Importance sampling (IS) is a powerful Monte Carlo (MC) methodology for approximating integrals, for instance in the context of Bayesian inference.
This research is superior to other review articles in this field due to the complete review of relevant articles and systematic write up.
Riemannian LBFGS (RLBFGS) is an extension of this method to Riemannian manifolds.
To address this limitation, we propose a framework to enhance the generalization power of existing DML methods in a Zero-Shot Learning (ZSL) setting by general yet discriminative representation learning and employing a class adversarial neural network.
In this paper, we tackle two important problems in low-rank learning, which are partial singular value decomposition and numerical rank estimation of huge matrices.
By this interpretation, we propose a Neural Generalization of Multiple Kernel Learning (NGMKL), which extends the conventional multiple kernel learning framework to a multi-layer neural network with nonlinear activation functions.
Also, the present work is extended for learning in the feature space induced by an RKHS kernel.
Ensemble techniques are powerful approaches that combine several weak learners to build a stronger one.