[134] Arash Vahdat et al. “DVAE++: Discrete Variational Autoencoders with Overlapping Transformations”. In: Proceedings of the 35th International Conference on Machine Learning. Ed. by Jennifer Dy and Andreas Krause. Vol. 80. Proceedings of Machine Learning Research. PMLR, July 2018, pp. 5035–5044. URL: https://proceedings.mlr.press/v80/vehdat18a.html.
[135] Xi Chen et al. "Variational Lossy Autoencoder". In: International Conference on Learning Representations. 2017. URL: https://openreview.net/forum?id=BysvGP5ee.
[136] Diederik P Kingma and Jimmy Ba. "Adam: A method for stochastic optimization". In: (2014). arXiv: 1412.6980 [cs.LG].
[137] Ilya Loshchilov and Frank Hutter. "SGDR: Stochastic Gradient Descent with Warm Restarts". In: International Conference on Learning Representations. 2017. URL: https://openreview.net/forum?id=Skq89Scxx.
[138] Yoav Benjamini and Yosef Hochberg. "Controlling the false discovery rate: a practical and powerful approach to multiple testing". In: Journal of the Royal Statistical Society: series B (Methodological) 57.1 (1995), pp. 289-300. DOI: 10.1111/J.2517-6161.1995.TB02031. X.
[139] Jacob Cohen. Statistical power analysis for the behavioral sciences. Academic press, 1988. DOI: 10.2307/2529115.
[140] Gregory C. DeAngelis and Dora E. Angelaki. "Visual-Vestibular Integration for Self-Motion Perception". In: The Neural Bases of Multisensory Processes (2012), pp. 629-644. DOI: 10.1201/9781439812174.
[141] Eduard Von Holst. "Relations between the central nervous system and the peripheral organs". In: British Journal of Animal Behaviour (1954). DOI: 10.1016/S0950-5601(54)80044-X.
[142] Paul R MacNeilage et al. "Vestibular facilitation of optic flow parsing". In: PLoS One 7.7 (2012), e40264. DOI: 10.1371/journal.pone.0040264.
[143] Kathleen E Cullen and Omid A Zobeiri. "Proprioception and the predictive sensing of active self-motion". In: Current Opinion in Physiology 20 (2021), pp. 29-38. DOI: 10.1016/j.cophys.2020.12.001.
[144] Constance S Royden and Ellen C Hildreth. "Human heading judgments in the presence of moving objects". In: Perception & Psychophysics 58 (1996), pp. 836-856. DOI: 10.3758/BF03205487.
[145] William H Warren Jr and Jeffrey A Saunders. "Perceiving heading in the presence of moving objects". In: Perception 24.3 (1995), pp. 315-331. DOI: 10.1068/p240315.
[146] Edward AB Horrocks et al. "Walking humans and running mice: perception and neural encoding of optic flow during self-motion". In: Philosophical Transactions of the Royal Society B 378.1869 (2023), p. 20210450. DOI: 10.1098/rstb.2021.0450.
[147] Jean-Paul Noel et al. "Causal inference during closed-loop navigation: parsing of self-and object-motion". In: Philosophical Transactions of the Royal Society B 378.1886 (2023), p. 20220344. DOI: 10.1098/rstb.2022.0344.
[148] Denis N Lee. “The optic flow field: The foundation of vision”. In: Philosophical Transactions of the Royal Society of London. B, Biological Sciences 290.1038 (1980), pp. 169–179. DOI: 10.1098/rstb.1980.0089.
[149] Markus Lappe et al. "Perception of self-motion from visual flow". In: Trends in Cognitive Sciences 3.9 (1999), pp. 329-336. DOI: 10.1016/S1364-6613(99)01364-9.
[150] Irina Higgins et al. "Symmetry-based representations for artificial and biological general intelligence". In: Frontiers in Computational Neuroscience 16 (2022), p. 836498. DOI: 10.3389/fncom.2022.836498.
[151] Fabio Anselmi et al. "On invariance and selectivity in representation learning". In: Information and Inference: A Journal of the IMA 5.2 (2016), pp. 134-158. DOI: 10.1093/imaiai/iaw009.
[152] Michael M Bronstein et al. "Geometric deep learning: Grids, groups, graphs, geodesics, and gauges". In: (2021). arXiv: 2104.13478 [cs.LG].
[153] Ishaan Gulrajani et al. "PixelVAE: A Latent Variable Model for Natural Images". In: International Conference on Learning Representations. 2017. URL: https://openreview.net/forum?id=BJKYvt5lg.