Search Results for author:
Found 3 papers, 3 papers with code
Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting
Through extensive experiments, we describe training probabilistic models and evaluate their predictive uncertainties based on empirical performance, reliability of confidence estimate, and practical applicability.
Information-Driven Adaptive Sensing Based on Deep Reinforcement Learning
In order to make better use of deep reinforcement learning in the creation of sensing policies for resource-constrained IoT devices, we present and study a novel reward function based on the Fisher information value.
Autonomous Management of Energy-Harvesting IoT Nodes Using Deep Reinforcement Learning
Reinforcement learning (RL) is capable of managing wireless, energy-harvesting IoT nodes by solving the problem of autonomous management in non-stationary, resource-constrained settings.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
