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Found 28 papers, 20 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.
SuperNOVA: Design Strategies and Opportunities for Interactive Visualization in Computational Notebooks
Through this work, we identify unique design opportunities and considerations for future notebook VA tools, such as using and manipulating multimodal data in notebooks as well as balancing the degree of visualization-notebook integration.
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.
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.
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.
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.
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.
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?
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.
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.
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.
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.
VisCUIT: Visual Auditor for Bias in CNN Image Classifier
CNN image classifiers are widely used, thanks to their efficiency and accuracy.
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.
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.
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.
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.
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?
Putting Humans in the Natural Language Processing Loop: A Survey
How can we design Natural Language Processing (NLP) systems that learn from human feedback?
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.
Mapping Researchers with PeopleMap
Discovering research expertise at universities can be a difficult task.
PeopleMap: Visualization Tool for Mapping Out Researchers using Natural Language Processing
Discovering research expertise at institutions can be a difficult task.
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.
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.
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.
