Pacific Rim International Conference on Artificial Intelligence最新文献

Egalitarian Price of Fairness for Indivisible Goods 不可分割商品的平等主义公平价格

Pacific Rim International Conference on Artificial Intelligence Pub Date : 2024-02-25 DOI: 10.1007/978-981-99-7019-3_3

Karen Frilya Celine, M. A. Dzulfikar, Ivan Koswara

引用次数: 0

CANAMRF: An Attention-Based Model for Multimodal Depression Detection CANAMRF：基于注意力的多模态抑郁检测模型

Pacific Rim International Conference on Artificial Intelligence Pub Date : 2024-01-04 DOI: 10.1007/978-981-99-7022-3_10

Yuntao Wei, Yuzhe Zhang, Shuyang Zhang, Hone Zhang

引用次数: 0

A Task-aware Dual Similarity Network for Fine-grained Few-shot Learning 用于细粒度少镜头学习的任务感知双相似网络

Pacific Rim International Conference on Artificial Intelligence Pub Date : 2022-10-22 DOI: 10.48550/arXiv.2210.12348

Yanjun Qi, Han Sun, Ningzhong Liu, Huiyu Zhou

引用次数: 1

APGKT: Exploiting Associative Path on Skills Graph for Knowledge Tracing APGKT:利用技能图上的关联路径进行知识追踪

Pacific Rim International Conference on Artificial Intelligence Pub Date : 2022-10-05 DOI: 10.48550/arXiv.2210.08971

H. Zhang, Chenyang Bu, Fei-Tsung Liu, Shuochen Liu, Yuhong Zhang, Xuegang Hu

{"title":"APGKT: Exploiting Associative Path on Skills Graph for Knowledge Tracing","authors":"H. Zhang, Chenyang Bu, Fei-Tsung Liu, Shuochen Liu, Yuhong Zhang, Xuegang Hu","doi":"10.48550/arXiv.2210.08971","DOIUrl":"https://doi.org/10.48550/arXiv.2210.08971","url":null,"abstract":"Knowledge tracing (KT) is a fundamental task in educational data mining that mainly focuses on students' dynamic cognitive states of skills. The question-answering process of students can be regarded as a thinking process that considers the following two problems. One problem is which skills are needed to answer the question, and the other is how to use these skills in order. If a student wants to answer a question correctly, the student should not only master the set of skills involved in the question but also think and obtain the associative path on the skills graph. The nodes in the associative path refer to the skills needed and the path shows the order of using them. The associative path is referred to as the skill mode. Thus, obtaining the skill modes is the key to answering questions successfully. However, most existing KT models only focus on a set of skills, without considering the skill modes. We propose a KT model, called APGKT, that exploits skill modes. Specifically, we extract the subgraph topology of the skills involved in the question and combine the difficulty level of the skills to obtain the skill modes via encoding; then, through multi-layer recurrent neural networks, we obtain a student's higher-order cognitive states of skills, which is used to predict the student's future answering performance. Experiments on five benchmark datasets validate the effectiveness of the proposed model.","PeriodicalId":272217,"journal":{"name":"Pacific Rim International Conference on Artificial Intelligence","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127748502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Features Fusion Framework for Multimodal Irregular Time-series Events 多模态不规则时间序列事件特征融合框架

Pacific Rim International Conference on Artificial Intelligence Pub Date : 2022-09-05 DOI: 10.48550/arXiv.2209.01728

Peiwang Tang, Xianchao Zhang

{"title":"Features Fusion Framework for Multimodal Irregular Time-series Events","authors":"Peiwang Tang, Xianchao Zhang","doi":"10.48550/arXiv.2209.01728","DOIUrl":"https://doi.org/10.48550/arXiv.2209.01728","url":null,"abstract":"Some data from multiple sources can be modeled as multimodal time-series events which have different sampling frequencies, data compositions, temporal relations and characteristics. Different types of events have complex nonlinear relationships, and the time of each event is irregular. Neither the classical Recurrent Neural Network (RNN) model nor the current state-of-the-art Transformer model can deal with these features well. In this paper, a features fusion framework for multimodal irregular time-series events is proposed based on the Long Short-Term Memory networks (LSTM). Firstly, the complex features are extracted according to the irregular patterns of different events. Secondly, the nonlinear correlation and complex temporal dependencies relationship between complex features are captured and fused into a tensor. Finally, a feature gate are used to control the access frequency of different tensors. Extensive experiments on MIMIC-III dataset demonstrate that the proposed framework significantly outperforms to the existing methods in terms of AUC (the area under Receiver Operating Characteristic curve) and AP (Average Precision).","PeriodicalId":272217,"journal":{"name":"Pacific Rim International Conference on Artificial Intelligence","volume":"155 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114767886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Moderately-Balanced Representation Learning for Treatment Effects with Orthogonality Information 正交信息治疗效果的中等平衡表征学习

Pacific Rim International Conference on Artificial Intelligence Pub Date : 2022-09-05 DOI: 10.48550/arXiv.2209.01956

Yiyan Huang, Cheuk Hang Leung, Shumin Ma, Qi Wu, Dongdong Wang, Zhixiang Huang

{"title":"Moderately-Balanced Representation Learning for Treatment Effects with Orthogonality Information","authors":"Yiyan Huang, Cheuk Hang Leung, Shumin Ma, Qi Wu, Dongdong Wang, Zhixiang Huang","doi":"10.48550/arXiv.2209.01956","DOIUrl":"https://doi.org/10.48550/arXiv.2209.01956","url":null,"abstract":"Estimating the average treatment effect (ATE) from observational data is challenging due to selection bias. Existing works mainly tackle this challenge in two ways. Some researchers propose constructing a score function that satisfies the orthogonal condition, which guarantees that the established ATE estimator is\"orthogonal\"to be more robust. The others explore representation learning models to achieve a balanced representation between the treated and the controlled groups. However, existing studies fail to 1) discriminate treated units from controlled ones in the representation space to avoid the over-balanced issue; 2) fully utilize the\"orthogonality information\". In this paper, we propose a moderately-balanced representation learning (MBRL) framework based on recent covariates balanced representation learning methods and orthogonal machine learning theory. This framework protects the representation from being over-balanced via multi-task learning. Simultaneously, MBRL incorporates the noise orthogonality information in the training and validation stages to achieve a better ATE estimation. The comprehensive experiments on benchmark and simulated datasets show the superiority and robustness of our method on treatment effect estimations compared with existing state-of-the-art methods.","PeriodicalId":272217,"journal":{"name":"Pacific Rim International Conference on Artificial Intelligence","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130603299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Multi-Head Convolutional Neural Network With Multi-path Attention improves Image Denoising 基于多路径注意的多头卷积神经网络改进了图像去噪

Pacific Rim International Conference on Artificial Intelligence Pub Date : 2022-04-27 DOI: 10.1007/978-3-031-20868-3_25

Jiahong Zhang, Meijun Qu, Ye Wang, Lihong Cao

引用次数: 3

VSEC: Transformer-based Model for Vietnamese Spelling Correction 基于转换的越南语拼写校正模型

Pacific Rim International Conference on Artificial Intelligence Pub Date : 2021-11-01 DOI: 10.1007/978-3-030-89363-7_20

Dinh-Truong Do, Nguyen Ha Thanh, Thang Bui, Dinh-Hieu Vo

引用次数: 7

SIN: Superpixel Interpolation Network SIN:超像素插值网络

Pacific Rim International Conference on Artificial Intelligence Pub Date : 2021-10-17 DOI: 10.1007/978-3-030-89370-5_22

Qing Yuan, Songfeng Lu, Yan Huang, Wuxin Sha

引用次数: 1

Multi-View Stereo Network with attention thin volume 多视点立体网络与注意薄体积

Pacific Rim International Conference on Artificial Intelligence Pub Date : 2021-10-16 DOI: 10.1007/978-3-031-20868-3_30

Zihang Wan

引用次数: 1