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This paper proposes Equivariant Poincaré ResNets, combining hyperbolic geometry with discrete symmetry groups to improve efficiency in learning visual representations by treating rotated features as symmetric rather than distinct hierarchical concepts.
This paper evaluates the biological plausibility and representational alignment of feedback alignment algorithms in convolutional networks, comparing them to standard backpropagation on CIFAR-10. The authors find that modified feedback alignment methods converge on internal representations similar to those produced by backpropagation, suggesting functional success through mimicking representational geometry.