Machine learning and knowledge discovery in databases : European Conference, ECML PKDD ... : proceedings. ECML PKDD (Conference)最新文献_第6页

Optimization of Annealed Importance Sampling Hyperparameters 退火重要抽样超参数的优化

Machine learning and knowledge discovery in databases : European Conference, ECML PKDD ... : proceedings. ECML PKDD (Conference) Pub Date : 2022-09-27 DOI: 10.48550/arXiv.2209.13226

Shirin Goshtasbpour, F. Pérez-Cruz

引用次数: 1

Probing Spurious Correlations in Popular Event-Based Rumor Detection Benchmarks 在流行的基于事件的谣言检测基准中探测虚假相关性

Machine learning and knowledge discovery in databases : European Conference, ECML PKDD ... : proceedings. ECML PKDD (Conference) Pub Date : 2022-09-19 DOI: 10.48550/arXiv.2209.08799

Jiaying Wu, Bryan Hooi

{"title":"Probing Spurious Correlations in Popular Event-Based Rumor Detection Benchmarks","authors":"Jiaying Wu, Bryan Hooi","doi":"10.48550/arXiv.2209.08799","DOIUrl":"https://doi.org/10.48550/arXiv.2209.08799","url":null,"abstract":"As social media becomes a hotbed for the spread of misinformation, the crucial task of rumor detection has witnessed promising advances fostered by open-source benchmark datasets. Despite being widely used, we find that these datasets suffer from spurious correlations, which are ignored by existing studies and lead to severe overestimation of existing rumor detection performance. The spurious correlations stem from three causes: (1) event-based data collection and labeling schemes assign the same veracity label to multiple highly similar posts from the same underlying event; (2) merging multiple data sources spuriously relates source identities to veracity labels; and (3) labeling bias. In this paper, we closely investigate three of the most popular rumor detection benchmark datasets (i.e., Twitter15, Twitter16 and PHEME), and propose event-separated rumor detection as a solution to eliminate spurious cues. Under the event-separated setting, we observe that the accuracy of existing state-of-the-art models drops significantly by over 40%, becoming only comparable to a simple neural classifier. To better address this task, we propose Publisher Style Aggregation (PSA), a generalizable approach that aggregates publisher posting records to learn writing style and veracity stance. Extensive experiments demonstrate that our method outperforms existing baselines in terms of effectiveness, efficiency and generalizability.","PeriodicalId":74091,"journal":{"name":"Machine learning and knowledge discovery in databases : European Conference, ECML PKDD ... : proceedings. ECML PKDD (Conference)","volume":"95 1","pages":"274-290"},"PeriodicalIF":0.0,"publicationDate":"2022-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89906508","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

Fast and Accurate Importance Weighting for Correcting Sample Bias 快速准确的校正样本偏差的重要性加权

Machine learning and knowledge discovery in databases : European Conference, ECML PKDD ... : proceedings. ECML PKDD (Conference) Pub Date : 2022-09-09 DOI: 10.48550/arXiv.2209.04215

Antoine de Mathelin, F. Deheeger, M. Mougeot, N. Vayatis

{"title":"Fast and Accurate Importance Weighting for Correcting Sample Bias","authors":"Antoine de Mathelin, F. Deheeger, M. Mougeot, N. Vayatis","doi":"10.48550/arXiv.2209.04215","DOIUrl":"https://doi.org/10.48550/arXiv.2209.04215","url":null,"abstract":"Bias in datasets can be very detrimental for appropriate statistical estimation. In response to this problem, importance weighting methods have been developed to match any biased distribution to its corresponding target unbiased distribution. The seminal Kernel Mean Matching (KMM) method is, nowadays, still considered as state of the art in this research field. However, one of the main drawbacks of this method is the computational burden for large datasets. Building on previous works by Huang et al. (2007) and de Mathelin et al. (2021), we derive a novel importance weighting algorithm which scales to large datasets by using a neural network to predict the instance weights. We show, on multiple public datasets, under various sample biases, that our proposed approach drastically reduces the computational time on large dataset while maintaining similar sample bias correction performance compared to other importance weighting methods. The proposed approach appears to be the only one able to give relevant reweighting in a reasonable time for large dataset with up to two million data.","PeriodicalId":74091,"journal":{"name":"Machine learning and knowledge discovery in databases : European Conference, ECML PKDD ... : proceedings. ECML PKDD (Conference)","volume":"22 1","pages":"659-674"},"PeriodicalIF":0.0,"publicationDate":"2022-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82601678","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: 3

Class-Incremental Learning via Knowledge Amalgamation 通过知识整合进行课堂增量学习

Machine learning and knowledge discovery in databases : European Conference, ECML PKDD ... : proceedings. ECML PKDD (Conference) Pub Date : 2022-09-05 DOI: 10.48550/arXiv.2209.02112

Marcus Vinícius de Carvalho, Mahardhika Pratama, Jie Zhang, Yajuan San

引用次数: 2

Scalable Adversarial Online Continual Learning 可扩展的对抗在线持续学习

Machine learning and knowledge discovery in databases : European Conference, ECML PKDD ... : proceedings. ECML PKDD (Conference) Pub Date : 2022-09-04 DOI: 10.48550/arXiv.2209.01558

T. Dam, Mahardhika Pratama, Md Meftahul Ferdaus, S. Anavatti, Hussein Abbas

引用次数: 1

An Ion Exchange Mechanism Inspired Story Ending Generator for Different Characters 基于离子交换机制的不同角色故事结局生成器

Machine learning and knowledge discovery in databases : European Conference, ECML PKDD ... : proceedings. ECML PKDD (Conference) Pub Date : 2022-09-01 DOI: 10.48550/arXiv.2209.00200

Xinyu Jiang, Qi Zhang, Chongyang Shi, Kaiying Jiang, Liang Hu, Shoujin Wang

{"title":"An Ion Exchange Mechanism Inspired Story Ending Generator for Different Characters","authors":"Xinyu Jiang, Qi Zhang, Chongyang Shi, Kaiying Jiang, Liang Hu, Shoujin Wang","doi":"10.48550/arXiv.2209.00200","DOIUrl":"https://doi.org/10.48550/arXiv.2209.00200","url":null,"abstract":"Story ending generation aims at generating reasonable endings for a given story context. Most existing studies in this area focus on generating coherent or diversified story endings, while they ignore that different characters may lead to different endings for a given story. In this paper, we propose a Character-oriented Story Ending Generator (CoSEG) to customize an ending for each character in a story. Specifically, we first propose a character modeling module to learn the personalities of characters from their descriptive experiences extracted from the story context. Then, inspired by the ion exchange mechanism in chemical reactions, we design a novel vector breaking/forming module to learn the intrinsic interactions between each character and the corresponding context through an analogical information exchange procedure. Finally, we leverage the attention mechanism to learn effective character-specific interactions and feed each interaction into a decoder to generate character-orient endings. Extensive experimental results and case studies demonstrate that CoSEG achieves significant improvements in the quality of generated endings compared with state-of-the-art methods, and it effectively customizes the endings for different characters.","PeriodicalId":74091,"journal":{"name":"Machine learning and knowledge discovery in databases : European Conference, ECML PKDD ... : proceedings. ECML PKDD (Conference)","volume":"34 1","pages":"553-570"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88988209","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: 0

EpiGNN: Exploring Spatial Transmission with Graph Neural Network for Regional Epidemic Forecasting EpiGNN:基于图神经网络的区域流行病预测的空间传播研究

Machine learning and knowledge discovery in databases : European Conference, ECML PKDD ... : proceedings. ECML PKDD (Conference) Pub Date : 2022-08-23 DOI: 10.48550/arXiv.2208.11517

Feng Xie, Zhong Zhang, Liang Li, B. Zhou, Yusong Tan

{"title":"EpiGNN: Exploring Spatial Transmission with Graph Neural Network for Regional Epidemic Forecasting","authors":"Feng Xie, Zhong Zhang, Liang Li, B. Zhou, Yusong Tan","doi":"10.48550/arXiv.2208.11517","DOIUrl":"https://doi.org/10.48550/arXiv.2208.11517","url":null,"abstract":"Epidemic forecasting is the key to effective control of epidemic transmission and helps the world mitigate the crisis that threatens public health. To better understand the transmission and evolution of epidemics, we propose EpiGNN, a graph neural network-based model for epidemic forecasting. Specifically, we design a transmission risk encoding module to characterize local and global spatial effects of regions in epidemic processes and incorporate them into the model. Meanwhile, we develop a Region-Aware Graph Learner (RAGL) that takes transmission risk, geographical dependencies, and temporal information into account to better explore spatial-temporal dependencies and makes regions aware of related regions' epidemic situations. The RAGL can also combine with external resources, such as human mobility, to further improve prediction performance. Comprehensive experiments on five real-world epidemic-related datasets (including influenza and COVID-19) demonstrate the effectiveness of our proposed method and show that EpiGNN outperforms state-of-the-art baselines by 9.48% in RMSE.","PeriodicalId":74091,"journal":{"name":"Machine learning and knowledge discovery in databases : European Conference, ECML PKDD ... : proceedings. ECML PKDD (Conference)","volume":"3 1","pages":"469-485"},"PeriodicalIF":0.0,"publicationDate":"2022-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73070490","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: 10

Cloud-Based Real-Time Molecular Screening Platform with MolFormer 基于云的MolFormer实时分子筛选平台

Machine learning and knowledge discovery in databases : European Conference, ECML PKDD ... : proceedings. ECML PKDD (Conference) Pub Date : 2022-08-13 DOI: 10.48550/arXiv.2208.06665

Brian M. Belgodere, V. Chenthamarakshan, Payel Das, Pierre L. Dognin, Toby Kurien, Igor Melnyk, Youssef Mroueh, Inkit Padhi, Mattia Rigotti, Jarret Ross, Yair Schiff, R. Young

引用次数: 1

Improving Micro-video Recommendation by Controlling Position Bias 通过控制位置偏差改进微视频推荐

Machine learning and knowledge discovery in databases : European Conference, ECML PKDD ... : proceedings. ECML PKDD (Conference) Pub Date : 2022-08-09 DOI: 10.48550/arXiv.2208.05315

Yisong Yu, Beihong Jin, Jiageng Song, Beibei Li, Y. Zheng, Wei-wei Zhu

{"title":"Improving Micro-video Recommendation by Controlling Position Bias","authors":"Yisong Yu, Beihong Jin, Jiageng Song, Beibei Li, Y. Zheng, Wei-wei Zhu","doi":"10.48550/arXiv.2208.05315","DOIUrl":"https://doi.org/10.48550/arXiv.2208.05315","url":null,"abstract":"As the micro-video apps become popular, the numbers of micro-videos and users increase rapidly, which highlights the importance of micro-video recommendation. Although the micro-video recommendation can be naturally treated as the sequential recommendation, the previous sequential recommendation models do not fully consider the characteristics of micro-video apps, and in their inductive biases, the role of positions is not in accord with the reality in the micro-video scenario. Therefore, in the paper, we present a model named PDMRec (Position Decoupled Micro-video Recommendation). PDMRec applies separate self-attention modules to model micro-video information and the positional information and then aggregate them together, avoid the noisy correlations between micro-video semantics and positional information being encoded into the sequence embeddings. Moreover, PDMRec proposes contrastive learning strategies which closely match with the characteristics of the micro-video scenario, thus reducing the interference from micro-video positions in sequences. We conduct the extensive experiments on two real-world datasets. The experimental results shows that PDMRec outperforms existing multiple state-of-the-art models and achieves significant performance improvements.","PeriodicalId":74091,"journal":{"name":"Machine learning and knowledge discovery in databases : European Conference, ECML PKDD ... : proceedings. ECML PKDD (Conference)","volume":"23 1","pages":"508-523"},"PeriodicalIF":0.0,"publicationDate":"2022-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78453246","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

No More Strided Convolutions or Pooling: A New CNN Building Block for Low-Resolution Images and Small Objects 不再跨行卷积或池化:低分辨率图像和小对象的新CNN构建块

Machine learning and knowledge discovery in databases : European Conference, ECML PKDD ... : proceedings. ECML PKDD (Conference) Pub Date : 2022-08-07 DOI: 10.48550/arXiv.2208.03641

Raja Sunkara, Tie Luo

{"title":"No More Strided Convolutions or Pooling: A New CNN Building Block for Low-Resolution Images and Small Objects","authors":"Raja Sunkara, Tie Luo","doi":"10.48550/arXiv.2208.03641","DOIUrl":"https://doi.org/10.48550/arXiv.2208.03641","url":null,"abstract":"Convolutional neural networks (CNNs) have made resounding success in many computer vision tasks such as image classification and object detection. However, their performance degrades rapidly on tougher tasks where images are of low resolution or objects are small. In this paper, we point out that this roots in a defective yet common design in existing CNN architectures, namely the use of strided convolution and/or pooling layers, which results in a loss of fine-grained information and learning of less effective feature representations. To this end, we propose a new CNN building block called SPD-Conv in place of each strided convolution layer and each pooling layer (thus eliminates them altogether). SPD-Conv is comprised of a space-to-depth (SPD) layer followed by a non-strided convolution (Conv) layer, and can be applied in most if not all CNN architectures. We explain this new design under two most representative computer vision tasks: object detection and image classification. We then create new CNN architectures by applying SPD-Conv to YOLOv5 and ResNet, and empirically show that our approach significantly outperforms state-of-the-art deep learning models, especially on tougher tasks with low-resolution images and small objects. We have open-sourced our code at https://github.com/LabSAINT/SPD-Conv.","PeriodicalId":74091,"journal":{"name":"Machine learning and knowledge discovery in databases : European Conference, ECML PKDD ... : proceedings. ECML PKDD (Conference)","volume":"116 1","pages":"443-459"},"PeriodicalIF":0.0,"publicationDate":"2022-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87897655","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: 40