129 citations · 586 across the 26 of their papers we have counts for
9 papers · 1 filter
Position: Every Ground Truth is a Human Construction, not an Objective Truth
Charlotte Högberg, Ericka Johnson, Kiri L. Wagstaff
Ground truth datasets play a fundamental role as reference values in the training and evaluation of machine learning models. This position paper argues that ground truths are not n…
Cosmic Microwave Background Recovery: A Graph-Based Bayesian Convolutional Network Approach
Jadie Adams, Steven Lu, Krzysztof M. Gorski +2
The cosmic microwave background (CMB) is a significant source of knowledge about the origin and evolution of our universe. However, observations of the CMB are contaminated by fore…
Using Machine Learning to Reduce Observational Biases When Detecting New Impacts on Mars
Kiri L. Wagstaff, Ingrid J. Daubar, Gary Doran +5
The current inventory of recent (fresh) impacts on Mars shows a strong bias towards areas of low thermal inertia. These areas are generally visually bright, and impacts create dark…
Hidden Heterogeneity: When to Choose Similarity-Based Calibration
Kiri L. Wagstaff, Thomas G. Dietterich
Trustworthy classifiers are essential to the adoption of machine learning predictions in many real-world settings. The predicted probability of possible outcomes can inform high-st…
Integrating Novelty Detection Capabilities with MSL Mastcam Operations to Enhance Data Analysis
Paul Horton, Hannah R. Kerner, Samantha Jacob +3
While innovations in scientific instrumentation have pushed the boundaries of Mars rover mission capabilities, the increase in data complexity has pressured Mars Science Laboratory…
Mars Image Content Classification: Three Years of NASA Deployment and Recent Advances
Kiri Wagstaff, Steven Lu, Emily Dunkel +8
The NASA Planetary Data System hosts millions of images acquired from the planet Mars. To help users quickly find images of interest, we have developed and deployed content-based c…
Visualizing Image Content to Explain Novel Image Discovery
Jake H. Lee, Kiri L. Wagstaff
The initial analysis of any large data set can be divided into two phases: (1) the identification of common trends or patterns and (2) the identification of anomalies or outliers t…
Interpretable Discovery in Large Image Data Sets
Kiri L. Wagstaff, Jake Lee
Automated detection of new, interesting, unusual, or anomalous images within large data sets has great value for applications from surveillance (e.g., airport security) to science…
Machine Learning that Matters
Kiri Wagstaff
Much of current machine learning (ML) research has lost its connection to problems of import to the larger world of science and society. From this perspective, there exist glaring…