no code implementations • 18 May 2024 • Yu Huang, Liang Guo, Wanqian Guo, Zhe Tao, Yang Lv, Zhihao Sun, Dongfang Zhao
In the field of environmental science, it is crucial to have robust evaluation metrics for large language models to ensure their efficacy and accuracy.
no code implementations • 7 Nov 2023 • Huidong Xie, Weijie Gan, Bo Zhou, Xiongchao Chen, Qiong Liu, Xueqi Guo, Liang Guo, Hongyu An, Ulugbek S. Kamilov, Ge Wang, Chi Liu
We extensively evaluated DDPET-3D on 100 patients with 6 different low-dose levels (a total of 600 testing studies), and demonstrated superior performance over previous diffusion models for 3D imaging problems as well as previous noise-aware medical image denoising models.
no code implementations • 13 Feb 2023 • Liang Guo, Zhongliang Li, Rachid Outbib
In the paper, a fuzzy reinforcement learning-based energy management strategy for fuel cell hybrid electric vehicles is proposed to reduce fuel consumption, maintain the batteries' long-term operation, and extend the lifetime of the fuel cells system.
no code implementations • 2 Sep 2022 • LianWu Chen, Xiguang Zheng, Chen Zhang, Liang Guo, Bing Yu
In recent years, deep neural networks (DNNs) based approaches have achieved the start-of-the-art performance for music source separation (MSS).
no code implementations • 26 Jan 2022 • Xu Zhang, LianWu Chen, Xiguang Zheng, Xinlei Ren, Chen Zhang, Liang Guo, Bing Yu
Speech enhancement methods based on deep learning have surpassed traditional methods.
1 code implementation • INTERSPEECH 2021 2021 • Xinlei Ren, Xu Zhang, LianWu Chen, Xiguang Zheng, Chen Zhang, Liang Guo, Bing Yu
In this work, a new causal U-net based multiple-in-multiple-out structure is proposed for real-time multi-channel speech enhancement.
no code implementations • 4 Feb 2021 • Liang Guo, Ducati Li, Cheng Yu
The existence of dissipative solutions to the compressible isentropic Navier-Stokes equations was established in this paper.
Analysis of PDEs
no code implementations • 4 Jul 2019 • Liang Guo, Jianya Liu, Ruodan Lu
Experiments with 156 benchmark datasets and three classifiers (logistic regression, decision tree and naive bayes) show that in general, our cross-validation procedure can extrude subsampling bias in the MCCV by lowering the EPE around 7. 18% and the variances around 26. 73%.