Zobrazeno 1 - 10
of 36
pro vyhledávání: '"Zhai, Pengyuan"'
Autor:
Zhai, Pengyuan
Are multi-layer parameterized quantum circuits (MPQCs) more expressive than classical neural networks (NNs)? How, why, and in what aspects? In this work, we survey and develop intuitive insights into the expressive power of MPQCs in relation to class
Externí odkaz:
http://arxiv.org/abs/2212.06380
Autor:
Zhai, Pengyuan, Yelin, Susanne
We present NFNet, a PyTorch-based framework for polynomial-time simulation of large-scale, continuously controlled quantum systems, supporting parallel matrix computation and auto-differentiation of network parameters. It is based on the non-interact
Externí odkaz:
http://arxiv.org/abs/2212.05779
Publikováno v:
Computer-Aided Civil and Infrastructure Engineering. 2021; 36: 1094-1113
In recent years, applying deep learning (DL) to assess structural damages has gained growing popularity in vision-based structural health monitoring (SHM). However, both data deficiency and class-imbalance hinder the wide adoption of DL in practical
Externí odkaz:
http://arxiv.org/abs/2211.15961
Autor:
Zhai, Pengyuan
The quantum circuit Born machine (QCBM) is a quantum physics inspired implicit generative model naturally suitable for learning binary images, with a potential advantage of modeling discrete distributions that are hard to simulate classically. As dat
Externí odkaz:
http://arxiv.org/abs/2211.10418
Autor:
Dai, Xili, Li, Mingyang, Zhai, Pengyuan, Tong, Shengbang, Gao, Xingjian, Huang, Shao-Lun, Zhu, Zhihui, You, Chong, Ma, Yi
Despite strong empirical performance for image classification, deep neural networks are often regarded as ``black boxes'' and they are difficult to interpret. On the other hand, sparse convolutional models, which assume that a signal can be expressed
Externí odkaz:
http://arxiv.org/abs/2210.12945
Autor:
Dai, Xili, Tong, Shengbang, Li, Mingyang, Wu, Ziyang, Psenka, Michael, Chan, Kwan Ho Ryan, Zhai, Pengyuan, Yu, Yaodong, Yuan, Xiaojun, Shum, Heung Yeung, Ma, Yi
This work proposes a new computational framework for learning a structured generative model for real-world datasets. In particular, we propose to learn a closed-loop transcription between a multi-class multi-dimensional data distribution and a linear
Externí odkaz:
http://arxiv.org/abs/2111.06636
Autor:
Tsai, Alicia Y., Gunay, Selim, Hwang, Minjune, Zhai, Pengyuan, Li, Chenglong, Ghaoui, Laurent El, Mosalam, Khalid M.
Post-hazard reconnaissance for natural disasters (e.g., earthquakes) is important for understanding the performance of the built environment, speeding up the recovery, enhancing resilience and making informed decisions related to current and future h
Externí odkaz:
http://arxiv.org/abs/2011.13087
Autor:
Jiang, Yuxiang, Xiao, Lairong, Zhai, Pengyuan, Li, Fengcheng, Li, Yanmiao, Zhang, Yafang, Zhong, Qi, Cai, Zhenyang, Liu, Sainan, Zhao, Xiaojun
Publikováno v:
In Surface & Coatings Technology 15 March 2023 456
Akademický článek
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Autor:
Li, Yanmiao, Zhao, Xiaojun, Zhai, Pengyuan, Fan, Pengyu, Xu, Jiahui, Xu, Yuefan, Yu, Zengkai, Li, Muyang, Zhang, Yongtong, Gao, Dawei, Liu, Sainan, Cai, Zhenyang, Xiao, Lairong
Publikováno v:
Materials (1996-1944); Jan2024, Vol. 17 Issue 1, p20, 13p