Zobrazeno 1 - 10
of 561
pro vyhledávání: '"JIN, DAVID"'
Autor:
Jin, David
State estimation is critical for robot operation. Most estimation algorithms assume that the robotic sensor measurements are contaminated by Gaussian noise. However, in practical applications, the noise is often non-Gaussian, heavy-tailed, or even mu
Externí odkaz:
https://hdl.handle.net/1721.1/157096
We perform detailed theoretical analysis of an expectation-maximization-based algorithm recently proposed in for solving a variation of the 3D registration problem, named multi-model 3D registration. Despite having shown superior empirical results, d
Externí odkaz:
http://arxiv.org/abs/2405.08991
This paper develops a new filtering approach for state estimation in polynomial systems corrupted by arbitrary noise, which commonly arise in robotics. We first consider a batch setup where we perform state estimation using all data collected from th
Externí odkaz:
http://arxiv.org/abs/2403.04712
We investigate a variation of the 3D registration problem, named multi-model 3D registration. In the multi-model registration problem, we are given two point clouds picturing a set of objects at different poses (and possibly including points belongin
Externí odkaz:
http://arxiv.org/abs/2402.10865
Various collaborative distributed machine learning (CDML) systems, including federated learning systems and swarm learning systems, with diferent key traits were developed to leverage resources for the development and use of machine learning(ML) mode
Externí odkaz:
http://arxiv.org/abs/2309.16584
Common document ranking pipelines in search systems are cascade systems that involve multiple ranking layers to integrate different information step-by-step. In this paper, we propose a novel re-ranker Fusion-in-T5 (FiT5), which integrates text match
Externí odkaz:
http://arxiv.org/abs/2305.14685
Autor:
Jin, David J.
We investigate the level of success a firm achieves depending on which of two common scoring algorithms is used to screen qualified applicants belonging to a disadvantaged group. Both algorithms are trained on data generated by a prejudiced decision-
Externí odkaz:
http://arxiv.org/abs/2212.00578
Autor:
Harker, F. Roger, Roigard, Christina M., Colonna, Ann E., Jin, David, Ryan, Grace, Chheang, Sok L., Hedderley, Duncan I., Dalziel, Paul
Publikováno v:
In Postharvest Biology and Technology November 2024 217
Autor:
Zhou, Xiaosu, Chen, Xue, Chen, Jiaqi, Wen, Lijun, Zhang, Zhanglin, Qin, Ya-Zhen, Cao, Panxiang, Xing, Haizhou, Mi, Yingchang, Wang, Wei, Zhang, Guangsen, Li, Ji, Wu, Huanling, Zhang, Zhifen, Zhang, Jian, Su, Zhan, Wang, Fang, Zhang, Yang, Ma, Xiaoli, Fang, Jiancheng, Wu, Ping, Wang, Tong, Fan, Gaowei, Zhao, Yang, Jin, David, Zhang, Xian, Ma, Xiujuan, Wu, Qisheng, Zhang, Zhihua, Wang, Linya, Ma, Futian, Xiao, Xia, Wu, Chengye, Sun, Kai, Tang, Ruijie, Zhang, Yun, Wu, Sanyun, Gao, Ran, Zhang, Leping, Zheng, Huyong, Zhao, Yanli, Zhu, Hong-Hu, Lu, Daopei, Lu, Peihua, Chen, Suning, Liu, Hongxing
Publikováno v:
In Blood 3 October 2024 144(14):1471-1485