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3D Reconstruction in Noisy Agricultural Environments: A Bayesian Optimization Perspective for View Planning
3D reconstruction is a fundamental task in robotics that gained attention due to its major impact in a wide variety of practical settings, including agriculture, underwater, and urban environments.
Weighted Ensembles for Active Learning with Adaptivity
Labeled data can be expensive to acquire in several application domains, including medical imaging, robotics, and computer vision.
Surrogate modeling for Bayesian optimization beyond a single Gaussian process
Tests on synthetic functions and real-world applications showcase the merits of the proposed method.
Unveiling Anomalous Edges and Nominal Connectivity of Attributed Networks
Uncovering anomalies in attributed networks has recently gained popularity due to its importance in unveiling outliers and flagging adversarial behavior in a gamut of data and network science applications including {the Internet of Things (IoT)}, finance, security, to list a few.
