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Found 33 papers, 26 papers with code
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.
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.
UnMask: Adversarial Detection and Defense Through Robust Feature Alignment
UnMask detects such attacks and defends the model by rectifying the misclassification, re-classifying the image based on its robust features.
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.
PeopleMap: Visualization Tool for Mapping Out Researchers using Natural Language Processing
Discovering research expertise at institutions can be a difficult task.
Mapping Researchers with PeopleMap
Discovering research expertise at universities can be a difficult task.
Bluff: Interactively Deciphering Adversarial Attacks on Deep Neural Networks
Deep neural networks (DNNs) are now commonly used in many domains.
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
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.
Putting Humans in the Natural Language Processing Loop: A Survey
How can we design Natural Language Processing (NLP) systems that learn from human feedback?
Dodrio: Exploring Transformer Models with Interactive Visualization
Why do large pre-trained transformer-based models perform so well across a wide variety of NLP tasks?
GAM Changer: Editing Generalized Additive Models with Interactive Visualization
Recent strides in interpretable machine learning (ML) research reveal that models exploit undesirable patterns in the data to make predictions, which potentially causes harms in deployment.
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on.
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.
StickyLand: Breaking the Linear Presentation of Computational Notebooks
We present StickyLand, a notebook extension for empowering users to freely organize their code in non-linear ways.
VisCUIT: Visual Auditor for Bias in CNN Image Classifier
CNN image classifiers are widely used, thanks to their efficiency and accuracy.
Overcoming Language Disparity in Online Content Classification with Multimodal Learning
Advances in Natural Language Processing (NLP) have revolutionized the way researchers and practitioners address crucial societal problems.
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
Visual Auditor: Interactive Visualization for Detection and Summarization of Model Biases
Recent research has developed algorithms for effectively identifying intersectional bias in the form of interpretable, underperforming subsets (or slices) of the data.
Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values
Machine learning (ML) interpretability techniques can reveal undesirable patterns in data that models exploit to make predictions--potentially causing harms once deployed.
TimberTrek: Exploring and Curating Sparse Decision Trees with Interactive Visualization
Given thousands of equally accurate machine learning (ML) models, how can users choose among them?
DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models
With recent advancements in diffusion models, users can generate high-quality images by writing text prompts in natural language.
GAM Coach: Towards Interactive and User-centered Algorithmic Recourse
Machine learning (ML) recourse techniques are increasingly used in high-stakes domains, providing end users with actions to alter ML predictions, but they assume ML developers understand what input variables can be changed.
WebSHAP: Towards Explaining Any Machine Learning Models Anywhere
As machine learning (ML) is increasingly integrated into our everyday Web experience, there is a call for transparent and explainable web-based ML.
Queer In AI: A Case Study in Community-Led Participatory AI
We present Queer in AI as a case study for community-led participatory design in AI.
Angler: Helping Machine Translation Practitioners Prioritize Model Improvements
Machine learning (ML) models can fail in unexpected ways in the real world, but not all model failures are equal.
SuperNOVA: Design Strategies and Opportunities for Interactive Visualization in Computational Notebooks
Through this study, we identify key design implications and trade-offs, such as leveraging multimodal data in notebooks as well as balancing the degree of visualization-notebook integration.
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.
WizMap: Scalable Interactive Visualization for Exploring Large Machine Learning Embeddings
Machine learning models often learn latent embedding representations that capture the domain semantics of their training data.
REVAMP: Automated Simulations of Adversarial Attacks on Arbitrary Objects in Realistic Scenes
Generating these adversarial objects in the digital space has been extensively studied, however successfully transferring these attacks from the digital realm to the physical realm has proven challenging when controlling for real-world environmental factors.
Wordflow: Social Prompt Engineering for Large Language Models
To investigate social prompt engineering, we introduce Wordflow, an open-source and social text editor that enables everyday users to easily create, run, share, and discover LLM prompts.
Farsight: Fostering Responsible AI Awareness During AI Application Prototyping
To address this, we present Farsight, a novel in situ interactive tool that helps people identify potential harms from the AI applications they are prototyping.
LLM Attributor: Interactive Visual Attribution for LLM Generation
Our library offers a new way to quickly attribute an LLM's text generation to training data points to inspect model behaviors, enhance its trustworthiness, and compare model-generated text with user-provided text.
