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Most ReLU Networks Admit Identifiable Parameters

Deep ReLU networks with input and hidden widths ≥2 have open sets of identifiable parameters, yielding exact functional dimensions and generic depth hierarchies.

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NeurIPS 2026SpotlightTechnical University of Denmark Max Planck Institute for Mathem UCLA Deep learning theory

The Symmetries of Three-Layer ReLU Networks

This paper characterizes generic parameter fibers of three-layer ReLU bottleneck networks via semi-algebraic descriptions, yielding polynomial-time functional equivalence tests and analyzing symmetry-induced gradient flow conservation laws.

Johanna Marie Gegenfurtner, Moritz Grillo, Guido Montufar

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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