Special Session

Research on special sessions focuses on developing efficient and robust methods for handling diverse data streams and system complexities. Current efforts concentrate on improving the reliability and efficiency of machine learning systems, including the use of lightweight architectures like All-ConvNets and novel approaches to data representation (e.g., lines and planes for LiDAR mapping). These advancements are crucial for enabling real-time applications in areas such as autonomous driving, human-robot interaction, and smart grids, addressing challenges in data variability, fault tolerance, and resource constraints.