Zobrazeno 1 - 10
of 2 122
pro vyhledávání: '"Li, ZiJian"'
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
Wireless networks are increasingly facing challenges due to their expanding scale and complexity. These challenges underscore the need for advanced AI-driven strategies, particularly in the upcoming 6G networks. In this article, we introduce Wireless
Externí odkaz:
http://arxiv.org/abs/2409.07964
Autor:
Song, Xiangchen, Li, Zijian, Chen, Guangyi, Zheng, Yujia, Fan, Yewen, Dong, Xinshuai, Zhang, Kun
Causal Temporal Representation Learning (Ctrl) methods aim to identify the temporal causal dynamics of complex nonstationary temporal sequences. Despite the success of existing Ctrl methods, they require either directly observing the domain variables
Externí odkaz:
http://arxiv.org/abs/2409.03142
Autor:
Wang, Hongyi, Sun, Ji, Liang, Jinzhe, Zhai, Li, Tang, Zitian, Li, Zijian, Zhai, Wei, Wang, Xusheng, Gao, Weihao, Gong, Sheng
The ionic bonding across the lattice and ordered microscopic structures endow crystals with unique symmetry and determine their macroscopic properties. Unconventional crystals, in particular, exhibit non-traditional lattice structures or possess exot
Externí odkaz:
http://arxiv.org/abs/2407.16131
Autor:
Chen, Xuexin, Cai, Ruichu, Zheng, Kaitao, Jiang, Zhifan, Huang, Zhengting, Hao, Zhifeng, Li, Zijian
Graph Out-of-Distribution (OOD), requiring that models trained on biased data generalize to the unseen test data, has considerable real-world applications. One of the most mainstream methods is to extract the invariant subgraph by aligning the origin
Externí odkaz:
http://arxiv.org/abs/2407.15273
Autor:
Zhuang, Ziyang, Miao, Chenfeng, Zou, Kun, Fang, Ming, Wei, Tao, Li, Zijian, Cheng, Ning, Hu, Wei, Wang, Shaojun, Xiao, Jing
Non-autoregressive (NAR) automatic speech recognition (ASR) models predict tokens independently and simultaneously, bringing high inference speed. However, there is still a gap in the accuracy of the NAR models compared to the autoregressive (AR) mod
Externí odkaz:
http://arxiv.org/abs/2406.08835
Autor:
Chen, Zhengming, Cai, Ruichu, Xie, Feng, Qiao, Jie, Wu, Anpeng, Li, Zijian, Hao, Zhifeng, Zhang, Kun
Unobserved discrete data are ubiquitous in many scientific disciplines, and how to learn the causal structure of these latent variables is crucial for uncovering data patterns. Most studies focus on the linear latent variable model or impose strict c
Externí odkaz:
http://arxiv.org/abs/2406.07020
Autor:
Lin, Nankai, Wu, Hongyan, Chen, Zhengming, Li, Zijian, Wang, Lianxi, Jiang, Shengyi, Zhou, Dong, Yang, Aimin
Hate speech on social media is ubiquitous but urgently controlled. Without detecting and mitigating the biases brought by hate speech, different types of ethical problems. While a number of datasets have been proposed to address the problem of hate s
Externí odkaz:
http://arxiv.org/abs/2406.04876
Retrieval augmented generation has revolutionized large language model (LLM) outputs by providing factual supports. Nevertheless, it struggles to capture all the necessary knowledge for complex reasoning questions. Existing retrieval methods typicall
Externí odkaz:
http://arxiv.org/abs/2406.06572
The rapid evolution of wireless technologies and the growing complexity of network infrastructures necessitate a paradigm shift in how communication networks are designed, configured, and managed. Recent advancements in Large Language Models (LLMs) h
Externí odkaz:
http://arxiv.org/abs/2405.17053