Search Results for author:
Found 22 papers, 7 papers with code
Improving Efficiency of Iso-Surface Extraction on Implicit Neural Representations Using Uncertainty Propagation
In this paper, we present an improved technique for range analysis by revisiting the arithmetic rules and analyzing the probability distribution of the network output within a spatial region.
PSRFlow: Probabilistic Super Resolution with Flow-Based Models for Scientific Data
The efficient sampling in the Gaussian latent space allows our model to perform uncertainty quantification for the super-resolved results.
Neural Stream Functions
Our approach takes a vector field as input and trains an implicit neural representation to learn a stream function for that vector field.
Adaptively Placed Multi-Grid Scene Representation Networks for Large-Scale Data Visualization
We address this shortcoming with an adaptively placed multi-grid SRN (APMGSRN) and propose a domain decomposition training and inference technique for accelerated parallel training on multi-GPU systems.
SKG: A Versatile Information Retrieval and Analysis Framework for Academic Papers with Semantic Knowledge Graphs
To extract knowledge from unstructured text, we develop a Knowledge Extraction Module that includes a semi-supervised pipeline for entity extraction and entity normalization.
VMap: An Interactive Rectangular Space-filling Visualization for Map-like Vertex-centric Graph Exploration
The resulting rectangular layout has better aspect ratio quality on synthetic data compared with the existing method for the rectangular partitioning of 2D points.
GNNInterpreter: A Probabilistic Generative Model-Level Explanation for Graph Neural Networks
In this paper, we propose a model-agnostic model-level explanation method for different GNNs that follow the message passing scheme, GNNInterpreter, to explain the high-level decision-making process of the GNN model.
IDLat: An Importance-Driven Latent Generation Method for Scientific Data
We qualitatively and quantitatively evaluate the effectiveness and efficiency of latent representations generated by our method with data from multiple scientific visualization applications.
VDL-Surrogate: A View-Dependent Latent-based Model for Parameter Space Exploration of Ensemble Simulations
In the model inference stage, we predict the latent representations at previously selected viewpoints and decode the latent representations to data space.
On the Importance and Applicability of Pre-Training for Federated Learning
To make our findings applicable to situations where pre-trained models are not directly available, we explore pre-training with synthetic data or even with clients' data in a decentralized manner, and found that they can already improve FL notably.
SmartGD: A GAN-Based Graph Drawing Framework for Diverse Aesthetic Goals
There are a few existing methods that have attempted to develop a flexible solution for optimizing different aesthetic aspects measured by different aesthetic criteria.
GNN-Surrogate: A Hierarchical and Adaptive Graph Neural Network for Parameter Space Exploration of Unstructured-Mesh Ocean Simulations
Our approach improves the efficiency of parameter space exploration with a surrogate model that predicts the simulation outputs accurately and efficiently.
Reinforcement Learning for Load-balanced Parallel Particle Tracing
Second, we propose a workload estimation model, helping RL agents estimate the workload distribution of processes in future computations.
DeepGD: A Deep Learning Framework for Graph Drawing Using GNN
In this paper, we propose a Convolutional Graph Neural Network based deep learning framework, DeepGD, which can draw arbitrary graphs once trained.
Deep Hierarchical Super Resolution for Scientific Data
We present a novel technique for hierarchical super resolution (SR) with neural networks (NNs), which upscales volumetric data represented with an octree data structure to a high-resolution uniform grid with minimal seam artifacts on octree node boundaries.
Local Latent Representation based on Geometric Convolution for Particle Data Feature Exploration
Feature related particle data analysis plays an important role in many scientific applications such as fluid simulations, cosmology simulations and molecular dynamics.
Document Domain Randomization for Deep Learning Document Layout Extraction
We present document domain randomization (DDR), the first successful transfer of convolutional neural networks (CNNs) trained only on graphically rendered pseudo-paper pages to real-world document segmentation.
VIS30K: A Collection of Figures and Tables from IEEE Visualization Conference Publications
We present the VIS30K dataset, a collection of 29, 689 images that represents 30 years of figures and tables from each track of the IEEE Visualization conference series (Vis, SciVis, InfoVis, VAST).
CNNPruner: Pruning Convolutional Neural Networks with Visual Analytics
It considers the importance of convolutional filters through both instability and sensitivity, and allows users to interactively create pruning plans according to a desired goal on model size or accuracy.
An Information-theoretic Visual Analysis Framework for Convolutional Neural Networks
Despite the great success of Convolutional Neural Networks (CNNs) in Computer Vision and Natural Language Processing, the working mechanism behind CNNs is still under extensive discussions and research.
InSituNet: Deep Image Synthesis for Parameter Space Exploration of Ensemble Simulations
We propose InSituNet, a deep learning based surrogate model to support parameter space exploration for ensemble simulations that are visualized in situ.
NNVA: Neural Network Assisted Visual Analysis of Yeast Cell Polarization Simulation
We utilize the trained network to perform interactive parameter sensitivity analysis of the original simulation at multiple levels-of-detail as well as recommend optimal parameter configurations using the activation maximization framework of neural networks.
