[58] A Ravishankar Rao and Guillermo A Cecchi. An objective function utilizing complex sparsity for efficient segmentation in multi-layer oscillatory networks. International Journal of Intelligent Computing and Cybernetics, 2010.
[59] A Ravishankar Rao and Guillermo A Cecchi. The effects of feedback and lateral connections on perceptual processing: A study using oscillatory networks. In International Joint Conference on Neural Networks, pp. 1177-1184, 2011.
[60] A Ravishankar Rao, Guillermo A Cecchi, Charles C Peck, and James R Kozloski. Unsupervised segmentation with dynamical units. IEEE Transactions on Neural Networks, 19(1):168-182, 2008.
[61] David P Reichert and Thomas Serre. Neuronal synchrony in complex-valued deep networks. International Conference on Learning Representations (ICLR), 2014.
[62] Mehdi SM Sajjadi, Daniel Duckworth, Aravindh Mahendran, Sjoerd van Steenkiste, Filip Pavetic, Mario Lucic, Leonidas J Guibas, Klaus Greff, and Thomas Kipf. Object scene representation transformer. Advances in Neural Information Processing Systems (NeurIPS), 35: 9512-9524, 2022.
[63] Adam Santoro, David Raposo, David G Barrett, Mateusz Malinowski, Razvan Pascanu, Peter Battaglia, and Timothy Lillicrap. A simple neural network module for relational reasoning. In Advances in Neural Information Processing Systems (NeurIPS), pp. 4967-4976, 2017.
[64] Maximilian Seitzer, Max Horn, Andrii Zadaianchuk, Dominik Zietlow, Tianjun Xiao, Carl-Johann Simon-Gabriel, Tong He, Zheng Zhang, Bernhard Schölkopf, Thomas Brox, and Francesco Locatello. Bridging the gap to real-world object-centric learning. International Conference on Learning Representations (ICLR), 2023.
[65] Wolf Singer. Neuronal synchrony: a versatile code for the definition of relations? Neuron, 24 (1):49-65, 1999.
[66] Wolf Singer. Distributed processing and temporal codes in neuronal networks. Cognitive Neurodynamics, 3(3):189-196, 2009.
[67] Wolf Singer and Charles M Gray. Visual feature integration and the temporal correlation hypothesis. Annual Review of Neuroscience, 18(1):555-586, 1995.
[68] Gautam Singh, Fei Deng, and Sungjin Ahn. Illiterate dall-e learns to compose. International Conference on Learning Representations (ICLR), 2022.
[69] Gautam Singh, Yi-Fu Wu, and Sungjin Ahn. Simple unsupervised object-centric learning for complex and naturalistic videos. Advances in Neural Information Processing Systems (NeurIPS), 2022.
[70] Gautam Singh, Yeongbin Kim, and Sungjin Ahn. Neural systematic binder. In International Conference on Learning Representations (ICLR), 2023.
[71] Cameron Smith, Hong-Xing Yu, Sergey Zakharov, Fredo Durand, Joshua B Tenenbaum, Jiajun Wu, and Vincent Sitzmann. Unsupervised discovery and composition of object light fields. arXiv preprint arXiv:2205.03923, 2022.
[72] Elizabeth S Spelke. Principles of object perception. Cognitive science, 14(1):29-56, 1990.
[73] Aleksandar Stanić, Anand Gopalakrishnan, Kazuki Irie, and Jürgen Schmidhuber. Contrastive training of complex-valued autoencoders for object discovery. arXiv preprint arXiv:2305.15001, 2023.
[74] Karl Stelzner, Robert Peharz, and Kristian Kersting. Faster attend-infer-repeat with tractable probabilistic models. In International Conference on Machine Learning (ICML), pp. 5966-5975, 2019.
[75] Karl Stelzner, Kristian Kersting, and Adam R Kosiorek. Decomposing 3d scenes into objects via unsupervised volume segmentation. arXiv preprint arXiv:2104.01148, 2021.
[76] Pieter Merkus Lambertus Tammes. On the origin of number and arrangement of the places of exit on the surface of pollen-grains. Recueil des travaux botaniques neerlandais, 27(1):1-84, 1930.
[77] Manuel Traub, Sebastian Otte, Tobias Menge, Matthias Karlbauer, Jannik Thuemmel, and Martin V Butz. Learning what and where: Disentangling location and identity tracking without supervision. In International Conference on Learning Representations (ICLR), 2023.