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Found 6 papers, 1 papers with code
Constrained Proximal Policy Optimization
The problem of constrained reinforcement learning (CRL) holds significant importance as it provides a framework for addressing critical safety satisfaction concerns in the field of reinforcement learning (RL).
Distributed Learning in Heterogeneous Environment: federated learning with adaptive aggregation and computation reduction
Although federated learning has achieved many breakthroughs recently, the heterogeneous nature of the learning environment greatly limits its performance and hinders its real-world applications.
Improving COVID-19 CT Classification of CNNs by Learning Parameter-Efficient Representation
And the achieved precision, sensitivity, and specificity for the COVID-19 pneumonia category are 98. 40%, 99. 59%, and 99. 50%, respectively.
Time-Frequency Distributions of Heart Sound Signals: A Comparative Study using Convolutional Neural Networks
The findings of this study provided the knowledge for selecting TFDs as CNN input and designing CNN architecture for heart sound classification.
UncertaintyFuseNet: Robust Uncertainty-aware Hierarchical Feature Fusion Model with Ensemble Monte Carlo Dropout for COVID-19 Detection
Differently from most of existing studies, which used either CT scan or X-ray images in COVID-19-case classification, we present a simple but efficient deep learning feature fusion model, called UncertaintyFuseNet, which is able to classify accurately large datasets of both of these types of images.
Channel Assignment in Uplink Wireless Communication using Machine Learning Approach
This letter investigates a channel assignment problem in uplink wireless communication systems.
