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Creators/Authors contains: "Bekkers, Erik"

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  1. ; ; ; ; ; ; (, Proceedings of Machine Learning Research - NeurIPS workshop)
    We propose a hierarchical neural network architecture for unsupervised learning of equivariant part-whole decompositions of visual scenes. In contrast to the global equivariance of group-equivariant networks, the proposed architecture exhibits equivariance to part-whole transformations throughout the hierarchy, which we term hierarchical equivariance. The model achieves these structured internal representations via hierarchical Bayesian inference, which gives rise to rich bottom-up, top-down, and lateral information flows, hypothesized to underlie the mechanisms of perceptual inference in visual cortex. We demonstrate these useful properties of the model on a simple dataset of scenes with multiple objects under independent rotations and translations. 
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    Accepted Manuscript Full Text Available
  2. ; ; ; ; ; ; ; ; ; ; et al (, Foundations and Trends® in Machine Learning)
    Free, publicly-accessible full text available January 1, 2026