no code implementations • 21 Jun 2023 • Shihang Feng, Hanchen Wang, Chengyuan Deng, Yinan Feng, Yanhua Liu, Min Zhu, Peng Jin, Yinpeng Chen, Youzuo Lin
We conduct comprehensive numerical experiments to explore the relationship between P-wave and S-wave velocities in seismic data.
no code implementations • 30 May 2023 • Chen Ling, Xujiang Zhao, Jiaying Lu, Chengyuan Deng, Can Zheng, Junxiang Wang, Tanmoy Chowdhury, Yun Li, Hejie Cui, Xuchao Zhang, Tianjiao Zhao, Amit Panalkar, Dhagash Mehta, Stefano Pasquali, Wei Cheng, Haoyu Wang, Yanchi Liu, Zhengzhang Chen, Haifeng Chen, Chris White, Quanquan Gu, Jian Pei, Carl Yang, Liang Zhao
In this article, we present a comprehensive survey on domain specification techniques for large language models, an emerging direction critical for large language model applications.
no code implementations • 4 May 2023 • Chengyuan Deng, Surya Teja Gavva, Karthik C. S., Parth Patel, Adarsh Srinivasan
Formally, we show that there exists a data set X in the Euclidean plane, for which there is a decision tree of depth k-1 whose k-means/k-median cost matches the optimal clustering cost of X, but every decision tree of depth less than k-1 has unbounded cost w. r. t.
no code implementations • 28 Nov 2021 • Chengyuan Deng, Youzuo Lin
For robustness, we prove the upper bounds of the deviation between the predictions from clean and noisy data.
2 code implementations • 4 Nov 2021 • Chengyuan Deng, Shihang Feng, Hanchen Wang, Xitong Zhang, Peng Jin, Yinan Feng, Qili Zeng, Yinpeng Chen, Youzuo Lin
The recent success of data-driven FWI methods results in a rapidly increasing demand for open datasets to serve the geophysics community.
no code implementations • 21 Oct 2020 • Chen Wang, Chengyuan Deng
Applying Global Self-attention (GSA) mechanism over features has achieved remarkable success on Convolutional Neural Networks (CNNs).
no code implementations • 27 Nov 2019 • Chen Wang, Chengyuan Deng, Vladimir Ivanov
Variational Autoencoders (VAEs) are powerful in data representation inference, but it cannot learn relations between features with its vanilla form and common variations.
no code implementations • 9 Aug 2019 • Chen Wang, Chengyuan Deng, Zhoulu Yu, Dafeng Hui, Xiaofeng Gong, Ruisen Luo
In addition, the proposed method has other preferred properties such as special advantages in dealing with highly imbalanced data, and it pioneers the research on the regularization for dynamic ensemble methods.
1 code implementation • 5 Aug 2019 • Chen Wang, Chengyuan Deng, Suzhen Wang
The paper presents Imbalance-XGBoost, a Python package that combines the powerful XGBoost software with weighted and focal losses to tackle binary label-imbalanced classification tasks.