Search Results for author:
Found 42 papers, 11 papers with code
HyperFLINT: Hypernetwork-based Flow Estimation and Temporal Interpolation for Scientific Ensemble Visualization
We present HyperFLINT (Hypernetwork-based FLow estimation and temporal INTerpolation), a novel deep learning-based approach for estimating flow fields, temporally interpolating scalar fields, and facilitating parameter space exploration in spatio-temporal scientific ensemble data.
SLIM: Spuriousness Mitigation with Minimal Human Annotations
Recent studies highlight that deep learning models often learn spurious features mistakenly linked to labels, compromising their reliability in real-world scenarios where such correlations do not hold.
GNNAnatomy: Systematic Generation and Evaluation of Multi-Level Explanations for Graph Neural Networks
GNNAnatomy uses graphlets, primitive graph substructures, to identify the most critical substructures in a graph class by analyzing the correlation between GNN predictions and graphlet frequencies.
A Reliable Framework for Human-in-the-Loop Anomaly Detection in Time Series
Time series anomaly detection is a critical machine learning task for numerous applications, such as finance, healthcare, and industrial systems.
AttributionScanner: A Visual Analytics System for Model Validation with Metadata-Free Slice Finding
Data slice finding is an emerging technique for validating machine learning (ML) models by identifying and analyzing subgroups in a dataset that exhibit poor performance, often characterized by distinct feature sets or descriptive metadata.
A Visual Analytics Design for Connecting Healthcare Team Communication to Patient Outcomes
Communication among healthcare professionals (HCPs) is crucial for the quality of patient treatment.
Visual Analytics for Efficient Image Exploration and User-Guided Image Captioning
On the one hand, by visually examining the captions automatically generated from language-image models for an image dataset, we gain deeper insights into the semantic underpinnings of the visual contents, unearthing data biases that may be entrenched within the dataset.
Classes are not Clusters: Improving Label-based Evaluation of Dimensionality Reduction
In this paper, we introduce two novel quality measures -- Label-Trustworthiness and Label-Continuity (Label-T&C) -- advancing the process of DR evaluation based on class labels.
A Multi-Level, Multi-Scale Visual Analytics Approach to Assessment of Multifidelity HPC Systems
This end-to-end log analysis system, coupled with visual analytics support, allows users to glean and promptly extract supercomputer usage and error patterns at varying temporal and spatial resolutions.
Photon Field Networks for Dynamic Real-Time Volumetric Global Illumination
In this paper, we present a novel method to enable real-time global illumination for volume data visualization.
HyperINR: A Fast and Predictive Hypernetwork for Implicit Neural Representations via Knowledge Distillation
Implicit Neural Representations (INRs) have recently exhibited immense potential in the field of scientific visualization for both data generation and visualization tasks.
Distributed Neural Representation for Reactive in situ Visualization
In this work, we develop a distributed volumetric neural representation and optimize it for in situ visualization.
How Does Attention Work in Vision Transformers? A Visual Analytics Attempt
Multi-head self-attentions are then applied to the sequence to learn the attention between patches.
Visual Analytics of Multivariate Networks with Representation Learning and Composite Variable Construction
Multivariate networks are commonly found in real-world data-driven applications.
Visual Analytics of Neuron Vulnerability to Adversarial Attacks on Convolutional Neural Networks
Adversarial attacks on a convolutional neural network (CNN) -- injecting human-imperceptible perturbations into an input image -- could fool a high-performance CNN into making incorrect predictions.
SUNY: A Visual Interpretation Framework for Convolutional Neural Networks from a Necessary and Sufficient Perspective
Researchers have proposed various methods for visually interpreting the Convolutional Neural Network (CNN) via saliency maps, which include Class-Activation-Map (CAM) based approaches as a leading family.
FoVolNet: Fast Volume Rendering using Foveated Deep Neural Networks
We introduce FoVolNet -- a method to significantly increase the performance of volume data visualization.
Interactive Volume Visualization via Multi-Resolution Hash Encoding based Neural Representation
Neural networks have shown great potential in compressing volume data for visualization.
Feature Learning for Nonlinear Dimensionality Reduction toward Maximal Extraction of Hidden Patterns
Dimensionality reduction (DR) plays a vital role in the visual analysis of high-dimensional data.
A Machine-Learning-Aided Visual Analysis Workflow for Investigating Air Pollution Data
In this paper, we present a methodology that utilizes multiple machine learning methods to uniformly explore these aspects.
VAC-CNN: A Visual Analytics System for Comparative Studies of Deep Convolutional Neural Networks
The rapid development of Convolutional Neural Networks (CNNs) in recent years has triggered significant breakthroughs in many machine learning (ML) applications.
A Deep Generative Model for Reordering Adjacency Matrices
Depending on the node ordering, an adjacency matrix can highlight distinct characteristics of a graph.
Interactive Dimensionality Reduction for Comparative Analysis
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.
HypperSteer: Hypothetical Steering and Data Perturbation in Sequence Prediction with Deep Learning
Deep Recurrent Neural Networks (RNN) continues to find success in predictive decision-making with temporal event sequences.
A Predictive Visual Analytics System for Studying Neurodegenerative Disease based on DTI Fiber Tracts
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.
P6: A Declarative Language for Integrating Machine Learning in Visual Analytics
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.
A Visual Analytics Approach to Debugging Cooperative, Autonomous Multi-Robot Systems' Worldviews
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
A Visual Analytics Framework for Reviewing Multivariate Time-Series Data with Dimensionality Reduction
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 Visual Analytics Framework for Contrastive Network Analysis
A common network analysis task is comparison of two networks to identify unique characteristics in one network with respect to the other.
Scalable Comparative Visualization of Ensembles of Call Graphs
Optimizing the performance of large-scale parallel codes is critical for efficient utilization of computing resources.
Distributed, Parallel, and Cluster Computing Performance
Network Comparison with Interpretable Contrastive Network Representation Learning
This analysis task could be greatly assisted by contrastive learning, which is an emerging analysis approach to discover salient patterns in one dataset relative to another.
A Visual Analytics System for Multi-model Comparison on Clinical Data Predictions
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.
Comparative Visual Analytics for Assessing Medical Records with Sequence Embedding
While we use an autoencoder for the event embedding, we apply its variant with the self-attention mechanism for the sequence embedding.
A Visual Analytics Framework for Reviewing Streaming Performance Data
To support streaming data analysis, we introduce a visual analytic framework comprising of three modules: data management, analysis, and interactive visualization.
Visual Summary of Value-level Feature Attribution in Prediction Classes with Recurrent Neural Networks
Deep Recurrent Neural Networks (RNN) is increasingly used in decision-making with temporal sequences.
Supporting Analysis of Dimensionality Reduction Results with Contrastive Learning
Dimensionality reduction (DR) is frequently used for analyzing and visualizing high-dimensional data as it provides a good first glance of the data.
An Incremental Dimensionality Reduction Method for Visualizing Streaming Multidimensional Data
We enhance an existing incremental PCA method in several ways to ensure its usability for visualizing streaming multidimensional data.
A Deep Generative Model for Graph Layout
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.
Multifaceted 4D Feature Segmentation and Extraction in Point and Field-based Datasets
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.
Visual Reasoning of Feature Attribution with Deep Recurrent Neural Networks
Deep Recurrent Neural Network (RNN) has gained popularity in many sequence classification tasks.
What Would a Graph Look Like in This Layout? A Machine Learning Approach to Large Graph Visualization
For a given graph, our approach can show what the graph would look like in different layouts and estimate their corresponding aesthetic metrics.
Learning to Compose with Professional Photographs on the Web
Photo composition is an important factor affecting the aesthetics in photography.
