Johan Verwey, Mo Wang, and John Wernsing · 2017
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Algorithmic regularization in learning deep homogeneous models: Layers are automatically balanced
Simon Du, Wei Hu, and Jason Lee · 2018
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Lessons learned from annotating 5 million images, 2019
Renaud Bauvin · 2019
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Sequential Experimental Design for Transductive Linear Bandits
Tanner Fiez, Lalit Jain, Kevin Jamieson, and Lillian Ratliff · 2019
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Sharp analysis of learning with discrete losses
Alex Nowak-Vila, Francis Bach, and Alessandro Rudi · 2019
Provable guarantees for self-supervised deep learning with spectral contrastive loss
Jeff HaoChen, Colin Wei, Adrien Gaidon, and Tengyu Ma · 2021
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Global convergence of gradient descent for asymmetric low-rank matrix factorization
Tian Ye and Simon Du · 2021
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Barlow twins: Self-supervised learning via redundancy reduction
Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and Stéphane Deny · 2021
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Contrastive and non-contrastive self-supervised learning recover global and local spectral embedding methods
Randall Balestriero and Yann LeCun · 2022
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Vicreg: Variance-invariance-covariance regularization for self-supervised learning
Adrien Bardes, Jean Ponce, and Yann LeCun · 2022
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Active labeling: streaming stochastic gradients
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S4l: Self-supervised semi-supervised learning
Xiaohua Zhai, Avital Oliver, Alexander Kolesnikov, and Lucas Beyer · 2019
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Deep batch active learning by diverse, uncertain gradient lower bounds
Jordan Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, and Alekh Agarwal · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Big self-supervised models are strong semi-supervised learners
Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey Hinton · 2020
Cited alongside, same era.
Big self-supervised models are strong semi-supervised learners
Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey Hinton · 2020
Cited alongside, same era.
Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
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Vivien Cabannes, Francis Bach, Vianney Perchet, and Alessandro Rudi · 2022
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Towards artificial general intelligence via a multimodal foundation model
Nanyi Fei, Zhiwu Lu, Yizhao Gao, Guoxing Yang, Yuqi Huo, Jingyuan Wen, Haoyu Lu, Ruihua Song, Xin Gao, Tao Xiang, Hao Sun, and Ji-Rong Wen · 2022
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Vision-Language Pre-training: Basics, Recent Advances, and Future Trends
Zhe Gan, Linjie Li, Chunyuan Li, Lijuan Wang, Zicheng Liu, and Jianfeng Gao · 2022
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Joint embedding self-supervised learning in the kernel regime
Bobak Kiani, Randall Balestriero, Yubei Chen, Seth Lloyd, and Yann LeCun · 2022
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A path towards autonomous machine intelligence
Yann LeCun · 2022
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Beyond neural scaling laws: beating power law scaling via data pruning
Ben Sorscher, Robert Geirhos, Shashank Shekhar, Surya Ganguli, and Ari Morcos · 2022
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Curriculum learning: A survey
Petru Soviany, Radu Tudor Ionescu, Paolo Rota, and Nicu Sebe · 2022
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Contrast to divide: Self-supervised pre-training for learning with noisy labels
Evgenii Zheltonozhskii, Chaim Baskin, Avi Mendelson, Alex M Bronstein, and Or Litany · 2022
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On minimal variations for unsupervised representation learning
Vivien Cabannes, Alberto Bietti, and Randall Balestriero · 2023
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The SSL interplay: Augmentations, inductive bias, and generalization
Vivien Cabannes, Bobak Kiani, Randall Balestriero, Yann LeCun, and Alberto Bietti · 2023
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Algorithmic regularization in model-free overparametrized asymmetric matrix factorization
Liwei Jiang, Yudong Chen, and Lijun Ding · 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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