RRN integrates graph convolutional operations within invertible residual blocks to perform spatially-aware normalization that addresses distribution shift in spatio-temporal forecasting.
Lipsformer: Introducing lipschitz continuity to vision transformers.ArXiv, abs/2304.09856
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Sinkhorn-normalized doubly stochastic attention preserves rank more effectively than Softmax row-stochastic attention, with both showing doubly exponential rank decay to one with network depth.
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Reversible Residual Normalization Alleviates Spatio-Temporal Distribution Shift
RRN integrates graph convolutional operations within invertible residual blocks to perform spatially-aware normalization that addresses distribution shift in spatio-temporal forecasting.
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Sinkhorn doubly stochastic attention rank decay analysis
Sinkhorn-normalized doubly stochastic attention preserves rank more effectively than Softmax row-stochastic attention, with both showing doubly exponential rank decay to one with network depth.