no code implementations • 22 Apr 2024 • Zixuan Zhou, Xuefei Ning, Ke Hong, Tianyu Fu, Jiaming Xu, Shiyao Li, Yuming Lou, Luning Wang, Zhihang Yuan, Xiuhong Li, Shengen Yan, Guohao Dai, Xiao-Ping Zhang, Yuhan Dong, Yu Wang
This paper presents a comprehensive survey of the existing literature on efficient LLM inference.
no code implementations • 2 Nov 2023 • Ke Hong, Guohao Dai, Jiaming Xu, Qiuli Mao, Xiuhong Li, Jun Liu, Kangdi Chen, Yuhan Dong, Yu Wang
A single and static dataflow may lead to a 50. 25% performance loss for GEMMs of different shapes in LLM inference.
no code implementations • 5 May 2023 • Taoyong Cui, Yuhan Dong
Inspired by the success of contrastive learning used in some high-level computer vision tasks, we bring in this idea to the low-level denoising task.
no code implementations • 21 Feb 2023 • Lu Liu, Lei Zhou, Yuhan Dong
This allows the camera to capture images with shallow depth-of-field, in which only a small area of the image is in sharp focus, while the rest of the image is blurred.
no code implementations • 13 Feb 2023 • Fei Kong, Xiyue Wang, Jinxi Xiang, Sen yang, Xinran Wang, Meng Yue, Jun Zhang, Junhan Zhao, Xiao Han, Yuhan Dong, Biyue Zhu, Fang Wang, Yueping Liu
We assessed the effectiveness of FACL in cancer diagnosis and Gleason grading tasks using 19, 461 whole-slide images of prostate cancer from multiple centers.
no code implementations • 25 Nov 2022 • Taoyong Cui, Jianze Li, Yuhan Dong, Li Liu
In the first stage, we propose a novel algorithm called polar decomposition-based orthogonal initialization (PDOI) to find a good initialization for the orthogonal optimization.
1 code implementation • 16 Jul 2022 • Zixuan Zhou, Xuefei Ning, Yi Cai, Jiashu Han, Yiping Deng, Yuhan Dong, Huazhong Yang, Yu Wang
Specifically, we train the supernet with a large sharing extent (an easier curriculum) at the beginning and gradually decrease the sharing extent of the supernet (a harder curriculum).
no code implementations • 26 May 2022 • Yaqi Sun, Shijing Si, Jianzong Wang, Yuhan Dong, Zhitao Zhu, Jing Xiao
More importantly, we apply the Gini coefficient and validation accuracy of clients in each communication round to construct a reward function for the reinforcement learning.
no code implementations • 6 May 2022 • Yang Liu, Ersi Zhang, Lulu Xu, Chufan Xiao, Xiaoyun Zhong, Lijin Lian, Fang Li, Bin Jiang, Yuhan Dong, Lan Ma, Qiming Huang, Ming Xu, Yongbing Zhang, Dongmei Yu, Chenggang Yan, Peiwu Qin
Deep learning techniques have shown great potential in medical image processing, particularly through accurate and reliable image segmentation on magnetic resonance imaging (MRI) scans or computed tomography (CT) scans, which allow the localization and diagnosis of lesions.
no code implementations • 6 May 2022 • Ruitao Zhang, Xueying Han, Ijaz Gul, Shiyao Zhai, Ying Liu, Yongbing Zhang, Yuhan Dong, Lan Ma, Dongmei Yu, Jin Zhou, Peiwu Qin
Although testing on the CAR-T cells dataset, a decent performance is observed, which is attributed to the limited size of the dataset.
no code implementations • CVPR 2022 • Xiaoyan Xing, Yanlin Qian, Sibo Feng, Yuhan Dong, Jiri Matas
In this paper, we present Point Cloud Color Constancy, in short PCCC, an illumination chromaticity estimation algorithm exploiting a point cloud.
no code implementations • 8 May 2018 • Dong Zhou, Huimin Ma, Yuhan Dong
To overcome this challenge, we propose a novel method that combines both the cognition-driven model and the data-driven model.