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Found 6 papers, 0 papers with code
Experimenting with an Evaluation Framework for Imbalanced Data Learning (EFIDL)
We compared the traditional data augmentation evaluation methods with our proposed cross-validation evaluation framework Results Using traditional data augmentation evaluation meta hods will give a false impression of improving the performance.
iMedBot: A Web-based Intelligent Agent for Healthcare Related Prediction and Deep Learning
Method: the iMedBot is a web application that we developed using the python Flask web framework and deployed on Amazon Web Services.
Empirical Study of Overfitting in Deep FNN Prediction Models for Breast Cancer Metastasis
We also find some interesting interacting pairs of hyperparameters such as learning rate and momentum, learning rate and decay, and batch size and epochs.
Eco-driving for Electric Connected Vehicles at Signalized Intersections: A Parameterized Reinforcement Learning approach
This paper proposes an eco-driving framework for electric connected vehicles (CVs) based on reinforcement learning (RL) to improve vehicle energy efficiency at signalized intersections.
Learning the policy for mixed electric platoon control of automated and human-driven vehicles at signalized intersection: a random search approach
Meanwhile, the simulation results demonstrate the effectiveness of the delay reward, which is designed to outperform distributed reward mechanism} Compared with normal car-following behavior, the sensitivity analysis reveals that the energy can be saved to different extends (39. 27%-82. 51%) by adjusting the relative importance of the optimization goal.
Distributed stochastic proximal algorithm with random reshuffling for non-smooth finite-sum optimization
We prove that local variable estimates generated by the proposed algorithm achieve consensus and are attracted to a neighborhood of the optimal solution in expectation with an $\mathcal{O}(\frac{1}{T}+\frac{1}{\sqrt{T}})$ convergence rate, where $T$ is the total number of iterations.
