3 papers
cs.LG2026
Stein-Rule Shrinkage for Stochastic Gradient Estimation in High Dimensions
M. Arashi, M. Amintoosi
Stochastic gradient methods are central to large-scale learning, but they treat mini-batch gradients as unbiased estimators, which classical decision theory shows are inadmissible…
cs.CV2021
Eigenbackground Revisited: Can We Model the Background with Eigenvectors?
Mahmood Amintoosi, Farzam Farbiz
Using dominant eigenvectors for background modeling (usually known as Eigenbackground) is a common technique in the literature. However, its results suffer from noticeable artifact…
cs.NE2019
An Upper Bound for Minimum True Matches in Graph Isomorphism with Simulated Annealing
Hashem Ezzati, Mahmood Amintoosi, Hashem Tabasi
Graph matching is one of the most important problems in graph theory and combinatorial optimization, with many applications in various domains. Although meta-heuristic algorithms h…