Zobrazeno 1 - 10
of 7 660
pro vyhledávání: '"Wu, Chung"'
Neural Radiance Fields (NeRF) face significant challenges in few-shot scenarios, primarily due to overfitting and long training times for high-fidelity rendering. Existing methods, such as FreeNeRF and SparseNeRF, use frequency regularization or pre-
Externí odkaz:
http://arxiv.org/abs/2410.16271
Autor:
Wu, Chung-Wen, Chen, Berlin
Automatic Speech Assessment (ASA) has seen notable advancements with the utilization of self-supervised features (SSL) in recent research. However, a key challenge in ASA lies in the imbalanced distribution of data, particularly evident in English te
Externí odkaz:
http://arxiv.org/abs/2406.10873
Publikováno v:
Journal of Medical Internet Research, Vol 23, Iss 3, p e23662 (2021)
BackgroundFilling a prescription on the web has become an alternative to in-person pharmacies for individuals to access their medications. However, the adoption of web-based filling has been gradual, and the use patterns remain to be unclear. Object
Externí odkaz:
https://doaj.org/article/3356acca9c7b4878ab6ca59f7553f12a
Autor:
Hsieh, Yu-Chiang, Lin, Zhen-You, Fung, Shin-Ji, Lu, Wen-Shin, Ho, Sheng-Chin, Hong, Siang-Ping, Ho, Sheng-Zhu, Huang, Chiu-Hua, Watanabe, Kenji, Taniguchi, Takashi, Chan, Yang-Hao, Chen, Yi-Chun, Wu, Chung-Lin, Chen, Tse-Ming
Publikováno v:
Nano Lett. 23, 7244-7251 (2023)
Strain engineering has quickly emerged as a viable option to modify the electronic, optical and magnetic properties of 2D materials. However, it remains challenging to arbitrarily control the strain. Here we show that by creating atomically-flat surf
Externí odkaz:
http://arxiv.org/abs/2401.01300
Radio Frequency Neural Networks (RFNNs) have demonstrated advantages in realizing intelligent applications across various domains. However, as the model size of deep neural networks rapidly increases, implementing large-scale RFNN in practice require
Externí odkaz:
http://arxiv.org/abs/2312.10343
Autor:
Yuan, Yifan, Kotiuga, Michele, Park, Tae Joon, Ni, Yuanyuan, Saha, Arnob, Zhou, Hua, Sadowski, Jerzy T., Al-Mahboob, Abdullah, Yu, Haoming, Du, Kai, Zhu, Minning, Deng, Sunbin, Bisht, Ravindra S., Lyu, Xiao, Wu, Chung-Tse Michael, Ye, Peide D., Sengupta, Abhronil, Cheong, Sang-Wook, Xu, Xiaoshan, Rabe, Karin M., Ramanathan, Shriram
Materials with field-tunable polarization are of broad interest to condensed matter sciences and solid-state device technologies. Here, using hydrogen (H) donor doping, we modify the room temperature metallic phase of a perovskite nickelate NdNiO3 in
Externí odkaz:
http://arxiv.org/abs/2311.12200
Autor:
Galloni, Alessandro R., Yuan, Yifan, Zhu, Minning, Yu, Haoming, Bisht, Ravindra S., Wu, Chung-Tse Michael, Grienberger, Christine, Ramanathan, Shriram, Milstein, Aaron D.
Design of hardware based on biological principles of neuronal computation and plasticity in the brain is a leading approach to realizing energy- and sample-efficient artificial intelligence and learning machines. An important factor in selection of t
Externí odkaz:
http://arxiv.org/abs/2310.00066
Owing to the data explosion and rapid development of artificial intelligence (AI), particularly deep neural networks (DNNs), the ever-increasing demand for large-scale matrix-vector multiplication has become one of the major issues in machine learnin
Externí odkaz:
http://arxiv.org/abs/2304.07378