… categorized into searching algorithms and trial schedulers. This section also evaluates the
efficiency and applicability of these algorithms for different machine learning models. Section …

… fuzzy rough neural network with the interpretability and prediction capability. In this article,
we propose modifications to the existing models of fuzzy rough neural network and then …

… parallel algorithms that are not theoretically-efficient. In this paper, we present implementations
of parallel algorithms … , and optimizations used to achieve good performance on graphs …

… to model and can lead to prediction inaccuracies. Despite these challenges, we believe ILP
based MP optimization … sue based on the observed improvements over manual optimization. …

… tion simulator estimates the performance of a … simulation algorithm that simulates a new
strategy using incremental updates to previous simulations and further improves performance …

… architectures do not fit the query performance prediction task. Finally, while previous work in
… , none of these approaches are ideal for query performance prediction, as we describe next. …

… This study uses machine intelligence algorithms with individual learners and ensemble
learners (bagging, boosting) to predict the strength of (HPC) prepared with waste materials. This …

… and robust hyperparameter optimization algorithm called ASHA, which exploits parallelism
and aggressive early-stopping to tackle large-scale hyperparameter optimization problems. …

… performance of a CNA hidden link prediction model developed using DRL techniques against
classical ML models … In this study, the performance of the DRL-CNA link prediction model …

… DCNN model. Parallel training algorithms for DCNN combined with parallel computing …
[20] developed a parallel algorithm for deep convolutional neural networks (MR-CNN), …