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pro vyhledávání: '"Chen, Yujie"'
Accurate traffic prediction faces significant challenges, necessitating a deep understanding of both temporal and spatial cues and their complex interactions across multiple variables. Recent advancements in traffic prediction systems are primarily d
Externí odkaz:
http://arxiv.org/abs/2409.17440
Autor:
Gu, Hao, Yi, JiangYan, Wang, Chenglong, Ren, Yong, Tao, Jianhua, Yan, Xinrui, Chen, Yujie, Zhang, Xiaohui
Fake audio detection is an emerging active topic. A growing number of literatures have aimed to detect fake utterance, which are mostly generated by Text-to-speech (TTS) or voice conversion (VC). However, countermeasures against impersonation remain
Externí odkaz:
http://arxiv.org/abs/2408.17009
Autor:
Zeng, Siding, Yi, Jiangyan, Tao, Jianhua, Chen, Yujie, Liang, Shan, Ren, Yong, Zhang, Xiaohui
When the task of locating manipulation regions in partially-fake audio (PFA) involves cross-domain datasets, the performance of deep learning models drops significantly due to the shift between the source and target domains. To address this issue, ex
Externí odkaz:
http://arxiv.org/abs/2407.08239
In the telephony scenarios, the fake speech detection (FSD) task to combat speech spoofing attacks is challenging. Data augmentation (DA) methods are considered effective means to address the FSD task in telephony scenarios, typically divided into ti
Externí odkaz:
http://arxiv.org/abs/2406.09664
Autor:
Chen, Yujie, Yi, Jiangyan, Xue, Jun, Wang, Chenglong, Zhang, Xiaohui, Dong, Shunbo, Zeng, Siding, Tao, Jianhua, Zhao, Lv, Fan, Cunhang
Fake artefacts for discriminating between bonafide and fake audio can exist in both short- and long-range segments. Therefore, combining local and global feature information can effectively discriminate between bonafide and fake audio. This paper pro
Externí odkaz:
http://arxiv.org/abs/2406.06086
Probabilistic graphical models that encode an underlying Markov random field are fundamental building blocks of generative modeling to learn latent representations in modern multivariate data sets with complex dependency structures. Among these, the
Externí odkaz:
http://arxiv.org/abs/2404.17763
Autor:
Gao, Yuan, Zhu, Yiheng, Cao, Yuanbin, Zhou, Yinzhi, Wu, Zhen, Chen, Yujie, Wu, Shenglan, Hu, Haoyuan, Dai, Xinyu
Open Domain Multi-Hop Question Answering (ODMHQA) plays a crucial role in Natural Language Processing (NLP) by aiming to answer complex questions through multi-step reasoning over retrieved information from external knowledge sources. Recently, Large
Externí odkaz:
http://arxiv.org/abs/2403.12393
Publikováno v:
(2024) Vol. 38 No. 8: AAAI-24 Technical Tracks 8 Vol. 38 No. 8: AAAI-24 Technical Tracks 8 Vol. 38 No. 8: AAAI-24 Technical Tracks 8 Proceedings of the AAAI Conference on Artificial Intelligence, 38(8), 8380-8388
In recent years, knowledge graph completion (KGC) models based on pre-trained language model (PLM) have shown promising results. However, the large number of parameters and high computational cost of PLM models pose challenges for their application i
Externí odkaz:
http://arxiv.org/abs/2401.12997
Autor:
Ma, Tengfei, Chen, Yujie, Tao, Wen, Zheng, Dashun, Lin, Xuan, Pang, Patrick Cheong-lao, Liu, Yiping, Wang, Yijun, Wang, Longyue, Song, Bosheng, Zeng, Xiangxiang, Yu, Philip S.
Molecular interaction prediction plays a crucial role in forecasting unknown interactions between molecules, such as drug-target interaction (DTI) and drug-drug interaction (DDI), which are essential in the field of drug discovery and therapeutics. A
Externí odkaz:
http://arxiv.org/abs/2312.06682
Low-precision training has emerged as a promising low-cost technique to enhance the training efficiency of deep neural networks without sacrificing much accuracy. Its Bayesian counterpart can further provide uncertainty quantification and improved ge
Externí odkaz:
http://arxiv.org/abs/2310.16320