no code implementations • 24 Mar 2024 • Liangrui Pan, Zhenyu Zhao, Ying Lu, Kewei Tang, Liyong Fu, Qingchun Liang, Shaoliang Peng
Influenced by ChatGPT, artificial intelligence (AI) large models have witnessed a global upsurge in large model research and development.
no code implementations • 20 Feb 2024 • Hongtao Zhu, Sizhe Zhang, Yang Su, Zhenyu Zhao, Nan Chen
In the domain of causal inference research, the prevalent potential outcomes framework, notably the Rubin Causal Model (RCM), often overlooks individual interference and assumes independent treatment effects.
no code implementations • 29 Nov 2023 • Shen Zhang, Zhaowei Chen, Zhenyu Zhao, Yuhao Chen, Yao Tang, Jiajun Liang
Extensive experiments demonstrate that our approach can address object duplication and heavy computation issues, achieving state-of-the-art performance on higher-resolution image synthesis tasks.
no code implementations • 6 Oct 2021 • Fei Fan, Zhenyu Zhao, Pengfei Tu, Huamin Jie, Kye Yak See
This paper investigates the influence of different motor stator failures on the common-mode (CM) current.
no code implementations • 6 Oct 2021 • Zhenyu Zhao, Fei Fan, Arjuna Weerasinghe, Pengfei Tu, Kye Yak See
The in-circuit common-mode (CM) impedance at the AC input of a motor drive system (MDS) provides valuable inputs for evaluating and estimating the CM electromagnetic interference (EMI) noise generated by the switching of power semiconductor devices in the MDS.
no code implementations • 1 Oct 2021 • Arjuna Weerasinghe, Zhenyu Zhao, Fei Fan, Pengfei Tu, Kye Yak See
The in-circuit differential-mode (DM) impedance at the AC input of a motor drive system (MDS) serves as a key parameter to evaluate and estimate the DM electromagnetic interference (EMI) noise caused by the switching of power semiconductor devices in the MDS.
no code implementations • COLING 2020 • Zhenyu Zhao, Shuangzhi Wu, Muyun Yang, Kehai Chen, Tiejun Zhao
Neural models have achieved great success on the task of machine reading comprehension (MRC), which are typically trained on hard labels.
1 code implementation • 5 May 2020 • Zhenyu Zhao, Yumin Zhang, Totte Harinen, Mike Yung
Uplift modeling is a causal learning technique that estimates subgroup-level treatment effects.
2 code implementations • 25 Feb 2020 • Huigang Chen, Totte Harinen, Jeong-Yoon Lee, Mike Yung, Zhenyu Zhao
CausalML is a Python implementation of algorithms related to causal inference and machine learning.
2 code implementations • 15 Aug 2019 • Zhenyu Zhao, Radhika Anand, Mallory Wang
This paper describes the approach to extend, evaluate, and implement the mRMR feature selection methods for classification problem in a marketing machine learning platform at Uber that automates creation and deployment of targeting and personalization models at scale.
no code implementations • 14 Aug 2019 • Zhenyu Zhao, Totte Harinen
An important but so far neglected use case for uplift modeling is an experiment with multiple treatment groups that have different costs, such as for example when different communication channels and promotion types are tested simultaneously.
no code implementations • 25 Nov 2015 • Chen Xu, Zhouchen Lin, Zhenyu Zhao, Hongbin Zha
We propose a new majorization-minimization (MM) method for non-smooth and non-convex programs, which is general enough to include the existing MM methods.