28 citations · 68 across the 25 of their papers we have counts for
4 papers · 1 filter
AdamL: A fast adaptive gradient method incorporating loss function
Lu Xia, Stefano Massei
Adaptive first-order optimizers are fundamental tools in deep learning, although they may suffer from poor generalization due to the nonuniform gradient scaling. In this work, we p…
Optimizing network robustness via Krylov subspaces
Stefano Massei, Francesco Tudisco
We consider the problem of attaining either the maximal increase or reduction of the robustness of a complex network by means of a bounded modification of a subset of the edge weig…
On the Convergence of the Gradient Descent Method with Stochastic Fixed-point Rounding Errors under the Polyak-Lojasiewicz Inequality
Lu Xia, Michiel E. Hochstenbach, Stefano Massei
When training neural networks with low-precision computation, rounding errors often cause stagnation or are detrimental to the convergence of the optimizers; in this paper we study…
A nested divide-and-conquer method for tensor Sylvester equations with positive definite hierarchically semiseparable coefficients
Stefano Massei, Leonardo Robol
Linear systems with a tensor product structure arise naturally when considering the discretization of Laplace type differential equations or, more generally, multidimensional opera…