With a carefully designed model visualization and explaining support, CNNC facilitates a highly interactive workflow that promptly presents both quantitative and qualitative information at each analysis stage.
However, existing DR methods provide limited capability and flexibility for such comparative analysis as each method is designed only for a narrow analysis target, such as identifying factors that most differentiate groups.
Deep Recurrent Neural Networks (RNN) continues to find success in predictive decision-making with temporal event sequences.
Diffusion tensor imaging (DTI) has been used to study the effects of neurodegenerative diseases on neural pathways, which may lead to more reliable and early diagnosis of these diseases as well as a better understanding of how they affect the brain.
We present P6, a declarative language for building high performance visual analytics systems through its support for specifying and integrating machine learning and interactive visualization methods.
Autonomous multi-robot systems, where a team of robots shares information to perform tasks that are beyond an individual robot's abilities, hold great promise for a number of applications, such as planetary exploration missions.
Human-Computer Interaction Multiagent Systems Robotics
Data-driven problem solving in many real-world applications involves analysis of time-dependent multivariate data, for which dimensionality reduction (DR) methods are often used to uncover the intrinsic structure and features of the data.
A common network analysis task is comparison of two networks to identify unique characteristics in one network with respect to the other.
Optimizing the performance of large-scale parallel codes is critical for efficient utilization of computing resources.
Distributed, Parallel, and Cluster Computing Performance
Contrastive learning (CL) is an emerging analysis approach that aims to discover unique patterns in one dataset relative to another.
While we use an autoencoder for the event embedding, we apply its variant with the self-attention mechanism for the sequence embedding.
Through a case study of a publicly available clinical dataset, we demonstrate the effectiveness of our visual analytics system to assist clinicians and researchers in comparing and quantitatively evaluating different machine learning methods.
To support streaming data analysis, we introduce a visual analytic framework comprising of three modules: data management, analysis, and interactive visualization.
Deep Recurrent Neural Networks (RNN) is increasingly used in decision-making with temporal sequences.
We enhance an existing incremental PCA method in several ways to ensure its usability for visualizing streaming multidimensional data.
Dimensionality reduction (DR) is frequently used for analyzing and visualizing high-dimensional data as it provides a good first glance of the data.
To provide users with an intuitive way to navigate the layout design space, we present a technique to systematically visualize a graph in diverse layouts using deep generative models.
In this work, we present a new 4D feature segmentation/extraction scheme that can operate on both the field and point/trajectory data types simultaneously.
Deep Recurrent Neural Network (RNN) has gained popularity in many sequence classification tasks.
For a given graph, our approach can show what the graph would look like in different layouts and estimate their corresponding aesthetic metrics.