Zobrazeno 1 - 10
of 46
pro vyhledávání: '"Lyu, Wenlong"'
Bayesian Optimization (BO) is a sample-efficient optimization algorithm widely employed across various applications. In some challenging BO tasks, input uncertainty arises due to the inevitable randomness in the optimization process, such as machinin
Externí odkaz:
http://arxiv.org/abs/2310.20145
Bayesian optimization (BO) is widely adopted in black-box optimization problems and it relies on a surrogate model to approximate the black-box response function. With the increasing number of black-box optimization tasks solved and even more to solv
Externí odkaz:
http://arxiv.org/abs/2308.04660
Autor:
Liu, Lin, Zhao, Mingming, Yuan, Shanxin, Lyu, Wenlong, Zhou, Wengang, Li, Houqiang, Wang, Yanfeng, Tian, Qi
Image compression aims to reduce the information redundancy in images. Most existing neural image compression methods rely on side information from hyperprior or context models to eliminate spatial redundancy, but rarely address the channel redundanc
Externí odkaz:
http://arxiv.org/abs/2306.05704
We proposed a new technique to accelerate sampling methods for solving difficult optimization problems. Our method investigates the intrinsic connection between posterior distribution sampling and optimization with Langevin dynamics, and then we prop
Externí odkaz:
http://arxiv.org/abs/2301.12640
Autor:
Song, Jihong, Yang, Xinru, Wu, Jieling, Wu, Zilan, Niu, Sitian, Zhuo, Litao, Lyu, Wenlong, Ye, Jinru, Fang, Yan, Zhan, Zhiying, Zhang, Hairong, Li, Xiaomei, Hong, Jinsheng, Su, Li
Publikováno v:
In Radiotherapy and Oncology August 2024 197
Autor:
Grosnit, Antoine, Tutunov, Rasul, Maraval, Alexandre Max, Griffiths, Ryan-Rhys, Cowen-Rivers, Alexander I., Yang, Lin, Zhu, Lin, Lyu, Wenlong, Chen, Zhitang, Wang, Jun, Peters, Jan, Bou-Ammar, Haitham
We introduce a method combining variational autoencoders (VAEs) and deep metric learning to perform Bayesian optimisation (BO) over high-dimensional and structured input spaces. By adapting ideas from deep metric learning, we use label guidance from
Externí odkaz:
http://arxiv.org/abs/2106.03609
Autor:
Cowen-Rivers, Alexander I., Lyu, Wenlong, Tutunov, Rasul, Wang, Zhi, Grosnit, Antoine, Griffiths, Ryan Rhys, Maraval, Alexandre Max, Jianye, Hao, Wang, Jun, Peters, Jan, Ammar, Haitham Bou
In this work we rigorously analyse assumptions inherent to black-box optimisation hyper-parameter tuning tasks. Our results on the Bayesmark benchmark indicate that heteroscedasticity and non-stationarity pose significant challenges for black-box opt
Externí odkaz:
http://arxiv.org/abs/2012.03826
Publikováno v:
2019 Design, Automation & Test in Europe Conference & Exhibition (DATE)
Bayesian optimization with Gaussian process as surrogate model has been successfully applied to analog circuit synthesis. In the traditional Gaussian process regression model, the kernel functions are defined explicitly. The computational complexity
Externí odkaz:
http://arxiv.org/abs/1912.00402
Publikováno v:
The 56th Annual Design Automation Conference 2019
This paper presents an efficient multi-fidelity Bayesian optimization approach for analog circuit synthesis. The proposed method can significantly reduce the overall computational cost by fusing the simple but potentially inaccurate low-fidelity mode
Externí odkaz:
http://arxiv.org/abs/1912.00392
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