1 citations · 2 across the 6 of their papers we have counts for
6 papers
Attention-based clustering
Rodrigo Maulen-Soto, Pierre Marion, Claire Boyer
Transformers have emerged as a powerful neural network architecture capable of tackling a wide range of learning tasks. In this work, we provide a theoretical analysis of their abi…
Inertial Methods with Viscous and Hessian driven Damping for Non-Convex Optimization
Rodrigo Maulen-Soto, Jalal Fadili, Peter Ochs
In this paper, we aim to study non-convex minimization problems via second-order (in-time) dynamics, including a non-vanishing viscous damping and a geometric Hessian-driven dampin…
An SDE Perspective on Stochastic Inertial Gradient Dynamics with Time-Dependent Viscosity and Geometric Damping
Rodrigo Maulen-Soto, Jalal Fadili, Hedy Attouch +1
Our approach is part of the close link between continuous dissipative dynamical systems and optimization algorithms. We aim to solve convex minimization problems by means of stocha…
Stochastic Inertial Dynamics Via Time Scaling and Averaging
Rodrigo Maulen-Soto, Jalal Fadili, Hedy Attouch +1
Our work is part of the close link between continuous-time dissipative dynamical systems and optimization algorithms, and more precisely here, in the stochastic setting. We aim to…
Tikhonov Regularization for Stochastic Non-Smooth Convex Optimization in Hilbert Spaces
Rodrigo Maulen-Soto, Jalal Fadili, Hedy Attouch
To solve convex optimization problems with a noisy gradient input, we analyze the global behavior of subgradient-like flows under stochastic errors. The objective function is compo…
An SDE perspective on stochastic convex optimization
Rodrigo Maulen-Soto, Jalal Fadili, Hedy Attouch
We analyze the global and local behavior of gradient-like flows under stochastic errors towards the aim of solving convex optimization problems with noisy gradient input. We first…