Zobrazeno 1 - 10
of 39
pro vyhledávání: '"XIE Tianpei"'
Autor:
Chen, Suiyao, Wu, Jing, Wang, Yunxiao, Ji, Cheng, Xie, Tianpei, Cociorva, Daniel, Sharps, Michael, Levasseur, Cecile, Brunzell, Hakan
Representation learning is a fundamental aspect of modern artificial intelligence, driving substantial improvements across diverse applications. While selfsupervised contrastive learning has led to significant advancements in fields like computer vis
Externí odkaz:
http://arxiv.org/abs/2411.11148
Autor:
Wu, Jing, Chen, Suiyao, Zhao, Qi, Sergazinov, Renat, Li, Chen, Liu, Shengjie, Zhao, Chongchao, Xie, Tianpei, Guo, Hanqing, Ji, Cheng, Cociorva, Daniel, Brunzel, Hakan
Publikováno v:
Association for the Advancement of Artificial Intelligence (AAAI), 2024
Self-supervised representation learning methods have achieved significant success in computer vision and natural language processing, where data samples exhibit explicit spatial or semantic dependencies. However, applying these methods to tabular dat
Externí odkaz:
http://arxiv.org/abs/2401.02013
Publikováno v:
康复学报, Vol 34, Pp 21-27 (2024)
ObjectiveTo observe the clinical effect of extracorporeal diaphragmatic pacing combined with four-point knee-ling position training on pulmonary function in patients with ischemic stroke.MethodsA total of 60 patients with ischemic stroke admitted to
Externí odkaz:
https://doaj.org/article/e828c1ec98ff4e7280e7b4d20682112d
Consider a social network where only a few nodes (agents) have meaningful interactions in the sense that the conditional dependency graph over node attribute variables (behaviors) is sparse. A company that can only observe the interactions between it
Externí odkaz:
http://arxiv.org/abs/1711.05391
In this paper, we propose a general framework to learn a robust large-margin binary classifier when corrupt measurements, called anomalies, caused by sensor failure might be present in the training set. The goal is to minimize the generalization erro
Externí odkaz:
http://arxiv.org/abs/1610.06806
In this paper, we propose a general framework to learn a robust large-margin binary classifier when corrupt measurements, called anomalies, caused by sensor failure might be present in the training set. The goal is to minimize the generalization erro
Externí odkaz:
http://arxiv.org/abs/1507.04540
In this paper, we consider multi-sensor classification when there is a large number of unlabeled samples. The problem is formulated under the multi-view learning framework and a Consensus-based Multi-View Maximum Entropy Discrimination (CMV-MED) algo
Externí odkaz:
http://arxiv.org/abs/1507.01269
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Akademický článek
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Autor:
Schwartz, Joel L, Muscat, Joshua E, Baker, Vikki, Larios, Eric, Stephenson, Gina Day, Guo, Wei, Xie, Tianpei, Gu, Xinbin, Chung, Fung-Lung
Publikováno v:
In Oral Oncology December 2003 39(8):842-854