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pro vyhledávání: '"ZHANG, Haipeng"'
Large language models (LLMs) have demonstrated impressive versatility across numerous tasks, yet their generalization capabilities remain poorly understood. To investigate these behaviors, arithmetic tasks serve as important venues. In previous studi
Externí odkaz:
http://arxiv.org/abs/2407.17963
Current directed graph embedding methods build upon undirected techniques but often inadequately capture directed edge information, leading to challenges such as: (1) Suboptimal representations for nodes with low in/out-degrees, due to the insufficie
Externí odkaz:
http://arxiv.org/abs/2406.05391
Position bias, i.e., users' preference of an item is affected by its placing position, is well studied in the recommender system literature. However, most existing methods ignore the widely coupled ranking bias, which is also related to the placing p
Externí odkaz:
http://arxiv.org/abs/2405.18971
The life trajectories of notable people have been studied to pinpoint the times and places of significant events such as birth, death, education, marriage, competition, work, speeches, scientific discoveries, artistic achievements, and battles. Under
Externí odkaz:
http://arxiv.org/abs/2406.00032
Autor:
Du, Xiaocong, Zhang, Haipeng
Achieving gender equality is a pivotal factor in realizing the UN's Global Goals for Sustainable Development. Gender bias studies work towards this and rely on name-based gender inference tools to assign individual gender labels when gender informati
Externí odkaz:
http://arxiv.org/abs/2405.06221
Autor:
Zhang, Haipeng, Dimitrov, Dimitar, Simpson, Lynn, Plaks, Nina, Singh, Balaji, Penney, Stephen, Charles, Jo, Sheehan, Rosemary, Flammini, Steven, Murphy, Shawn, Landman, Adam
Publikováno v:
JMIR Formative Research, Vol 4, Iss 10, p e19533 (2020)
BackgroundAs of July 17, 2020, the COVID-19 pandemic has affected over 14 million people worldwide, with over 3.68 million cases in the United States. As the number of COVID-19 cases increased in Massachusetts, the Massachusetts Department of Public
Externí odkaz:
https://doaj.org/article/f5b68a72ab834ec3b1481a5c374d8d45
Autor:
Li, Ran, Zhang, Haipeng, Sun, Mingyang, Teng, Fei, Wan, Can, Pineda, Salvador, Kariniotakis, Georges
Better forecasts may not lead to better decision-making. To address this challenge, decision-oriented learning (DOL) has been proposed as a new branch of machine learning that replaces traditional statistical loss with a decision loss to form an end-
Externí odkaz:
http://arxiv.org/abs/2401.03680
Forecast-then-optimize is a widely-used framework for decision-making problems in power systems. Traditionally, statistical losses have been employed to train forecasting models, but recent research demonstrated that improved decision utility in down
Externí odkaz:
http://arxiv.org/abs/2312.13501