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Compositional generalization is a crucial step towards developing data-efficient intelligent machines that generalize in human-like ways.
Pragmatics and intensional logic
Richard Montague · 1970
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Connectionism and cognitive architecture: A critical analysis
Jerry A Fodor and Zenon W Pylyshyn · 1988
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Mapping part-whole hierarchies into connectionist networks
Geoffrey E Hinton · 1990
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Holographic reduced representations: Convolution algebra for compositional distributed representations
Tony Plate et al · 1991
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Training products of experts by minimizing contrastive divergence
Geoffrey E Hinton · 2002
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Causal inference using the algorithmic markov condition
Dominik Janzing and Bernhard Schölkopf · 2010
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The caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel R Bowman · 2017
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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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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Learning independent causal mechanisms
Giambattista Parascandolo, Niki Kilbertus, Mateo Rojas-Carulla, and Bernhard Schölkopf · 2018
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Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
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Nuanced metrics for measuring unintended bias with real data for text classification
Daniel Borkan, Lucas Dixon, Jeffrey Sorensen, Nithum Thain, and Lucy Vasserman · 2019
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Permutation equivariant models for compositional generalization in language
Jonathan Gordon, David Lopez-Paz, Marco Baroni, and Diane Bouchacourt · 2019
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Decoupling representation and classifier for long-tailed recognition
Bingyi Kang, Saining Xie, Marcus Rohrbach, Zhicheng Yan, Albert Gordo, Jiashi Feng, and Yannis Kalantidis · 2019
Cited alongside, same era.
Compositional generalization in image captioning
Mitja Nikolaus, Mostafa Abdou, Matthew Lamm, Rahul Aralikatte, and Desmond Elliott · 2019
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Shiori Sagawa, Pang Wei Koh, Tatsunori B Hashimoto, and Percy Liang · 2019
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Empirical or invariant risk minimization? a sample complexity perspective
Kartik Ahuja, Jun Wang, Amit Dhurandhar, Karthikeyan Shanmugam, and Kush R Varshney · 2020
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Domain generalization: A survey
Kaiyang Zhou, Ziwei Liu, Yu Qiao, Tao Xiang, and Chen Change Loy · 2022
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Provably learning object-centric representations
Jack Brady, Roland S Zimmermann, Yash Sharma, Bernhard Schölkopf, Julius Von Kügelgen, and Wieland Brendel · 2023
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Human-like systematic generalization through a meta-learning neural network
Brenden M Lake and Marco Baroni · 2023
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A survey on compositional generalization in applications
Baihan Lin, Djallel Bouneffouf, and Irina Rish · 2023
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Causal triplet: An open challenge for intervention-centric causal representation learning
Yuejiang Liu, Alexandre Alahi, Chris Russell, Max Horn, Dominik Zietlow, Bernhard Schölkopf, and Francesco Locatello · 2023
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Compositional visual generation with energy based models
Y. Du, S. Li, and I. Mordatch · 2020
Cited alongside, same era.
Compositionality decomposed: How do neural networks generalise?
Dieuwke Hupkes, Verna Dankers, Mathijs Mul, and Elia Bruni · 2020
Cited alongside, same era.
Cogs: A compositional generalization challenge based on semantic interpretation
Najoung Kim and Tal Linzen · 2020
Cited alongside, same era.
Long-tail learning via logit adjustment
Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Himanshu Jain, Andreas Veit, and Sanjiv Kumar · 2020
Cited alongside, same era.
Balanced meta-softmax for long-tailed visual recognition
Jiawei Ren, Cunjun Yu, Xiao Ma, Haiyu Zhao, Shuai Yi, et al · 2020
Cited alongside, same era.
The risks of invariant risk minimization
Elan Rosenfeld, Pradeep Ravikumar, and Andrej Risteski · 2020
Cited alongside, same era.
Unsupervised learning of compositional energy concepts
Y. Du, S. Li, Y. Sharma, J. Tenenbaum, and I. Mordatch · 2021
Cited alongside, same era.
Out-of-distribution generalization via risk extrapolation (rex)
David Krueger, Ethan Caballero, Joern-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Remi Le Priol, and Aaron Courville · 2021
Cited alongside, same era.
Mohammad Pezeshki, Diane Bouchacourt, Mark Ibrahim, Nicolas Ballas, Pascal Vincent, and David Lopez-Paz · 2023
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Topology-aware robust optimization for out-of-distribution generalization
Fengchun Qiao and Xi Peng · 2023
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Discovering modular solutions that generalize compositionally
Simon Schug, Seijin Kobayashi, Yassir Akram, Maciej Wołczyk, Alexandra Proca, Johannes Von Oswald, Razvan Pascanu, João Sacramento, and Angelika Steger · 2023
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Provable compositional generalization for object-centric learning
Thaddäus Wiedemer, Jack Brady, Alexander Panfilov, Attila Juhos, Matthias Bethge, and Wieland Brendel · 2023
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Improving domain generalization with domain relations
Huaxiu Yao, Xinyu Yang, Xinyi Pan, Shengchao Liu, Pang Wei Koh, and Chelsea Finn · 2023
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Nico++: Towards better benchmarking for domain generalization
Xingxuan Zhang, Yue He, Renzhe Xu, Han Yu, Zheyan Shen, and Peng Cui · 2023
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Additive decoders for latent variables identification and cartesian-product extrapolation
Sébastien Lachapelle, Divyat Mahajan, Ioannis Mitliagkas, and Simon Lacoste-Julien · 2024
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A survey on compositional learning of ai models: Theoretical and experimetnal practices
Sania Sinha, Tanawan Premsri, and Parisa Kordjamshidi · 2024
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Compositional image decomposition with diffusion models
Jocelin Su, Nan Liu, Yanbo Wang, Joshua B Tenenbaum, and Yilun Du · 2024
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Group robust classification without any group information
Christos Tsirigotis, Joao Monteiro, Pau Rodriguez, David Vazquez, and Aaron C Courville · 2024
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Concept algebra for (score-based) text-controlled generative models
Zihao Wang, Lin Gui, Jeffrey Negrea, and Victor Veitch · 2024
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Compositional generalization from first principles
Thaddäus Wiedemer, Prasanna Mayilvahanan, Matthias Bethge, and Wieland Brendel · 2024
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The pitfalls of memorization: When memorization hurts generalization
Reza Bayat, Mohammad Pezeshki, Elvis Dohmatob, David Lopez-Paz, and Pascal Vincent · 2025
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