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Contrastive self-supervised learning (SSL) learns an embedding space that maps similar data pairs closer and dissimilar data pairs farther apart.
Asymptotic evaluation of certain markov process expectations for large time, i
M. D. Donsker and S. S. Varadhan · 1975
Earlier work this paper cites.
Partial association measures and an application to qualitative regression
J. Daudin · 1980
Earlier work this paper cites.
LSAC national longitudinal bar passage study
L. F. Wightman · 1998
Earlier work this paper cites.
Information theory, inference and learning algorithms
D. J. MacKay, D. J. Mac Kay, et al · 2003
Earlier work this paper cites.
Dimensionality reduction for supervised learning with reproducing kernel hilbert spaces
K. Fukumizu, F. R. Bach, and M. I. Jordan · 2004
Earlier work this paper cites.
On mutual information in contrastive learning for visual representations
M. Wu, C. Zhuang, M. Mosse, D. Yamins, and N. Goodman · 2005
Earlier work this paper cites.
Big self-supervised models are strong semi-supervised learners
T. Chen, S. Kornblith, K. Swersky, M. Norouzi, and G. Hinton · 2006
Earlier work this paper cites.
Kernel measures of conditional dependence
K. Fukumizu, A. Gretton, X. Sun, and B. Schölkopf · 2007
Earlier work this paper cites.
Conditional mutual information based feature selection for classification task
J. Novovičová, P. Somol, M. Haindl, and P. Pudil · 2007
Earlier work this paper cites.
Visualizing data using t-sne
L. Van der Maaten and G. Hinton · 2008
Earlier work this paper cites.
Estimating divergence functionals and the likelihood ratio by convex risk minimization
X. Nguyen, M. J. Wainwright, and M. I. Jordan · 2010
Earlier work this paper cites.
Conditional negative sampling for contrastive learning of visual representations
M. Wu, M. Mosse, C. Zhuang, D. Yamins, and N. Goodman · 2010
Earlier work this paper cites.
Kernel embeddings of conditional distributions: A unified kernel framework for nonparametric inference in graphical models
L. Song, K. Fukumizu, and A. Gretton · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Earlier work this paper cites.
Certifying and removing disparate impact
M. Feldman, S. A. Friedler, J. Moeller, C. Scheidegger, and S. Venkatasubramanian · 2015
Earlier work this paper cites.
Distilling the knowledge in a neural network
G. Hinton, O. Vinyals, J. Dean, et al · 2015
Earlier work this paper cites.
Machine bias
J. Angwin, J. Larson, S. Mattu, and L. Kirchner · 2016
Earlier work this paper cites.
Equality of opportunity in supervised learning
M. Hardt, E. Price, and N. Srebro · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
UCI machine learning repository, 2017
D. Dua and C. Graff · 2017
Earlier work this paper cites.
Mutual information neural estimation
M. I. Belghazi, A. Baratin, S. Rajeshwar, S. Ozair, Y. Bengio, A. Courville, and D. Hjelm · 2018
Earlier work this paper cites.
Learning deep representations by mutual information estimation and maximization
R. D. Hjelm, A. Fedorov, S. Lavoie-Marchildon, K. Grewal, P. Bachman, A. Trischler, and Y. Bengio · 2018
Cited alongside, same era.
Disentangling by factorising
H. Kim and A. Mnih · 2018
Cited alongside, same era.
Learning adversarially fair and transferable representations
D. Madras, E. Creager, T. Pitassi, and R. Zemel · 2018
Cited alongside, same era.
Representation learning with contrastive predictive coding
A. v. d. Oord, Y. Li, and O. Vinyals · 2018
Cited alongside, same era.
Mitigating unwanted biases with adversarial learning
B. H. Zhang, B. Lemoine, and M. Mitchell · 2018
Cited alongside, same era.
A theoretical analysis of contrastive unsupervised representation learning
A maximal correlation approach to imposing fairness in machine learning
J. Lee, Y. Bu, P. Sattigeri, R. Panda, G. Wornell, L. Karlinsky, and R. Feris · 2020
Later among the works it cites.
Towards debiasing sentence representations
P. P. Liang, I. M. Li, E. Zheng, Y. C. Lim, R. Salakhutdinov, and L.-P. Morency · 2020
Later among the works it cites.
Sensitivenets: Learning agnostic representations with application to face images
A. Morales, J. Fierrez, R. Vera-Rodriguez, and R. Tolosana · 2020
Later among the works it cites.
Unsupervised pretraining transfers well across languages
M. Rivière, A. Joulin, P.-E. Mazaré, and E. Dupoux · 2020
Later among the works it cites.
Learning certified individually fair representations
A. Ruoss, M. Balunović, M. Fischer, and M. Vechev · 2020
Later among the works it cites.
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S. Arora, H. Khandeparkar, M. Khodak, O. Plevrakis, and N. Saunshi · 2019
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Learning representations by maximizing mutual information across views
P. Bachman, R. D. Hjelm, and W. Buchwalter · 2019
Cited alongside, same era.
Flexibly fair representation learning by disentanglement
E. Creager, D. Madras, J.-H. Jacobsen, M. Weis, K. Swersky, T. Pitassi, and R. Zemel · 2019
Cited alongside, same era.
Wasserstein dependency measure for representation learning
S. Ozair, C. Lynch, Y. Bengio, A. v. d. Oord, S. Levine, and P. Sermanet · 2019
Cited alongside, same era.
On variational bounds of mutual information
B. Poole, S. Ozair, A. Van Den Oord, A. Alemi, and G. Tucker · 2019
Cited alongside, same era.
The woman worked as a babysitter: On biases in language generation
E. Sheng, K.-W. Chang, P. Natarajan, and N. Peng · 2019
Cited alongside, same era.
Learning controllable fair representations
J. Song, P. Kalluri, A. Grover, S. Zhao, and S. Ermon · 2019
Cited alongside, same era.
Social bias frames: Reasoning about social and power implications of language
M. Sap, S. Gabriel, L. Qin, D. Jurafsky, N. A. Smith, and Y. Choi · 2020
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What makes for good views for contrastive learning?
Y. Tian, C. Sun, B. Poole, D. Krishnan, C. Schmid, and P. Isola · 2020
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