Time series experiments, in which experimental units receive a sequence of treatments over time, are frequently employed in ...

The ROCC group is currently on the lookout for talented graduate students interested in learning how systems and controls theory can be used to solve a variety of real-world applications. If you would ...

The Constraint Reasoning and Optimization group, led by Professor Matti Järvisalo, focuses on the development and analysis of state-of-the-art decision, search, and optimization procedures, and their ...

编辑：LRST 【新智元导读】CGPO框架通过混合评审机制和约束优化器，有效解决了RLHF在多任务学习中的奖励欺骗和多目标优化问题，显著提升了语言模型在多任务环境中的表现。CGPO的设计为未来多任务学习提供了新的优化路径，有望进一步提升大型 ...

In a paper published in the journal Manufacturing Letters, researchers addressed limitations in machine learning (ML) for ...

This course offers a holistic and hands-on introduction to the fundamentals of mathematical optimization for machine learning and deep learning. Using a range of real datasets and basic ...

Various geometrical problems are then presented, and there is detailed discussion of unconstrained and constrained minimization problems, and interior-point methods. The focus of the book is on ...

The key is not just improving quantum hardware or algorithms but optimizing the entire stack—from hardware and software to ...

A novel method of electrical stimulation to precisely control neural activity for sensory restoration exhibits improvements in visual stimulus reconstruction, enables efficient hardware design, and ...

This shift towards miniaturization and efficiency recalls an idea put forth over 60 years ago by physicist Richard Feynman in ...

More information: Tatsuhiko Shirai et al, Post-Processing Variationally Scheduled Quantum Algorithm for Constrained Combinatorial Optimization Problems, IEEE Transactions on Quantum Engineering (2024) ...