Ming Jin

Revolutionizing how individuals manage energy consumption using AI technology.

A new framework for reinforcement learning adapts to dynamic environments.

New method detects anomalies in time series data despite missing values.

This paper discusses the role of LLMs in time series analysis.

A fresh approach to estimating how training data impacts model predictions.

This study examines fairness in fluid antenna systems for improved wireless communication.

Learn how diffusion models enhance time series data analysis across various fields.

This study examines the benefits of pausing updates in reinforcement learning systems.

Discover how antifragility can strengthen machine learning systems against challenges.

A new framework tackles fairness conflicts in machine learning effectively.

This paper examines fluid antennas and intelligent surfaces to enhance wireless connections.

Fluid antennas enhance wireless information and power transfer systems significantly.

An analysis of rare, unexpected events and their impact on decision-making.

Exploring the impact of FAS and ARIS on wireless communication technologies.

Exploring new methods for improved mobile communication and sensing capabilities.

Combining fluid antennas and intelligent surfaces enhances wireless communication in dynamic environments.

A new framework improves offline reinforcement learning reliability and performance.

A look at HMIMO technology and its impact on wireless communication.

A new model architecture improves forecasting accuracy and efficiency.

Adaptive antennas enhance wireless communication by optimizing signal detection.

Exploring how fluid antennas enhance robust and covert communication.

Discover how machine unlearning improves AI safety and image quality.

Discover how fluid antenna systems are transforming wireless communication today.