318 citations
- Apple (United States)US4 papers
- Carnegie Mellon UniversityUS3 papers
- Stanford UniversityUS3 papers
- University of California, BerkeleyUS3 papers
- Google (United States)US2 papers
- Microsoft Research (United Kingdom)GB2 papers
- Allen InstituteUS1 paper
- Amazon (Germany)DE1 paper
- Berkeley CollegeUS1 paper
- Duke UniversityUS1 paper
- Instituto Superior TécnicoPT1 paper
- Karlsruhe Institute of TechnologyDE1 paper
24 papers
Synthetic Query Generation using Large Language Models for Virtual Assistants
Sonal Sannigrahi, Thiago Fraga-Silva, Youssef Oualil +1
Virtual Assistants (VAs) are important Information Retrieval platforms that help users accomplish various tasks through spoken commands. The speech recognition system (speech-to-te…
Symphony: Composing Interactive Interfaces for Machine Learning
Alex Bäuerle, Ángel Alexander Cabrera, Fred Hohman +5
Interfaces for machine learning (ML), information and visualizations about models or data, can help practitioners build robust and responsible ML systems. Despite their benefits, r…
Sketch-based Creativity Support Tools using Deep Learning
Forrest Huang, Eldon Schoop, David Ha +2
Sketching is a natural and effective visual communication medium commonly used in creative processes. Recent developments in deep-learning models drastically improved machines' abi…
Visual Intelligence through Human Interaction
Ranjay Krishna, Mitchell Gordon, Li Fei-Fei +1
Over the last decade, Computer Vision, the branch of Artificial Intelligence aimed at understanding the visual world, has evolved from simply recognizing objects in images to descr…
Neo: Generalizing Confusion Matrix Visualization to Hierarchical and Multi-Output Labels
Jochen Görtler, Fred Hohman, Dominik Moritz +5
The confusion matrix, a ubiquitous visualization for helping people evaluate machine learning models, is a tabular layout that compares predicted class labels against actual class…
Private Adaptive Gradient Methods for Convex Optimization
Hilal Asi, John Duchi, Alireza Fallah +2
We study adaptive methods for differentially private convex optimization, proposing and analyzing differentially private variants of a Stochastic Gradient Descent (SGD) algorithm w…