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Variance-Invariance-Covariance Regularization (VICReg) is a self-supervised learning (SSL) method that has shown promising results on a variety of tasks.
On variational bounds of mutual information
Ben Poole, Sherjil Ozair, Aäron van den Oord, Alexander A. Alemi, and George Tucker · 1905
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An omnibus test of normality for moderate and large size samples
Ralph B. D’Agostino · 1971
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Entropy expressions and their estimators for multivariate distributions
N.A. Ahmed and D.V. Gokhale · 1989
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Rademacher and gaussian complexities: Risk bounds and structural results
Peter L Bartlett and Shahar Mendelson · 2002
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Estimation of the entropy of a multivariate normal distribution
Neeraj Misra, Harshinder Singh, and Eugene Demchuk · 2003
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Estimation of entropy and mutual information
Liam Paninski · 2003
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On entropy approximation for gaussian mixture random vectors
Marco Huber, Tim Bailey, Hugh Durrant-Whyte, and Uwe Hanebeck · 2008
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Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Algorithms for learning kernels based on centered alignment
Corinna Cortes, Mehryar Mohri, and Afshin Rostamizadeh · 2012
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Foundations of machine learning
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar · 2012
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Fine-grained visual classification of aircraft
Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi · 2013
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Food-101–mining discriminative components with random forests
Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool · 2014
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On the number of linear regions of deep neural networks
Guido F Montufar, Razvan Pascanu, Kyunghyun Cho, and Yoshua Bengio · 2014
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Understanding machine learning: From theory to algorithms
Shai Shalev-Shwartz and Shai Ben-David · 2014
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Deep variational information bottleneck
Alexander A Alemi, Ian Fischer, Joshua V Dillon, and Kevin Murphy · 2016
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Testing the manifold hypothesis
Charles Fefferman, Sanjoy Mitter, and Hariharan Narayanan · 2016
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Deep Learning , volume 1
I. Goodfellow, Y. Bengio, and A. Courville · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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f-gan: Training generative neural samplers using variational divergence minimization
Sebastian Nowozin, Botond Cseke, and Ryota Tomioka · 2016
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Computing entropies with nested sampling
Brendon J Brewer · 2017
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Implicit regularization in matrix factorization
Suriya Gunasekar, Blake E Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, and Nati Srebro · 2017
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Estimating mixture entropy with pairwise distances
Artemy Kolchinsky and Brendan D Tracey · 2017
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Compression of deep neural networks via information, (2017)
Ravid Shwartz-Ziv and Naftali Tishby · 2017
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Information-theoretic analysis of generalization capability of learning algorithms
Aolin Xu and Maxim Raginsky · 2017
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A spline theory of deep networks
Randall Balestriero and Richard Baraniuk · 2018
Bootstrap your own latent-a new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Formal limitations on the measurement of mutual information
David McAllester and Karl Stratos · 2020
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Self-supervised learning of pretext-invariant representations
Ishan Misra and Laurens van der Maaten · 2020
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The dual information bottleneck
Zoe Piran, Ravid Shwartz-Ziv, and Naftali Tishby · 2020
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Cited alongside, same era.
Mine: mutual information neural estimation
Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeswar, Sherjil Ozair, Yoshua Bengio, Aaron Courville, and R Devon Hjelm · 2018
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Attentioned convolutional lstm inpaintingnetwork for anomaly detection in videos
Itamar Ben-Ari and Ravid Shwartz-Ziv · 2018
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Estimating information flow in deep neural networks
Ziv Goldfeld, Ewout van den Berg, Kristjan Greenewald, Igor Melnyk, Nam Nguyen, Brian Kingsbury, and Yury Polyanskiy · 2018
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Implicit bias of gradient descent on linear convolutional networks
Suriya Gunasekar, Jason D Lee, Daniel Soudry, and Nati Srebro · 2018
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Learning deep representations by mutual information estimation and maximization
R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio · 2018
Cited alongside, same era.
Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
Cited alongside, same era.
Information in infinite ensembles of infinitely-wide neural networks
Ravid Shwartz-Ziv and Alexander A Alemi · 2020
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Reasoning about generalization via conditional mutual information
Thomas Steinke and Lydia Zakynthinou · 2020
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Understanding contrastive representation learning through alignment and uniformity on the hypersphere
Tongzhou Wang and Phillip Isola · 2020
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Vicreg: Variance-invariance-covariance regularization for self-supervised learning
Adrien Bardes, Jean Ponce, and Yann LeCun · 2021
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Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
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Exploring simple siamese representation learning
Xinlei Chen and Kaiming He · 2021
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Lossy compression for lossless prediction
Yann Dubois, Benjamin Bloem-Reddy, Karen Ullrich, and Chris J Maddison · 2021
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Permute, quantize, and fine-tune: Efficient compression of neural networks
Julieta Martinez, Jashan Shewakramani, Ting Wei Liu, Ioan Andrei Bârsan, Wenyuan Zeng, and Raquel Urtasun · 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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Understanding neural networks with logarithm determinant entropy estimator
Zhanghao Zhouyin and Ding Liu · 2021
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Contrastive learning inverts the data generating process
Roland S Zimmermann, Yash Sharma, Steffen Schneider, Matthias Bethge, and Wieland Brendel · 2021
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Understanding dimensional collapse in contrastive self-supervised learning
Li Jing, Pascal Vincent, Yann LeCun, and Yuandong Tian · 2022
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Robustness Implies Generalization via Data-Dependent Generalization Bounds
Kenji Kawaguchi, Zhun Deng, Kyle Luh, and Jiaoyang Huang · 2022
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A convnet for the 2020s
Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie · 2022
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Information flow in deep neural networks
Ravid Shwartz-Ziv · 2022
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Reverse engineering self-supervised learning
Ido Ben-Shaul, Ravid Shwartz-Ziv, Tomer Galanti, Shai Dekel, and Yann LeCun · 2023
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How does information bottleneck help deep learning?
Kenji Kawaguchi, Zhun Deng, Xu Ji, and Jiaoyang Huang · 2023
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To compress or not to compress–self-supervised learning and information theory: A review
Ravid Shwartz-Ziv and Yann LeCun · 2023
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