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Leveraging the compositional nature of our world to expedite learning and facilitate generalization is a hallmark of human perception.
Connectionism and cognitive architecture: A critical analysis
Jerry A. Fodor and Zenon W. Pylyshyn · 1988
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Covariate shift adaptation by importance weighted cross validation
Masashi Sugiyama, Matthias Krauledat, and Klaus-Robert Müller · 2007
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A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan · 2010
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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
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Aspects of the Theory of Syntax , volume 11
Noam Chomsky · 2014
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Domain adaptation–can quantity compensate for quality?
Shai Ben-David and Ruth Urner · 2014
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Attention is All you Need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks
Brenden Lake and Marco Baroni · 2018
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Rearranging the familiar: Testing compositional generalization in recurrent networks
Joao Loula, Marco Baroni, and Brenden M Lake · 2018
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Exploiting semantics in neural machine translation with graph convolutional networks
Diego Marcheggiani, Jasmijn Bastings, and Ivan Titov · 2018
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Matrix capsules with EM routing
Geoffrey E Hinton, Sara Sabour, and Nicholas Frosst · 2018
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Measuring compositional generalization: A comprehensive method on realistic data
Daniel Keysers, Nathanael Schärli, Nathan Scales, Hylke Buisman, Daniel Furrer, Sergii Kashubin, Nikola Momchev, Danila Sinopalnikov, Lukasz Stafiniak, Tibor Tihon, et al · 2019
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On the fairness of disentangled representations
Francesco Locatello, Gabriele Abbati, Thomas Rainforth, Stefan Bauer, Bernhard Schölkopf, and Olivier Bachem · 2019
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Are disentangled representations helpful for abstract visual reasoning?
Sjoerd van Steenkiste, Francesco Locatello, Jürgen Schmidhuber, and Olivier Bachem · 2019
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Compositional generalization in a deep seq2seq model by separating syntax and semantics
Jake Russin, Jason Jo, Randall C O’Reilly, and Yoshua Bengio · 2019
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Multi-object representation learning with iterative variational inference
Klaus Greff, Raphaël Lopez Kaufman, Rishabh Kabra, Nick Watters, Christopher Burgess, Daniel Zoran, Loic Matthey, Matthew Botvinick, and Alexander Lerchner · 2019
Self-supervised learning with data augmentations provably isolates content from style
Julius Von Kügelgen, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Schölkopf, Michel Besserve, and Francesco Locatello · 2021
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Conditional object-centric learning from video
Thomas Kipf, Gamaleldin F Elsayed, Aravindh Mahendran, Austin Stone, Sara Sabour, Georg Heigold, Rico Jonschkowski, Alexey Dosovitskiy, and Klaus Greff · 2021
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Bi-linear value networks for multi-goal reinforcement learning
Zhang-Wei Hong, Ge Yang, and Pulkit Agrawal · 2021
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Towards Out-Of-Distribution Generalization: A Survey
Zheyan Shen, Jiashuo Liu, Yue He, Xingxuan Zhang, Renzhe Xu, Han Yu, and Peng Cui · 2021
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Visual Representation Learning Does Not Generalize Strongly Within the Same Domain
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MONet: Unsupervised Scene Decomposition and Representation, January 2019
Christopher P. Burgess, Loic Matthey, Nicholas Watters, Rishabh Kabra, Irina Higgins, Matt Botvinick, and Alexander Lerchner · 2019
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Training neural networks to encode symbols enables combinatorial generalization
Ivan I. Vankov and Jeffrey S. Bowers · 2019
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Learning to recombine and resample data for compositional generalization
Ekin Akyürek, Afra Feyza Akyürek, and Jacob Andreas · 2020
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Object-Centric Learning with Slot Attention
Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, and Thomas Kipf · 2020
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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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The role of Disentanglement in Generalisation
Milton Llera Montero, Casimir JH Ludwig, Rui Ponte Costa, Gaurav Malhotra, and Jeffrey Bowers
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Lost in Latent Space: Examining failures of disentangled models at combinatorial generalisation
Milton L. Montero, Jeffrey Bowers, Rui Ponte Costa, Casimir JH Ludwig, and Gaurav Malhotra
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Lukas Schott, Julius von Kügelgen, Frederik Träuble, Peter Gehler, Chris Russell, Matthias Bethge, Bernhard Schölkopf, Francesco Locatello, and Wieland Brendel · 2022
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Embrace the gap: VAEs perform independent mechanism analysis
Patrik Reizinger, Luigi Gresele, Jack Brady, Julius Von Kügelgen, Dominik Zietlow, Bernhard Schölkopf, Georg Martius, Wieland Brendel, and Michel Besserve · 2022
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Complex-Valued Autoencoders for Object Discovery, November 2022
Sindy Löwe, Phillip Lippe, Maja Rudolph, and Max Welling · 2022
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First Steps Toward Understanding the Extrapolation of Nonlinear Models to Unseen Domains
Kefan Dong and Tengyu Ma · 2022
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Learning and generalization of compositional representations of visual scenes, March 2023
E. Paxon Frady, Spencer Kent, Quinn Tran, Pentti Kanerva, Bruno A. Olshausen, and Friedrich T. Sommer · 2023
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Learning to Extrapolate: A Transductive Approach
Aviv Netanyahu, Abhishek Gupta, Max Simchowitz, Kaiqing Zhang, and Pulkit Agrawal · 2023
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