[1] Kartik Ahuja, Ethan Caballero, Dinghuai Zhang, Jean-Christophe Gagnon-Audet, Yoshua Bengio, Ioannis Mitliagkas, and Irina Rish. "Invariance principle meets information bottleneck for out-of-distribution generalization." NeurIPS. Volume 34. 2021, pages 3438-3450.
[2] Mohammad Sadegh Akhondzadeh, Vijay Lingam, and Aleksandar Bojchevski. "Probing Graph Representation." AISTATS. 2023.
[3] Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz. "Invariant risk minimization" (2019). arXiv: 1907.02893.
[4] Hangbo Bao, Li Dong, Songhao Piao, and Furu Wei. "BEiT: BERT Pre-Training of Image Transformers." ICLR. 2022.
[5] Yoshua Bengio, Tristan Deleu, Nasim Rahaman, Nan Rosemary Ke, Sebastien Lachapelle, Olexa Bilaniuk, Anirudh Goyal, and Christopher Pal. “A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms.” ICLR. 2020.
[6] Yoshua Bengio, Nicholas Léonard, and Aaron Courville. "Estimating or propagating gradients through stochastic neurons for conditional computation" (2013). arXiv: 1308.3432.
[7] Aaron Beppu and Thomas Griffiths. "Iterated learning and the cultural ratchet." Proceedings of the Annual Meeting of the Cognitive Science Society. Volume 31. 2009.
[8] Henry Brighton and Simon Kirby. "Understanding linguistic evolution by visualizing the emergence of topographic mappings." Artificial Life 12.2 (2006), pages 229-242.
[9] Chris Burgess and Hyunjik Kim. 3D Shapes Dataset. 2018. URL: https://github.com/deepmind/3d-shapes/.
[10] Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. “Emerging properties in self-supervised vision transformers.” CVPR. 2021.
[11] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. "A simple framework for contrastive learning of visual representations." ICML. 2020.
[12] Grégoire Deletang, Anian Ruoss, Paul-Ambroise Duquenne, Elliot Catt, Tim Genewein, Christopher Mattern, Jordi Grau-Moya, Li Kevin Wenliang, Matthew Aitchison, Laurent Orseau, Marcus Hutter, and Joel Veness. Language modeling is compression. 2023. arXiv: 2309.10668.
[13] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding." *NAACL-HLT*. 2019, pages 4171–4186.
[14] Vijay Prakash Dwivedi and Xavier Bresson. “A generalization of transformer networks to graphs.” AAAI 2021 Workshop on Deep Learning on Graphs: Methods and Applications. 2020. arXiv: 2012.09699.
[15] Vijay Prakash Dwivedi, Chaitanya K Joshi, Thomas Laurent, Yoshua Bengio, and Xavier Bresson. "Benchmarking graph neural networks." Journal of Machine Learning Research 24.43 (2023), pages 1-48.
[16] Vijay Prakash Dwivedi, Ladislav Rampášek, Mikhail Galkin, Ali Parviz, Guy Wolf, Anh Tuan Luu, and Dominique Beaini. "Long Range Graph Benchmark." Neural Information Processing Systems (NeurIPS 2022), Track on Datasets and Benchmarks (2022).
[17] Jerry A Fodor and Ernest Lepore. The compositionality papers. Oxford University Press, 2002.
[18] Jerry A Fodor and Zenon W Pylyshyn. "Connectionism and cognitive architecture: A critical analysis." Cognition 28.1-2 (1988), pages 3-71.
[19] Tommaso Furlanello, Zachary Lipton, Michael Tschannen, Laurent Itti, and Anima Anandkumar. "Born again neural networks." ICML. 2018.
[20] Robert Geirhos, Jorn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A Wichmann. "Shortcut learning in deep neural networks." Nature Machine Intelligence 2.11 (2020), pages 665-673.
[21] Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. "Neural message passing for quantum chemistry." ICML. 2017.
[22] Jonathan Godwin, Michael Schaarschmidt, Alexander L Gaunt, Alvaro Sanchez-Gonzalez, Yulia Rubanova, Petar Velicković, James Kirkpatrick, and Peter Battaglia. "Simple GNN Regularisation for 3D Molecular Property Prediction and Beyond." *ICLR*. 2022.