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This paper studies separating invariants: mappings on $D$ dimensional domains which are invariant to an appropriate group action, and which separate orbits.
The Classical Groups: Their Invariants and Representations
H. Weyl · 1946
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A. Rényi · 1952
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Isomorphism of graphs with bounded eigenvalue multiplicity
L. Babai, D. Y. Grigoryev, and D. M. Mount · 1982
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J. Harris · 2013
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J. M. Lee · 2013
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J. R. Munkres · 2013
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Generic global rigidity in complex and pseudo-euclidean spaces
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