no code implementations • IWSLT 2017 • Hao Qin, Takahiro Shinozaki, Kevin Duh
Neural machine translation (NMT) systems have demonstrated promising results in recent years.
1 code implementation • 3 May 2024 • Xianzhou Zeng, Hao Qin, Ming Kong, Luyuan Chen, Qiang Zhu
The accuracy and robustness of 3D human pose estimation (HPE) are limited by 2D pose detection errors and 2D to 3D ill-posed challenges, which have drawn great attention to Multi-Hypothesis HPE research.
no code implementations • 6 Apr 2024 • Yuhong Mo, Hao Qin, Yushan Dong, Ziyi Zhu, Zhenglin Li
The test set confusion matrix and accuracy show that the model has 99% prediction accuracy for AI-generated text, with a precision of 0. 99, a recall of 1, and an f1 score of 0. 99, achieving a very high classification accuracy.
no code implementations • 31 Aug 2023 • Hao Qin, Zhaozhou Wu, Xingqi Zhang
Additionally, a thorough study of varying weather conditions on trajectory design is provided, and the impact of weight coefficients in the problem formulation is discussed.
1 code implementation • NeurIPS 2023 • Hao Qin, Kwang-Sung Jun, Chicheng Zhang
Maillard sampling \cite{maillard13apprentissage}, an attractive alternative to Thompson sampling, has recently been shown to achieve competitive regret guarantees in the sub-Gaussian reward setting \cite{bian2022maillard} while maintaining closed-form action probabilities, which is useful for offline policy evaluation.
no code implementations • 1 Mar 2021 • Keyu Li, Jian Wang, Yangxin Xu, Hao Qin, Dongsheng Liu, Li Liu, Max Q. -H. Meng
Furthermore, we propose a confidence-based approach to encode the optimization of image quality in the learning process.
no code implementations • 17 Feb 2021 • Poompong Chaiwongkhot, Jiaqiang Zhong, Anqi Huang, Hao Qin, Sheng-cai Shi, Vadim Makarov
We study potential security vulnerabilities of a single-photon detector based on superconducting transition-edge sensor.
Quantum Physics
1 code implementation • 10 Oct 2019 • Haoran Dou, Xin Yang, Jikuan Qian, Wufeng Xue, Hao Qin, Xu Wang, Lequan Yu, Shujun Wang, Yi Xiong, Pheng-Ann Heng, Dong Ni
In this study, we propose a novel reinforcement learning (RL) framework to automatically localize fetal brain standard planes in 3D US.