no code implementations • NAACL (ACL) 2022 • Noujoud Ahbali, Xinyuan Liu, Albert Nanda, Jamie Stark, Ashit Talukder, Rupinder Paul Khandpur
To this end, we propose a novel deep learning-powered approach to automate news analysis and credit adverse events detection to score the credit sentiment associated with a company.
no code implementations • 20 Dec 2023 • Xinyuan Liu, Lizhi Wang, Lingen Li, Chang Chen, Xue Hu, Fenglong Song, Youliang Yan
Computational spectral imaging is drawing increasing attention owing to the snapshot advantage, and amplitude, phase, and wavelength encoding systems are three types of representative implementations.
1 code implementation • 17 May 2023 • Hang Xu, Xinyuan Liu, Haonan Xu, Yike Ma, Zunjie Zhu, Chenggang Yan, Feng Dai
We decouple reversibility and joint-optim from single smoothing function into two distinct entities, which for the first time achieves the objectives of both correcting angular boundary and blending angle with other parameters. Extensive experiments on multiple datasets show that boundary discontinuity problem is well-addressed.
no code implementations • 16 May 2023 • Yifei Wang, Yiyang Zhou, Jihua Zhu, Xinyuan Liu, Wenbiao Yan, Zhiqiang Tian
Label distribution learning (LDL) is a new machine learning paradigm for solving label ambiguity.
no code implementations • CVPR 2023 • Hang Xu, Xinyuan Liu, Qiang Zhao, Yike Ma, Chenggang Yan, Feng Dai
Therefore, we propose GLDL-ATSS as a better training sample selection strategy for objects of the spherical image, which can alleviate the drawback of IoU threshold-based strategy of scale-sample imbalance.
no code implementations • 7 Jul 2020 • Xinyuan Liu, Jihua Zhu, Qinghai Zheng, Zhongyu Li, Ruixin Liu, Jun Wang
More specifically, this novel loss function not only considers the mapping errors generated from the projection of the input space into the output one but also accounts for the reconstruction errors generated from the projection of the output space back to the input one.
no code implementations • 7 Apr 2020 • Qinghai Zheng, Jihua Zhu, Haoyu Tang, Xinyuan Liu, Zhongyu Li, Huimin Lu
Recently, label distribution learning (LDL) has drawn much attention in machine learning, where LDL model is learned from labelel instances.