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The first article discusses PID tuning using Cross-Entropy Deep Learning for Lyapunov Stability Analysis in Underwater Unmanned Vehicles (UUVs). The authors Hector Kohler, Benoit Clement, Thomas Chaffre, and Gilles Le Chenadec propose experiments to study the stability of a learning-based adaptive control system. The system combines model-based control structures with deep model-free learning algorithms to compensate for external disturbing forces acting on UUVs.
The second article focuses on validating quantum advantage claims in Gaussian Boson Sampling (GBS) by using linear cross-entropy certification. Authors Javier Martínez-Cifuentes, Hubert de Guise, and Nicolás Quesada argue that verifying GBS implementations using their ideal distributions directly can avoid issues related to assuming ground truth distributions are hard to sample. They introduce a measure called the LXE score to assess how close a GBS implementation is to its ideal model, without relying on computationally hard assumptions. This approach aims to prevent classical algorithms from efficiently simulating experiments and undermining quantum advantage claims.
Overall, both articles explore advanced techniques in control theory and quantum computing, highlighting the importance of empirical studies and innovative methods for stability analysis and validation in complex systems.
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Source link: https://medium.com/@monocosmo77/best-examples-of-cross-entropy-part1-machine-learning-2024-df60bb3067d7?source=rss——artificial_intelligence-5
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