Search Results for author: YuanHao Liu

Found 7 papers, 4 papers with code

A Dual-Fusion Cognitive Diagnosis Framework for Open Student Learning Environments

1 code implementation19 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.

cognitive diagnosis

MCNS: Mining Causal Natural Structures Inside Time Series via A Novel Internal Causality Scheme

no code implementations13 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.

Causal Inference Time Series +1

Popularity Debiasing from Exposure to Interaction in Collaborative Filtering

1 code implementation9 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.

Collaborative Filtering Recommendation Systems

TSFool: Crafting Highly-Imperceptible Adversarial Time Series through Multi-Objective Attack

2 code implementations14 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).

Adversarial Attack Time Series +2

Meta Pattern Concern Score: A Novel Evaluation Measure with Human Values for Multi-classifiers

1 code implementation14 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.

Attribute-Based Robotic Grasping with One-Grasp Adaptation

no code implementations6 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.

Attribute Object +1

Towards Powerful Graph Neural Networks: Diversity Matters

no code implementations1 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.

Diversity Graph Representation Learning +1

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