Our research aims to unify existing works' diverging opinions on how
arc...
Large language models (LLMs) have skyrocketed in popularity in recent ye...
There has been an explosion in interest in machine learning (ML) in rece...
Machine learning models often learn latent embedding representations tha...
Computational notebooks such as Jupyter Notebook have become data scient...
Diffusion-based generative models' impressive ability to create convinci...
As machine learning (ML) is increasingly integrated into our everyday We...
Machine learning (ML) recourse techniques are increasingly used in
high-...
Transformers have become the de facto models of choice in machine learni...
While anomaly detection stands among the most important and valuable pro...
Adversarial Training is the most effective approach for improving the
ro...
With recent advancements in diffusion models, users can generate high-qu...
Discovering and making sense of relevant literature is fundamental in an...
We present our ongoing work NeuroMapper, an in-browser visualization too...
As the information on the Internet continues growing exponentially,
unde...
Class imbalance is a ubiquitous phenomenon occurring in real world data
...
Given thousands of equally accurate machine learning (ML) models, how ca...
Advanced surgical procedures for congenital heart diseases (CHDs) requir...
Machine learning (ML) interpretability techniques can reveal undesirable...
As machine learning (ML) systems become increasingly widespread, it is
n...
How can we develop visual analytics (VA) tools that can be easily adopte...
CNN image classifiers are widely used, thanks to their efficiency and
ac...
As automatic speech recognition (ASR) systems are now being widely deplo...
Person tracking using computer vision techniques has wide ranging
applic...
Deep neural networks (DNNs) have been widely used for decision making,
p...
Google Play Store's policy forbids the use of incentivized installs, rat...
Recent strides in interpretable machine learning (ML) research reveal th...
Discovering and making sense of relevant research literature is fundamen...
Manually grading D3 data visualizations is a challenging endeavor, and i...
Keeping track of scientific challenges, advances and emerging directions...
Existing research on making sense of deep neural networks often focuses ...
As job markets worldwide have become more competitive and applicant sele...
The study of network robustness is a critical tool in the characterizati...
The advent of larger machine learning (ML) models have improved
state-of...
Why do large pre-trained transformer-based models perform so well across...
With the widespread use of toxic language online, platforms are increasi...
Computer vision is playing an increasingly important role in automated
m...
Skeleton-based human action recognition technologies are increasingly us...
Mixed-initiative visual analytics systems incorporate well-established d...
With the rapid emergence of graph representation learning, the construct...
With the recent release of AI interaction guidelines from Apple, Google,...
Interpreting how persuasive language influences audiences has implicatio...
This paper systematically derives design dimensions for the structured
e...
Deep neural networks (DNNs) are now commonly used in many domains. Howev...
Discovering research expertise at universities can be a difficult task.
...
Graph data have become increasingly common. Visualizing them helps peopl...
Discovering research expertise at institutions can be a difficult task.
...
The study of network robustness is a critical tool in the characterizati...
Deep learning's great success motivates many practitioners and students ...
Deep learning models are being integrated into a wide range of high-impa...