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Visualization facilitates uncertainty evaluation of multiple-point geostatistical stochastic simulation
Stochastic simulation is an essential method for modeling complex geological structures in geosciences. Evaluating the uncertainty of the...
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Interactive Input and Visualization for Planning with Temporal Uncertainty
When planning series of events or processes, everyone has to cope with temporal uncertainty. Popular examples are holiday planning or train trips....
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Communicating Uncertainty in Machine Learning Explanations: A Visualization Analytics Approach for Predictive Process Monitoring
As data-driven intelligent systems advance, the need for reliable and transparent decision-making mechanisms has become increasingly important....
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Visualization
3D computer-based visualization refers to all those methodologies adopted to produce, represent, describe, transmit, and present graphically/visually...
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Uncertainty-aware visual analytics: scope, opportunities, and challenges
In many applications, visual analytics (VA) has developed into a standard tool to ease data access and knowledge generation. VA describes a holistic...
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A workflow to systematically design uncertainty-aware visual analytics applications
Visual analytics (VA) is a paradigm for insight generation by using visual analysis techniques and automated reasoning by transforming data into...
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Evidential uncertainty sampling strategies for active learning
Recent studies in active learning, particularly in uncertainty sampling, have focused on the decomposition of model uncertainty into reducible and...
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Handling Uncertainty in Spatiotemporal Data
Spatial technologies forge massive datasets fast and constantly. This gigantic dataset consists of the time series forecasting or spatial...
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Information Visualization Techniques
Data have become ubiquitous, and it is much needed to provide efficient ways for people to understand and use them. It is often stated that...
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Uncertainty Modeling for Group Re-Identification
Group re-identification (GReID) aims to correctly associate images containing the same group members captured with non-overlap** camera networks,...
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Uncertainty-Aware Face Recognition
In this chapter, we introduced the motivation of data uncertainty estimation in deep face recognition systems as well as its applications. From a...
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Visualizing Uncertainty in Different Domains: Commonalities and Potential Impacts on Human Decision-Making
Visualizing uncertainty is a difficult but important task. Many techniques for visualizing uncertainty are designed for a specific domain, such as...
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Dynamic visualization of high-dimensional data
Dimensionality reduction (DR) is commonly used to project high-dimensional data into lower dimensions for visualization, which could then generate...
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Uncertainty Quantification for Scale-Space Blob Detection
We consider the problem of blob detection for uncertain images, such as images that have to be inferred from noisy measurements. Extending recent...
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A survey of uncertainty in deep neural networks
Over the last decade, neural networks have reached almost every field of science and become a crucial part of various real world applications. Due to...
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Uncertainty-aware enhanced dark experience replay for continual learning
The replay-based approaches are a notable family of methods among many efforts on Continual Learning, where memory sampling strat- egy and rehearsal...
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Design Cognition in Data Visualization
In this chapter I introduce the topic of design cognition and its relevance to data visualization. I outline two historically dominant paradigms of...
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Conformal Prediction Masks: Visualizing Uncertainty in Medical Imaging
Estimating uncertainty in image-to-image recovery networks is an important task, particularly as such networks are being increasingly deployed in the...
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Self-Supervised Monocular Depth Estimation by Digging into Uncertainty Quantification
Based on well-designed network architectures and objective functions, self-supervised monocular depth estimation has made great progress. However,...
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Uncertainty-aware hierarchical reinforcement learning for long-horizon tasks
Hierarchical reinforcement learning excels at dividing difficult task goals into easily achievable subgoals. It provides an effective means to solve...
