anchor.diff:  VAST,TVCG,MAQUI: Interweaving Queries and Pattern Mining for Recursive Event Sequence Exploration,J),"Po-Ming Law, Zhicheng Liu, Sana Malik, Rahul Basole",http://www.zcliu.org/maqui/MAQUI_vast18.pdf,"Exploring event sequences by defining queries alone or by using mining algorithms alone is often not sufficient to support analysis. Analysts often interweave querying and mining in a recursive manner during event sequence analysis: sequences extracted as query results are used for mining patterns, patterns generated are incorporated into a new query for segmenting the sequences, and the resulting segments are mined or queried again. To support flexible analysis, we propose a framework that describes the process of interwoven querying and mining. Based on this framework, we developed MAQUI, a Mining And Querying User Interface that enables recursive event sequence exploration. To understand the efficacy of MAQUI, we conducted two case studies with domain experts. The findings suggest that the capability of interweaving querying and mining helps the participants articulate their questions and gain novel insights from their data.",,https://github.com/terrancelaw/MAQUI,,,vimeo 289787863,10.1109/TVCG.2018.2864886,2018,2018,VAST,"Convention Hall 1, Section C",Friday,"Event, Sequence, and ML",26-Oct,9:00 AM,9:20 AM
   VAST,TVCG,iForest: Interpreting Random Forests via Visual Analytics,J),"Xun Zhao, Yanhong Wu, Dik Lun Lee, Weiwei Cui",http://zhaoxun.me/publication/iForest.pdf,"As an ensemble model that consists of many independent decision trees, random forests generate predictions by feeding the input to internal trees and summarizing their outputs. The ensemble nature of the model helps random forests outperform any individual decision tree. However, it also leads to a poor model interpretability, which significantly hinders the model from being used in fields that require transparent and explainable predictions, such as medical diagnosis and financial fraud detection. The interpretation challenges stem from the variety and complexity of the contained decision trees. Each decision tree has its unique structure and properties, such as the features used in the tree and the feature threshold in each tree node. Thus, a data input may lead to a variety of decision paths. To understand how a final prediction is achieved, it is desired to understand and compare all decision paths in the context of all tree structures, which is a huge challenge for any users. In this paper, we propose a visual analytic system aiming at interpreting random forest models and predictions. In addition to providing users with all the tree information, we summarize the decision paths in random forests, which eventually reflects the working mechanism of the model and reduces users’ mental burden of interpretation. To demonstrate the effectiveness of our system, two usage scenarios and a qualitative user study are conducted.",,,,,vimeo 289787165,10.1109/tvcg.2018.2864475,2018,2018,VAST,"Convention Hall 1, Section C",Friday,"Event, Sequence, and ML",26-Oct,9:20 AM,9:40 AM
   VAST,TVCG,Visual Progression Analysis of Event Sequence Data,J),"Shunan Guo, Zhuochen Jin, David Gotz, Fan Du, Hongyuan Zha, Nan Cao",http://gotz.web.unc.edu/files/2018/08/2018_VAST_EventThread_V2_Preprint.pdf,"Event sequence data is common to a broad range of application domains, from security to health care to scholarly communication. This form of data captures information about the progression of events for an individual entity (e.g., a computer network device; a patient; an author) in the form of a series of time-stamped observations. Moreover, each event is associated with an event type (e.g., a computer login attempt, or a hospital discharge). Analyses of event sequence data have been shown to help reveal important temporal patterns, such as clinical paths resulting in improved outcomes, or an understanding of common career trajectories for scholars. Moreover, recent research has demonstrated a variety of techniques designed to overcome methodological challenges such as large volumes of data and high dimensionality. However, the effective identication and analysis of latent stages of progression, which can allow for variation within different but similarly evolving event sequences, remain a signicant challenge with important real-world motivations. In this paper, we propose an unsupervised stage analysis algorithm to identify semantically meaningful progression stages as well as the critical events which help dene those stages. The algorithm follows three key steps: (1) event representation estimation, (2) event sequence warping and alignment, and (3) sequence segmentation. We also present a novel visualization system, ET2 , which interactively illustrates the results of the stage analysis algorithm to help reveal evolution patterns across stages. Finally, we report three forms of evaluation for ET2 : (1) case studies with two real-world datasets, (2) interviews with domain expert users, and (3) a performance evaluation on the progression analysis algorithm and the visualization design",,,,,vimeo 289787414,10.1109/TVCG.2018.2864885,2018,2018,VAST,"Convention Hall 1, Section C",Friday,"Event, Sequence, and ML",26-Oct,9:40 AM,10:00 AM
  -TVCG,TVCG,StreamExplorer: A Multi-Stage System for Visually Exploring Events in Social Streams,T),"Yingcai Wu, Zhutian Chen, Guodao Sun, Xiao Xie, Nan Cao, Shixia Liu, Weiwei Cui",,,,,,,vimeo 289788908,10.1109/tvcg.2017.2764459,,2018,VAST,"Convention Hall 1, Section C",Friday,"Event, Sequence, and ML",26-Oct,10:00 AM,10:20 AM
  +TVCG,TVCG,StreamExplorer: A Multi-Stage System for Visually Exploring Events in Social Streams,T),"Yingcai Wu, Zhutian Chen, Guodao Sun, Xiao Xie, Nan Cao, Shixia Liu, Weiwei Cui",,,,,,,vimeo 289788908,10.1109/tvcg.2017.2764459,2017,2018,VAST,"Convention Hall 1, Section C",Friday,"Event, Sequence, and ML",26-Oct,10:00 AM,10:20 AM
   VAST,TVCG,Duet: Helping Data Analysis Novices Conduct Pairwise Comparisons by Minimal Specification,J),"Po-Ming Law, Rahul Basole, Yanhong Wu",https://terrancelaw.github.io/publications/duet_vast18.pdf,"Data analysis novices often encounter barriers in executing low-level operations for pairwise comparisons. They may also run into barriers in interpreting the artifacts (e.g., visualizations) created as a result of the operations. We developed Duet, a visual analysis system designed to help data analysis novices conduct pairwise comparisons by addressing execution and interpretation barriers. To reduce the barriers in executing low-level operations during pairwise comparison, Duet employs minimal specication: when one object group (i.e. a group of records in a data table) is specied, Duet recommends object groups that are similar to or different from the specied one; when two object groups are specied, Duet recommends similar and different attributes between them. To lower the barriers in interpreting its recommendations, Duet explains the recommended groups and attributes using both visualizations and textual descriptions. We conducted a qualitative evaluation with eight participants to understand the effectiveness of Duet. The results suggest that minimal specication is easy to use and Duet's explanations are helpful for interpreting the recommendations despite some usability issues.",,,,,vimeo 289787850,10.1109/TVCG.2018.2864526,2018,2018,VAST,"Convention Hall 1, Section C",Friday,"Event, Sequence, and ML",26-Oct,10:20 AM,10:40 AM
  -InfoVis,TVCG,Visualizing Uncertain Tropical Cyclone Predictions using Representative Samples from Ensembles of Forecast Tracks,J),"Le Liu, Lace M. K. Padilla, Sarah Creem-Regehr, Donald House",http://lacepadilla.com/Downloads/publications/Liuetal_2018.pdf,"A common approach to sampling the space of a prediction is the generation of an ensemble of potential outcomes, where the ensemble's distribution reveals the statistical structure of the prediction space. For example, the US National Hurricane Center generates multiple day predictions for a storm's path, size, and wind speed, and then uses a Monte Carlo approach to sample this prediction into a large ensemble of potential storm outcomes. Various forms of summary visualizations are generated from such an ensemble, often using spatial spread to indicate its statistical characteristics. However, studies have shown that changes in the size of such summary glyphs, representing changes in the uncertainty of the prediction, are frequently confounded with other attributes of the phenomenon, such as its size or strength. In addition, simulation ensembles typically encode multivariate information, which can be difficult or confusing to include in a summary display. This problem can be overcome by directly displaying the ensemble as a set of annotated trajectories, however this solution will not be effective if ensembles are densely overdrawn or structurally disorganized. We propose to overcome these difficulties by selectively sampling the original ensemble, constructing a smaller representative and spatially well organized ensemble. This can be drawn directly as a set of paths that implicitly reveals the underlying spatial uncertainty distribution of the prediction. Since this approach does not use a visual channel to encode uncertainty, additional information can more easily be encoded in the display without leading to visual confusion. To demonstrate our argument, we describe the development of a visualization for ensembles of tropical cyclone forecast tracks, explaining how their spatial and temporal predictions, as well as other crucial storm characteristics such as size and intensity, can be clearly revealed. We verify the effectiveness of this visualization approach through a cognitive study exploring how storm damage estimates are affected by the density of tracks drawn, and by the presence or absence of annotating information on storm size and intensity.",,https://osf.io/hy3ba/,https://osf.io/hy3ba/,,vimeo 289784601,,2018,2018,InfoVis,"Convention Hall 1, Section D",Friday,Uncertainty & Error,26-Oct,9:00 AM,9:20 AM
  +InfoVis,TVCG,Visualizing Uncertain Tropical Cyclone Predictions using Representative Samples from Ensembles of Forecast Tracks,J),"Le Liu, Lace M. K. Padilla, Sarah Creem-Regehr, Donald House",http://lacepadilla.com/Downloads/publications/Liuetal_2018.pdf,"A common approach to sampling the space of a prediction is the generation of an ensemble of potential outcomes, where the ensemble's distribution reveals the statistical structure of the prediction space. For example, the US National Hurricane Center generates multiple day predictions for a storm's path, size, and wind speed, and then uses a Monte Carlo approach to sample this prediction into a large ensemble of potential storm outcomes. Various forms of summary visualizations are generated from such an ensemble, often using spatial spread to indicate its statistical characteristics. However, studies have shown that changes in the size of such summary glyphs, representing changes in the uncertainty of the prediction, are frequently confounded with other attributes of the phenomenon, such as its size or strength. In addition, simulation ensembles typically encode multivariate information, which can be difficult or confusing to include in a summary display. This problem can be overcome by directly displaying the ensemble as a set of annotated trajectories, however this solution will not be effective if ensembles are densely overdrawn or structurally disorganized. We propose to overcome these difficulties by selectively sampling the original ensemble, constructing a smaller representative and spatially well organized ensemble. This can be drawn directly as a set of paths that implicitly reveals the underlying spatial uncertainty distribution of the prediction. Since this approach does not use a visual channel to encode uncertainty, additional information can more easily be encoded in the display without leading to visual confusion. To demonstrate our argument, we describe the development of a visualization for ensembles of tropical cyclone forecast tracks, explaining how their spatial and temporal predictions, as well as other crucial storm characteristics such as size and intensity, can be clearly revealed. We verify the effectiveness of this visualization approach through a cognitive study exploring how storm damage estimates are affected by the density of tracks drawn, and by the presence or absence of annotating information on storm size and intensity.",,https://osf.io/hy3ba/,https://osf.io/hy3ba/,,vimeo 289784601,10.1109/TVCG.2018.2865193,2018,2018,InfoVis,"Convention Hall 1, Section D",Friday,Uncertainty & Error,26-Oct,9:00 AM,9:20 AM
   InfoVis,TVCG,Hypothetical Outcome Plots Help Untrained Observers Judge Trends in Ambiguous Data,J),"Alex Kale, Francis Nguyen, Matthew Kay, Jessica Hullman",http://users.eecs.northwestern.edu/~jhullman/hops_jobs_pfs.pdf,"Animated representations of outcomes drawn from distributions (hypothetical outcome plots, or HOPs) are used in the media and other public venues to communicate uncertainty. HOPs greatly improve multivariate probability estimation over conventional static uncertainty visualizations and leverage the ability of the visual system to quickly, accurately, and automatically process the summary statistical properties of ensembles. However, it is unclear how well HOPs support applied tasks resembling real world judgments posed in uncertainty communication. We identify and motivate an appropriate task to investigate realistic judgments of uncertainty in the public domain through a qualitative analysis of uncertainty visualizations in the news. We contribute two crowdsourced experiments comparing the effectiveness of HOPs, error bars, and line ensembles for supporting perceptual decision-making from visualized uncertainty. Participants infer which of two possible underlying trends is more likely to have produced a sample of time series data by referencing uncertainty visualizations which depict the two trends with variability due to sampling error. By modeling each participant's accuracy as a function of the level of evidence presented over many repeated judgments, we find that observers are able to correctly infer the underlying trend in samples conveying a lower level of evidence when using HOPs rather than static aggregate uncertainty visualizations as a decision aid. Modeling approaches like ours contribute theoretically grounded and richly descriptive accounts of user perceptions to visualization evaluation.",https://medium.com/@uwdata/hypothetical-outcome-plots-hops-help-users-separate-signal-from-noise-870d4e2b75d7,,https://github.com/kalealex/jobs-report-hops,https://osf.io/8cte3/registrations/,vimeo 289785258,10.1109/tvcg.2018.2864909,2018,2018,InfoVis,"Convention Hall 1, Section D",Friday,Uncertainty & Error,26-Oct,9:20 AM,9:40 AM
   InfoVis,TVCG,In Pursuit of Error: A Survey of Uncertainty Visualization Evaluation,J),"Jessica Hullman, Xiaoli Qiao, Michael Correll, Alex Kale, Matthew Kay",https://github.com/jhullman/uncertaintyVisEval/blob/master/uncertainty_vis_eval.pdf,"Understanding and accounting for uncertainty is critical to effectively reasoning about visualized data. However, evaluating the impact of an uncertainty visualization is complex due to the difficulties that people have interpreting uncertainty and the challenge of defining correct behavior with uncertainty information. Currently, evaluators of uncertainty visualization must rely on general purpose visualization evaluation frameworks which can be ill-equipped to provide guidance with the unique difficulties of assessing judgments under uncertainty. To help evaluators navigate these complexities, we present a taxonomy for characterizing decisions made in designing an evaluation of an uncertainty visualization. Our taxonomy differentiates six levels of decisions that comprise an uncertainty visualization evaluation: the behavioral targets of the study, expected effects from an uncertainty visualization, evaluation goals, measures, elicitation techniques, and analysis approaches. Applying our taxonomy to 86 user studies of uncertainty visualizations, we find that existing evaluation practice, particularly in visualization research, focuses on Performance and Satisfaction-based measures that assume more predictable and statistically-driven judgment behavior than is suggested by research on human judgment and decision making. We reflect on common themes in evaluation practice concerning the interpretation and semantics of uncertainty, the use of confidence reporting, and a bias toward evaluating performance as accuracy rather than decision quality. We conclude with a concrete set of recommendations for evaluators designed to reduce the mismatch between the conceptualization of uncertainty in visualization versus other fields.",,https://github.com/jhullman/uncertaintyVisEval,,,vimeo 289785271,10.1109/tvcg.2018.2864889,2018,2018,InfoVis,"Convention Hall 1, Section D",Friday,Uncertainty & Error,26-Oct,9:40 AM,10:00 AM
  -InfoVis,TVCG,Where's my data? Evaluating Visualizations with Missing Data,J),"Hayeong Song, Danielle Albers Szafir",http://cmci.colorado.edu/visualab/papers/song_VIS_2018.pdf,Many real-world datasets are incomplete due to factors such as data collection failures or misalignments between fused datasets. Visualizations of incomplete datasets should allow analysts to draw conclusions from their data while effectively reasoning about the quality of the data and resulting conclusions. We conducted a pair of crowdsourced studies to measure how the methods used to impute and visualize missing data may influence analysts' perceptions of data quality and their confidence in their conclusions. Our experiments used different design choices for line graphs and bar charts to estimate averages and trends in incomplete time series datasets. Our results provide preliminary guidance for visualization designers to consider when working with incomplete data in different domains and scenarios.,,,http://cmci.colorado.edu/visualab/MissingData/,,vimeo 289785184,,2018,2018,InfoVis,"Convention Hall 1, Section D",Friday,Uncertainty & Error,26-Oct,10:00 AM,10:20 AM
  +InfoVis,TVCG,Where's my data? Evaluating Visualizations with Missing Data,J),"Hayeong Song, Danielle Albers Szafir",http://cmci.colorado.edu/visualab/papers/song_VIS_2018.pdf,Many real-world datasets are incomplete due to factors such as data collection failures or misalignments between fused datasets. Visualizations of incomplete datasets should allow analysts to draw conclusions from their data while effectively reasoning about the quality of the data and resulting conclusions. We conducted a pair of crowdsourced studies to measure how the methods used to impute and visualize missing data may influence analysts' perceptions of data quality and their confidence in their conclusions. Our experiments used different design choices for line graphs and bar charts to estimate averages and trends in incomplete time series datasets. Our results provide preliminary guidance for visualization designers to consider when working with incomplete data in different domains and scenarios.,,,http://cmci.colorado.edu/visualab/MissingData/,,vimeo 289785184,10.1109/TVCG.2018.2864914,2018,2018,InfoVis,"Convention Hall 1, Section D",Friday,Uncertainty & Error,26-Oct,10:00 AM,10:20 AM
   InfoVis,TVCG,A Framework for Externalizing Implicit Error Using Visualization,,"Nina McCurdy, Julie Gerdes, Miriah Meyer",http://sci.utah.edu/~vdl/papers/2018_infovis_IE-Framework.pdf,"This paper presents a framework for externalizing and analyzing expert knowledge about discrepancies in data through the use of visualization. Grounded in an 18-month design study with global health experts, the framework formalizes the notion of data discrepancies as implicit error, both in global health data and more broadly. We use the term implicit error to describe measurement error that is inherent to and pervasive throughout a dataset, but that isn't explicitly accounted for or defined. Instead, implicit error exists in the minds of experts, is mainly qualitative, and is accounted for subjectively during expert interpretation of the data. Externalizing knowledge surrounding implicit error can assist in synchronizing, validating, and enhancing interpretation, and can inform error analysis and mitigation. The framework consists of a description of implicit error components that are important for downstream analysis, along with a process model for externalizing and analyzing implicit error using visualization. As a second contribution, we provide a rich, reflective, and verifiable description of our research process as an exemplar summary toward the ongoing inquiry into ways of increasing the validity and transferability of design study research.",,,,,vimeo 289785202,10.1109/tvcg.2018.2864913,2018,2018,InfoVis,"Convention Hall 1, Section D",Friday,Uncertainty & Error,26-Oct,10:20 AM,10:40 AM
   SciVis,TVCG,Exploring Time-Varying Multivariate Volume Data Using Matrix of Isosurface Similarity Maps,J),"Jun Tao, Martin Imre, Chaoli Wang, Nitesh V Chawla, Hanqi Guo, Gökhan Sever, Seung Hyun Kim",,,,,,,vimeo 290325769,10.1109/tvcg.2018.2864808,2018,2018,SciVis,Estrel Hall C,Friday,Time-varying Data,26-Oct,9:00 AM,9:20 AM
   SciVis,TVCG,Visual Analysis of Spatio-temporal Relations of Pairwise Attributes in Unsteady Flow,J),"Marzieh Berenjkoub, Rodolfo Ostilla Monico, Robert S. Laramee, Guoning Chen",,,,,,,vimeo 290325964,10.1109/tvcg.2018.2864817,2018,2018,SciVis,Estrel Hall C,Friday,Time-varying Data,26-Oct,9:20 AM,9:40 AM
   SciVis,TVCG,Time-Dependent Flow seen through Approximate Observer Killing Fields,J),"Markus Hadwiger, Matej Mlejnek, Thomas Theussl, Peter Rautek",,,,,,,vimeo 290327432,10.1109/tvcg.2018.2864839,2018,2018,SciVis,Estrel Hall C,Friday,Time-varying Data,26-Oct,9:40 AM,10:00 AM
  -TVCG,TVCG,An Exploratory Framework for Cyclone Identification and Tracking,T),"Akash Anil Valsangkar, Joy Merwin Monteiro, Vidya Narayanan, Ingrid Hotz, Vijay Natarajan",,,,,,,vimeo 289789170,10.1109/tvcg.2018.2810068,,2018,SciVis,Estrel Hall C,Friday,Time-varying Data,26-Oct,10:00 AM,10:20 AM
  -TVCG,TVCG,Popup-Plots: Warping Temporal Data Visualization,T),"Johanna Schmidt, Dominik Fleischmann, Bernhard Preim, Norbert Brandle, Gabriel Mistelbauer",,,,,,,vimeo 289789393,10.1109/tvcg.2018.2841385,,2018,SciVis,Estrel Hall C,Friday,Time-varying Data,26-Oct,10:20 AM,10:40 AM+TVCG,TVCG,An Exploratory Framework for Cyclone Identification and Tracking,T),"Akash Anil Valsangkar, Joy Merwin Monteiro, Vidya Narayanan, Ingrid Hotz, Vijay Natarajan",,,,,,,vimeo 289789170,10.1109/tvcg.2018.2810068,2018,2018,SciVis,Estrel Hall C,Friday,Time-varying Data,26-Oct,10:00 AM,10:20 AM
  +TVCG,TVCG,Popup-Plots: Warping Temporal Data Visualization,T),"Johanna Schmidt, Dominik Fleischmann, Bernhard Preim, Norbert Brandle, Gabriel Mistelbauer",,,,,,,vimeo 289789393,10.1109/tvcg.2018.2841385,2018,2018,SciVis,Estrel Hall C,Friday,Time-varying Data,26-Oct,10:20 AM,10:40 AM
positive.code: SciVis,SciVis,Ordering Perceptions about Perceptual Order,,"Roxana Bujack, Terece L Turton, David Rogers, James Ahrens",,,,,,,vimeo 290328692,,2018,2018,SciVis Short Papers,Estrel Hall C,Thursday,"Visual Abstractions, Perceptual Study and Immersive Visualization",25-Oct,3:35 PM,3:47 PM
  SciVis,SciVis,QuFlow: Visualizing Parameter Flow in Quantum Circuits for Understanding Quantum Computation,,"Siyuan Lin, Hao Jiang, Lingyun Sun",,,,,,,vimeo 290328716,,2018,2018,SciVis Short Papers,Estrel Hall C,Thursday,"Visual Abstractions, Perceptual Study and Immersive Visualization",25-Oct,3:47 PM,3:59 PM
  VAST,TVCG,RuleMatrix: Visualizing and Understanding Classifiers with Rules,J),"Yao Ming, Huamin Qu, Enrico Bertini",https://arxiv.org/pdf/1807.06228.pdf,"With the growing adoption of machine learning techniques, there is a surge of research interest towards making machine learning systems more transparent and interpretable. Various visualizations have been developed to help model developers understand, diagnose, and refine machine learning models. However, a large number of potential but neglected users are the domain experts with little knowledge of machine learning but are expected to work with machine learning systems. In this paper, we present an interactive visualization technique to help users with little expertise in machine learning to understand, explore and validate predictive models. By viewing the model as a black box, we extract a standardized rule-based knowledge representation from its input-output behavior. We design RuleMatrix, a matrix-based visualization of rules to help users navigate and verify the rules and the black-box model. We evaluate the effectiveness of RuleMatrix via two use cases and a usability study.",http://myaooo.com/projects/rule-matrix/,,,,vimeo 289787299,10.1109/TVCG.2018.2864812,2018,2018,VAST,"Convention Hall 1, Section C",Thursday,Explainable ML,25-Oct,4:20 PM,4:40 PM
  VAST,TVCG,Seq2Seq-Vis: A Visual Debugging Tool for Sequence to Sequence Models,J),"Hendrik Strobelt, Sebastian Gehrmann, Michael Behrisch, Adam Perer, Hanspeter Pfister, Alexander M. Rush",https://arxiv.org/pdf/1804.09299.pdf,"Neural sequence-to-sequence models have proven to be accurate and robust for many sequence prediction tasks, and have become the standard approach for automatic translation of text. The models work with a five-stage blackbox pipeline that begins with encoding a source sequence to a vector space and then decoding out to a new target sequence. This process is now standard, but like many deep learning methods remains quite difficult to understand or debug. In this work, we present a visual analysis tool that allows interaction and ”what if”-style exploration of trained sequence-to-sequence models through each stage of the translation process. The aim is to identify which patterns have been learned, to detect model errors, and to probe the model with counterfactual scenario. We demonstrate the utility of our tool through several real-world sequence-to-sequence use cases on large-scale models.",,https://github.com/HendrikStrobelt/Seq2Seq-Vis,,,vimeo 289787650,,2018,2018,VAST,"Convention Hall 1, Section C",Thursday,Explainable ML,25-Oct,4:40 PM,5:00 PM
  VAST,TVCG,Manifold: A Model-Agnostic Framework for Interpretation and Diagnosis of Machine Learning Models,J),"Jiawei Zhang, Yang Wang, Piero Molino, Lezhi Li, David Ebert",https://arxiv.org/pdf/1808.00196.pdf,"Interpretation and diagnosis of machine learning models have gained renewed interest in recent years with breakthroughs in new approaches. We present Manifold, a framework that utilizes visual analysis techniques to support interpretation, debugging, and comparison of machine learning models in a more transparent and interactive manner. Conventional techniques usually focus on visualizing the internal logic of a specific model type (i.e., deep neural networks), lacking the ability to extend to a more complex scenario where different model types are integrated. To this end, Manifold is designed as a generic framework that does not rely on or access the internal logic of the model and solely observes the input (i.e., instances or features) and the output (i.e., the predicted result and probability distribution). We describe the workflow of Manifold as an iterative process consisting of three major phases that are commonly involved in the model development and diagnosis process: inspection (hypothesis), explanation (reasoning), and refinement (verification). The visual components supporting these tasks include a scatterplot-based visual summary that overviews the models’ outcome and a customizable tabular view that reveals feature discrimination. We demonstrate current applications of the framework on the classification and regression tasks and discuss other potential machine learning use scenarios where Manifold can be applied.",,,,,vimeo 289787203,10.1109/tvcg.2018.2864499,2018,2018,VAST,"Convention Hall 1, Section C",Thursday,Explainable ML,25-Oct,5:00 PM,5:20 PM
  VAST,TVCG,Visual Analytics for Topic Model Optimization based on User-Steerable Speculative Execution,J),"Mennatallah El-Assady, Fabian Sperrle, Oliver Deussen, Daniel Keim, Christopher Collins",http://graphics.uni-konstanz.de/publikationen/ElAssady2018VisualAnalytic/visualanalyticstopic.pdf,"To effectively assess the potential consequences of human interventions in model-driven analytics systems, we establish the concept of speculative execution as a visual analytics paradigm for creating user-steerable preview mechanisms. This paper presents an explainable, mixed-initiative topic modeling framework that integrates speculative execution into the algorithmic decisionmaking process. Our approach visualizes the model-space of our novel incremental hierarchical topic modeling algorithm, unveiling its inner-workings. We support the active incorporation of the user’s domain knowledge in every step through explicit model manipulation interactions. In addition, users can initialize the model with expected topic seeds, the backbone priors. For a more targeted optimization, the modeling process automatically triggers a speculative execution of various optimization strategies, and requests feedback whenever the measured model quality deteriorates. Users compare the proposed optimizations to the current model state and preview their effect on the next model iterations, before applying one of them. This supervised human-in-the-loop process targets maximum improvement for minimum feedback and has proven to be effective in three independent studies that confirm topic model quality improvements.",,,,,vimeo 289787264,10.1109/tvcg.2018.2864769,2018,2018,VAST,"Convention Hall 1, Section C",Thursday,Explainable ML,25-Oct,5:20 PM,5:40 PM
  VAST,TVCG,VIS4ML: An Ontology for Visual Analytics Assisted Machine Learning,J),"Dominik Sacha, Matthias Kraus, Daniel Keim, Min Chen",https://bib.dbvis.de/uploadedFiles/TVCG2864838.pdf,"While many VA workflows make use of machine-learned models to support analytical tasks, VA workflows have become increasingly important in understanding and improving Machine Learning (ML) processes. In this paper, we propose an ontology (VIS4ML) for a subarea of VA, namely “VA-assisted ML”. The purpose of VIS4ML is to describe and understand existing VA workflows used in ML as well as to detect gaps in ML processes and the potential of introducing advanced VA techniques to such processes. Ontologies have been widely used to map out the scope of a topic in biology, medicine, and many other disciplines. We adopt the scholarly methodologies for constructing VIS4ML, including the specification, conceptualization, formalization, implementation, and validation of ontologies. In particular, we reinterpret the traditional VA pipeline to encompass model-development workflows. We introduce necessary definitions, rules, syntaxes, and visual notations for formulating VIS4ML and make use of semantic web technologies for implementing it in the Web Ontology Language (OWL). VIS4ML captures the high-level knowledge about previous workflows where VA is used to assist in ML. It is consistent with the established VA concepts and will continue to evolve along with the future developments in VA and ML. While this ontology is an effort for building the theoretical foundation of VA, it can be used by practitioners in real-world applications to optimize model-development workflows by systematically examining the potential benefits that can be brought about by either machine or human capabilities. Meanwhile, VIS4ML is intended to be extensible and will continue to be updated to reflect future advancements in using VA for building high-quality data-analytical models or for building such models rapidly",,,,,vimeo 289787814,10.1109/TVCG.2018.2864838,2018,2018,VAST,"Convention Hall 1, Section C",Thursday,Explainable ML,25-Oct,5:40 PM,6:00 PM
  InfoVis,TVCG,Mitigating the Attraction Effect with Visualizations,J),"Evanthia Dimara, Gilles Bailly, Anastasia Bezerianos, Steven Franconeri",https://hal.inria.fr/hal-01845004v2/document,"Human decisions are prone to biases, and this is no less true for decisions made within data visualizations. Bias mitigation strategies often focus on the person, by educating people about their biases, typically with little success. We focus instead on the system, presenting the first evidence that altering the design of an interactive visualization tool can mitigate a strong bias � the attraction effect. Participants viewed 2D scatterplots where choices between superior alternatives were affected by the placement of other suboptimal points. We found that highlighting the superior alternatives weakened the bias, but did not eliminate it. We then tested an interactive approach where participants completely removed locally dominated points from the view, inspired by the elimination by aspects strategy in the decision-making literature. This approach strongly decreased the bias, leading to a counterintuitive suggestion: tools that allow removing inappropriately salient or distracting data from a view may help lead users to make more rational decisions.",,https://aviz.fr/deletion,https://aviz.fr/deletion,,vimeo 289784657,,2018,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Perception & Cognition 2,25-Oct,4:20 PM,4:40 PM
  InfoVis,TVCG,Face to Face: Evaluating Visual Comparison,J),"Brian David Ondov, Nicole Jardine, Niklas Elmqvist, Steven Franconeri",http://users.umiacs.umd.edu/~elm/projects/face2face/face2face.pdf,"Data are often viewed as a single set of values, but those values frequently must be compared with another set. The existing evaluations of designs that facilitate these comparisons tend to be based on intuitive reasoning, rather than quantifiable measures. We build on this work with a series of crowdsourced experiments that use low-level perceptual comparison tasks that arise frequently in comparisons within data visualizations (e.g., which value changes the most between the two sets of data?). Participants completed these tasks across a variety of layouts: overlaid, two arrangements of juxtaposed small multiples, mirror-symmetric small multiples, and animated transitions. A staircase procedure sought the difficulty level (e.g., value change delta) that led to equivalent accuracy for each layout. Confirming prior intuition, we observe high levels of performance for overlaid versus standard small multiples. However, we also find performance improvements for both mirror symmetric small multiples and animated transitions. While some results are incongruent with common wisdom in data visualization, they align with previous work in perceptual psychology, and thus have potentially strong implications for visual comparison designs.",,,,,vimeo 289785372,10.1109/tvcg.2018.2864884,2018,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Perception & Cognition 2,25-Oct,4:40 PM,5:00 PM
  TVCG,TVCG,Task-Based Effectiveness of Basic Visualizations,T),"Bahador Saket, Alex Endert, Çagatay Demiralp",https://arxiv.org/pdf/1709.08546.pdf,"Visualizations of tabular data are widely used; understanding their effectiveness in different task and data contexts is fundamental to scaling their impact. However, little is known about how basic tabular data visualizations perform across varying data analysis tasks. In this paper, we report results from a crowdsourced experiment to evaluate the effectiveness of five small scale (5-34 data points) two-dimensional visualization types�Table, Line Chart, Bar Chart, Scatterplot, and Pie Chart�across ten common data analysis tasks using two datasets. We find the effectiveness of these visualization types significantly varies across task, suggesting that visualization design would benefit from considering context-dependent effectiveness. Based on our findings, we derive recommendations on which visualizations to choose based on different tasks. We finally train a decision tree on the data we collected to drive a recommender, showcasing how to effectively engineer experimental user data into practical visualization systems.",,,https://github.com/gtvalab/ChartsEffectiveness/tree/master/Raw-Data,,vimeo 289789506,10.1109/tvcg.2018.2829750,2018,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Perception & Cognition 2,25-Oct,5:00 PM,5:20 PM
  InfoVis,TVCG,At a Glance: Approximate Entropy as a Measure of Line Chart Visualization Complexity,J),"Eugene Wu, Remco Chang, Abigail Mosca, Gabriel Ryan",,,,,,,vimeo 289784475,,2018,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Perception & Cognition 2,25-Oct,5:20 PM,5:40 PM
  TVCG,TVCG,Correlation Judgment and Visualization Features: A Comparative Study,T),"Fumeng Yang, Lane Harrison, Ronald A. Rensink, Steven Franconeri, Remco Chang",https://github.com/Fumeng-Yang/VisualFeature_TVCG/blob/master/paper/VF_preprint.pdf,"Recent visualization research efforts have incorporated experimental techniques and perceptual models from the vision science community. Perceptual laws such as Weber's law, for example, have been used to model the perception of correlation in scatterplots. While this thread of research has progressively refined the modeling of the perception of correlation in scatterplots, it remains unclear as to why such perception can be modeled using relatively simple functions, e.g., linear and log-linear. In this paper, we investigate a longstanding hypothesis that people use visual features in a chart as a proxy for statistical measures like correlation. For a given scatterplot, we extract 49 candidate visual features and evaluate which best align with existing models and participant judgments. The results support the hypothesis that people attend to a small number of visual features when discriminating correlation in scatterplots. We discuss how this result may account for prior conflicting findings, and how visual features provide a baseline for future model-based approaches in visualization evaluation and design.",,,,,vimeo 289789613,10.1109/tvcg.2018.2810918,2018,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Perception & Cognition 2,25-Oct,5:40 PM,6:00 PM
  SciVis,TVCG,Firefly: Illumination Drones for Interactive Visualization,J),"Sergej Stoppel, Magnus Paulson Erga, Stefan Bruckner",,,,,,,vimeo 290325505,,2018,2018,SciVis,Estrel Hall A+B,Thursday,Interaction and Multivariate Data,25-Oct,4:20 PM,4:40 PM
  SciVis,TVCG,CoDDA: A Flexible Copula-based Distribution Driven Analysis Framework for Large-Scale Multivariate Data,J),"Subhashis Hazarika, Soumya Dutta, Han-Wei Shen, Jen-Ping Chen",,,,,,,vimeo 290325658,10.1109/tvcg.2018.2864801,2018,2018,SciVis,Estrel Hall A+B,Thursday,Interaction and Multivariate Data,25-Oct,4:40 PM,5:00 PM
  SciVis,TVCG,"Details-First, Show Context, Overview Last: Supporting Exploration of Viscous Fingers in Large-Scale Ensemble Simulations ",J),"Timothy Basil Luciani, Andrew T Burks, Cassiano Sugiyama, Jonathan Komperda, Liz G.Elisabeta Marai",,,,,,,vimeo 290328058,,2018,2018,SciVis,Estrel Hall A+B,Thursday,Interaction and Multivariate Data,25-Oct,5:00 PM,5:20 PM
  TVCG,TVCG,A Model of Spatial Directness in Interactive Visualization,T),"Stefan Bruckner, Tobias Isenberg, Timo Ropinski, Alexander Wiebel",,,,,,,vimeo 289789140,10.1109/tvcg.2018.2848906,,2018,SciVis,Estrel Hall A+B,Thursday,Interaction and Multivariate Data,25-Oct,5:20 PM,5:40 PM
  TVCG,TVCG,Decal-Lenses: Interactive Lenses on Surfaces for Multivariate Visualization,T),"Allan Rocha, Julio Daniel Silva, Usman R. Alim, Sheelagh Carpendale, Mario Costa Sousa",,,,,,,vimeo 289789528,10.1109/tvcg.2018.2850781,,2018,SciVis,Estrel Hall A+B,Thursday,Interaction and Multivariate Data,25-Oct,5:40 PM,6:00 PM
  SciVis,SciVis,Cluster-Based Visualization for Merger Tree Data: The Challenge of Missing Expectations,,"Annie Preston, Kwan-Liu Ma",,,,,,,vimeo 290328886,,2018,2018,SciVis Short Papers,Estrel Hall C,Thursday,"Flow, Astrophysics, and Computationally Intensive Data Visualization",25-Oct,4:20 PM,4:32 PM
  SciVis,SciVis,Visualization of Uncertainty for Computationally Intensive Simulations Using High Fidelity Emulators,,"Ayan Biswas, Earl Lawrence, James Ahrens",,,,,,,vimeo 290328642,,2018,2018,SciVis Short Papers,Estrel Hall C,Thursday,"Flow, Astrophysics, and Computationally Intensive Data Visualization",25-Oct,4:32 PM,4:44 PM
  SciVis,SciVis,A Lagrangian Method for Extracting Eddy Boundaries in the Red Sea and the Gulf of Aden,,"Anke Friederici, Habib Toye, Ibrahim Hoteit, Tino Weinkauf, Holger Theisel, Markus Hadwiger",,,,,,,vimeo 290328836,,2018,2018,SciVis Short Papers,Estrel Hall C,Thursday,"Flow, Astrophysics, and Computationally Intensive Data Visualization",25-Oct,4:45 PM,4:57 PM
  
negative.code: SciVis,TVCG,Robust and Fast Extraction of 3D Symmetric Tensor Field Topology,J),"Lawrence Roy, Prashant Kumar, Yue Zhang, Eugene Zhang",,,,,,,vimeo 290325698,10.1109/tvcg.2018.2864768,2018,2018,SciVis,Estrel Hall A+B,Thursday,Tensors,25-Oct,9:00 AM,9:20 AM
  SciVis,TVCG,DT-MRI Streamsurfaces Revisited,J),"Michael Ankele, Thomas Schultz",,,,,,,vimeo 290327825,10.1109/tvcg.2018.2864845,2018,2018,SciVis,Estrel Hall A+B,Thursday,Tensors,25-Oct,9:20 AM,9:40 AM
  SciVis,TVCG,Tensor Field Visualization using Fiber Surfaces of Invariant Space,J),"Felix Raith, Christian Blecha, Thomas Nagel, Francesco Parisio, Olaf Kolditz, Fabian Günther, Markus Stommel, Gerik Scheuermann",,,,,,,vimeo 290327914,10.1109/tvcg.2018.2864846,2018,2018,SciVis,Estrel Hall A+B,Thursday,Tensors,25-Oct,9:40 AM,10:00 AM
  TVCG,TVCG,Tensor Decompositions for Integral Histogram Compression and Look-Up,T),"Rafael Ballester-Ripoll, Renato Pajarola",,,,,,,vimeo 289788992,10.1109/tvcg.2018.2802521,2018,2018,SciVis,Estrel Hall A+B,Thursday,Tensors,25-Oct,10:00 AM,10:20 AM
  VAST,TVCG,An Interactive Method to Improve Crowdsourced Annotations,J),"Shixia Liu, Changjian Chen, Yafeng Lu, Fangxin Ouyang, Bin Wang",http://www.shixialiu.com/publications/LabelInspect/paper.pdf,"In order to effectively infer correct labels from noisy crowdsourced annotations, learning-from-crowds models have introduced expert validation. However, little research has been done on facilitating the validation procedure. In this paper, we propose an interactive method to assist experts in verifying uncertain instance labels and unreliable workers. Given the instance labels and worker reliability inferred from a learning-from-crowds model, candidate instances and workers are selected for expert validation. The influence of verified results is propagated to relevant instances and workers through the learning-from-crowds model. To facilitate the validation of annotations, we have developed a confusion visualization to indicate the confusing classes for further exploration, a constrained projection method to show the uncertain labels in context, and a scatter-plot-based visualization to illustrate worker reliability. The three visualizations are tightly integrated with the learning-from-crowds model to provide an iterative and progressive environment for data validation. Two case studies were conducted that demonstrate our approach offers an efficient method for validating and improving crowdsourced annotations.",,,,,vimeo 289787374,10.1109/TVCG.2018.2864843,2018,2018,VAST,Estrel Hall C,Thursday,Interactive Analytics and Design,25-Oct,9:00 AM,9:20 AM
  VAST,TVCG,RegressionExplorer: Interactive Exploration of Logistic Regression Models with Subgroup Analysis,J),"Dennis Dingen, Marcel van 't Veer, Patrick Houthuizen, Eveline H. J. Mestrom, Erik H.H.M. Korsten, Arthur R.A. Bouwman, Jarke van Wijk",,,,,,,vimeo 289787606,10.1109/tvcg.2018.2865043,2018,2018,VAST,Estrel Hall C,Thursday,Interactive Analytics and Design,25-Oct,9:20 AM,9:40 AM
  VAST,TVCG,Drag and Track:  A Direct Manipulation Interface for Contextualizing Data Instances within a Continuous Parameter Space,J),"Daniel Orban, Daniel Keefe, Ayan Biswas, James Ahrens, David Rogers",,,,,,,vimeo 289787986,10.1109/TVCG.2018.2865051,2018,2018,VAST,Estrel Hall C,Thursday,Interactive Analytics and Design,25-Oct,9:40 AM,10:00 AM
  VAST,TVCG,Clustrophile 2: Guided Visual Clustering Analysis,J),"Marco Cavallo, Çagatay Demiralp",https://arxiv.org/pdf/1804.03048.pdf,"Data clustering is a common unsupervised learning method frequently used in exploratory data analysis. However, identifying relevant structures in unlabeled, high-dimensional data is nontrivial, requiring iterative experimentation with clustering parameters as well as data features and instances. The number of possible clusterings for a typical dataset is vast, and navigating in this vast space is also challenging. The absence of ground-truth labels makes it impossible to define an optimal solution, thus requiring user judgment to establish what can be considered a satisfiable clustering result. Data scientists need adequate interactive tools to effectively explore and navigate the large clustering space so as to improve the effectiveness of exploratory clustering analysis. We introduce Clustrophile 2, a new interactive tool for guided clustering analysis. Clustrophile 2 guides users in clustering-based exploratory analysis, adapts user feedback to improve user guidance, facilitates the interpretation of clusters, and helps quickly reason about differences between clusterings. To this end, Clustrophile 2 contributes a novel feature, the Clustering Tour, to help users choose clustering parameters and assess the quality of different clustering results in relation to current analysis goals and user expectations. We evaluate Clustrophile 2 through a user study with 12 data scientists, who used our tool to explore and interpret sub-cohorts in a dataset of Parkinson’s disease patients. Results suggest that Clustrophile 2 improves the speed and effectiveness of exploratory clustering analysis for both experts and non-experts.",,,,,vimeo 289787185,10.1109/TVCG.2018.2864477,2018,2018,VAST,Estrel Hall C,Thursday,Interactive Analytics and Design,25-Oct,10:00 AM,10:20 AM
  VAST,TVCG,InkPlanner: Supporting Prewriting via Intelligent Visual Diagramming,J),"Zhicong Lu, Mingming Fan, Yun Wang, Jian Zhao, Michelle Annett, Daniel Wigdor",http://mingmingfan.com/papers/InkPlanner_TVCG.pdf,"Prewriting is the process of generating and organizing ideas before drafting a document. Although often overlooked by novice writers and writing tool developers, prewriting is a critical process that improves the quality of a final document. To better understand current prewriting practices, we first conducted interviews with writing learners and experts. Based on the learners’ needs and experts’ recommendations, we then designed and developed InkPlanner, a novel pen and touch visualization tool that allows writers to utilize visual diagramming for ideation during prewriting. InkPlanner further allows writers to sort their ideas into a logical and sequential narrative by using a novel widget— NarrativeLine. Using a NarrativeLine, InkPlanner can automatically generate a document outline to guide later drafting exercises. Inkplanner is powered by machine-generated semantic and structural suggestions that are curated from various texts. To qualitatively review the tool and understand how writers use InkPlanner for prewriting, two writing experts were interviewed and a user study was conducted with university students. The results demonstrated that InkPlanner encouraged writers to generate more diverse ideas and also enabled them to think more strategically about how to organize their ideas for later drafting.",,,,,vimeo 289787883,10.1109/tvcg.2018.2864887,2018,2018,VAST,Estrel Hall C,Thursday,Interactive Analytics and Design,25-Oct,10:20 AM,10:40 AM
  TVCG,TVCG,Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers,T),"Fred Hohman, Minsuk Kahng, Robert Pienta, Duen Horng Chau",,,,,,,vimeo 289789596,10.1109/tvcg.2018.2843369,2018,2018,VAST,"Convention Hall 1, Section C",Thursday,Deep Learning,25-Oct,11:00 AM,11:20 AM
  VAST,VAST,Analyzing the Noise Robustness of Deep Neural Networks,C),"Mengchen Liu, Shixia Liu, Hang Su, Kelei Cao, Jun Zhu",http://www.shixialiu.com/publications/aevis/paper.pdf,"Deep neural networks (DNNs) are vulnerable to maliciously generated adversarial examples. These examples are intentionally designed by making imperceptible perturbations and often mislead a DNN into making an incorrect prediction. This phenomenon means that there is significant risk in applying DNNs to safety-critical applications, such as driverless cars. To address this issue, we present a visual analytics approach to explain the primary cause of the wrong predictions introduced by adversarial examples. The key is to analyze the datapaths of the adversarial examples and compare them with those of the normal examples. A datapath is a group of critical neurons and their connections. To this end, we formulate the datapath extraction as a subset selection problem and approximately solve it based on back-propagation. A multi-level visualization consisting of a segmented DAG (layer level), an Euler diagram (feature map level), and a heat map (neuron level), has been designed to help experts investigate datapaths from the high-level layers to the detailed neuron activations. Two case studies are conducted that demonstrate the promise of our approach in support of explaining the working mechanism of adversarial examples.",,,,,vimeo 289787703,,2018,2018,VAST,"Convention Hall 1, Section C",Thursday,Deep Learning,25-Oct,11:20 AM,11:40 AM
  VAST,TVCG,DQNViz: A Visual Analytics Approach to Understand Deep Q-Networks,J),"Junpeng Wang, Liang Gou, Han-Wei Shen, Hao Yang",http://edda-project.github.io/files/2018-08-13/dqnvis.pdf,"Deep Q-Network (DQN), as one type of deep reinforcement learning model, targets to train an intelligent agent that acquires optimal actions while interacting with an environment. The model is well known for its ability to surpass professional human players across many Atari 2600 games. Despite the superhuman performance, in-depth understanding of the model and interpreting the sophisticated behaviors of the DQN agent remain to be challenging tasks, due to the long-time model training process and the large number of experiences dynamically generated by the agent. In this work, we propose DQNViz, a visual analytics system to expose details of the blind training process in four levels, and enable users to dive into the large experience space of the agent for comprehensive analysis. As an initial attempt in visualizing DQN models, our work focuses more on Atari games with a simple action space, most notably the Breakout game. From our visual analytics of the agent’s experiences, we extract useful action/reward patterns that help to interpret the model and control the training. Through multiple case studies conducted together with deep learning experts, we demonstrate that DQNViz can effectively help domain experts to understand, diagnose, and potentially improve DQN models.",,,,,vimeo 289787246,10.1109/tvcg.2018.2864504,2018,2018,VAST,"Convention Hall 1, Section C",Thursday,Deep Learning,25-Oct,11:40 AM,12:00 PM
  VAST,TVCG,RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Network on Electronic Medical Records,J),"Bum Chul Kwon, Min-Je Choi, Joanne Kim, Edward Choi, Young Bin Kim, Soonwook Kwon, Jimeng Sun, Jaegul Choo",https://arxiv.org/pdf/1805.10724.pdf,"We have recently seen many successful applications of recurrent neural networks (RNNs) on electronic medical records (EMRs), which contain histories of patients’ diagnoses, medications, and other various events, in order to predict the current and future states of patients. Despite the strong performance of RNNs, it is often challenging for users to understand why the model makes a particular prediction. Such black-box nature of RNNs can impede its wide adoption in clinical practice. Furthermore, we have no established methods to interactively leverage users’ domain expertise and prior knowledge as inputs for steering the model. Therefore, our design study aims to provide a visual analytics solution to increase interpretability and interactivity of RNNs via a joint effort of medical experts, artificial intelligence scientists, and visual analytics researchers. Following the iterative design process between the experts, we design, implement, and evaluate a visual analytics tool called RetainVis, which couples a newly improved, interpretable, and interactive RNN-based model called RetainEX and visualizations for users’ exploration of EMR data in the context of prediction tasks. Our study shows the effective use of RetainVis for gaining insights into how individual medical codes contribute to making risk predictions, using EMRs of patients with heart failure and cataract symptoms. Our study also demonstrates how we made substantial changes to the state-of-the-art RNN model called RETAIN in order to make use of temporal information and increase interactivity. This study will provide a useful guideline for researchers that aim to design an interpretable and interactive visual analytics tool for RNNs",,,,,vimeo 289787946,10.1109/TVCG.2018.2865027,2018,2018,VAST,"Convention Hall 1, Section C",Thursday,Deep Learning,25-Oct,12:00 PM,12:20 PM
  