Zobrazeno 1 - 10
of 178
pro vyhledávání: '"Zhou, Zirui"'
We present $\textit{Learn2Aggregate}$, a machine learning (ML) framework for optimizing the generation of Chv\'atal-Gomory (CG) cuts in mixed integer linear programming (MILP). The framework trains a graph neural network to classify useful constraint
Externí odkaz:
http://arxiv.org/abs/2409.06559
Scoliosis presents significant diagnostic challenges, particularly in adolescents, where early detection is crucial for effective treatment. Traditional diagnostic and follow-up methods, which rely on physical examinations and radiography, face limit
Externí odkaz:
http://arxiv.org/abs/2407.05726
Autor:
Li, Xijun, Zhu, Fangzhou, Zhen, Hui-Ling, Luo, Weilin, Lu, Meng, Huang, Yimin, Fan, Zhenan, Zhou, Zirui, Kuang, Yufei, Wang, Zhihai, Geng, Zijie, Li, Yang, Liu, Haoyang, An, Zhiwu, Yang, Muming, Li, Jianshu, Wang, Jie, Yan, Junchi, Sun, Defeng, Zhong, Tao, Zhang, Yong, Zeng, Jia, Yuan, Mingxuan, Hao, Jianye, Yao, Jun, Mao, Kun
In an era of digital ubiquity, efficient resource management and decision-making are paramount across numerous industries. To this end, we present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVer
Externí odkaz:
http://arxiv.org/abs/2401.05960
The rapid advancement of artificial intelligence (AI) techniques has opened up new opportunities to revolutionize various fields, including operations research (OR). This survey paper explores the integration of AI within the OR process (AI4OR) to en
Externí odkaz:
http://arxiv.org/abs/2401.03244
Publikováno v:
ECML PKDD 2023
Combinatorial optimization finds an optimal solution within a discrete set of variables and constraints. The field has seen tremendous progress both in research and industry. With the success of deep learning in the past decade, a recent trend in com
Externí odkaz:
http://arxiv.org/abs/2311.13843
Autor:
Zhou, Chenkun, Wang, Di, Lagunas, Francisco, Atterberry, Benjamin, Lei, Ming, Hu, Huicheng, Zhou, Zirui, Filatov, Alexander S., Jiang, De-en, Rossini, Aaron J., Klie, Robert F., Talapin, Dmitri V.
Two-dimensional (2D) transition-metal carbides and nitrides (MXenes) show impressive performance in applications, such as supercapacitors, batteries, electromagnetic interference shielding, or electrocatalysis. These materials combine the electronic
Externí odkaz:
http://arxiv.org/abs/2305.17566
Autor:
Ramamonjison, Rindranirina, Yu, Timothy T., Li, Raymond, Li, Haley, Carenini, Giuseppe, Ghaddar, Bissan, He, Shiqi, Mostajabdaveh, Mahdi, Banitalebi-Dehkordi, Amin, Zhou, Zirui, Zhang, Yong
The Natural Language for Optimization (NL4Opt) Competition was created to investigate methods of extracting the meaning and formulation of an optimization problem based on its text description. Specifically, the goal of the competition is to increase
Externí odkaz:
http://arxiv.org/abs/2303.08233
Autor:
Ramamonjison, Rindranirina, Li, Haley, Yu, Timothy T., He, Shiqi, Rengan, Vishnu, Banitalebi-Dehkordi, Amin, Zhou, Zirui, Zhang, Yong
We describe an augmented intelligence system for simplifying and enhancing the modeling experience for operations research. Using this system, the user receives a suggested formulation of an optimization problem based on its description. To facilitat
Externí odkaz:
http://arxiv.org/abs/2209.15565
Autor:
Fan, Zhenan, Zhou, Zirui, Pei, Jian, Friedlander, Michael P., Hu, Jiajie, Li, Chengliang, Zhang, Yong
Federated learning is an emerging technique for training models from decentralized data sets. In many applications, data owners participating in the federated learning system hold not only the data but also a set of domain knowledge. Such knowledge i
Externí odkaz:
http://arxiv.org/abs/2208.07530
Autor:
Bajaj, Mohit, Chu, Lingyang, Romaniello, Vittorio, Singh, Gursimran, Pei, Jian, Zhou, Zirui, Wang, Lanjun, Zhang, Yong
The popularity of machine learning has increased the risk of unfair models getting deployed in high-stake applications, such as justice system, drug/vaccination design, and medical diagnosis. Although there are effective methods to train fair models
Externí odkaz:
http://arxiv.org/abs/2207.05811