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This paper introduces Mixture of Channel Experts (MoCE), a structured sparse layer that replaces dense pointwise projections in convolutional networks to reduce computational cost while maintaining or improving performance.
This paper shows that sequence-pooled normalization in convolutional networks provides global context beyond the receptive field, supplying most of the context needed for sequence labeling and affecting attribution in network ablation studies.
The article revisits the Efficient Channel Attention (ECA) paper and presents experiments using chess data that challenge the central hypothesis about cross-channel interaction.
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.