45 citations · 68 across the 19 of their papers we have counts for
6 papers · 1 filter
Identifying Information from Observations with Uncertainty and Novelty
Derek S. Prijatelj, Timothy J. Ireland, Walter J. Scheirer
A machine that learns a task from observations must encounter and process uncertainty and novelty, especially when it is to maintain performance when observing new information and…
This Probably Looks Exactly Like That: An Invertible Prototypical Network
Zachariah Carmichael, Timothy Redgrave, Daniel Gonzalez Cedre +1
We combine concept-based neural networks with generative, flow-based classifiers into a novel, intrinsically explainable, exactly invertible approach to supervised learning. Protot…
How Well Do Feature-Additive Explainers Explain Feature-Additive Predictors?
Zachariah Carmichael, Walter J. Scheirer
Surging interest in deep learning from high-stakes domains has precipitated concern over the inscrutable nature of black box neural networks. Explainable AI (XAI) research has led…
Pitfalls in Machine Learning Research: Reexamining the Development Cycle
Stella Biderman, Walter J. Scheirer
Machine learning has the potential to fuel further advances in data science, but it is greatly hindered by an ad hoc design process, poor data hygiene, and a lack of statistical ri…
Modeling Score Distributions and Continuous Covariates: A Bayesian Approach
Mel McCurrie, Hamish Nicholson, Walter J. Scheirer +1
Computer Vision practitioners must thoroughly understand their model's performance, but conditional evaluation is complex and error-prone. In biometric verification, model performa…
SHADHO: Massively Scalable Hardware-Aware Distributed Hyperparameter Optimization
Jeff Kinnison, Nathaniel Kremer-Herman, Douglas Thain +1
Computer vision is experiencing an AI renaissance, in which machine learning models are expediting important breakthroughs in academic research and commercial applications. Effecti…