Zobrazeno 1 - 10
of 21 035
pro vyhledávání: '"Zhang, Kun"'
Autor:
Zhang, Kun, Troeger, Willi, Kuhn, Matthias, Wiedemann, Stephan, Ibrahim, Karim, Pfluecke, Christian, Sveric, Krunoslav M., Winzer, Robert, Fedders, Dieter, Ruf, Tobias F., Strasser, Ruth H., Linke, Axel, Quick, Silvio, Heidrich, Felix M.
Background: Systemic inflammation can occur after transcatheter aortic valve replacement (TAVR) and correlates with adverse outcome. The impact of remote ischemic preconditioning (RIPC) on TAVR associated systemic inflammation is unknown and was focu
Externí odkaz:
https://tud.qucosa.de/id/qucosa%3A89145
https://tud.qucosa.de/api/qucosa%3A89145/attachment/ATT-0/
https://tud.qucosa.de/api/qucosa%3A89145/attachment/ATT-0/
Autor:
Sun, Yuewen, Kong, Lingjing, Chen, Guangyi, Li, Loka, Luo, Gongxu, Li, Zijian, Zhang, Yixuan, Zheng, Yujia, Yang, Mengyue, Stojanov, Petar, Segal, Eran, Xing, Eric P., Zhang, Kun
Prevalent in biological applications (e.g., human phenotype measurements), multimodal datasets can provide valuable insights into the underlying biological mechanisms. However, current machine learning models designed to analyze such datasets still l
Externí odkaz:
http://arxiv.org/abs/2411.06518
Autor:
Essofi, Abdelmajid, Salahuddeen, Ridwan, Nwadike, Munachiso, Zhalieva, Elnura, Zhang, Kun, Xing, Eric, Neiswanger, Willie, Ho, Qirong
The training or fine-tuning of machine learning, vision, and language models is often implemented as a pipeline: a sequence of stages encompassing data preparation, model training and evaluation. In this paper, we exploit pipeline structures to reduc
Externí odkaz:
http://arxiv.org/abs/2411.03731
This paper studies games of voluntary disclosure in which a sender discloses evidence to a receiver who then offers an allocation and transfers. We characterize the set of equilibrium payoffs in this setting. Our main result establishes that any payo
Externí odkaz:
http://arxiv.org/abs/2411.03608
Quantum batteries (QBs) exploit principles of quantum mechanics to accelerate the charging process and aim to achieve optimal energy storage. However, analytical results for investigating these problems remain lacking due to the challenges associated
Externí odkaz:
http://arxiv.org/abs/2411.01175
Learning the structure of Directed Acyclic Graphs (DAGs) presents a significant challenge due to the vast combinatorial search space of possible graphs, which scales exponentially with the number of nodes. Recent advancements have redefined this prob
Externí odkaz:
http://arxiv.org/abs/2410.23862
When solving long-horizon tasks, it is intriguing to decompose the high-level task into subtasks. Decomposing experiences into reusable subtasks can improve data efficiency, accelerate policy generalization, and in general provide promising solutions
Externí odkaz:
http://arxiv.org/abs/2410.21616
Recent advances in differentiable structure learning have framed the combinatorial problem of learning directed acyclic graphs as a continuous optimization problem. Various aspects, including data standardization, have been studied to identify factor
Externí odkaz:
http://arxiv.org/abs/2410.18396
Autor:
Wu, Anpeng, Kuang, Kun, Zhu, Minqin, Wang, Yingrong, Zheng, Yujia, Han, Kairong, Li, Baohong, Chen, Guangyi, Wu, Fei, Zhang, Kun
Recent breakthroughs in artificial intelligence have driven a paradigm shift, where large language models (LLMs) with billions or trillions of parameters are trained on vast datasets, achieving unprecedented success across a series of language tasks.
Externí odkaz:
http://arxiv.org/abs/2410.15319
Pseudocode is extensively used in introductory programming courses to instruct computer science students in algorithm design, utilizing natural language to define algorithmic behaviors. This learning approach enables students to convert pseudocode in
Externí odkaz:
http://arxiv.org/abs/2410.21282