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Found 7 papers, 4 papers with code
Practical Insights on Incremental Learning of New Human Physical Activity on the Edge
Edge Machine Learning (Edge ML), which shifts computational intelligence from cloud-based systems to edge devices, is attracting significant interest due to its evident benefits including reduced latency, enhanced data privacy, and decreased connectivity reliance.
AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors in Agents
Autonomous agents empowered by Large Language Models (LLMs) have undergone significant improvements, enabling them to generalize across a broad spectrum of tasks.
Opportunistic Air Quality Monitoring and Forecasting with Expandable Graph Neural Networks
Air Quality Monitoring and Forecasting has been a popular research topic in recent years.
Unleashing Realistic Air Quality Forecasting: Introducing the Ready-to-Use PurpleAirSF Dataset
Air quality forecasting has garnered significant attention recently, with data-driven models taking center stage due to advancements in machine learning and deep learning models.
On Handling Catastrophic Forgetting for Incremental Learning of Human Physical Activity on the Edge
Human activity recognition (HAR) has been a classic research problem.
Graph Convolutional Networks for Traffic Forecasting with Missing Values
To this end, we propose GCN-M, a Graph Convolutional Network model with the ability to handle the complex missing values in the Spatio-temporal context.
SMATE: Semi-Supervised Spatio-Temporal Representation Learning on Multivariate Time Series
Learning from Multivariate Time Series (MTS) has attracted widespread attention in recent years.
