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Education
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My research is focussed on learning of equivariant representations for data-efficient deep learning. Besides improving data-efficiency, “equivariance to symmetry transformations” provides one of the first rational design principles for deep neural networks, and allows them to be more easily interpreted in geometrical terms than ordinary black-box networks.