Zobrazeno 1 - 10
of 789
pro vyhledávání: '"He, YongJun"'
Autor:
Depoutovitch, Alex, Chen, Chong, Chen, Jin, Larson, Paul, Lin, Shu, Ng, Jack, Cui, Wenlin, Liu, Qiang, Huang, Wei, Xiao, Yong, He, Yongjun
Publikováno v:
Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data
Using cloud Database as a Service (DBaaS) offerings instead of on-premise deployments is increasingly common. Key advantages include improved availability and scalability at a lower cost than on-premise alternatives. In this paper, we describe the de
Externí odkaz:
http://arxiv.org/abs/2412.02792
Mutual Information-based Representations Disentanglement for Unaligned Multimodal Language Sequences
The key challenge in unaligned multimodal language sequences lies in effectively integrating information from various modalities to obtain a refined multimodal joint representation. Recently, the disentangle and fuse methods have achieved the promisi
Externí odkaz:
http://arxiv.org/abs/2409.12408
Autor:
Lu, Yao, Bian, Song, Chen, Lequn, He, Yongjun, Hui, Yulong, Lentz, Matthew, Li, Beibin, Liu, Fei, Li, Jialin, Liu, Qi, Liu, Rui, Liu, Xiaoxuan, Ma, Lin, Rong, Kexin, Wang, Jianguo, Wu, Yingjun, Wu, Yongji, Zhang, Huanchen, Zhang, Minjia, Zhang, Qizhen, Zhou, Tianyi, Zhuo, Danyang
In this paper, we investigate the intersection of large generative AI models and cloud-native computing architectures. Recent large models such as ChatGPT, while revolutionary in their capabilities, face challenges like escalating costs and demand fo
Externí odkaz:
http://arxiv.org/abs/2401.12230
Autor:
Guan, Yadong, Han, Jiqing, Song, Hongwei, Song, Wenjie, Zheng, Guibin, Zheng, Tieran, He, Yongjun
Overlapping sound events are ubiquitous in real-world environments, but existing end-to-end sound event detection (SED) methods still struggle to detect them effectively. A critical reason is that these methods represent overlapping events using shar
Externí odkaz:
http://arxiv.org/abs/2401.05850
Classical machine learning models, such as linear models and tree-based models, are widely used in industry. These models are sensitive to data distribution, thus feature preprocessing, which transforms features from one distribution to another, is a
Externí odkaz:
http://arxiv.org/abs/2310.02540
Autor:
Huang, Qiang, Jiang, Jiawei, Rao, Xi Susie, Zhang, Ce, Han, Zhichao, Zhang, Zitao, Wang, Xin, He, Yongjun, Xu, Quanqing, Zhao, Yang, Hu, Chuang, Shang, Shuo, Du, Bo
To handle graphs in which features or connectivities are evolving over time, a series of temporal graph neural networks (TGNNs) have been proposed. Despite the success of these TGNNs, the previous TGNN evaluations reveal several limitations regarding
Externí odkaz:
http://arxiv.org/abs/2308.16385
Autor:
Wang, Jue, Yuan, Binhang, Rimanic, Luka, He, Yongjun, Dao, Tri, Chen, Beidi, Re, Christopher, Zhang, Ce
Communication compression is a crucial technique for modern distributed learning systems to alleviate their communication bottlenecks over slower networks. Despite recent intensive studies of gradient compression for data parallel-style training, com
Externí odkaz:
http://arxiv.org/abs/2206.01299
Autor:
Yuan, Binhang, He, Yongjun, Davis, Jared Quincy, Zhang, Tianyi, Dao, Tri, Chen, Beidi, Liang, Percy, Re, Christopher, Zhang, Ce
Training foundation models, such as GPT-3 and PaLM, can be extremely expensive, often involving tens of thousands of GPUs running continuously for months. These models are typically trained in specialized clusters featuring fast, homogeneous intercon
Externí odkaz:
http://arxiv.org/abs/2206.01288
Publikováno v:
In Acta Materialia 1 December 2024 281
Publikováno v:
In Computerized Medical Imaging and Graphics October 2024 117