Zobrazeno 1 - 10
of 278
pro vyhledávání: '"Gu, Weiwei"'
Continual and interactive robot learning is a challenging problem as the robot is present with human users who expect the robot to learn novel skills to solve novel tasks perpetually with sample efficiency. In this work we present a framework for rob
Externí odkaz:
http://arxiv.org/abs/2409.03166
Graph neural networks (GNNs) can learn effective node representations that significantly improve link prediction accuracy. However, most GNN-based link prediction algorithms are incompetent to predict weak ties connecting different communities. Most
Externí odkaz:
http://arxiv.org/abs/2406.11904
Autor:
Gu, Weiwei, Wang, Senquan
Blood Glucose (BG) control involves keeping an individual's BG within a healthy range through extracorporeal insulin injections is an important task for people with type 1 diabetes. However,traditional patient self-management is cumbersome and risky.
Externí odkaz:
http://arxiv.org/abs/2403.07566
Publikováno v:
Journal of Social Computing, 2023, 4(4): 326-336
Complex networks are widely used to represent an abundance of real-world relations ranging from social networks to brain networks. Inferring missing links or predicting future ones based on the currently observed network is known as the link predicti
Externí odkaz:
http://arxiv.org/abs/2403.04282
We present a framework for robots to learn novel visual concepts and tasks via in-situ linguistic interactions with human users. Previous approaches have either used large pre-trained visual models to infer novel objects zero-shot, or added novel con
Externí odkaz:
http://arxiv.org/abs/2312.13219
We present an empirical study on methods for span finding, the selection of consecutive tokens in text for some downstream tasks. We focus on approaches that can be employed in training end-to-end information extraction systems, and find there is no
Externí odkaz:
http://arxiv.org/abs/2210.06824
Autor:
Chen, Yunmo, Gantt, William, Gu, Weiwei, Chen, Tongfei, White, Aaron Steven, Van Durme, Benjamin
We present a novel iterative extraction model, IterX, for extracting complex relations, or templates (i.e., N-tuples representing a mapping from named slots to spans of text) within a document. Documents may feature zero or more instances of a templa
Externí odkaz:
http://arxiv.org/abs/2210.06600
Autor:
Zhang, Youyi, Hu, Jiabao, Li, Yuanbo, Gu, Weiwei, Feng, Zukang, Yan, Kaiheng, Zhang, Man, Li, Yaya, Yuan, Zi, Sun, Xiaomei, Zhang, Lu, Xu, Shanliang, Wang, Yajun, Yan, Xiaojun
Publikováno v:
In Aquaculture 15 November 2024 592
Autor:
Zhang, Man, Hu, Jiabao, Zhu, Jiajie, Tang, Mengke, Zhang, Youyi, Li, Yaya, Gu, Weiwei, Jiang, Huan, Wang, Danli, Xu, Shanliang, Yan, Xiaojun, Wang, Yajun
Publikováno v:
In Aquaculture 15 September 2024 590
Autor:
Hu, Jiabao, Zhang, Youyi, Li, Yuanbo, Gu, Weiwei, Feng, Zukang, Yan, Kaiheng, Zhang, Man, Li, Yaya, Zheng, Rongyue, Xu, Shanliang, Wang, Yajun, Yan, Xiaojun
Publikováno v:
In Aquaculture 15 September 2024 590