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Found 9 papers, 2 papers with code
Exploiting Unary Relations with Stacked Learning for Relation Extraction
Relation extraction models typically cast the problem of determining whether there is a relation between a pair of entities as a single decision.
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Evaluating Terrain-Dependent Performance for Martian Frost Detection in Visible Satellite Observations
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
The NASA Planetary Data System (PDS) hosts millions of images of planets, moons, and other bodies collected throughout many missions.
Cosmic Microwave Background Recovery: A Graph-Based Bayesian Convolutional Network Approach
The cosmic microwave background (CMB) is a significant source of knowledge about the origin and evolution of our universe.
Using Machine Learning to Reduce Observational Biases When Detecting New Impacts on Mars
This study investigates the use of a trained machine learning classifier to increase the detection of fresh impacts on Mars using CTX data.
Hidden Heterogeneity: When to Choose Similarity-Based Calibration
However, these methods are unable to detect subpopulations where calibration could also improve prediction accuracy.
Integrating Novelty Detection Capabilities with MSL Mastcam Operations to Enhance Data Analysis
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.
Visualizing Image Content to Explain Novel Image Discovery
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.
Interpretable Discovery in Large Image Data Sets
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).
