Zobrazeno 1 - 10
of 71
pro vyhledávání: '"Tang, Weijing"'
In many real-world networks, relationships often go beyond simple dyadic presence or absence; they can be positive, like friendship, alliance, and mutualism, or negative, characterized by enmity, disputes, and competition. To understand the formation
Externí odkaz:
http://arxiv.org/abs/2409.06172
Optimizing Cox regression and its neural network variants poses substantial computational challenges in large-scale studies. Stochastic gradient descent (SGD), known for its scalability in model optimization, has recently been adapted to optimize Cox
Externí odkaz:
http://arxiv.org/abs/2408.02839
Modern complex datasets often consist of various sub-populations. To develop robust and generalizable methods in the presence of sub-population heterogeneity, it is important to guarantee a uniform learning performance instead of an average one. In m
Externí odkaz:
http://arxiv.org/abs/2405.01709
Autor:
Tang, Weijing, Zhu, Ji
Statistical network models are useful for understanding the underlying formation mechanism and characteristics of complex networks. However, statistical models for \textit{signed networks} have been largely unexplored. In signed networks, there exist
Externí odkaz:
http://arxiv.org/abs/2309.00193
Autor:
Wang, Di, Ye, Wen, Zhu, Ji, Xu, Gongjun, Tang, Weijing, Zawistowski, Matthew, Fritsche, Lars G., He, Kevin
Polygenic hazard score (PHS) models designed for European ancestry (EUR) individuals provide ample information regarding survival risk discrimination. Incorporating such information can improve the performance of risk discrimination in an internal sm
Externí odkaz:
http://arxiv.org/abs/2302.11123
Neural Network (Deep Learning) is a modern model in Artificial Intelligence and it has been exploited in Survival Analysis. Although several improvements have been shown by previous works, training an excellent deep learning model requires a huge amo
Externí odkaz:
http://arxiv.org/abs/2208.05100
This paper introduces an Ordinary Differential Equation (ODE) notion for survival analysis. The ODE notion not only provides a unified modeling framework, but more importantly, also enables the development of a widely applicable, scalable, and easy-t
Externí odkaz:
http://arxiv.org/abs/2009.03449
In this paper, we propose a flexible model for survival analysis using neural networks along with scalable optimization algorithms. One key technical challenge for directly applying maximum likelihood estimation (MLE) to censored data is that evaluat
Externí odkaz:
http://arxiv.org/abs/2008.08637
We investigate the Plackett-Luce (PL) model based listwise learning-to-rank (LTR) on data with partitioned preference, where a set of items are sliced into ordered and disjoint partitions, but the ranking of items within a partition is unknown. Given
Externí odkaz:
http://arxiv.org/abs/2006.05067
Autor:
Torre, Denis, Fstkchyan, Yesai S., Ho, Jessica Sook Yuin, Cheon, Youngseo, Patel, Roosheel S., Degrace, Emma J., Mzoughi, Slim, Schwarz, Megan, Mohammed, Kevin, Seo, Ji-Seon, Romero-Bueno, Raquel, Demircioglu, Deniz, Hasson, Dan, Tang, Weijing, Mahajani, Sameehan U., Campisi, Laura, Zheng, Simin, Song, Won-Suk, Wang, Ying-chih, Shah, Hardik, Francoeur, Nancy, Soto, Juan, Salfati, Zelda, Weirauch, Matthew T., Warburton, Peter, Beaumont, Kristin, Smith, Melissa L., Mulder, Lubbertus, Villalta, S. Armando, Kessenbrock, Kai, Jang, Cholsoon, Lee, Daeyoup, De Rubeis, Silvia, Cobos, Inma, Tam, Oliver, Hammell, Molly Gale, Seldin, Marcus, Shi, Yongsheng, Basu, Uttiya, Sebastiano, Vittorio, Byun, Minji, Sebra, Robert, Rosenberg, Brad R., Benner, Chris, Guccione, Ernesto, Marazzi, Ivan
Publikováno v:
In Molecular Cell 7 December 2023 83(23):4255-4271