52 citations · 58 across the 4 of their papers we have counts for
4 papers
VAE Explainer: Supplement Learning Variational Autoencoders with Interactive Visualization
Donald Bertucci, Alex Endert
Variational Autoencoders are widespread in Machine Learning, but are typically explained with dense math notation or static code examples. This paper presents VAE Explainer, an int…
Zeno: An Interactive Framework for Behavioral Evaluation of Machine Learning
Ángel Alexander Cabrera, Erica Fu, Donald Bertucci +4
Machine learning models with high accuracy on test data can still produce systematic failures, such as harmful biases and safety issues, when deployed in the real world. To detect…
Beyond Value: CHECKLIST for Testing Inferences in Planning-Based RL
Kin-Ho Lam, Delyar Tabatabai, Jed Irvine +4
Reinforcement learning (RL) agents are commonly evaluated via their expected value over a distribution of test scenarios. Unfortunately, this evaluation approach provides limited e…
DendroMap: Visual Exploration of Large-Scale Image Datasets for Machine Learning with Treemaps
Donald Bertucci, Md Montaser Hamid, Yashwanthi Anand +4
In this paper, we present DendroMap, a novel approach to interactively exploring large-scale image datasets for machine learning (ML). ML practitioners often explore image datasets…