22 citations · 65 across the 6 of their papers we have counts for
6 papers
Optimization on manifolds: A symplectic approach
Guilherme França, Alessandro Barp, Mark Girolami +1
Optimization tasks are crucial in statistical machine learning. Recently, there has been great interest in leveraging tools from dynamical systems to derive accelerated and robust…
On dissipative symplectic integration with applications to gradient-based optimization
Guilherme França, Michael I. Jordan, René Vidal
Recently, continuous-time dynamical systems have proved useful in providing conceptual and quantitative insights into gradient-based optimization, widely used in modern machine lea…
Investigating the signatures of long-range persistence in seismic sequences along Circum-Pacific subduction zones
D. B. de Freitas, G. S. França, T. Scheerer +2
In the present paper, we analyze the signatures of long-range persistence in seismic sequences along Circum-Pacific subduction zones, from Chile to Kermadec, extracted from the Nat…
Conformal Symplectic and Relativistic Optimization
Guilherme França, Jeremias Sulam, Daniel P. Robinson +1
Arguably, the two most popular accelerated or momentum-based optimization methods in machine learning are Nesterov's accelerated gradient and Polyaks's heavy ball, both correspondi…
How is Distributed ADMM Affected by Network Topology?
Guilherme França, José Bento
When solving consensus optimization problems over a graph, there is often an explicit characterization of the convergence rate of Gradient Descent (GD) using the spectrum of the gr…
A theory for the zeros of Riemann and other -functions (updated)
Guilherme França, André LeClair
In these lectures we first review the important properties of the Riemann -function that are necessary to understand the nature and importance of the Riemann hypothesis (RH). In…