4 papers
A Non-Monotone Preconditioned Trust-Region Method for Neural Network Training
Andrea Angino, Bindi Ãapriqi, Shega Likaj +2
Training deep neural networks at scale can benefit from domain decomposition, where the network is split into subdomains trained in parallel and coupled by a global trust-region me…
Protect the Brain When Treating the Heart: Feasibility of 2.5D U-Net for Real-Time Gaseous Microemboli Detection
Andrea Angino, Ken Trotti, Diego Ulisse Pizzagalli +3
Gaseous microemboli (GME) represent a common complication of cardiac structural interventions across both surgical and transcatheter approaches. Intraoperative transesophageal echo…
An Additively Preconditioned Trust Region Strategy for Machine Learning
Samuel Cruz AlegrÃa, Bindi Ãapriqi, Shega Likaj +2
Modern machine learning, especially the training of deep neural networks, depends on solving large-scale, highly nonconvex optimization problems, whose objective function exhibit a…
Data-Parallel Neural Network Training via Nonlinearly Preconditioned Trust-Region Method
Samuel A. Cruz AlegrÃa, Ken Trotti, Alena KopaniÄáková +1
Parallel training methods are increasingly relevant in machine learning (ML) due to the continuing growth in model and dataset sizes. We propose a variant of the Additively Precond…