Search Results for author: Kiri L. Wagstaff

Found 9 papers, 2 papers with code

Evaluating Terrain-Dependent Performance for Martian Frost Detection in Visible Satellite Observations

no code implementations2 Mar 2024 Gary Doran, Serina Diniega, Steven Lu, Mark Wronkiewicz, Kiri L. Wagstaff

Seasonal frosting and defrosting on the surface of Mars is hypothesized to drive both climate processes and the formation and evolution of geomorphological features such as gullies.

Interactive Mars Image Content-Based Search with Interpretable Machine Learning

no code implementations19 Jan 2024 Bhavan Vasu, Steven Lu, Emily Dunkel, Kiri L. Wagstaff, Kevin Grimes, Michael McAuley

The NASA Planetary Data System (PDS) hosts millions of images of planets, moons, and other bodies collected throughout many missions.

Interpretable Machine Learning

Cosmic Microwave Background Recovery: A Graph-Based Bayesian Convolutional Network Approach

no code implementations24 Feb 2023 Jadie Adams, Steven Lu, Krzysztof M. Gorski, Graca Rocha, Kiri L. Wagstaff

The cosmic microwave background (CMB) is a significant source of knowledge about the origin and evolution of our universe.

Hidden Heterogeneity: When to Choose Similarity-Based Calibration

1 code implementation3 Feb 2022 Kiri L. Wagstaff, Thomas G. Dietterich

However, these methods are unable to detect subpopulations where calibration could also improve prediction accuracy.

Classifier calibration Decision Making

Integrating Novelty Detection Capabilities with MSL Mastcam Operations to Enhance Data Analysis

no code implementations23 Mar 2021 Paul Horton, Hannah R. Kerner, Samantha Jacob, Ernest Cisneros, Kiri L. Wagstaff, James Bell

We address this need by creating products for MSLWEB that use novelty detection to help operations staff identify unusual data that might be diagnostic of new or atypical compositions or mineralogies detected within an imaging scene.

Novelty Detection

Visualizing Image Content to Explain Novel Image Discovery

no code implementations14 Aug 2019 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 that deviate from those trends.

Novelty Detection

Interpretable Discovery in Large Image Data Sets

no code implementations21 Jun 2018 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 (observations that don't fit a given theory can lead to new discoveries).

General Classification Novelty Detection

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