Zobrazeno 1 - 10
of 356
pro vyhledávání: '"ZHANG, Ziyao"'
Current generative AI models like ChatGPT, Claude, and Gemini are widely used for knowledge dissemination, task decomposition, and creative thinking. However, their linear interaction methods often force users to repeatedly compare and copy contextua
Externí odkaz:
http://arxiv.org/abs/2410.10570
Autor:
Zhang, Ziyao, Chen, Minjia, Ma, Rui, Sun, Bohao, Wonfor, Adrian, Penty, Richard, Cheng, Qixiang
Photonic integrated switches that are both space and wavelength selective are a highly promising technology for data-intensive applications as they benefit from multi-dimensional manipulation of optical signals. However, scaling these switches normal
Externí odkaz:
http://arxiv.org/abs/2410.05541
Code generation aims to automatically generate code from input requirements, significantly enhancing development efficiency. Recent large language models (LLMs) based approaches have shown promising results and revolutionized code generation task. De
Externí odkaz:
http://arxiv.org/abs/2409.20550
Autor:
Yang, Yanwu, Ye, Chenfei, Su, Guinan, Zhang, Ziyao, Chang, Zhikai, Chen, Hairui, Chan, Piu, Yu, Yue, Ma, Ting
Foundation models pretrained on large-scale datasets via self-supervised learning demonstrate exceptional versatility across various tasks. Due to the heterogeneity and hard-to-collect medical data, this approach is especially beneficial for medical
Externí odkaz:
http://arxiv.org/abs/2403.01433
Publikováno v:
Engineering, Construction and Architectural Management, 2023, Vol. 31, Issue 12, pp. 4809-4830.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/ECAM-06-2022-0563
Autor:
Piotrowski, Dariusz, Korzeniowski, Renard, Falai, Alessio, Cygert, Sebastian, Pokora, Kamil, Tinchev, Georgi, Zhang, Ziyao, Yanagisawa, Kayoko
In this work, we introduce a framework for cross-lingual speech synthesis, which involves an upstream Voice Conversion (VC) model and a downstream Text-To-Speech (TTS) model. The proposed framework consists of 4 stages. In the first two stages, we us
Externí odkaz:
http://arxiv.org/abs/2309.08255
Autor:
Zhang, Ziyao1 (AUTHOR) 2023201328@buct.edu.cn, Zhang, Fangming1 (AUTHOR) 2023210972@buct.edu.cn, Xie, Wenjing2 (AUTHOR) xwj15761987183@163.com, Niu, Yubo1 (AUTHOR) 2021110034@buct.edu.cn, Wang, Haonan1 (AUTHOR) 2020080009@buct.edu.cn, Li, Guofeng1 (AUTHOR) ligf@mail.buct.edu.cn, Zhao, Lingyun3 (AUTHOR) lyzhao@mail.tsinghua.edu.cn, Wang, Xing1 (AUTHOR) wangxing@mail.buct.edu.cn, Xie, Wensheng1 (AUTHOR) wangxing@mail.buct.edu.cn
Publikováno v:
International Journal of Molecular Sciences. Oct2024, Vol. 25 Issue 19, p10760. 15p.
Multimodal Brain Disease Classification with Functional Interaction Learning from Single fMRI Volume
In neuroimaging analysis, fMRI can well assess the function changes for brain diseases with no obvious structural lesions. To date, most deep-learning-based fMRI studies have employed functional connectivity (FC) as the basic feature for disease clas
Externí odkaz:
http://arxiv.org/abs/2208.03028
An essential design decision for multilingual Neural Text-To-Speech (NTTS) systems is how to represent input linguistic features within the model. Looking at the wide variety of approaches in the literature, two main paradigms emerge, unified and sep
Externí odkaz:
http://arxiv.org/abs/2207.01547
Training multilingual Neural Text-To-Speech (NTTS) models using only monolingual corpora has emerged as a popular way for building voice cloning based Polyglot NTTS systems. In order to train these models, it is essential to understand how the compos
Externí odkaz:
http://arxiv.org/abs/2207.01507