Parameter symmetries determine representational geometry in overparameterized nonlinear networks
Representations are routinely used across machine learning, psychology, and neuroscience to draw inferences about the computations of biological and artificial systems. Such inferences presume a meaningful link between representational geometry and the computation being performed. For artificial neural networks, howeve...
Marvin Theiss, Lukas Braun, Andrew M. Saxe et al.
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