Zobrazeno 1 - 10
of 2 280
pro vyhledávání: '"WONG, JASON"'
Motivation: The three-dimensional (3D) organization of the genome plays a critical role in regulating gene expression and maintaining cellular homeostasis. Disruptions in this spatial organization can result in abnormal chromatin interactions, contri
Externí odkaz:
http://arxiv.org/abs/2412.03005
Autor:
Kamalaruban, Parameswaran, Pi, Yulu, Burrell, Stuart, Drage, Eleanor, Skalski, Piotr, Wong, Jason, Sutton, David
Ensuring fairness in transaction fraud detection models is vital due to the potential harms and legal implications of biased decision-making. Despite extensive research on algorithmic fairness, there is a notable gap in the study of bias in fraud det
Externí odkaz:
http://arxiv.org/abs/2409.04373
Lomics: Generation of Pathways and Gene Sets using Large Language Models for Transcriptomic Analysis
Autor:
Wong, Chun-Ka, Choo, Ali, Cheng, Eugene C. C., San, Wing-Chun, Cheng, Kelvin Chak-Kong, Lau, Yee-Man, Lin, Minqing, Li, Fei, Liang, Wei-Hao, Liao, Song-Yan, Ng, Kwong-Man, Hung, Ivan Fan-Ngai, Tse, Hung-Fat, Wong, Jason Wing-Hon
Interrogation of biological pathways is an integral part of omics data analysis. Large language models (LLMs) enable the generation of custom pathways and gene sets tailored to specific scientific questions. These targeted sets are significantly smal
Externí odkaz:
http://arxiv.org/abs/2407.09089
Autor:
Perez, Iker, Wong, Jason, Skalski, Piotr, Burrell, Stuart, Mortier, Richard, McAuley, Derek, Sutton, David
Global financial crime activity is driving demand for machine learning solutions in fraud prevention. However, prevention systems are commonly serviced to financial institutions in isolation, and few provisions exist for data sharing due to fears of
Externí odkaz:
http://arxiv.org/abs/2401.02450
Towards a Foundation Purchasing Model: Pretrained Generative Autoregression on Transaction Sequences
Publikováno v:
4th ACM International Conference on AI in Finance (ICAIF '23), November 27-29, 2023, Brooklyn, NY, USA
Machine learning models underpin many modern financial systems for use cases such as fraud detection and churn prediction. Most are based on supervised learning with hand-engineered features, which relies heavily on the availability of labelled data.
Externí odkaz:
http://arxiv.org/abs/2401.01641
Autor:
Zhang, Wei, Kam-Kwai, Wong, Chen, Yitian, Jia, Ailing, Wang, Luwei, Zhang, Jian-Wei, Cheng, Lechao, Qu, Huamin, Chen, Wei
The study of cultural artifact provenance, tracing ownership and preservation, holds significant importance in archaeology and art history. Modern technology has advanced this field, yet challenges persist, including recognizing evidence from diverse
Externí odkaz:
http://arxiv.org/abs/2306.08834
Autor:
Chan, Ken Ka Pang1,2 (AUTHOR) chankapang@cuhk.edu.hk, Wong, Jason Siu Hang3 (AUTHOR), Yip, Wing Ho3 (AUTHOR)
Publikováno v:
Respirology Case Reports. Nov2024, Vol. 12 Issue 11, p1-5. 5p.
Autor:
Zhou, Jiehui, Wang, Xumeng, Wong, Jason K., Wang, Huanliang, Wang, Zhongwei, Yang, Xiaoyu, Yan, Xiaoran, Feng, Haozhe, Qu, Huamin, Ying, Haochao, Chen, Wei
Publikováno v:
IEEE Transactions on Visualization and Computer Graphics, vol. 29, no. 1, pp. 809-819, Jan. 2023
Data privacy is an essential issue in publishing data visualizations. However, it is challenging to represent multiple data patterns in privacy-preserving visualizations. The prior approaches target specific chart types or perform an anonymization mo
Externí odkaz:
http://arxiv.org/abs/2208.13418
Autor:
Zhang, Wei, Wong, Jason K., Wang, Xumeng, Gong, Youcheng, Zhu, Rongchen, Liu, Kai, Yan, Zihan, Tan, Siwei, Qu, Huamin, Chen, Siming, Chen, Wei
In history research, cohort analysis seeks to identify social structures and figure mobilities by studying the group-based behavior of historical figures. Prior works mainly employ automatic data mining approaches, lacking effective visual explanatio
Externí odkaz:
http://arxiv.org/abs/2208.09237
Composing Graph Neural Networks (GNNs) of operations that respect physical symmetries has been suggested to give better model performance with a smaller number of learnable parameters. However, real-world applications, such as in high energy physics
Externí odkaz:
http://arxiv.org/abs/2202.06941