Although many deep-learning-based super-resolution approaches have been
...
We present a neural network approach to compute stream functions, which ...
Scene representation networks (SRNs) have been recently proposed for
com...
The number of published research papers has experienced exponential grow...
We present VMap, a map-like rectangular space-filling visualization, to
...
We present and discuss the results of a two-year qualitative analysis of...
Recently, Graph Neural Networks (GNNs) have significantly advanced the
p...
Deep learning based latent representations have been widely used for num...
We propose VDL-Surrogate, a view-dependent neural-network-latent-based
s...
Particle tracing through numerical integration is a well-known approach ...
In most of the literature on federated learning (FL), neural networks ar...
A multitude of studies have been conducted on graph drawing, but many
ex...
We propose GNN-Surrogate, a graph neural network-based surrogate model t...
Public opinion surveys constitute a powerful tool to study peoples' atti...
We explore an online learning reinforcement learning (RL) paradigm for
o...
In the past decades, many graph drawing techniques have been proposed fo...
Analyzing particle data plays an important role in many scientific
appli...
We present document domain randomization (DDR), the first successful tra...
We present the VIS30K dataset, a collection of 29,689 images that repres...
We present the Feature Tracking Kit (FTK), a framework that simplifies,
...
Convolutional neural networks (CNNs) have demonstrated extraordinarily g...
Despite the great success of Convolutional Neural Networks (CNNs) in Com...
Feature tracking and the visualizations of the resulting trajectories ma...
Viscous and gravitational flow instabilities cause a displacement front ...
We propose InSituNet, a deep learning based surrogate model to support
p...
Complex computational models are often designed to simulate real-world
p...