6 citations · 16 across the 31 of their papers we have counts for
3 papers · 1 filter
Manifold meta-learning for reduced-complexity neural system identification
Marco Forgione, Ankush Chakrabarty, Dario Piga +2
System identification has greatly benefited from deep learning techniques, particularly for modeling complex, nonlinear dynamical systems with partially unknown physics where tradi…
Exact Gauss-Newton Optimization for Training Deep Neural Networks
Mikalai Korbit, Adeyemi D. Adeoye, Alberto Bemporad +1
We present Exact Gauss-Newton (EGN), a stochastic second-order optimization algorithm that combines the generalized Gauss-Newton (GN) Hessian approximation with low-rank linear alg…
Regularized Gauss-Newton for Optimizing Overparameterized Neural Networks
Adeyemi D. Adeoye, Philipp Christian Petersen, Alberto Bemporad
The generalized Gauss-Newton (GGN) optimization method incorporates curvature estimates into its solution steps, and provides a good approximation to the Newton method for large-sc…