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Joint-Embedding Predictive Architectures (JEPAs) have recently emerged as a novel and powerful technique for self-supervised representation learning.
Signature verification using a" siamese" time delay neural network
Jane Bromley, Isabelle Guyon, Yann LeCun, Eduard Säckinger, and Roopak Shah · 1993
Earlier work this paper cites.
Autoencoders, minimum description length and helmholtz free energy
Geoffrey E Hinton and Richard Zemel · 1993
Earlier work this paper cites.
Random walks on graphs
László Lovász · 1993
Earlier work this paper cites.
Hyperbolic geometry on a hyperboloid
William F. Reynolds · 1993
Earlier work this paper cites.
A fast and high quality multilevel scheme for partitioning irregular graphs
George Karypis and Vipin Kumar · 1998
Earlier work this paper cites.
Training products of experts by minimizing contrastive divergence
Geoffrey E Hinton · 2002
Earlier work this paper cites.
The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
Earlier work this paper cites.
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Earlier work this paper cites.
Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
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Earlier work this paper cites.
Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
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Earlier work this paper cites.
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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
Earlier work this paper cites.
Auto-encoding variational bayes
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Earlier work this paper cites.
Infants hierarchically organize memory representations
R. Rosenberg and L. Feigenson · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Fast r-cnn
Ross Girshick · 2015
Earlier work this paper cites.
Xavier Bresson and Thomas Laurent · 2017
Earlier work this paper cites.
Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
Earlier work this paper cites.
Relational autoencoder for feature extraction
Qinxue Meng, Daniel Catchpoole, David Skillicom, and Paul J. Kennedy · 2017
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Poincaré embeddings for learning hierarchical representations
Maximillian Nickel and Douwe Kiela · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
Earlier work this paper cites.
On the tradeoff between mode collapse and sample quality in generative adversarial networks
Sudarshan Adiga, Mohamed Adel Attia, Wei-Ting Chang, and Ravi Tandon · 2018
Earlier work this paper cites.
Representation tradeoffs for hyperbolic embeddings
Frederic Sala, Chris De Sa, Albert Gu, and Christopher Re · 2018
Earlier work this paper cites.
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
Earlier work this paper cites.
Hierarchical graph representation learning with differentiable pooling
Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton, and Jure Leskovec · 2018
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Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan E. Lenssen · 2019
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Strategies for pre-training graph neural networks
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec · 2019
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Weisfeiler and leman go neural: Higher-order graph neural networks
Christopher Morris, Martin Ritzert, Matthias Fey, William L Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe · 2019
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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, et al · 2019
Cited alongside, same era.
Self-supervised learning: Generative or contrastive
Xiao Liu, Fanjin Zhang, Zhenyu Hou, Li Mian, Zhaoyu Wang, Jing Zhang, and Jie Tang · 2021
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Deep Learning on Graphs
Yao Ma and Jiliang Tang · 2021
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Graph hierarchy: a novel framework to analyse hierarchical structures in complex networks
Giannis Moutsinas, Choudhry Shuaib, Weisi Guo, and Stephen Jarvis · 2021
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Adversarial graph augmentation to improve graph contrastive learning
Susheel Suresh, Pan Li, Cong Hao, and Jennifer Neville · 2021
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Large-scale representation learning on graphs via bootstrapping
Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Mehdi Azabou, Eva L Dyer, Remi Munos, Petar Veličković, and Michal Valko · 2021
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On memorization in probabilistic deep generative models
Gerrit van den Burg and Chris Williams · 2021
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Fan-Yun Sun, Jordan Hoffman, Vikas Verma, and Jian Tang · 2019
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Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger · 2019
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The surprising power of graph neural networks with random node initialization
Ralph Abboud, Ismail Ilkan Ceylan, Martin Grohe, and Thomas Lukasiewicz · 2020
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 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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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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A generalization of transformer networks to graphs
Vijay Prakash Dwivedi and Xavier Bresson · 2020
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Representing hyperbolic space accurately using multi-component floats
Tao Yu and Christopher M De Sa · 2021
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From canonical correlation analysis to self-supervised graph neural networks
Hengrui Zhang, Qitian Wu, Junchi Yan, David Wipf, and Philip S Yu · 2021
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Data2vec: A general framework for self-supervised learning in speech, vision and language
Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, and Michael Auli · 2022
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Long range graph benchmark
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Co-sne: Dimensionality reduction and visualization for hyperbolic data
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Masked autoencoders are scalable vision learners
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Graphmae: Self-supervised masked graph autoencoders
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A path towards autonomous machine intelligence version 0.9. 2, 2022-06-27
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Augmentation-free self-supervised learning on graphs
Namkyeong Lee, Junseok Lee, and Chanyoung Park · 2022
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Diagnosing and fixing manifold overfitting in deep generative models
Gabriel Loaiza-Ganem, Brendan Leigh Ross, Jesse C Cresswell, and Anthony L Caterini · 2022
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Self-supervised learning from images with a joint-embedding predictive architecture
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A-jepa: Joint-embedding predictive architecture can listen
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A generalization of vit/mlp-mixer to graphs
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The edge of orthogonality: A simple view of what makes byol tick
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S2gae: Self-supervised graph autoencoders are generalizable learners with graph masking
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Everything is connected: Graph neural networks
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Modeling graphs beyond hyperbolic: Graph neural networks in symmetric positive definite matrices
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