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Simplicial Embeddings (SEM) are representations learned through self-supervised learning (SSL), wherein a representation is projected into $L$ simplices of $V$ dimensions each using a softmax operation.
Revisiting self-supervised visual representation learning
Alexander Kolesnikov, Xiaohua Zhai, and Lucas Beyer · 1901
Earlier work this paper cites.
Momentum Contrast for Unsupervised Visual Representation Learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 1911
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Hierarchical mixtures of experts and the em algorithm
M.I. Jordan and R.A. Jacobs · 1993
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Emergence of simple-cell receptive field properties by learning a sparse code for natural images
B.A. Olshausen and D.J. Field · 1996
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Weak Convergence and Empirical Processes
Aad W. van der Vaart and Jon A. Wellner · 1996
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Independent component analysis: algorithms and applications
Aapo Hyvärinen and Erkki Oja · 2000
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Learning Overcomplete Representations
Michael S. Lewicki and Terrence J. Sejnowski · 2000
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Energy-based models for sparse overcomplete representations
Yee Whye Teh, Max Welling, Simon Osindero, and Geoffrey E. Hinton · 2003
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Stable recovery of sparse overcomplete representations in the presence of noise
D.L. Donoho, M. Elad, and V.N. Temlyakov · 2005
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The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization
Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, Dawn Song, Jacob Steinhardt, and Justin Gilmer · 2006
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On Robustness and Transferability of Convolutional Neural Networks
Josip Djolonga, Jessica Yung, Michael Tschannen, Rob Romijnders, Lucas Beyer, Alexander Kolesnikov, Joan Puigcerver, Matthias Minderer, Alexander D’Amour, Dan Moldovan, Sylvain Gelly, Neil Houlsby, Xiaohua Zhai, and Mario Lucic · 2007
Earlier work this paper cites.
Sparse deep belief net model for visual area v2
Honglak Lee, Chaitanya Ekanadham, and Andrew Ng · 2007
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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, 2009
Alex Krizhevsky · 2009
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Learning fast approximations of sparse coding
Karol Gregor and Yann LeCun · 2010
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Contrastive Learning of General-Purpose Audio Representations
Aaqib Saeed, David Grangier, and Neil Zeghidour · 2010
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Sun database: Large-scale scene recognition from abbey to zoo
Jianxiong Xiao, James Hays, Krista A Ehinger, Aude Oliva, and Antonio Torralba · 2010
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Graph contrastive learning with augmentations
Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen · 2010
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The importance of encoding versus training with sparse coding and vector quantization
Adam Coates and Andrew Y. Ng · 2011
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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Large-scale feature learning with spike-and-slab sparse coding
Ian J. Goodfellow, Aaron Courville, and Yoshua Bengio · 2012
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Learning effective and interpretable semantic models using non-negative sparse embedding
Brian Murphy, Partha Pratim Talukdar, and Tom Michael Mitchell · 2012
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Highly overcomplete sparse coding
Bruno A. Olshausen · 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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Describing textures in the wild
Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi · 2014
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Do ImageNet Classifiers Generalize to ImageNet?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 2019
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Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
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Adversarial robustness: From self-supervised pre-training to fine-tuning
Tianlong Chen, Sijia Liu, Shiyu Chang, Yu Cheng, Lisa Amini, and Zhangyang Wang · 2020
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Exploring simple siamese representation learning
Xinlei Chen and Kaiming He · 2020
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Alex Graves, Greg Wayne, and Ivo Danihelka · 2014
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Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Sparse overcomplete word vector representations
Manaal Faruqui, Yulia Tsvetkov, Dani Yogatama, Chris Dyer, and Noah A. Smith · 2015
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A compositional and interpretable semantic space
Alona Fyshe, Leila Wehbe, Partha P. Talukdar, Brian Murphy, and Tom M. Mitchell · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton · 2016
Cited alongside, same era.
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Robustness Metrics, 2020
Josip Djolonga, Frances Hubis, Matthias Minderer, Zachary Nado, Jeremy Nixon, Rob Romijnders, Dustin Tran, and Mario Lucic · 2020
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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, Bilal Piot, koray kavukcuoglu, Remi Munos, and Michal Valko · 2020
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Array programming with NumPy
Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, Robert Kern, Matti Picus, Stephan Hoyer, Marten H. van Kerkwijk, Matthew Brett, Allan Haldane, Jaime Fernández del Río, Mark Wiebe, Pearu Peterson, Pierre Gérard-Marchant, Kevin Sheppard, Tyler Reddy, Warren Weckesser, Hameer Abbasi, Christoph Gohlke, and Travis E. Oliphant · 2020
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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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Solo-learn: A library of self-supervised methods for visual representation learning, 2021
Victor G. Turrisi da Costa, Enrico Fini, Moin Nabi, Nicu Sebe, and Elisa Ricci · 2021
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Interpretable agent communication from scratch(with a generic visual processor emerging on the side)
Roberto Dessì, Eugene Kharitonov, and Marco Baroni · 2021
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Interpretable agent communication from scratch (with a generic visual processor emerging on the side)
Roberto Dessi, Eugene Kharitonov, and Marco Baroni · 2021
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Compressive Visual Representations
Kuang-Huei Lee, Anurag Arnab, Sergio Guadarrama, John Canny, and Ian Fischer · 2021
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Discrete-valued neural communication
Dianbo Liu, Alex M Lamb, Kenji Kawaguchi, Anirudh Goyal ALIAS PARTH GOYAL, Chen Sun, Michael C Mozer, and Yoshua Bengio · 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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VICReg: Variance-invariance-covariance regularization for self-supervised learning
Adrien Bardes, Jean Ponce, and Yann LeCun · 2022
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Epistimio/orion: Asynchronous Distributed Hyperparameter Optimization, March 2022
Xavier Bouthillier, Christos Tsirigotis, François Corneau-Tremblay, Thomas Schweizer, Lin Dong, Pierre Delaunay, Fabrice Normandin, Mirko Bronzi, Dendi Suhubdy, Reyhane Askari, Michael Noukhovitch, Chao Xue, Satya Ortiz-Gagné, Olivier Breuleux, Arnaud Bergeron, Olexa Bilaniuk, Steven Bocco, Hadrien Bertrand, Guillaume Alain, Dmitriy Serdyuk, Peter Henderson, Pascal Lamblin, and Christopher Beckham · 2022
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Coordination among neural modules through a shared global workspace
Anirudh Goyal, Aniket Rajiv Didolkar, Alex Lamb, Kartikeya Badola, Nan Rosemary Ke, Nasim Rahaman, Jonathan Binas, Charles Blundell, Michael Curtis Mozer, and Yoshua Bengio · 2022
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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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