no code implementations • 21 Dec 2023 • Wenbin Hu, Fernando Acero, Eleftherios Triantafyllidis, Zhaocheng Liu, Zhibin Li
We present a modular framework designed to enable a robot hand-arm system to learn how to catch flying objects, a task that requires fast, reactive, and accurately-timed robot motions.
no code implementations • 17 Dec 2023 • Kun Li, Wenbin Hu
At the same time, in order to enhance the continuous representation capability of the numerical text, a common-sense numerical knowledge graph is introduced.
no code implementations • 9 Dec 2023 • Chuang Liu, Yibing Zhan, Xueqi Ma, Liang Ding, Dapeng Tao, Jia Wu, Wenbin Hu, Bo Du
Graph Transformers (GTs) have achieved impressive results on various graph-related tasks.
no code implementations • 21 Nov 2023 • Chuang Liu, Wenhang Yu, Kuang Gao, Xueqi Ma, Yibing Zhan, Jia Wu, Bo Du, Wenbin Hu
Graph pooling has been increasingly recognized as crucial for Graph Neural Networks (GNNs) to facilitate hierarchical graph representation learning.
1 code implementation • 5 Oct 2023 • Kun Li, Yong Luo, Xiantao Cai, Wenbin Hu, Bo Du
In this paper, we propose a zero-shot learning solution for the DRP task in preclinical drug screening.
no code implementations • 12 Sep 2023 • Yong Lin, Hangyu Lin, Wei Xiong, Shizhe Diao, Jianmeng Liu, Jipeng Zhang, Rui Pan, Haoxiang Wang, Wenbin Hu, Hanning Zhang, Hanze Dong, Renjie Pi, Han Zhao, Nan Jiang, Heng Ji, Yuan YAO, Tong Zhang
Building on the analysis and the observation that averaging different layers of the transformer leads to significantly different reward-tax trade-offs, we propose Adaptive Model Averaging (AMA) to adaptively find various combination ratios of model layers.
1 code implementation • 19 Jul 2023 • Yize Cheng, Wenbin Hu, Minhao Cheng
Deep neural networks (DNNs) have shown unprecedented success in object detection tasks.
1 code implementation • 22 Jun 2023 • Chuang Liu, Yibing Zhan, Baosheng Yu, Liu Liu, Bo Du, Wenbin Hu, Tongliang Liu
A pooling operation is essential for effective graph-level representation learning, where the node drop pooling has become one mainstream graph pooling technology.
1 code implementation • 20 Apr 2023 • Chiaming Hsu, Changtong Zan, Liang Ding, Longyue Wang, Xiaoting Wang, Weifeng Liu, Fu Lin, Wenbin Hu
Experiments on WMT17-EnZh XRE also show the effectiveness of our Prompt-XRE against other competitive baselines.
no code implementations • 14 Jan 2023 • Zhenyu Yang, Ge Zhang, Jia Wu, Jian Yang, Quan Z. Sheng, Shan Xue, Chuan Zhou, Charu Aggarwal, Hao Peng, Wenbin Hu, Edwin Hancock, Pietro Liò
Traditional approaches to learning a set of graphs heavily rely on hand-crafted features, such as substructures.
no code implementations • 18 Jul 2022 • Chuang Liu, Xueqi Ma, Yibing Zhan, Liang Ding, Dapeng Tao, Bo Du, Wenbin Hu, Danilo Mandic
However, the LTH-based methods suffer from two major drawbacks: 1) they require exhaustive and iterative training of dense models, resulting in an extremely large training computation cost, and 2) they only trim graph structures and model parameters but ignore the node feature dimension, where significant redundancy exists.
1 code implementation • 15 Jun 2022 • Xiaowen Wei, Xiuwen Gong, Yibing Zhan, Bo Du, Yong Luo, Wenbin Hu
Experimental results on real-world networks demonstrate that CLNode is a general framework that can be combined with various GNNs to improve their accuracy and robustness.
no code implementations • 31 May 2022 • Ge Zhang, Jia Wu, Jian Yang, Shan Xue, Wenbin Hu, Chuan Zhou, Hao Peng, Quan Z. Sheng, Charu Aggarwal
To frame this survey, we propose a systematic taxonomy covering GLNNs upon deep neural networks, graph neural networks, and graph pooling.
1 code implementation • 15 Apr 2022 • Chuang Liu, Yibing Zhan, Jia Wu, Chang Li, Bo Du, Wenbin Hu, Tongliang Liu, DaCheng Tao
Graph neural networks have emerged as a leading architecture for many graph-level tasks, such as graph classification and graph generation.
no code implementations • 1 Mar 2022 • Jiejun Tan, Wenbin Hu, Weiwei Liu
To address these issues, a novel paradigm, Entity Pre-typing Relation Classification with Prompt Answer Centralizing(EPPAC) is proposed in this paper.
no code implementations • 29 Nov 2021 • Weiliang Tao, Yan Liu, Zhimin Ma, Wenbin Hu
This paper proposes a novel particle image velocimetry (PIV) technique to generate an instantaneous two-dimensional velocity field for sediment-laden fluid based on the optical flow algorithm of ultrasound imaging.
no code implementations • ACL 2021 • YUREN MAO, Zekai Wang, Weiwei Liu, Xuemin Lin, Wenbin Hu
Task variance regularization, which can be used to improve the generalization of Multi-task Learning (MTL) models, remains unexplored in multi-task text classification.
no code implementations • 26 May 2021 • Xing Su, Shan Xue, Fanzhen Liu, Jia Wu, Jian Yang, Chuan Zhou, Wenbin Hu, Cecile Paris, Surya Nepal, Di Jin, Quan Z. Sheng, Philip S. Yu
A community reveals the features and connections of its members that are different from those in other communities in a network.
1 code implementation • 19 Jun 2020 • Pinghua Xu, Wenbin Hu, Jia Wu, Weiwei Liu
However, the practical significance of the existing studies on this subject is limited for two reasons.
Social and Information Networks Computer Science and Game Theory J.4
1 code implementation • 17 May 2020 • Fanzhen Liu, Shan Xue, Jia Wu, Chuan Zhou, Wenbin Hu, Cecile Paris, Surya Nepal, Jian Yang, Philip S. Yu
As communities represent similar opinions, similar functions, similar purposes, etc., community detection is an important and extremely useful tool in both scientific inquiry and data analytics.
no code implementations • 15 Feb 2020 • Zhaole Sun, Kai Yuan, Wenbin Hu, Chuanyu Yang, Zhibin Li
In robotic grasping, objects are often occluded in ungraspable configurations such that no pregrasp pose can be found, eg large flat boxes on the table that can only be grasped from the side.
Robotics
no code implementations • 11 Feb 2020 • Wenbin Hu, Chuanyu Yang, Kai Yuan, Zhibin Li
The performance of learned policy is evaluated on three different tasks: grasping a static target, grasping a dynamic target, and re-grasping.
Robotics