1 code implementation • 18 Sep 2023 • Zoe De Simone, Angie Boggust, Arvind Satyanarayan, Ashia Wilson
Generative text-to-image (TTI) models produce high-quality images from short textual descriptions and are widely used in academic and creative domains.
1 code implementation • 28 Jun 2023 • Benny J. Tang, Angie Boggust, Arvind Satyanarayan
Captions that describe or explain charts help improve recall and comprehension of the depicted data and provide a more accessible medium for people with visual disabilities.
1 code implementation • 7 Jun 2022 • Angie Boggust, Harini Suresh, Hendrik Strobelt, John V. Guttag, Arvind Satyanarayan
Moreover, with saliency cards, we are able to analyze the research landscape in a more structured fashion to identify opportunities for new methods and evaluation metrics for unmet user needs.
1 code implementation • 22 Nov 2021 • Hussein Mozannar, Arvind Satyanarayan, David Sontag
For this collaboration to perform properly, the human decision maker must have a mental model of when and when not to rely on the agent.
1 code implementation • EMNLP (ACL) 2021 • Hendrik Strobelt, Benjamin Hoover, Arvind Satyanarayan, Sebastian Gehrmann
While different language models are ubiquitous in NLP, it is hard to contrast their outputs and identify which contexts one can handle better than the other.
no code implementations • 8 Oct 2021 • Alan Lundgard, Arvind Satyanarayan
To demonstrate how our model can be applied to evaluate the effectiveness of visualization descriptions, we conduct a mixed-methods evaluation with 30 blind and 90 sighted readers, and find that these reader groups differ significantly on which semantic content they rank as most useful.
1 code implementation • 20 Jul 2021 • Angie Boggust, Benjamin Hoover, Arvind Satyanarayan, Hendrik Strobelt
Saliency methods -- techniques to identify the importance of input features on a model's output -- are a common step in understanding neural network behavior.
no code implementations • 8 Mar 2021 • Ariel Levy, Monica Agrawal, Arvind Satyanarayan, David Sontag
Automated decision support can accelerate tedious tasks as users can focus their attention where it is needed most.
Decision Making Human-Computer Interaction
no code implementations • 17 Feb 2021 • Harini Suresh, Kathleen M. Lewis, John V. Guttag, Arvind Satyanarayan
Interpretability methods aim to help users build trust in and understand the capabilities of machine learning models.
no code implementations • 24 Jan 2021 • Harini Suresh, Steven R. Gomez, Kevin K. Nam, Arvind Satyanarayan
To ensure accountability and mitigate harm, it is critical that diverse stakeholders can interrogate black-box automated systems and find information that is understandable, relevant, and useful to them.
1 code implementation • 10 Dec 2019 • Angie Boggust, Brandon Carter, Arvind Satyanarayan
Embeddings mapping high-dimensional discrete input to lower-dimensional continuous vector spaces have been widely adopted in machine learning applications as a way to capture domain semantics.
2 code implementations • 25 May 2019 • Madelon Hulsebos, Kevin Hu, Michiel Bakker, Emanuel Zgraggen, Arvind Satyanarayan, Tim Kraska, Çağatay Demiralp, César Hidalgo
Correctly detecting the semantic type of data columns is crucial for data science tasks such as automated data cleaning, schema matching, and data discovery.
1 code implementation • 12 May 2019 • Kevin Hu, Neil Gaikwad, Michiel Bakker, Madelon Hulsebos, Emanuel Zgraggen, César Hidalgo, Tim Kraska, Guoliang Li, Arvind Satyanarayan, Çağatay Demiralp
Researchers currently rely on ad hoc datasets to train automated visualization tools and evaluate the effectiveness of visualization designs.
1 code implementation • Distill 2018 • Chris Olah, Arvind Satyanarayan, Ian Johnson, Shan Carter, Ludwig Schubert, Katherine Ye, Alexander Mordvintsev
In this article, we treat existing interpretability methods as fundamental and composable building blocks for rich user interfaces.