Search Results for author:
Found 4 papers, 0 papers with code
Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxonomy, and Methods
With extensive pre-trained knowledge and high-level general capabilities, large language models (LLMs) emerge as a promising avenue to augment reinforcement learning (RL) in aspects such as multi-task learning, sample efficiency, and task planning.
Applying Large Language Models to Power Systems: Potential Security Threats
Applying large language models (LLMs) to modern power systems presents a promising avenue for enhancing decision-making and operational efficiency.
Fed-NILM: A Federated Learning-based Non-Intrusive Load Monitoring Method for Privacy-Protection
However, inadequate load data and the risk of power consumer privacy breaches may be encountered by local data owners during the NILM model training.
Super-Resolution Perception for Industrial Sensor Data
In this paper, we present the problem formulation and methodology framework of Super-Resolution Perception (SRP) on industrial sensor data.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
