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pro vyhledávání: '"Su, Guinan"'
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
Large language models (LLMs) have demonstrated remarkable potential in natural language understanding and generation, making them valuable tools for enhancing conversational interactions. However, LLMs encounter challenges such as lacking multi-step
Externí odkaz:
http://arxiv.org/abs/2311.05114
In recent years, audio-driven 3D facial animation has gained significant attention, particularly in applications such as virtual reality, gaming, and video conferencing. However, accurately modeling the intricate and subtle dynamics of facial express
Externí odkaz:
http://arxiv.org/abs/2311.04766
Generative Adversarial Networks (GANs) are formulated as minimax game problems, whereby generators attempt to approach real data distributions by virtue of adversarial learning against discriminators. The intrinsic problem complexity poses the challe
Externí odkaz:
http://arxiv.org/abs/2006.09134
Autor:
Wang, Yujing, Yang, Yaming, Chen, Yiren, Bai, Jing, Zhang, Ce, Su, Guinan, Kou, Xiaoyu, Tong, Yunhai, Yang, Mao, Zhou, Lidong
Learning text representation is crucial for text classification and other language related tasks. There are a diverse set of text representation networks in the literature, and how to find the optimal one is a non-trivial problem. Recently, the emerg
Externí odkaz:
http://arxiv.org/abs/1912.10729
Akademický článek
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BrainMass: Advancing Brain Network Analysis for Diagnosis With Large-Scale Self-Supervised Learning.
Publikováno v:
IEEE transactions on medical imaging [IEEE Trans Med Imaging] 2024 Nov; Vol. 43 (11), pp. 4004-4016. Date of Electronic Publication: 2024 Nov 04.
