Papers Data Visualization
“Data Visualization” 태그가 달린 논문 335편 · 필터 해제
Polis: Scaling Deliberation by Mapping High Dimensional Opinion Spaces
Deliberative and participatory approaches to democracy seek to directly include citizens in decision-making and agenda-setting processes. These methods date back to the very foundations of democracy in Athens, where regu…
Data VisualizationDecision MakingFilament Plots for Data Visualization
The efficiency of modern computer graphics allows us to explore collections of space curves simultaneously with "drag-to-rotate" interfaces. This inspires us to replace "scatterplots of points" with "scatterplots of curv…
Data VisualizationExplainable Debugger for Black-box Machine Learning Models
The research around developing methods for debugging and refining Machine Learning (ML) models is still in its infancy. We believe employing tailored tools in the development process can help developers in creating more …
Anomaly DetectionBIG-bench Machine LearningData VisualizationDecision Making+3DIVINE: Diverse Influential Training Points for Data Visualization and Model Refinement
As the complexity of machine learning (ML) models increases, resulting in a lack of prediction explainability, several methods have been developed to explain a model's behavior in terms of the training data points that m…
Data VisualizationFairnessApplications of knowledge graphs for food science and industry
The deployment of various networks (e.g., Internet of Things [IoT] and mobile networks), databases (e.g., nutrition tables and food compositional databases), and social media (e.g., Instagram and Twitter) generates huge …
Data Visualizationgraph constructionKnowledge GraphsNutrition+2Self-service Data Classification Using Interactive Visualization and Interpretable Machine Learning
Machine learning algorithms often produce models considered as complex black-box models by both end users and developers. They fail to explain the model in terms of the domain they are designed for. The proposed Iterativ…
BIG-bench Machine LearningClassificationData VisualizationInterpretable Machine LearningGenerative locally linear embedding: A module for manifold unfolding and visualization
Data often have nonlinear patterns in machine learning. One can unfold the nonlinear manifold of a dataset for low-dimensional visualization and feature extraction. Locally Linear Embedding (LLE) is a nonlinear spectral …
Data VisualizationDimensionality ReductionVariational InferenceA Domain-Oblivious Approach for Learning Concise Representations of Filtered Topological Spaces for Clustering
Persistence diagrams have been widely used to quantify the underlying features of filtered topological spaces in data visualization. In many applications, computing distances between diagrams is essential; however, compu…
Data VisualizationGenerative Adversarial NetworkTheoretical Foundations of t-SNE for Visualizing High-Dimensional Clustered Data
This paper investigates the theoretical foundations of the t-distributed stochastic neighbor embedding (t-SNE) algorithm, a popular nonlinear dimension reduction and data visualization method. A novel theoretical framewo…
ClusteringData VisualizationDimensionality ReductionVocal Bursts Intensity Predictionmaplet: An extensible R toolbox for modular and reproducible omics pipelines
This paper presents maplet, an open-source R package for the creation of highly customizable, fully reproducible statistical pipelines for omics data analysis, with a special focus on metabolomics-based methods. It build…
Data VisualizationPlot2API: Recommending Graphic API from Plot via Semantic Parsing Guided Neural Network
Plot-based Graphic API recommendation (Plot2API) is an unstudied but meaningful issue, which has several important applications in the context of software engineering and data visualization, such as the plotting guidance…
Data AugmentationData Visualizationimage-classificationImage Classification+3GEPSA, a tool for monitoring social challenges in digital press
This papers presents a platform for monitoring press narratives with respect to several social challenges, including gender equality, migrations and minority languages. As narratives are encoded in natural language, we h…
ClassificationData VisualizationDocument ClassificationKeyword Extraction+4Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges
Interpretability in machine learning (ML) is crucial for high stakes decisions and troubleshooting. In this work, we provide fundamental principles for interpretable ML, and dispel common misunderstandings that dilute th…
Additive modelsBIG-bench Machine LearningCausal InferenceData Visualization+3A Discriminative Vectorial Framework for Multi-modal Feature Representation
Due to the rapid advancements of sensory and computing technology, multi-modal data sources that represent the same pattern or phenomenon have attracted growing attention. As a result, finding means to explore useful inf…
Audio Emotion RecognitionData VisualizationEmotion RecognitionFace Recognition+3Visualizing changes to US federal environmental agency websites, 2016–2020
Websites have become the primary means by which the US federal government communicates about its operations and presents information for public consumption. However, the alteration or removal of critical information from…
Data VisualizationMultimodal Data Visualization and Denoising with Integrated Diffusion
We propose a method called integrated diffusion for combining multimodal datasets, or data gathered via several different measurements on the same system, to create a joint data diffusion operator. As real world data suf…
ClusteringData VisualizationDenoisingBroader terms curriculum mapping: Using natural language processing and visual-supported communication to create representative program planning experiences
Accreditation bodies call for curriculum development processes open to all stakeholders, reflecting viewpoints of students, industry, university faculty and society. However, communication difficulties between faculty an…
Data VisualizationRobust Principal Component Analysis: A Median of Means Approach
Principal Component Analysis (PCA) is a fundamental tool for data visualization, denoising, and dimensionality reduction. It is widely popular in Statistics, Machine Learning, Computer Vision, and related fields. However…
Data VisualizationDenoisingDimensionality ReductionPhilosophyFits and Starts: Enterprise Use of AutoML and the Role of Humans in the Loop
AutoML systems can speed up routine data science work and make machine learning available to those without expertise in statistics and computer science. These systems have gained traction in enterprise settings where poo…
AutoMLData VisualizationApproximate Query Processing for Group-By Queries based on Conditional Generative Models
The Group-By query is an important kind of query, which is common and widely used in data warehouses, data analytics, and data visualization. Approximate query processing is an effective way to increase the querying effi…
Data Visualization