Zobrazeno 1 - 10
of 256
pro vyhledávání: '"Yang, Sean"'
Autor:
Roth, Holger R., Beutel, Daniel J., Cheng, Yan, Marques, Javier Fernandez, Pan, Heng, Chen, Chester, Zhang, Zhihong, Wen, Yuhong, Yang, Sean, Isaac, Yang, Hsieh, Yuan-Ting, Xu, Ziyue, Xu, Daguang, Lane, Nicholas D., Feng, Andrew
Several open-source systems, such as Flower and NVIDIA FLARE, have been developed in recent years while focusing on different aspects of federated learning (FL). Flower is dedicated to implementing a cohesive approach to FL, analytics, and evaluation
Externí odkaz:
http://arxiv.org/abs/2407.00031
Autor:
Roth, Holger R., Xu, Ziyue, Hsieh, Yuan-Ting, Renduchintala, Adithya, Yang, Isaac, Zhang, Zhihong, Wen, Yuhong, Yang, Sean, Lu, Kevin, Kersten, Kristopher, Ricketts, Camir, Xu, Daguang, Chen, Chester, Cheng, Yan, Feng, Andrew
In the ever-evolving landscape of artificial intelligence (AI) and large language models (LLMs), handling and leveraging data effectively has become a critical challenge. Most state-of-the-art machine learning algorithms are data-centric. However, as
Externí odkaz:
http://arxiv.org/abs/2402.07792
Movement paths are used widely in intelligent transportation and smart city applications. To serve such applications, path representation learning aims to provide compact representations of paths that enable efficient and accurate operations when use
Externí odkaz:
http://arxiv.org/abs/2307.10171
Autor:
Roth, Holger R., Cheng, Yan, Wen, Yuhong, Yang, Isaac, Xu, Ziyue, Hsieh, Yuan-Ting, Kersten, Kristopher, Harouni, Ahmed, Zhao, Can, Lu, Kevin, Zhang, Zhihong, Li, Wenqi, Myronenko, Andriy, Yang, Dong, Yang, Sean, Rieke, Nicola, Quraini, Abood, Chen, Chester, Xu, Daguang, Ma, Nic, Dogra, Prerna, Flores, Mona, Feng, Andrew
Publikováno v:
IEEE Data Eng. Bull., Vol. 46, No. 1, 2023
Federated learning (FL) enables building robust and generalizable AI models by leveraging diverse datasets from multiple collaborators without centralizing the data. We created NVIDIA FLARE as an open-source software development kit (SDK) to make it
Externí odkaz:
http://arxiv.org/abs/2210.13291
In step with the digitalization of transportation, we are witnessing a growing range of path-based smart-city applications, e.g., travel-time estimation and travel path ranking. A temporal path(TP) that includes temporal information, e.g., departure
Externí odkaz:
http://arxiv.org/abs/2203.16110
Path representations are critical in a variety of transportation applications, such as estimating path ranking in path recommendation systems and estimating path travel time in navigation systems. Existing studies often learn task-specific path repre
Externí odkaz:
http://arxiv.org/abs/2106.09373
Publikováno v:
Journal of Economics & Finance. Mar2024, Vol. 48 Issue 1, p238-261. 24p.
Figures are an important channel for scientific communication, used to express complex ideas, models and data in ways that words cannot. However, this visual information is mostly ignored in analyses of the scientific literature. In this paper, we de
Externí odkaz:
http://arxiv.org/abs/1908.07465
Autor:
Yang, Sean Bin, Yang, Bin
Modern navigation services often provide multiple paths connecting the same source and destination for users to select. Hence, ranking such paths becomes increasingly important, which directly affects the service quality. We present PathRank, a data-
Externí odkaz:
http://arxiv.org/abs/1907.04028
We propose JECL, a method for clustering image-caption pairs by training parallel encoders with regularized clustering and alignment objectives, simultaneously learning both representations and cluster assignments. These image-caption pairs arise fre
Externí odkaz:
http://arxiv.org/abs/1901.01860