2018 IEEE Workshop on Machine Learning from User Interaction for Visualization and Analytics (MLUI)最新文献

ModelSpace: Visualizing the Trails of Data Models in Visual Analytics Systems ModelSpace:可视化分析系统中数据模型的轨迹

2018 IEEE Workshop on Machine Learning from User Interaction for Visualization and Analytics (MLUI) Pub Date : 2018-10-22 DOI: 10.1109/MLUI52768.2018.10075649

Eli T. Brown, Sriram Yarlagadda, Kristin A. Cook, Remco Chang, A. Endert

{"title":"ModelSpace: Visualizing the Trails of Data Models in Visual Analytics Systems","authors":"Eli T. Brown, Sriram Yarlagadda, Kristin A. Cook, Remco Chang, A. Endert","doi":"10.1109/MLUI52768.2018.10075649","DOIUrl":"https://doi.org/10.1109/MLUI52768.2018.10075649","url":null,"abstract":"User interactions with visualization systems have been shown to encode a great deal of information about the the users’ thinking processes, and analyzing their interaction trails can teach us more about the users, their approach, and how they arrived at insights. This deeper understanding is critical to improving their experience and outcomes, and there are tools available to visualize logs of interactions. It can be difficult to determine the structurally interesting parts of interaction data, though, like what set of button clicks constitutes an action that matters. In the case of visual analytics systems that use machine learning models, there is a convenient marker of when the user has significantly altered the state of the system via interaction: when the model is updated based on new information. We present a method for numerical analytic provenance using high-dimensional visualization to show and compare the trails of these sequences of model states of the system. We evaluate this approach with a prototype tool, ModelSpace, applied to two case studies on experimental data from model-steering visual analytics tools. ModelSpace reveals individual user’s progress, the relationships between their paths, and the characteristics of certain regions of the space of possible models.","PeriodicalId":421877,"journal":{"name":"2018 IEEE Workshop on Machine Learning from User Interaction for Visualization and Analytics (MLUI)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126937700","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: 16

HyperTuner: Visual Analytics for Hyperparameter Tuning by Professionals HyperTuner:由专业人员进行超参数调优的可视化分析

2018 IEEE Workshop on Machine Learning from User Interaction for Visualization and Analytics (MLUI) Pub Date : 2018-10-22 DOI: 10.1109/MLUI52768.2018.10075647

Tianyi Li, G. Convertino, Wenbo Wang, Haley Most, Tristan Zajonc, Yi-Hsun Tsai

{"title":"HyperTuner: Visual Analytics for Hyperparameter Tuning by Professionals","authors":"Tianyi Li, G. Convertino, Wenbo Wang, Haley Most, Tristan Zajonc, Yi-Hsun Tsai","doi":"10.1109/MLUI52768.2018.10075647","DOIUrl":"https://doi.org/10.1109/MLUI52768.2018.10075647","url":null,"abstract":"While training a machine learning model, data scientists often need to determine some hyperparameters to set up the model. The values of hyperparameters configure the structure and other characteristics of the model and can significantly influence the training result. However, given the complexity of the model algorithms and the training processes, identifying a sweet spot in the hyperparameter space for a specific problem can be challenging. This paper characterizes user requirements for hyperparameter tuning and proposes a prototype system to provide model-agnostic support. We conducted interviews with data science practitioners in industry to collect user requirements and identify opportunities for leveraging interactive visual support. We present HyperTuner, a prototype system that supports hyperparameter search and analysis via interactive visual analytics. The design treats models as black boxes with the hyperparameters and data as inputs, and the predictions and performance metrics as outputs. We discuss our preliminary evaluation results, where the data science practitioners deem HyperTuner as useful and desired to help gain insights into the influence of hyperparameters on model performance and convergence. The design also triggered additional requirements such as involving more advanced support for automated tuning and debugging.","PeriodicalId":421877,"journal":{"name":"2018 IEEE Workshop on Machine Learning from User Interaction for Visualization and Analytics (MLUI)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117006186","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: 18

Providing Contextual Assistance in Response to Frustration in Visual Analytics Tasks 在视觉分析任务中提供上下文帮助以应对挫折

2018 IEEE Workshop on Machine Learning from User Interaction for Visualization and Analytics (MLUI) Pub Date : 2018-10-22 DOI: 10.1109/MLUI52768.2018.10075561

P. Panwar, A. Bradley, C. Collins

引用次数: 4

A Human-in-the-Loop Software Platform 人在循环软件平台

2018 IEEE Workshop on Machine Learning from User Interaction for Visualization and Analytics (MLUI) Pub Date : 2018-10-22 DOI: 10.1109/MLUI52768.2018.10075650

Fang Cao, David J. Scroggins, Lebna V. Thomas, Eli T. Brown

{"title":"A Human-in-the-Loop Software Platform","authors":"Fang Cao, David J. Scroggins, Lebna V. Thomas, Eli T. Brown","doi":"10.1109/MLUI52768.2018.10075650","DOIUrl":"https://doi.org/10.1109/MLUI52768.2018.10075650","url":null,"abstract":"Human-in-the-Loop (HIL) analytics systems blend the intuitive sensemaking abilities of humans with the raw number-crunching capability of machine learning. The web and front-end visualization libraries, such as D3.js, make it easier than ever to develop cross-platform HIL systems for wide distribution. Analytics toolkits such as scikit-learn provide straightforward, coherent interfaces for a variety of machine learning algorithms. However, creating novel HIL systems requires expertise in a range of skills including data visualization, web engineering, and machine learning. The Library for Interactive Human-Computer Analytics (LIHCA) is a platform to simplify creating applications that use interactive visualizations to steer back-end machine learners. Developers can enhance their interactive visualizations by connecting to a LIHCA API back end that manages data, runs machine learning algorithms, and returns the results in a visualization-convenient format. We provide a discussion of design considerations for HIL systems, an implementation of LIHCA to satisfy those considerations, and a set of implemented examples to illustrate the usage of the library.","PeriodicalId":421877,"journal":{"name":"2018 IEEE Workshop on Machine Learning from User Interaction for Visualization and Analytics (MLUI)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116935267","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

Speculative Execution for Guided Visual Analytics 引导视觉分析的投机执行

2018 IEEE Workshop on Machine Learning from User Interaction for Visualization and Analytics (MLUI) Pub Date : 2018-10-22 DOI: 10.1109/MLUI52768.2018.10075559

F. Sperrle, J. Bernard, M. Sedlmair, D. Keim, Mennatallah El-Assady

引用次数: 18

Using Hidden Markov Models to Determine Cognitive States of Visual Analytic Users 利用隐马尔可夫模型确定视觉分析用户的认知状态

2018 IEEE Workshop on Machine Learning from User Interaction for Visualization and Analytics (MLUI) Pub Date : 2018-10-22 DOI: 10.1109/MLUI52768.2018.10075648

M. Aboufoul, Ryan Wesslen, Isaac Cho, Wenwen Dou, Samira Shaikh

{"title":"Using Hidden Markov Models to Determine Cognitive States of Visual Analytic Users","authors":"M. Aboufoul, Ryan Wesslen, Isaac Cho, Wenwen Dou, Samira Shaikh","doi":"10.1109/MLUI52768.2018.10075648","DOIUrl":"https://doi.org/10.1109/MLUI52768.2018.10075648","url":null,"abstract":"Many visual analytics tools exist to assist users in examining large amounts of information at once via coordinated views that include graphs, network connections and maps. However, the cognitive processes that those users undergo while using such tools remain a mystery. Many psychological studies suggest that individuals may undergo some planning stage followed by analysis before finally making conclusions when examining large amounts of analytical data with the goal of reaching a decision. While the general order of these cognitive states has been theorized, the exact states of individuals at specific points during their interaction with visual analytic systems remain unclear. In this work, we developed models to determine the cognitive states of users based solely on their interactions with visual analytics systems via Hidden Markov Models. Hidden Markov Models allow for the classification of observations through hidden states (cognitive states in our case) as well as the prediction of future cognitive states. We generate these models through unsupervised learning and use established metrics such as AIC and BIC metrics to evaluate our models. Our solutions are designed to help improve visual analytics tools by providing a better understanding of cognitive thought processes of users during data intensive analysis tasks.","PeriodicalId":421877,"journal":{"name":"2018 IEEE Workshop on Machine Learning from User Interaction for Visualization and Analytics (MLUI)","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128173885","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: 4

Computer-supported Interactive Assignment of Keywords for Literature Collections 计算机支持的文献馆藏关键词交互赋值

2018 IEEE Workshop on Machine Learning from User Interaction for Visualization and Analytics (MLUI) Pub Date : 2018-10-22 DOI: 10.1109/MLUI52768.2018.10075564

S. Agarwal, Fabian Beck

引用次数: 4

A Bidirectional Pipeline for Semantic Interaction 语义交互的双向管道

2018 IEEE Workshop on Machine Learning from User Interaction for Visualization and Analytics (MLUI) Pub Date : 2018-10-22 DOI: 10.1109/MLUI52768.2018.10075562

Michelle Dowling, John E. Wenskovitch, P. Hauck, A. Binford, Nicholas F. Polys, Chris North

引用次数: 18