1 code implementation • 19 Oct 2024 • YuanHao Liu, Shuo Liu, Yimeng Liu, Jingwen Yang, Hong Qian
To this end, this paper proposes a dual-fusion cognitive diagnosis framework (DFCD) to address the challenge of aligning two different modalities, i. e., textual semantic features and response-relevant features.
no code implementations • 13 Sep 2023 • YuanHao Liu, Dehui Du, Zihan Jiang, Anyan Huang, Yiyang Li
To address these challenges, we propose a novel framework called Mining Causal Natural Structure (MCNS), which is automatic and domain-agnostic and helps to find the causal natural structures inside time series via the internal causality scheme.
1 code implementation • 9 May 2023 • YuanHao Liu, Qi Cao, HuaWei Shen, Yunfan Wu, Shuchang Tao, Xueqi Cheng
In this paper, we propose a new criterion for popularity debiasing, i. e., in an unbiased recommender system, both popular and unpopular items should receive Interactions Proportional to the number of users who Like it, namely IPL criterion.
2 code implementations • 14 Sep 2022 • Yanyun Wang, Dehui Du, Haibo Hu, Zi Liang, YuanHao Liu
Recent years have witnessed the success of recurrent neural network (RNN) models in time series classification (TSC).
1 code implementation • 14 Sep 2022 • Yanyun Wang, Dehui Du, YuanHao Liu
And a case study shows it can not only find the ideal model reducing 0. 53% of dangerous cases by only sacrificing 0. 04% of training accuracy, but also refine the learning rate to train a new model averagely outperforming the original one with a 1. 62% lower value of itself and 0. 36% fewer number of dangerous cases.
no code implementations • 6 Apr 2021 • Yang Yang, YuanHao Liu, Hengyue Liang, Xibai Lou, Changhyun Choi
In this work, we introduce an end-to-end learning method of attribute-based robotic grasping with one-grasp adaptation capability.
no code implementations • 1 Jan 2021 • Xu Bingbing, HuaWei Shen, Qi Cao, YuanHao Liu, Keting Cen, Xueqi Cheng
For a target node, diverse sampling offers it diverse neighborhoods, i. e., rooted sub-graphs, and the representation of target node is finally obtained via aggregating the representation of diverse neighborhoods obtained using any GNN model.