On Making $\mathrm{SE}(2)$-Invariant Networks Optimal
Respecting a-priori known symmetries of underlying data is a key principle in designing efficient neural architectures. Convolutional neural networks are a canonical example, encoding translation equivariance. Extending this inductive bias to richer symmetry groups such as $\mathrm{SE}(2)$ in the most effective way pos...
Tomáš Karella, Emily Shinkle, Alice E. A. Allen et al.
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