A novel decentralized intersection data-sharing and assimilation protocol for multi-agent Gaussian processes exploits posterior discrepancies to improve team-level predictive performance while preserving locality.
Scalar field mapping with adaptive high-intensity region avoidance
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
SP-ICL integrates L1 regularization with integral concurrent learning using sliding modes to recover sparse parameters online and proves ultimate boundedness of closed-loop trajectories via non-smooth Lyapunov analysis.
citing papers explorer
-
Decentralized Scalar Field Mapping using Gaussian Process
A novel decentralized intersection data-sharing and assimilation protocol for multi-agent Gaussian processes exploits posterior discrepancies to improve team-level predictive performance while preserving locality.
-
Adaptive Control with Sparse Identification of Nonlinear Dynamics
SP-ICL integrates L1 regularization with integral concurrent learning using sliding modes to recover sparse parameters online and proves ultimate boundedness of closed-loop trajectories via non-smooth Lyapunov analysis.