3 papers
cs.LG2026
Parameter symmetries determine representational geometry in overparameterized nonlinear networks
Marvin Theiss, Lukas Braun, Andrew M. Saxe +1
Representations are routinely used across machine learning, psychology, and neuroscience to draw inferences about the computations of biological and artificial systems. Such infere…
cs.LG2026
Not all solutions are created equal: An analytical dissociation of functional and representational similarity in deep linear neural networks
Lukas Braun, Erin Grant, Andrew M. Saxe
A foundational principle of connectionism is that perception, action, and cognition emerge from parallel computations among simple, interconnected units that generate and rely on n…
cs.LG2024
From Lazy to Rich: Exact Learning Dynamics in Deep Linear Networks
Clémentine C. J. Dominé, Nicolas Anguita, Alexandra M. Proca +4
Biological and artificial neural networks develop internal representations that enable them to perform complex tasks. In artificial networks, the effectiveness of these models reli…