19 citations · 30 across the 5 of their papers we have counts for
7 papers
Improving dermatology classifiers across populations using images generated by large diffusion models
Luke W. Sagers, James A. Diao, Matthew Groh +3
Dermatological classification algorithms developed without sufficiently diverse training data may generalize poorly across populations. While intentional data collection and annota…
Deceptive AI Systems That Give Explanations Are Just as Convincing as Honest AI Systems in Human-Machine Decision Making
Valdemar Danry, Pat Pataranutaporn, Ziv Epstein +2
The ability to discern between true and false information is essential to making sound decisions. However, with the recent increase in AI-based disinformation campaigns, it has bec…
Social influence leads to the formation of diverse local trends
Ziv Epstein, Matthew Groh, Abhimanyu Dubey +1
How does the visual design of digital platforms impact user behavior and the resulting environment? A body of work suggests that introducing social signals to content can increase…
Interpolating GANs to Scaffold Autotelic Creativity
Ziv Epstein, Océane Boulais, Skylar Gordon +1
The latent space modeled by generative adversarial networks (GANs) represents a large possibility space. By interpolating categories generated by GANs, it is possible to create nov…
Towards a new social laboratory: An experimental study of search through community participation at Burning Man
Ziv Epstein, Micah Epstein, Christian Almenar +6
The "small world phenomenon," popularized by Stanley Milgram, suggests that individuals from across a social network are connected via a short path of mutual friends and can levera…
Evaluating Generative Adversarial Networks on Explicitly Parameterized Distributions
Shayne O'Brien, Matt Groh, Abhimanyu Dubey
The true distribution parameterizations of commonly used image datasets are inaccessible. Rather than designing metrics for feature spaces with unknown characteristics, we propose…