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"Deep Learning for Multitask Management in Nonlinear Optimization with Excavation-based Neural Networks"
"In this paper, we propose a novel multitask optimization framework that has been successfully applied to multivariate regression problem. We first introduce a novel image-level model, called excavation-based neural network (EBN), which can model the spatial and temporal dependencies between two latent variables. Our model requires the covariance matrix, and has strong nonlinearity in the nonlinear space, which is essential for agent-based search. We apply the EBN to a nonlinear optimization problem of the same problem. We show that the proposed model can be used for a wide range of tasks, including multivariate regression, multivariate clustering, multivariate analysis, multivariate signal processing and multivariate support vector machines. The proposed framework is tested on synthetic and real-world datasets, and its performance is compared to state-of-the-art multitask optimization methods. We evaluate the proposed model on all of the datasets, including a new benchmark dataset, and show that the proposed model is able to outperform the state-of-the-art strategies in these datasets."