Výsledky vyhledávání

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A Systematic Survey on Large Language Models for Algorithm Design

Autor: Liu, Fei, Yao, Yiming, Guo, Ping, Yang, Zhiyuan, Zhao, Zhe, Lin, Xi, Tong, Xialiang, Yuan, Mingxuan, Lu, Zhichao, Wang, Zhenkun, Zhang, Qingfu

Algorithm Design (AD) is crucial for effective problem-solving across various domains. The advent of Large Language Models (LLMs) has notably enhanced the automation and innovation within this field, offering new perspectives and promising solutions.

Externí odkaz: http://arxiv.org/abs/2410.14716

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Evolve Cost-aware Acquisition Functions Using Large Language Models

Autor: Yao, Yiming, Liu, Fei, Cheng, Ji, Zhang, Qingfu

Many real-world optimization scenarios involve expensive evaluation with unknown and heterogeneous costs. Cost-aware Bayesian optimization stands out as a prominent solution in addressing these challenges. To approach the global optimum within a limi

Externí odkaz: http://arxiv.org/abs/2404.16906

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Residual Channel Attention Network for Brain Glioma Segmentation

Autor: Yao, Yiming, Qian, Peisheng, Zhao, Ziyuan, Zeng, Zeng

Publikováno v: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

A glioma is a malignant brain tumor that seriously affects cognitive functions and lowers patients' life quality. Segmentation of brain glioma is challenging because of interclass ambiguities in tumor regions. Recently, deep learning approaches have

Externí odkaz: http://arxiv.org/abs/2205.10758

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