Zobrazeno 1 - 10
of 1 153
pro vyhledávání: '"Wei Xiaoli"'
This paper studies the extended mean-field control problems with state-control joint law dependence and Poissonian common noise. We develop the stochastic maximum principle (SMP) and establish the connection to the Hamiltonian-Jacobi-Bellman (HJB) eq
Externí odkaz:
http://arxiv.org/abs/2407.05356
This paper studies the continuous-time q-learning in the mean-field jump-diffusion models from the representative agent's perspective. To overcome the challenge when the population distribution may not be directly observable, we introduce the integra
Externí odkaz:
http://arxiv.org/abs/2407.04521
Interactive video object segmentation is a crucial video task, having various applications from video editing to data annotating. However, current approaches struggle to accurately segment objects across diverse domains. Recently, Segment Anything Mo
Externí odkaz:
http://arxiv.org/abs/2406.05485
Contemporary automatic first break (FB) picking methods typically analyze 1D signals, 2D source gathers, or 3D source-receiver gathers. Utilizing higher-dimensional data, such as 2D or 3D, incorporates global features, improving the stability of loca
Externí odkaz:
http://arxiv.org/abs/2404.08408
The problem of how to take the right actions to make profits in sequential process continues to be difficult due to the quick dynamics and a significant amount of uncertainty in many application scenarios. In such complicated environments, reinforcem
Externí odkaz:
http://arxiv.org/abs/2310.00642
Autor:
Wei, Xiaoli, Zhang, Chunxia, Wang, Hongtao, Tan, Chengli, Xiong, Deng, Jiang, Baisong, Zhang, Jiangshe, Kim, Sang-Woon
Accurate interpolation of seismic data is crucial for improving the quality of imaging and interpretation. In recent years, deep learning models such as U-Net and generative adversarial networks have been widely applied to seismic data interpolation.
Externí odkaz:
http://arxiv.org/abs/2307.04226
Autor:
Wei, Xiaoli, Yu, Xiang
This paper studies the q-learning, recently coined as the continuous time counterpart of Q-learning by Jia and Zhou (2023), for continuous time Mckean-Vlasov control problems in the setting of entropy-regularized reinforcement learning. In contrast t
Externí odkaz:
http://arxiv.org/abs/2306.16208
In seismic exploration, first break (FB) picking is a crucial aspect in the determination of subsurface velocity models, significantly influencing the placement of wells. Many deep neural networks (DNNs)-based automatic picking methods have been prop
Externí odkaz:
http://arxiv.org/abs/2305.13799
The marriage between mean-field theory and reinforcement learning has shown a great capacity to solve large-scale control problems with homogeneous agents. To break the homogeneity restriction of mean-field theory, a recent interest is to introduce g
Externí odkaz:
http://arxiv.org/abs/2209.04808
Autor:
Wang, Hongtao, Zhang, Jiangshe, Wei, Xiaoli, Zhang, Chunxia, Guo, Zhenbo, Long, Li, Wang, Yicheng
Picking the first arrival times of prestack gathers is called First Arrival Time (FAT) picking, which is an indispensable step in seismic data processing, and is mainly solved manually in the past. With the current increasing density of seismic data
Externí odkaz:
http://arxiv.org/abs/2209.03132