Phase Transition from Random Features to Fully Trainable Networks

arXiv:2610.31663 · math.NA, cs.LG, cs.NA, math.ST, stat.ML, stat.TH · Submitted 2026-10-22 · Read on arXiv

math.NA, cs.LG, cs.NA, math.ST, stat.ML, stat.TH

Submitted: 2026-10-22

Updated: 2026-10-22

Terminology

• Phase Transition — 1× in this paper · explained in 3 episodes

Related papers

• Do physics-informed neural networks (PINNs) need to be deep? Shallow PINNs using the Levenberg-Marquardt algorithm
• A Neural-preconditioned Poisson Solver for Mixed Dirichlet and Neumann Boundary Conditions
• Advanced Linear Algebra with Applications - Part I (Numerical linear algebra for PDEs, machine learning, and data assimilation)
• Operator learning meets inverse problems: A probabilistic perspective
• Second-order consistency for learning chaotic dynamics via randomized Jacobian matching
• Windowed thinning and query complexity for the bouncy particle and Zigzag samplers