1 citations · 1 across the 1 of their papers we have counts for
5 papers
Greedy Learning to Optimize with Convergence Guarantees
Patrick Fahy, Mohammad Golbabaee, Matthias J. Ehrhardt
Learning to optimize (L2O) is an approach that leverages training data to accelerate the solution of optimization problems. Many approaches use unrolling to parametrize the update…
Importance-Aware Scheduling for High-Dimensional Hyperparameter Optimization
Ruinan Wang, Ian Nabney, Mohammad Golbabaee
Hyperparameter Optimization (HPO) is essential for building high-performing ML/DL models, yet conventional optimizers often struggle in high-dimensional spaces where evaluations ar…
MRI2Qmap: multi-parametric quantitative mapping with MRI-driven denoising priors
Mohammad Golbabaee, Matteo Cencini, Carolin Pirkl +3
Magnetic Resonance Fingerprinting (MRF) and other highly accelerated transient-state parameter mapping techniques enable simultaneous quantification of multiple tissue properties,…
Physics informed guided diffusion for accelerated multi-parametric MRI reconstruction
Perla Mayo, Carolin M. Pirkl, Alin Achim +2
We introduce MRF-DiPh, a novel physics informed denoising diffusion approach for multiparametric tissue mapping from highly accelerated, transient-state quantitative MRI acquisitio…
Grouped Sequential Optimization Strategy -- the Application of Hyperparameter Importance Assessment in Deep Learning
Ruinan Wang, Ian Nabney, Mohammad Golbabaee
Hyperparameter optimization (HPO) is a critical component of machine learning pipelines, significantly affecting model robustness, stability, and generalization. However, HPO is of…