MESU is a Bayesian framework that balances learning and forgetting by leveraging synaptic uncertainty, enabling continual learning without task boundaries while mitigating catastrophic forgetting, and ...

Journal Editorial Report: The week's best and worst from Jason Riley, Allysia Finley and Kyle Peterson. Scale drives efficiency—for almost a century, industrial planners have relied on this simple ...

Abstract: Automated Class Imbalance Learning (AutoCIL) is an emerging paradigm that leverages Combined Algorithm Selection and Hyperparameter Optimization (CASH) to automate the configuration of ...

Accurate prediction of crown convergence in Tunnel Boring Machine (TBM) tunnels is critical for ensuring construction safety, optimizing support design, and improving construction efficiency. This ...

1 Department of Mechanical and Process Engineering (D-MAVT), Eidgenössische Technische Hochschule (ETH) Zürich, Zürich, Switzerland 2 CREATE Lab, École Polytechnique Fédérale de Lausanne (EPFL), ...

Department of Engineering, University of Cambridge, Cambridge CB2 1CB2 1PZ, U.K.

Abstract: This paper introduces an intelligent optimization framework that integrates Digital Twin (DT) technology, deep learning, and a tailored Multi-Restart Bayesian Optimization with Random ...

For the uninitiated, every Soulslike feels like a steep learning curve defines it, but those in the know are well aware that there's a wide spectrum of Soulslike difficulty. Some of the most difficult ...