Search Results for author:
Found 16 papers, 7 papers with code
Diffusion Explainer: Visual Explanation for Text-to-image Stable Diffusion
Diffusion Explainer tightly integrates a visual overview of Stable Diffusion's complex components with detailed explanations of their underlying operations, enabling users to fluidly transition between multiple levels of abstraction through animations and interactive elements.
NeuroMapper: In-browser Visualizer for Neural Network Training
We present our ongoing work NeuroMapper, an in-browser visualization tool that helps machine learning (ML) developers interpret the evolution of a model during training, providing a new way to monitor the training process and visually discover reasons for suboptimal training.
Concept Evolution in Deep Learning Training: A Unified Interpretation Framework and Discoveries
We present ConceptEvo, a unified interpretation framework for deep neural networks (DNNs) that reveals the inception and evolution of learned concepts during training.
DetectorDetective: Investigating the Effects of Adversarial Examples on Object Detectors
With deep learning based systems performing exceedingly well in many vision-related tasks, a major concern with their widespread deployment especially in safety-critical applications is their susceptibility to adversarial attacks.
NeuroCartography: Scalable Automatic Visual Summarization of Concepts in Deep Neural Networks
Through a large-scale human evaluation, we demonstrate that our technique discovers neuron groups that represent coherent, human-meaningful concepts.
Quantifying the Impact of Human Capital, Job History, and Language Factors on Job Seniority with a Large-scale Analysis of Resumes
As job markets worldwide have become more competitive and applicant selection criteria have become more opaque, and different (and sometimes contradictory) information and advice is available for job seekers wishing to progress in their careers, it has never been more difficult to determine which factors in a r\'esum\'e most effectively help career progression.
RECAST: Enabling User Recourse and Interpretability of Toxicity Detection Models with Interactive Visualization
With the widespread use of toxic language online, platforms are increasingly using automated systems that leverage advances in natural language processing to automatically flag and remove toxic comments.
SkeletonVis: Interactive Visualization for Understanding Adversarial Attacks on Human Action Recognition Models
Skeleton-based human action recognition technologies are increasingly used in video based applications, such as home robotics, healthcare on aging population, and surveillance.
A Comparative Analysis of Industry Human-AI Interaction Guidelines
We have then used this framework to compare each of the surveyed companies to find differences in areas of emphasis.
Human-Computer Interaction
Bluff: Interactively Deciphering Adversarial Attacks on Deep Neural Networks
Deep neural networks (DNNs) are now commonly used in many domains.
CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization
Deep learning's great success motivates many practitioners and students to learn about this exciting technology.
Massif: Interactive Interpretation of Adversarial Attacks on Deep Learning
Deep neural networks (DNNs) are increasingly powering high-stakes applications such as autonomous cars and healthcare; however, DNNs are often treated as "black boxes" in such applications.
CNN 101: Interactive Visual Learning for Convolutional Neural Networks
The success of deep learning solving previously-thought hard problems has inspired many non-experts to learn and understand this exciting technology.
RECAST: Interactive Auditing of Automatic Toxicity Detection Models
As toxic language becomes nearly pervasive online, there has been increasing interest in leveraging the advancements in natural language processing (NLP), from very large transformer models to automatically detecting and removing toxic comments.
NeuralDivergence: Exploring and Understanding Neural Networks by Comparing Activation Distributions
As deep neural networks are increasingly used in solving high-stake problems, there is a pressing need to understand their internal decision mechanisms.
Summit: Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations
Deep learning is increasingly used in decision-making tasks.
