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Found 5 papers, 2 papers with code
Verifying message-passing neural networks via topology-based bounds tightening
Since graph neural networks (GNNs) are often vulnerable to attack, we need to know when we can trust them.
Practical Path-based Bayesian Optimization
There has been a surge in interest in data-driven experimental design with applications to chemical engineering and drug manufacturing.
Edge-assisted U-Shaped Split Federated Learning with Privacy-preserving for Internet of Things
To address these challenges, we present an innovative Edge-assisted U-Shaped Split Federated Learning (EUSFL) framework, which harnesses the high-performance capabilities of edge servers to assist IoT devices in model training and optimization process.
SnAKe: Bayesian Optimization with Pathwise Exploration
Bayesian Optimization is a very effective tool for optimizing expensive black-box functions.
Efficient Weingarten Map and Curvature Estimation on Manifolds
In this paper, we propose an efficient method to estimate the Weingarten map for point cloud data sampled from manifold embedded in Euclidean space.
